Datasets:

afg1

/

litscan-epmc-subset-hq

like 0

PMC9640536

Yonghao Tian,Ruijuan Liu,Xiaoyan Hou,Zhixiao Gao,Xinyu Liu,Weifang Zhang

SIRT2 promotes the viability, invasion and metastasis of osteosarcoma cells by inhibiting the degradation of Snail

07-11-2022

Bone cancer,Cell invasion,Bone metastases

Osteosarcomas (OS) are highly metastatic and usually lead to poor outcomes. Epithelial-mesenchymal transition (EMT) is reported to be a critical event in metastasis. SIRT2 exerts dual functions in many different tumors. However, the underlying molecular mechanisms of SIRT2 in osteosarcoma cell metastasis and the question of whether SIRT2 regulates EMT have not been fully explored. In this study, we confirmed that SIRT2 was highly-expressed in human osteosarcoma MG63 and Saos-2 cell lines. The viability, migration and invasion of osteosarcoma cells were inhibited by knockdown of SIRT2 and were enhanced by overexpression of SIRT2. Moreover, SIRT2 positively regulated EMT and upregulated the protein levels of the mesenchymal markers N-cadherin and Vimentin and the levels of MMP2 and MMP9. A xenograft mouse model showed that SIRT2 knockdown in osteosarcoma cells led to reduced tumor growth, decreased expression of mesenchymal markers and impaired lung and liver metastasis in vivo. Furthermore, we showed that SIRT2 interacted with and upregulated the protein level of the EMT-associated transcription factor Snail. SIRT2 inhibited Snail degradation via its deacetylase activity. Knockdown of Snail abrogated the promoting effects of SIRT2 on migration and invasion of osteosarcoma cells. In conclusion, SIRT2 plays a crucial role in osteosarcoma metastasis by inhibiting Snail degradation and may serve as a novel therapeutic target to manage osteosarcoma.

SIRT2 promotes the viability, invasion and metastasis of osteosarcoma cells by inhibiting the degradation of Snail Osteosarcomas (OS) are highly metastatic and usually lead to poor outcomes. Epithelial-mesenchymal transition (EMT) is reported to be a critical event in metastasis. SIRT2 exerts dual functions in many different tumors. However, the underlying molecular mechanisms of SIRT2 in osteosarcoma cell metastasis and the question of whether SIRT2 regulates EMT have not been fully explored. In this study, we confirmed that SIRT2 was highly-expressed in human osteosarcoma MG63 and Saos-2 cell lines. The viability, migration and invasion of osteosarcoma cells were inhibited by knockdown of SIRT2 and were enhanced by overexpression of SIRT2. Moreover, SIRT2 positively regulated EMT and upregulated the protein levels of the mesenchymal markers N-cadherin and Vimentin and the levels of MMP2 and MMP9. A xenograft mouse model showed that SIRT2 knockdown in osteosarcoma cells led to reduced tumor growth, decreased expression of mesenchymal markers and impaired lung and liver metastasis in vivo. Furthermore, we showed that SIRT2 interacted with and upregulated the protein level of the EMT-associated transcription factor Snail. SIRT2 inhibited Snail degradation via its deacetylase activity. Knockdown of Snail abrogated the promoting effects of SIRT2 on migration and invasion of osteosarcoma cells. In conclusion, SIRT2 plays a crucial role in osteosarcoma metastasis by inhibiting Snail degradation and may serve as a novel therapeutic target to manage osteosarcoma. Osteosarcoma (OS) is the most common primary malignant bone tumor, and is mainly composed of osteoid and cartilaginous matrix as well as fibrous tissues [1]. The annual incidence of osteosarcoma is 3 to 4 cases per million [2]. The incidence in adolescents aged 15-19 is the highest, and compared with females (10.7 per million), males have a higher incidence (19.3 per million), which suggests that males are more likely to be affected by osteosarcoma [3]. Although osteosarcoma can occur in any bone, the metaphysis of long bones is the most common site [4]. Osteosarcoma is highly aggressive, and it can quickly invade surrounding tissues and disseminate through the body. Due to its strong metastasis, osteosarcoma is associated with a high mortality rate, with a 5-year survival rate of approximately 50–60% [5]. Tumor recurrence, high lung metastasis and chemotherapy resistance are the main reasons for the poor prognosis [5]. Therefore, it is essential to explore the molecular mechanism of metastasis and develop new strategies for the treatment of osteosarcoma. Epithelial-mesenchymal transition (EMT) plays a key role in wound healing, embryonic development and tumor metastasis [6]. During the progression of EMT, cell-cell junctions and apico-basal polarity are lost, and invasive properties are acquired. EMT transforms early tumors into aggressive malignant tumors [7]. During cancer progression, tumor cells that undergo EMT fall off and enter lymphatic and blood vessels, causing the systemic spread and development of secondary tumors in distant organs [8]. EMT is the key step in metastatic malignant tumors of epithelial origin. In addition, mounting evidence has proven the crucial role of EMT in tumors of mesenchymal origin, such as osteosarcoma [9, 10]. Sirtuins (silent information regulators) are NAD+ (nicotinamide adenine dinucleotide)-dependent type III histone deacetylases (HDACs), which include a family of proteins with homology to silent information regulator 2 (Sir2) in Saccharomyces cerevisiae [11]. The seven sirtuin family members (SIRT1-SIRT7) show diversity in subcellular localization and function. SIRT1, SIRT6 and SIRT7 are mainly located in the nucleus, and SIRT3, SIRT4 and SIRT5 are located in the mitochondria, while SIRT2 is the only sirtuin mainly located in the cytoplasm [12]. The N-terminus of SIRT2 has a leucine-rich nuclear export signal (NES), which can regulate its nucleoplasmic localization [13]. SIRT2 levels increase during mitosis and accumulate in the nucleus when treated with a nuclear export inhibitor, and overexpression of SIRT2 markedly prolongs the mitotic phase [14, 15]. Mechanistically, SIRT2 is localized on chromatin and deacetylates histone H4K16Ac, which is vital for chromatin condensation during mitotic phase [16]. Thus, SIRT2 can instantly migrate to the nucleus during the transition period of G2/M and is involved in the regulation of mitosis. Current research shows that SIRT2 plays an important role in many physiological and pathological processes, such as proliferation, the cell cycle, apoptosis, genome integrity, cell metabolism, infection and inflammation. In different tumor types, SIRT2 may act as an activator or inhibitor [17]. There are many studies on SIRT2 in breast cancer, liver cancer, lung cancer, leukemia and other malignant tumors, but no relevant research on its role in the development of osteosarcoma has been reported. In this study, we identified the oncogenic role of SIRT2 in osteosarcoma and explored the underlying molecular mechanism of SIRT2 in the invasion and metastasis of osteosarcoma cells through in vivo and in vitro experiments. Importantly, we report that SIRT2 promoted the invasion and metastasis by inhibiting the degradation of the transcription factor Snail via its deacetylase activity. Therefore, SIRT2 might be a promising candidate for use against the metastasis of human osteosarcoma. SIRT1, SIRT2, SIRT6 and SIRT7 proteins are mainly localized in the nucleus or cytoplasm and are more likely to play important roles in the development of osteosarcoma. Previous reports have shown that SIRT6 and SIRT7 proteins were elevated in osteosarcoma cells, and they promoted the migration and invasion of osteosarcoma cells by different mechanisms [18, 19]. Thus, we focused on SIRT1 and SIRT2 in this study and detected the expression of these two proteins in hFOB1.19 (human osteoblasts) and several osteosarcoma cell lines MG63, HOS, U2OS and Saos-2. The protein level of SIRT2 was significantly upregulated in MG63 and Saos-2 cells compared with human osteoblasts (Fig. 1A). However, the expression of SIRT1 protein did not change greatly in osteosarcoma cells, which suggests that SIRT2 may play a more important role than SIRT1 in the development of osteosarcoma. The mRNA level of SIRT2 was also significantly increased in osteosarcoma MG63 and Saos-2 cells (Fig. 1B). Transwell assay showed that in addition to MG63 and Saos-2 cells, U2OS cells also had increased cell migration and invasion compared with hFOB1.19 control cells (Fig. 1C). The wound-healing experiment showed that the migrating speed of cells to the scratch was significantly increased in all four osteosarcoma cell lines (Fig. 1D, E). This may be because the proliferation rate of hFOB1.19 cells is the slowest and the migrating speed depends largely on the proliferation rate of cells [20]. Since osteosarcoma is a malignant stromal tumor, the expressions of epithelial and mesenchymal proteins were detected. The epithelial marker E-cadherin was decreased in all osteosarcoma cell lines compared with hFOB1.19 cells (Fig. 1F). The expression of mesenchymal marker Vimentin and the matrix metalloproteinase 2 (MMP-2) which degrades and remodels the extracellular matrix (ECM) was increased in most of the osteosarcoma cell lines. However, an obvious increase of MMP-9 was only observed in Saos-2 cells. Unexpectedly, the mesenchymal marker N-cadherin was decreased in all osteosarcoma cell lines. Our data showed that not all the mesenchymal proteins and MMP proteins were increased in MG63 and Saos-2 cells that expressed high level of SIRT2. We suppose that there might be other proteins that regulate the expression of N-cadherin and MMP9 in these cells. Since SIRT2 is highly expressed in osteosarcoma cell lines MG63 and Saos-2, two small interfering RNAs (siRNAs) of SIRT2, Si-SIRT2-1 (abbreviated as Si-1) and Si-SIRT2-2 (abbreviated as Si-2) were used to transfect MG63 and Saos-2 cells respectively (Fig. 2A). The CCK-8 assay showed that SIRT2 knockdown inhibited the viability of osteosarcoma cells (Fig. 2B). The results of Transwell assay showed that the number of cells passing through the chamber was decreased after SIRT2 knockdown, indicating that SIRT2 knockdown inhibited the migration and invasion of osteosarcoma cells (Fig. 2C, D). The results of the wound-healing experiment showed that the migrating speed to the scratch was reduced after SIRT2 knockdown (Fig. 2E). The expression level of SIRT2 in osteosarcoma U2OS cells was relatively low, so the pENTER-SIRT2-c-Flag-His plasmid was transfected into U2OS cells (Fig. 2F). Our results showed that the overexpression of SIRT2 greatly increased the viability, migration and invasion of osteosarcoma cells (Fig. 2G–I). In summary, SIRT2 is increased in osteosarcoma cells and promotes the viability, migration and invasion of osteosarcoma cells. EMT is an important way to promote cell invasion and metastasis. We then detected the expression of EMT-related proteins after SIRT2 knockdown in MG63 and Saos-2 cells. The results showed that the expression levels of the mesenchymal markers N-cadherin and Vimentin were decreased after SIRT2 knockdown; MMP-2 and MMP-9 were also decreased (Fig. 3A, B). The expression of N-cadherin and Vimentin was further detected by immunofluorescence. The intensity of immunofluorescence of N-cadherin and Vimentin was significantly weakened after SIRT2 knockdown (Fig. 3C, D). N-cadherin was distributed in the nucleus and cytoplasm, while Vimentin was mainly distributed in the cytoplasm. SIRT2 overexpression downregulated the expression of the epithelial marker E-cadherin and upregulated the expression of N-cadherin, Vimentin, MMP2, and MMP9 in U2OS cells (Fig. 4A). The gelatin zymogram assay was used to detect the activity of MMPs. SIRT2 overexpression significantly increased the enzymatic activity of MMP2, while SIRT2 knockdown decreased the enzymatic activity of MMP2 (Fig. 4B). The immunofluorescence intensity of N-cadherin and Vimentin was notably increased with SIRT2 overexpression (Fig. 4C, D). In contrast, SIRT2 overexpression significantly reduced the immunofluorescence intensity of E-cadherin (Fig. 4E). Thus, SIRT2 promotes EMT by upregulating the mesenchymal proteins and downregulating the epithelial marker. We infected MG63 cells with SIRT2 shRNA lentivirus and established an osteosarcoma cell line with stable SIRT2 knockdown (MG63-shSIRT2) (Fig. 5A, B). MG63-shSIRT2 cells or control cells were injected subcutaneously into the left forelimb of nude mice. The tumor sizes in the nude mice were observed and compared, and the tumor volumes were measured regularly to record the tumor growth curves (Fig. 5C). It was shown that SIRT2 knockdown significantly inhibited tumor growth. The mice were sacrificed 12 days after injection, and the tumors were isolated, photographed and weighed. The results showed that the volume and weight of tumors in the SIRT2 knockdown group were significantly lower than those in the control group (Fig. 5D, E), indicating that SIRT2 knockdown inhibited the proliferation of osteosarcoma cells in nude mice. H&E staining of tumor tissues showed that SIRT2 knockdown group had a complete envelope of tumors, while the tumor envelope was disrupted in the control group, indicating that SIRT2 knockdown inhibited tumor invasiveness (Fig. 5F). The expression levels of EMT-related proteins in tumors of the SIRT2 knockdown group and control group were detected (Fig. 5G). N-cadherin, Vimentin, MMP2, and MMP9 were all decreased in the tumors of the SIRT2 knockdown group. To explore the effect of SIRT2 on the metastatic ability of osteosarcoma cells in vivo, MG63-shSIRT2 or control cells were injected into the tail vein of nude mice. Our results showed that the fluorescence intensity of nude mice in the control group was obviously higher than that in the SIRT2 knockdown group, and the fluorescence was mainly distributed in the chest and abdomen (Fig. 6A). Importantly, in mice xenografted with MG63-shSIRT2 cells, the fluorescence intensity of the lung and liver was attenuated, and metastatic nodules were greatly reduced (Fig. 6B, C). H&E staining further confirmed that the SIRT2 knockdown group had significantly fewer metastatic nodules in the lung and liver (Fig. 6D, E). To explore the mechanism by which SIRT2 promotes the invasion and metastasis of osteosarcoma cells, we used the BioGRID database (https://thebiogrid.org/116593/table/homo-sapiens/sirt2.html) and HitPredict database (http://www.hitpredict.org/htp_int.php?Value=5142) to search for the molecules that interact with SIRT2. Both databases showed that SIRT2 may interact with the transcription factor Snail. We overexpressed SIRT2 (pENTER-SIRT2-c-Flag-His) and Snail (pcDNA3.1-Snail-c-HA) in MG63 cells and performed co-immunoprecipitation (Co-IP). We showed that SIRT2 bound to Snail (Fig. 7A, B). To clarify the regulatory relationship between SIRT2 and Snail, we detected the expression of Snail in osteosarcoma cells, and Snail expression was upregulated in MG63 and Saos-2 cells (with high SIRT2 expression) compared with hFOB1.19 control cells (with low SIRT2 expression) (Fig. 7C). In addition, the expression of Snail was decreased after SIRT2 knockdown in MG63 and Saos-2 cells (Fig. 7D). Overexpression of SIRT2 in U2OS cells upregulated the expression of Snail (Fig. 7E). We performed histochemical staining of SIRT2 and Snail in the tumor tissues of nude mice subcutaneously injected with MG63-shSIRT2 or control cells, and showed that the Snail staining was weakened after SIRT2 knockdown (Fig. 7F). To explore whether SIRT2 regulates Snail at the transcriptional level, qRT-PCR was employed. Knockdown or overexpression of SIRT2 did not affect the mRNA level of Snail (Fig. 7G, H). In summary, our data confirm the positive regulation of Snail by SIRT2 in vitro and in vivo. We further explored whether SIRT2 regulated Snail at the protein level. AGK2 is a selective inhibitor of SIRT2 activity, and Snail expression was decreased when the cells were treated with AGK2 in a dose-dependent manner (Fig. 8A), suggesting that SIRT2 upregulates Snail through its deacetylase activity. We chose to treat cells with 30 μM AGK2 and further confirmed the inhibition of Snail (Fig. 8B). The protein synthesis inhibitor, cycloheximide (CHX) was used to treat MG63 cells to inhibit protein synthesis, and then the proteasome inhibitor, MG132 was used to treat the cells. The expression of Snail was obviously increased with MG132 treatment, indicating that Snail was degraded through the proteasome pathway (Fig. 8C). Compared with CHX treatment alone, the expression of Snail was decreased more quickly with AGK2 treatment, further indicating that SIRT2 upregulated Snail through its deacetylase activity (Fig. 8D). Combined treatment with MG132 and AGK2 rescued the decrease in Snail level caused by AGK2 treatment, confirming that SIRT2 upregulated Snail by inhibiting Snail’s proteasomal degradation (Fig. 8E). AGK2 treatment decreased the protein expression of Snail in SIRT2-overexpressing cells, which further suggests that SIRT2 regulates Snail via its deacetylase activity (Fig. 8F). We then knocked down Snail in SIRT2-overexpressing cells (Fig. 8G) and showed that Snail knockdown abrogated the promoting effect of SIRT2 on the migration and invasion of osteosarcoma cells (Fig. 8H, I), suggesting that SIRT2 promotes the invasion and metastasis of osteosarcoma cells via Snail. Osteosarcoma is one of the leading causes of cancer-related death in adolescents [3, 21]. Osteosarcoma is highly aggressive and rapidly invades surrounding tissues and causes the development of early hematogenous metastases, with the most common site being the lungs [22]. Surgical resection combined with chemotherapy is effective in approximately 70% of patients with osteosarcoma, but the 5-year survival rate for osteosarcoma patients with metastases at diagnosis is only approximately 20% [23]. Osteosarcoma cells are prone to invasion and metastasis, which is the main reason for poor prognosis [5]. SIRT2 may act as an inducer or inhibitor in cancer, which may be related to cancer subtypes, subcellular localization, changes in deacetylase activity, and differences in substrate expression levels [17]. In this study, we aimed to explore the role of SIRT2 in the development and migration of osteosarcoma, which to date has remained unclear. We showed that the expression of SIRT2 but not SIRT1 was upregulated in osteosarcoma cells, which suggests that SIRT2 may contribute to the development of osteosarcoma. In addition, knockdown of SIRT2 inhibited osteosarcoma cell viability, migration and invasion, while overexpression of SIRT2 promoted osteosarcoma cell viability, migration and invasion. During EMT of tumors, N-cadherin is upregulated and E-cadherin is downregulated. This “cadherin switch” reduces cell adhesion, enhances migration and invasion, and leads to a low survival rate [24, 25].The ECM is a complex network composed of extracellular macromolecules such as collagen and glycoproteins. The migration of normal cells is restricted by the ECM, while tumor cells and macrophages secrete matrix metalloproteinases. MMP2 and MMP9 degrade ECM to promote cancer cell invasion [26]. We showed that in osteosarcoma cells the expression of the mesenchymal markers N-cadherin and Vimentin and of MMP2 and MMP9 was all downregulated with SIRT2 knockdown, while the epithelial marker E-cadherin was decreased and N-cadherin, Vimentin, MMP2 and MMP9 were increased with SIRT2 overexpression. In addition, the enzymatic activity of MMP2 was increased with SIRT2 overexpression and decreased with SIRT2 knockdown. These results suggest that osteosarcoma cells activate EMT progression by upregulating SIRT2 to promote the “cadherin switch” and promote tumor invasion and metastasis. To explore the mechanism by which SIRT2 regulates EMT, we searched the BioGRID database and HitPredict database for the molecules that interact with SIRT2. Snail was found to be one of the candidates. We proved the binding of SIRT2 and Snail in osteosarcoma cells and showed that SIRT2 positively regulated the expression of Snail. Studies have shown that Snail overexpression in a variety of tumors is associated with tumor grade, lymph node metastasis and tumor recurrence, and leads to poor prognosis [27]. Snail inhibits E-cadherin transcription by binding to the E-box of the E-cadherin promoter [28]. Snail also inhibits other epithelial molecules such as the tight junction proteins claudin, occludin and zona occludin-1 (ZO-1) [29]. Snail upregulates the expression of genes associated with aggressive phenotypes, such as N-cadherin, Vimentin, MMP2, and MMP9 [30–32]. Thus, we infer that SIRT2 promoted EMT by upregulating Snail. The Snail protein is very unstable, with a half-life of approximately 25 minutes. Snail not only exists in the nucleus, but also enters the cytoplasm through nuclear export. Posttranslational modifications (PTMs), including phosphorylation and ubiquitination, affect Snail protein stability, subcellular localization and activity. Casein kinase 1 (CK1) first phosphorylates Snail at serine 107 (Ser107) and serine 104 (Ser104) [33]. Glycogen synthase kinase 3β (GSK-3β) then phosphorylates Snail Ser100 and Ser96 to enable it to exit the nucleus. Phosphorylation at Ser96 and Ser100 provides a recognition site for binding to the E3 ubiquitin ligase β-TrCP1, resulting in Snail ubiquitination and proteasomal degradation [34, 35]. We showed that SIRT2 inhibits Snail ubiquitination and degradation, ultimately stabilizes Snail. We suppose that SIRT2 may associate with CK1, GSK-3β or β-TrCP1 to inhibit the phosphorylation, nuclear export and ubiquitin-mediated degradation of Snail, or that SIRT2 directly deacetylates Snail to inhibit its degradation. Both of these possibilities need to be further explored. In this study, SIRT2 showed increased levels in the osteosarcoma cell lines MG63 and Saos-2. Its overexpression enhanced and its knockdown restrained cell viability, migration, invasion and EMT. SIRT2 interacted with the transcription factor Snail and inhibited the proteasomal degradation of Snail to promote EMT. Snail knockdown reduced SIRT2-promoted cell invasion and metastasis. SIRT2 knockdown inhibited both tumorigenesis and lung and liver metastasis of osteosarcoma via Snail in vivo. Therefore, SIRT2 might be a promising therapeutic target for treating osteosarcoma. The human osteoblast cell line hFOB1.19 was purchased from the Cell Bank of the Typical Culture Preservation Committee of the Chinese Academy of Sciences. The human OS cell line Saos-2 was purchased from Shanghai Gaining Biotechnology. The human OS cell lines HOS, MG-63 and U2OS were purchased from the American Type Culture Collection (ATCC). The hFOB1.19 cells were maintained in DMEM and Ham’s F12 medium (DMEM/F12, Gibco BRL, USA) (1:1) with 0.3 mg/mL G418. HOS cells were cultured in Dulbecco’s modified Eagle medium (DMEM, Gibco BRL, USA). MG63 cells were cultured in RPMI 1640. Saos-2 and U2OS cells were cultured in McCoy’s 5 A medium. All cells were supplemented with 10% FBS (Gibco BRL, USA), penicillin (100 U/mL) and streptomycin (100 mg/mL). The hFOB1.19 cells were cultured in 5% CO2 at 33.5 °C, and the other cells were cultured in a humidified atmosphere of 5% CO2 at 37 °C. Specific small interfering RNA (siRNA) targeting SIRT2, Snail and negative control siRNAs were purchased from GenePharma (China). MG63 and Saos-2 cells were seeded in 6-well plates, and the cells were transfected with 20 nM siRNAs using jetPRIME transfection reagent according to the manufacturer’s instructions. For overexpression experiments, U2OS cells were transfected with the pENTER-SIRT2-c-Flag-His plasmid using Lipofectamine 2000. The siRNA sequences were as follows: si-SIRT2-1: 5'-CCTAGAGGCCAAGGCTTAAdTdT-3'; si-SIRT2-2 (si-SIRT2): 5'-GAGGCCAUCUUUGAGAUCAGCUAUU-3'; si-Snail: 5'-CCUGUCAGAUGAGGACAGUGGGAAA-3'; si-NC: 5'-UUCUCCGAACGUGUCACGUTT -3'. Lentivirus shRNAs targeting SIRT2 (shSIRT2) were purchased from GeneCopoeia (China). To acquire an MG63 cell line with stable SIRT2 knockdown (MG63-shSIRT2), MG63 cells were infected with shSIRT2 lentivirus (Multiplicity of infection, MOI = 80) using 5 µg/mL polybrene transfection reagent (GenePharma, China) and cells were selected with 0.5 µg /mL puromycin. RNA was extracted using TRIzol reagent (Invitrogen, USA) and reverse-transcribed to cDNA by the HiScript Q RT SuperMix (Vazyme, China) according to the manufacturer’s instructions. ChamQ SYBR qPCR Master Mix (Vazyme, China) was used to amplify the products, which were monitored on a CFX96 Touch Real-Time PCR detection system (Bio-Rad, USA). The PCR primer sequences were as follows: SIRT2-F: CTGCGGAACTTATTCTCCCAGAC; SIRT2-R: CCACCAAACAGATGACTCTGCG; Snail-F: TTCTCACTGCCATGGAATTCC; Snail-R: GCAGAGGACACAGAACCAGAAA; GAPDH-F: GCACCGTCAAGGCTGAGAAC; GAPDH-R: GCCTTCTCCATGGTGGTGAA. Total cellular proteins were extracted with radioimmunoprecipitation assay (RIPA, Beyotime Biotechnology, China) lysis buffer with phenylmethanesulfonyl fluoride (PMSF, Beyotime Biotechnology, China) and protease inhibitor (BestBio, China) at a 100:1:1 ratio. Proteins were separated by polyacrylamide gel electrophoresis (PAGE) and transferred to PVDF membranes (Millipore, USA). The membrane was incubated with primary antibodies against: SIRT1 (1:1000, 13161-1-AP, Proteintech), SIRT2 (1:1000, 19655-1-AP, Proteintech), E-cadherin (1:1000, 20874-1-AP, Proteintech), EMT Antibody Sample Kit (1:1000, 9782, Cell Signaling), MMP2 (1:1000, 40994, Cell Signaling), MMP9 (1:1000,13667, Cell Signaling), Snail (1:1000, 3879, Cell Signaling), Snail (1:1000, 13099-1-AP, Proteintech), GAPDH (1:1000, 10494-1-AP, Proteintech), β-tubulin (1:1000, 86298, Cell Signaling), Flag (1:1000, 20543-1-AP, Proteintech) and HA (1:1000, 66006-2-Ig, Proteintech). The following day, the membranes were incubated with goat anti-mouse HRP-conjugated secondary antibody (ZB-2305) or goat anti-rabbit HRP-conjugated secondary antibody (ZB-2301) for 1 h. The proteins were visualized with the Immobilon Western Chemiluminescent HRP Substrate kit (Millipore, USA). Cell viability was assessed using a Cell Counting Kit 8 (CCK-8, BestBio, China) according to the manufacturer’s protocol. MG63 and Saos-2 cells with SIRT2 knockdown and U2OS cells with SIRT2 overexpression were seeded into a 96-well plate. At 0, 24, 48, 72 and 96 h after seeding (every 2 days of total 8 days for Saos-2 cells), 10 μL CCK-8 was added and incubated for 2 h at 37 °C, and then the absorbance of the solution was measured at a wavelength of 450 nm. Cell migration was examined using the wound-healing assay. Briefly, the cells were plated and cultured overnight to approximately 80–90% confluence in a 6-well plate. A wound was created by scraping a straight scratch in the confluent cell layer with a pipette tip (200 μL). The cells were washed 3 times with PBS to remove the floating cells and serum-free culture medium was added. A computer-based microscopy imaging system was used to capture images of scratched positions at 0 h and 24 h. The migration distance was calculated and compared. For the migration assay, 8 × 104 MG63 or Saos-2 cells in 200 μL culture medium without FBS were seeded in Transwell chambers and the lower chambers were filled with 500 μL 20% FBS complete medium as a chemoattractant. After incubation for 36 h, cells that had migrated to the lower chamber were washed with PBS, fixed with 4% paraformaldehyde for 30 min and stained with 0.1% crystal violet for 10 min. The number of migrated cells was counted in five randomly selected fields under phase contrast microscope. For the invasion assay, a layer of artificially reconstituted basement membrane material Matrigel was coated on the bottom of the upper surface of the cell membrane (the dilution ratio of Matrigel to serum-free medium was 1:6). Transfected cells were seeded in 24-well plates containing slides. When the cells were 50% confluent, the cell slides were fixed in 4% paraformaldehyde for 15 min, and permeabilized with 0.2% Triton X-100 (Sigma, USA) for 30 min. The cell slides were blocked in 5% BSA for 1 h, and then incubated with N-cadherin (1:100, 22018-1-AP, Proteintech), Vimentin (1:100, 10366-1-AP, Proteintech) or E-cadherin (1:100, 20874-1-AP, Proteintech) antibody at 4 °C overnight. The following day, the cells were incubated with Dylight 488-conjugated goat anti-rabbit IgG (H + L) secondary antibody or Dylight 594-conjugated goat anti-mouse IgG (H + L) secondary antibody in the dark for 2 h. Next, DNA was stained with DAPI (Beyotime Biotechnology, China) for 5 min. Finally, the cell slides were sealed with antifade mounting medium and stored in the dark at 4 °C. Immunofluorescence images were obtained using a fluorescence microscope. When the cells grew to approximately 80% confluence, they were washed twice with sterile PBS and incubated in serum-free media at 37 °C in a CO2 incubator for at least 16-20 hours. The media was centrifuged (400×g for 5 min at 4 °C) to remove cells debris, and the supernatant was retained as a sample to be tested with a Gelatin Zymograpgy Analysis Kit (Real-Times Biotechnology, Beijing, China). MG63 cells were co-transfected with SIRT2 (pENTER-SIRT2-c-Flag-His) and Snail (pcDNA3.1-Snail-c-HA) plasmids. Total proteins were extracted with weak RIPA lysis buffer (Beyotime, China). Protein A/G Plus Magnetic Beads (MedChemExpress, USA) were pre-incubated with the IgG (normal rabbit IgG, B900610, Proteintech), SIRT2 antibody or Snail antibody for 60 min on a spinning wheel at 4 °C. The bead-antibody complexes were washed three times and suspended with the protein lysate on a spinning wheel at 4 °C overnight. The beads were washed four times with PBST buffer, and were collected with a magnetic stand. The immunoprecipitates were then eluted by boiling with 2×loading buffer for Western blot analysis. Six-week-old male BALB/c athymic nude mice were subcutaneously injected into the flanks with 1 × 107 MG63-shSIRT2 cells or MG63-shNC cells in 0.1 mL of PBS (n = 6 per group). Tumor growth was monitored, and tumor volume was measured every 2 days with calipers. The tumor volume was calculated with the formula: (length × width2)/2. Twelve days after injection, the mice were sacrificed, and the tumors were harvested and weighed. H&E staining, immunochemical staining of SIRT2 and Snail, and Western blot analysis of EMT-related proteins were performed. For the tumor metastatic assay, 2 × 106/100 µL MG63-shSIRT2 cells or MG63-shNC cells were injected into the tail vein of 6-week-old male BALB/c athymic nude mice (n = 6 per group). Five weeks later, experimental lung and liver metastasis was determined. All animal experimental procedures and protocols were approved by the Experimental Animal Ethics Committee of Shandong University School of Medicine. Data are expressed as the mean ± standard deviation (SD). Statistical analysis was carried out using SPSS 20.0 (IBM, Chicago, IL, USA). The significant differences between the two groups were assessed by two-tailed Student’s t-test. Values of P < 0.05 were considered statistically significance. checklist Uncropped WB

PMC9640567

Hongquan Wang,Yan Wang,Shihui Lai,Liang Zhao,Wenhui Liu,Shiqian Liu,Haiqiang Chen,Jinhua Wang,Guanhua Du,Bo Tang

LINC01468 drives NAFLD-HCC progression through CUL4A-linked degradation of SHIP2

07-11-2022

Experimental models of disease,Hepatocellular carcinoma

Accumulating evidence suggests that long noncoding RNAs (lncRNAs) are deregulated in hepatocellular carcinoma (HCC) and play a role in the pathogenesis of non-alcoholic fatty liver disease (NAFLD). However, the current understanding of the role of lncRNAs in NAFLD-associated HCC is limited. In this study, transcriptomic profiling analysis of three paired human liver samples from patients with NAFLD-driven HCC and adjacent samples showed that LINC01468 expression was significantly upregulated. In vitro and in vivo gain- and loss-of-function experiments showed that LINC01468 promotes the proliferation of HCC cells through lipogenesis. Mechanistically, LINC01468 binds SHIP2 and promotes cullin 4 A (CUL4A)-linked ubiquitin degradation, thereby activating the PI3K/AKT/mTOR signaling pathway, resulting in the promotion of de novo lipid biosynthesis and HCC progression. Importantly, the SHIP2 inhibitor reversed the sorafenib resistance induced by LINC01468 overexpression. Moreover, ALKBH5-mediated N6-methyladenosine (m6A) modification led to stabilization and upregulation of LINC01468 RNA. Taken together, the findings indicated a novel mechanism by which LINC01468-mediated lipogenesis promotes HCC progression through CUL4A-linked degradation of SHIP2. LINC01468 acts as a driver of HCC progression from NAFLD, highlights the potential of the LINC01468-SHIP2 axis as a therapeutic target for HCC.

LINC01468 drives NAFLD-HCC progression through CUL4A-linked degradation of SHIP2 Accumulating evidence suggests that long noncoding RNAs (lncRNAs) are deregulated in hepatocellular carcinoma (HCC) and play a role in the pathogenesis of non-alcoholic fatty liver disease (NAFLD). However, the current understanding of the role of lncRNAs in NAFLD-associated HCC is limited. In this study, transcriptomic profiling analysis of three paired human liver samples from patients with NAFLD-driven HCC and adjacent samples showed that LINC01468 expression was significantly upregulated. In vitro and in vivo gain- and loss-of-function experiments showed that LINC01468 promotes the proliferation of HCC cells through lipogenesis. Mechanistically, LINC01468 binds SHIP2 and promotes cullin 4 A (CUL4A)-linked ubiquitin degradation, thereby activating the PI3K/AKT/mTOR signaling pathway, resulting in the promotion of de novo lipid biosynthesis and HCC progression. Importantly, the SHIP2 inhibitor reversed the sorafenib resistance induced by LINC01468 overexpression. Moreover, ALKBH5-mediated N6-methyladenosine (m6A) modification led to stabilization and upregulation of LINC01468 RNA. Taken together, the findings indicated a novel mechanism by which LINC01468-mediated lipogenesis promotes HCC progression through CUL4A-linked degradation of SHIP2. LINC01468 acts as a driver of HCC progression from NAFLD, highlights the potential of the LINC01468-SHIP2 axis as a therapeutic target for HCC. Hepatocellular carcinoma (HCC), the most common primary liver cancer, is considered the second-most common cause of cancer-related death globally and is the fifth-most common cancer worldwide [1]. HCC is known to be caused by cirrhosis resulting from chronic infection (hepatitis B virus and hepatitis C virus) and alcohol-induced injury [2]. However, despite the reduction in the incidence of chronic infection-related HCC with the development of anti-HCV drugs and effective vaccines for HBV [1], HCC-associated mortality has been rising prominently, suggesting that other risk factors likely account for this increase. With a global rise in type 2 diabetes (T2DM) and obesity, non-alcoholic fatty liver disease (NAFLD), now known as metabolic dysfunction-associated fatty liver disease (MAFLD) [3, 4], is becoming an increasingly important etiology of HCC [5, 6]. NAFLD is considered to indicate a metabolic predisposition to liver cancer [7], and is now becoming the dominant cause of HCC worldwide [8]. However, the molecular mechanisms underlying the progression of NAFLD to HCC remain largely unknown [9, 10]. Long noncoding RNAs (lncRNAs) are a novel class of RNAs >200 nucleotides in length that lack the ability to encode proteins. lncRNAs are deregulated in HCC and exert crucial roles in the occurrence and progression of HCC [11], and some lncRNAs act as vital metabolic regulators that are involved in the etiology of NAFLD [12–15]. Although lncRNAs may contribute to the progression of NAFLD and HCC [16–20], their role in NAFLD-associated HCC is not well-understood, indicating the need to delineate the relevant mechanisms underlying NAFLD-HCC progression. LINC01468 is a newly identified lncRNA [21, 22] that functions as an oncogene contributing to the progression of lung adenocarcinoma [23]. However, the roles and underlying mechanisms of LINC01468 in HCC remain unclear, and the role of LINC01468 in NAFLD-related HCC has not yet been reported. In this study, we identified significant upregulation of LINC01468 in NAFLD and HCC. LINC01468 silencing inhibited HCC tumorigenesis via lipid metabolism and suppressed the chemoresistance of HCC cells. Mechanistically, LINC01468 directly interacted with SHIP2 and destabilized SHIP2 by enhancing E3 ubiquitin ligase cullin 4 A (CUL4A) ubiquitination-dependent SHIP2 degradation. Taken together, the findings of the present study revealed a new mechanism by which LINC01468-mediated lipogenesis promotes hepatocellular carcinoma progression through the CUL4A-linked degradation of SHIP2. To reveal the role of lncRNA in NAFLD-associated HCC, we first analyzed three paired human liver tumor tissues and adjacent normal tissues (n = 3) from patients with NAFLD-driven HCC by RNA-seq. In comparison with paired adjacent normal tissues, 5944 genes were upregulated and 104 were downregulated in NAFLD-HCC (Fig. 1A and Supplementary Fig. 1A). An analysis of the differentially expressed genes in human NAFLD-HCC showed a significant overlap of 17 genes with those in mice NAFLD-HCC (Fig. 1B). The results of hierarchical clustering and heatmap analysis of the significantly differentially expressed genes between human and mouse NAFLD-HCC are shown in Fig. 1C. Overexpression of lncRNAs in HCC tissues was confirmed by qRT-PCR with 26 paired samples. LINC01468 was the most significantly upregulated among the four lncRNAs, and showed the highest log2 fold-change values (Fig. 1D). The expression of lipogenic pathway enzymes such as SREBP1, FASN, ACLY, ACAC, and SCD1 was detected by qRT-PCR, and progressive induction of SREBP1, ACLY, FASN, ACAC, and SCD1 was observed in HCC tissues (Fig. 1E). To explore the functions of the four selected lncRNAs in HCC, we investigated their effects on the expression of lipogenic pathway enzymes. Overexpression of lncRNAs upregulated the protein-level expression of lipogenic pathway enzymes in HCC cell lines, with LINC01468 inducing a significant upregulation (Fig. 1F). Scatter-plot analysis indicated a positive correlation between the mRNA levels of LINC01468 and SREBP1 (r = 0.6358, p < 0.005), ACLY (r = 0.7074, p < 0.01) (Fig. 1G), SLC7A11-AS1, SREBP1 (r = 0.5280, p = 0.0056), and ACLY (r = 0.4271, p = 0.0296) (Supplementary Fig. 1B), SCAMP1-AS1, SREBP1 (r = 0.4783, p = 0.0134), and ACLY (r = 0.4202, p = 0.0326) (Supplementary Fig. 1C), MCM3AP-AS1 and SREBP1 (r = 0.4963, p < 0.001), and ACLY (r = 0.4724, p = 0.0148) (Supplementary Fig. 1D). Next, the correlation between LINC01468 expression and clinicopathological findings in 52 NAFLD-HCC cases was examined. Based on the median expression levels of LINC01468 detected by qRT-PCR, patients were divided into two groups. LINC01468 levels were significantly related to hemoglobin A1C (HbA1C), triglyceride (TG), and total cholesterol (TC), and cirrhosis levels, tumor size, tumor stage, TNM stage, and microvascular invasion (Table 1). We further examined the correlation between LINC01468 expression and the 5-year follow-up data of the patients. Patients with high LINC01468 expression showed a significantly lower overall survival when the median LINC01468 expression level in 52 patients was used as the cutoff point (Fig. 1H). Considering the upregulation of LINC01468 expression in NAFLD-HCC, we explored the function of LINC01468 in HCC. LINC01468 silencing reduced the proliferative capacity of HCC cells (Fig. 2A, all P < 0.01), and LINC01468 knockdown inhibited the migration and invasion of HCC cells (Fig. 2B, all P < 0.05). Similarly, LINC01468 knockdown reduced the tumorigenesis of HCC cells in vivo, indicating that HCC cells with LINC01468 knockdown showed slower and less sustainable tumor growth in the xenograft model than in the scrambled control group (Fig. 2C). Overall, these findings indicate that LINC01468 promotes HCC development through lipid accumulation. Reprogramming of lipid metabolism is closely related to drug resistance in cancer [24]. Therefore, we assessed the effects of LINC01468 on lenvatinib (LVB) and sorafenib (SOR) sensitivity. Sorafenib was the first multi-tyrosine kinase inhibitor approved for the treatment of patients with unresectable HCC [25], while lenvatinib is another tyrosine kinase inhibitor that received approval for first-line treatment of patients with advanced HCC [26]. LINC01468 silencing sensitized SNU-449 cells to LVB, as reflected by a reduction in cell viability (Fig. 2D), colony formation (Fig. 2E), and tumorigenicity (Fig. 2F, G). Concurrently, exogenously overexpressing LINC01468 reduced the sensitivity of Huh7 cells to SOR (Fig. 2H–K). Together, these results suggest that LINC01468 promotes HCC proliferation and metastasis, thereby conferring drug chemoresistance. To explore the functions of LINC01468 in HCC, we performed RNA-seq in HCC cells transfected with shLINC01468 or a scrambled control. A total of 2056 unique transcripts were identified using three independent biological replicates, including 1345 upregulated and 711 downregulated mRNAs (Fig. 3A). KEGG pathway enrichment analysis suggested that these genes, including NAFLD genes, were enriched in cancer-related pathways (Fig. 3B and C). The differentially expressed gene sets were related to mammalian target of rapamycin (mTOR) and fatty acid (FA) metabolism, which showed a significantly positive correlation with LINC01468 expression in the gene set enrichment analysis (GSEA), indicating a pivotal role of LINC01468 in lipid metabolism regulation (Fig. 3D). To confirm that LINC01468 regulates mTOR, we investigated the effect of LINC01468 disruption on the expression of the Akt/mTOR pathway. LINC01468 knockdown decreased protein expression of the Akt/mTOR pathway (Fig. 3E), and the protein levels of the Akt/mTOR pathway increased after LINC01468 overexpression (Fig. 3F). Moreover, the mTORC1 inhibitor rapamycin significantly diminished the activation of the Akt/mTOR pathway by LINC01468 overexpression (Fig. 3G). We also evaluated the effect of LINC01468 on the lipid content in HCC. As shown in Fig. 3H, LINC01468 silencing significantly decreased the level of neutral lipid staining by oil red O. These results were further corroborated by the findings for the cellular lipid content, indicating that LINC01468 silencing significantly decreased the levels of intracellular TG and TC (Fig. 3I). LINC01468 silencing had significantly decreased levels of neutral lipid in vivo (Fig. 3J). To examine the functional consequences of LINC01468 in vivo, we established orthotopic xenografts derived from control- or LINC01468-expressing HCCs. Tumors overexpressing LINC01468 grew faster than those in the control group and became resistant to sorafenib (Fig. 3K). The mTOR pathway is involved in many hallmarks of cancer, including cell growth, metabolic reprogramming, proliferation, and inhibition of apoptosis, and is upregulated in HCC tissue samples. Pharmacological inhibition of the mTOR pathway (e.g., by rapamycin or everolimus) can hamper tumor progression both in vitro and in animal models. Everolimus, an mTOR inhibitor, exhibits antitumor activity by disrupting various signaling pathways [27], and has been studied in combination with sorafenib in patients with unresectable or metastatic HCC [28]. Sorafenib combined with everolimus (an mTOR inhibitor) significantly reduced tumor growth and restored sensitivity to sorafenib therapy in LINC01468-overexpressing tumors (Fig. 3K, L, and M). Since most lncRNAs have been suggested to exert their actions by interacting with their counterpart proteins [29–32], we performed an RNA pull-down assay followed by mass spectrometry and western blot analysis to identify the proteins associated with LINC01468 (Fig. 4A). The Src homology 2 (SH2)-domain-containing PtdIns(3,4,5)P3 5-phosphatase-2 (SHIP2), which specifically hydrolyzes the phosphate at the 5ʹ position of the inositol ring to produce PtdIns(3,4)P2 from PtdIns(3,4,5)P3 [33], was the most-enriched LINC01468-interacting protein (Fig. 4B). Using biotin-LINC01468 pull-down lysates, we subsequently confirmed that LINC01468 and SHIP2 interacted in a dose-dependent manner (Fig. 4C). RNA immunoprecipitation (RIP) with an SHIP2 antibody was used to validate the association between LINC01468 and SHIP2. Notably, LINC01468 was enriched approximately 15-fold in precipitates with SHIP2 antibodies (Fig. 4D). A combination of fluorescence in situ hybridization (FISH) and immunofluorescence staining showed that endogenous LINC01468 was mainly colocalized with SHIP2 (Fig. 4E). We then determined the unique binding region of LINC01468 responsible for its interaction with SHIP2 and constructed a series of deletion mutants of LINC01468. RNA pull-down assays showed that LINC01468 mutants containing nucleotides 400–600 bound to SHIP2 as efficiently as full-length LINC01468, whereas other mutants completely lost their binding capacity, indicating that nucleotides 400–600 of LINC01468 are required for association with SHIP2 (Fig. 4F, G). Taken together, these results implied that LINC01468 directly interacts with SHIP2. Therefore, we performed expression analysis of SHIP2 from HCC tissues and para-cancerous tissues, which showed mRNA- (Fig. 4H) and protein-level (Fig. 4I) reductions in SHIP2 expression and a negative correlation between SHIP2 expression and the LINC01468 level in 26 paired tumors and adjacent normal tissues from human NAFLD-associated HCCs (Fig. 4J). SHIP2 was downregulated in the NAFLD cell model established using SNU-182 cells induced by palmitic acid (PA) and oleic acid (OA) (Fig. 4K–N). Thus, LINC01468 silencing inhibits HCC tumorigenesis via lipid metabolism. Since lncRNAs destabilize their binding proteins through ubiquitination-mediated degradation [34–36], we hypothesized that LINC01468 might bind to SHIP2 to regulate its stability. We found that LINC01468 silencing upregulated SHIP2 protein levels (Fig. 5A), whereas LINC01468 overexpression decreased SHIP2 protein levels in HCC cells (Fig. 5B). However, overexpression or silencing of LINC01468 had no effect on the SHIP2 mRNA (Fig. 5C). To determine whether LINC01468 regulates SHIP2 stability through ubiquitination-mediated degradation, we treated SNU-182 and Huh7 cells with the de novo protein synthesis inhibitor cycloheximide and the potent cell-permeable reversible proteasome inhibitor MG132, respectively. LINC01468 overexpression led to a robust decrease in SHIP2 protein levels (Fig. 5D, E), and MG132 rescued this reduction (Fig. 5F, G), suggesting that LINC01468 could promote SHIP2 for proteasome-dependent degradation. Furthermore, LINC01468 overexpression increased SHIP2 ubiquitination in both SNU-182 and Huh7 cells (Fig. 5H–K). Thus, LINC01468 can destabilize the SHIP2 protein by promoting its ubiquitination-mediated degradation. LncRNAs can participate in ubiquitin-mediated protein degradation by acting as scaffolds. To identify the E3 ubiquitin ligase targeting SHIP2 for degradation in HCC cells, we co-immunoprecipitated SHIP2 from the lysates of HCC cells and analyzed the immunoprecipitated proteins by liquid chromatography-mass spectrometry. CUL4A was identified as a candidate E3 ligase that binds to LINC01468, which mediates the ubiquitination of SHIP2 (Fig. 6A). RNA pull-down also revealed the interaction of LINC01468 with CUL4A (Fig. 6B). RNA-immunprecipitation (RIP) assays followed by qRT-PCR validated that LINC01468 was markedly enriched in the RNA-protein complexes precipitated with the anti-CUL4A antibody (Fig. 6C). We then validated the interaction between endogenous SHIP2 and CUL4A in HCC cells by immunoprecipitation (Fig. 6D). Importantly, CUL4A silencing increased the level of SHIP2 protein (Fig. 6E), whereas CUL4A overexpression reduced SHIP2 protein levels (Fig. 6F). As expected, CUL4A overexpression increased SHIP2 ubiquitination (Fig. 6G). Next, we examined whether LINC01468 affected the SHIP2-CUL4A interaction. We found that LINC01468 silencing markedly decreased the interaction of SHIP2 with CUL4A (Fig. 6H). To confirm whether SHIP2 degradation is mediated by CUL4A, we silenced CUL4A and detected the SHIP2 protein level, and showed that CUL4A silencing decreased LINC01468-dependent SHIP2 degradation (Fig. 6I). The degradation assay showed that the half-life of SHIP2 was prolonged (Fig. 6J). Moreover, LINC01468 or CUL4A silencing dramatically reduced SHIP2 ubiquitination (Figs. 6K and 4L). Thus, CUL4A is an E3 ligase that regulates SHIP2 ubiquitination. Although SHIP2 can suppress PI3K/Akt signaling and inhibits cancer progression [37–39], its role in regulating the PI3K/AKT/mTOR signaling pathway in HCC remains poorly understood. We found that LINC01468 silencing decreased the levels of phosphorylated AKT (S473), phosphorylated mTOR, phosphorylated S6K, and 4EBP1, which recovered after SHIP2 knockdown (Fig. 7A). Consistent with the changes in the expression of these Akt/mTOR proteins, we silenced SHIP2 in LINC01468-knockdown cells and confirmed that LINC01468-mediated metabolic regulation is indeed channeled through SHIP2 (Fig. 7B). Accordingly, the LNC04168-knockdown-induced reduction in tumor growth was reversed by SHIP2 knockdown in a SNU-449 HCC model stably transfected with an shRNA for LNC04168 in vivo, suggesting that LNC04168 acts through SHIP2 downregulation to promote the growth of HCC tumors (Supplementary Fig. 2A and B). Conversely, LINC01468 overexpression increased Akt and mTOR levels, whereas SHIP2 overexpression abolished LINC01468-induced activation of PI3K/AKT/mTOR signaling (Fig. 7C). In accordance with these changes, we confirmed that LINC01468-mediated metabolic regulation is channeled through SHIP2 after ectopic expression of SHIP2 in LINC01468-overexpression cells (Fig. 7D). We also found that SHIP2 silencing led to an increased level of mTOR protein (Fig. 7E), and enforced expression of SHIP2 decreased mTOR protein (Fig. 7F), indicating that SHIP2 negatively regulates PI3K/Akt signaling in HCC. Taken together, these data suggest that the LINC01468/SHIP2 axis activates the PI3K/AKT/mTOR signaling pathway. To confirm that the LINC01468-mediated metabolic regulation is channeled through SHIP2/ phosphatidylinositol-3,4,5 -trisphosphate (PIP3), we used the PIP3 inhibitor PIT-1 in LINC01468-overexpressed cells. PIT-1, a small molecule PIP3 antagonist (PIT) that blocks pleckstrin homology (PH) domain interaction, including activation of Akt, significantly inhibits tumor angiogenesis and metastasis [40, 41]. PIT-1 was able to inhibit the LINC01468 overexpression induced SHIP2/PIP3-dependant activation of Akt/mTOR (Fig. 7G) and rescue LINC01468-induced metabolic phenotypes. The rescued phenotypes included a lower ability for migration and invasion and decreased lipid production (Fig. 7H). Thus, SHIP2/PIP3 are the effectors of LINC01468 in modulating lipid metabolism. The expression of LINC01468, SHIP2, and mTOR pathways was confirmed in the xenograft by IHC, and Ki67 staining indicated cell proliferation in these tumors (Fig. 7I). Therefore, LINC01468 is a potential therapeutic target for HCC and drug resistance. Since m6A dysregulation enhances lipogenesis and NAFLD-HCC progression [42], we analyzed whether LINC01468 was modified or upregulated by m6A modification. Many m6A sites were found with LINC01468 using the RMvar (rmvar.renlab.org) prediction. In comparison with normal THLE2 liver cells, m6A was more significantly abundant in Huh7 and SNU-449 cells in RIP and RT-qPCR results (Fig. 8A). To screen the m6A enzyme-regulated LINC01468 modification, antibodies against different m6A-related proteins were used to perform an RIP assay and detect the expression of LINC01468 in the pulled products. METTL3 and ALKBH5 significantly enriched LINC01468, suggesting that METTL3 and ALKBH5 play roles in m6A modification of LINC01468. Interestingly, ALKBH5 expression was negatively correlated with LINC01468 expression in HCC (Fig. 8B). In comparison with para-cancerous tissues, HCC tissues showed significantly reduced ALKBH5 levels (Fig. 8C). Further experiments validated that site 52455230 could be modified by ALKBH5. ALKBH5 overexpression led to increased luciferase activity in the wild-type LINC01468 group, whereas luciferase activity was unchanged in the mutant-type LINC01468 group (Fig. 8D). ALKBH5 overexpression decreased LINC01468 mRNA expression in HCC cells (Fig. 8E), whereas ALKBH5 silencing had the opposite result (Fig. 8F). RIP qPCR assays showed that ALKBH5 overexpression reduced the m6A modification of LINC01468 in HCC cells (Fig. 8G), whereas ALKBH5 silencing produced the opposite effect (Fig. 8H). In the presence of actinomycin D, an inhibitor of de novo synthesis of RNA, ALKBH5 overexpression decreased the stability of LINC01468, whereas ALKBH5 silencing showed the opposite result (Fig. 8I). These data reveal the critical role of ALKBH5 in upregulating LINC01468 in HCC. The carcinogenic pathways leading to HCC tumorigenesis in NAFLD patients are complex and poorly understood. Epigenetics has been implicated in the etiology of NAFLD-associated HCC [43, 44], and the role of lncRNAs in several NAFLD-associated cancer-related processes participating in HCC tumorigenesis, such as epigenetic regulation and cell metabolism, has received much attention [45]. Although some lncRNAs may contribute to NAFLD-HCC progression [46–48], their role in NAFLD-associated HCC is largely unclear. The present study investigated the role of LINC01468 in the progression of NAFLD-HCC and showed that LINC01468 mediates lipogenesis, thereby promoting HCC progression through CUL4A-linked degradation of SHIP2 (Fig. 8J). Many lncRNAs are dysregulated in HCC and play critical roles in tumorigenesis and HCC progression [49, 50], and some HCC-related lncRNAs play crucial roles in the initiation and progression of HCC by regulating lipid metabolic reprogramming [51–54]. In examining the role of lncRNAs in NAFLD-associated HCC, the authors found that LINC01468 was upregulated in liver tissues during NAFLD-HCC and that LINC01468 silencing inhibited HCC tumorigenesis via lipid metabolism. Since lncRNAs have been shown to mediate resistance to treatment and malignant progression of HCC [55, 56], these results suggest that LINC01468 promotes HCC proliferation and confers drug chemoresistance in HCC cells. Thus, we uncovered a new role of LINC01468 in HCC development. Certain lncRNAs function biologically by interacting with other proteins [29–32], while others regulate their binding proteins through post-translational modifications. To identify the molecular mechanisms underlying the oncogenic role of LINC01468 in HCC, an RNA pull-down assay and western blot analysis were used to determine whether SHIP2 is associated with LINC01468. RNA-IP was used to validate the association between LINC01468 and SHIP2. Since LncRNAs destabilize their binding proteins by promoting ubiquitination-mediated degradation [34–36], we postulated that LINC01468 might bind to SHIP2 to regulate its stability. Our results showed that in HCC cells, LINC01468 silencing upregulated SHIP2 protein levels, and LINC01468 overexpression decreased SHIP2 protein levels, which were rescued by MG132; thus, LINC01468 could promote SHIP2 for proteasome-dependent degradation. Additionally, mechanistic details relating to the ability of LINC01468 to regulate SHIP2 suggested that LINC01468 promotes SHIP2 ubiquitination by enhancing its binding to CUL4A, a ubiquitin E3 ligase, thereby leading to CUL4A-dependent SHIP2 ubiquitinated degradation. SHIP2 regulates the PI3K/AKT pathway, which plays a crucial role in cancer progression, by producing PI(3,4)P2 to increase AKT activation and cancer cell survival. SHIP2 plays a central role in cancer development and progression, including HCC [57, 58], and SHIP2 has been shown to negatively regulate PI3K/Akt signaling and suppresses cancer progression [37–39]. Mechanistic details related to SHIP2 modulation may involve proteasome-dependent degradation, and a recent study showed that S-phase kinase-associated protein 2 (SKP2), a component of the E3 ubiquitin ligase complex, downregulates SHIP2 through polyubiquitination. Our results confirmed that LINC01468 increases CUL4A-mediated degradation of SHIP2, by which SHIP2 negatively regulates PI3K/Akt, thereby promoting lipogenesis and HCC progression; thus, SHIP2 functions as a tumor suppressor in NAFLD-HCC. Moreover, owing to its ability to produce PI(3,4)P2, SHIP2 can actually promote Akt activation, and SHIP2 inhibition can kill breast and colon cancer cells; thus, SHIP2 may function as an oncogene as well [59, 60]. However, the role of SHIP2 in different tumors remains to be determined. Taken together, the present results reveal a new mechanism by which LINC01468-mediated lipogenesis promotes NAFLD-HCC progression through the CUL4A-linked degradation of SHIP2. LINC01468 acts as a crucial driver of NAFLD-HCC progression and chemoresistance, highlighting the value of the LINC01468-SHIP2 axis as a potential therapeutic target for HCC. Normal human hepatocyte THLE2 and the HCC cell lines (Huh7, SNU-449, SNU-182, and HCC-LM3) were obtained from the American Type Culture Collection (ATCC, USA) (Supplementary Table 1). The clinical tumor and adjacent matched non-tumor tissues were collected from patients with NAFLD-HCC (n = 26) at the First Affiliated Hospital of Guangxi Medical University (Table 1). All studies involving human samples were reviewed and approved by the ethics committee of the First Affiliated Hospital of Guangxi Medical University, and written informed consent was obtained from all patients based on the Declaration of Helsinki. pTNM classification advocated by the International Union against Cancer was uused to determine tumor grade and classification. CCK8 assay, Reverse transcription quantitative polymerase chain reaction (RT-qPCR),cell proliferation assay and drug treatment, Immunohistochemistry (IHC), methylated RNA immunoprecipitation qPCR (MeRIP-qPCR), xenograft assay, RNA immunoprecipitation (RIP) assay, RNA pull-down assay, Histological analysis for lipid droplet determination, triglyceride and cholesterol assay, m6A quantification, coimmunoprecipitation, and western blot analysis, RNA fuorescence in situ hybridization (RNA-FISH) assay are further described in Supplementary materials and methods. Statistical analysis was conducted in the GraphPad Prism v8.0 (GraphPad, Inc., USA) and the Statistical Software Package for Social Sciences (v 22.0; SPSS, Inc., Chicago, IL, USA). Differences were considered statistically significant at P < 0.05. Pearson’s correlation analysis was fitted between two selected genes in clinical tumor tissues. Supplementary Figures Supplementary materials and methods Supplementary Tables Unprocessed WB

PMC9640654

Aoran Luo,Xiaoxiao Lan,Qiongzi Qiu,Qing Zhou,Jia Li,Mengting Wu,Pengyuan Liu,Honghe Zhang,Bingjian Lu,Yan Lu,Weiguo Lu

LncRNA SFTA1P promotes cervical cancer progression by interaction with PTBP1 to facilitate TPM4 mRNA degradation

07-11-2022

Cervical cancer,Diagnostic markers

Long non-coding RNAs (lncRNAs) play key roles in cancer development and progression. However, the biological function and clinical significance of most lncRNAs in cervical cancer remain elusive. In this study, we explore the function and mechanism of lncRNA surfactant associated 1 (SFTA1P) in cervical cancer. We firstly identified SFTA1P by analyzing the RNA sequencing data of cervical cancer from our previous study and from The Cancer Genome Atlas (TCGA). We then verified SFTA1P expression by qRT-PCR. The cell proliferation and migration capacity of SFTA1P was assessed by using CCK-8, colony formation, transwell and wound healing assays. RNA pull-down, RNA immunoprecipitation (RIP), RNA stability and western blot assays were used to reveal potential mechanisms. Athymic nude mice were used to evaluate tumorigenicity and metastasis in vivo. SFTA1P is upregulated in cervical tumor tissues and its high expression is associated with poor prognosis. Biologically, knockdown of SFTA1P inhibited the proliferation, migration, and invasion of cervical cancer cells in vitro, as well as tumorigenesis and metastasis in vivo. Mechanistically, SFTA1P was shown to interact with polypyrimidine tract binding protein 1 (PTBP1) to regulate the stability of tropomyosin 4 (TPM4) mRNA, thereby resulting in malignant cell phenotypes. TPM4 knockdown could attenuate the suppression of cell progression induced by either SFTA1P or PTBP1 knockdown. Our findings demonstrate that SFTA1P can promote tumor progression by mediating the degradation of TPM4 mRNA through its interaction with PTBP1 protein. This provides a potential therapeutic strategy to target the SFTA1P-PTBP1-TPM4 axis in cervical cancer.

LncRNA SFTA1P promotes cervical cancer progression by interaction with PTBP1 to facilitate TPM4 mRNA degradation Long non-coding RNAs (lncRNAs) play key roles in cancer development and progression. However, the biological function and clinical significance of most lncRNAs in cervical cancer remain elusive. In this study, we explore the function and mechanism of lncRNA surfactant associated 1 (SFTA1P) in cervical cancer. We firstly identified SFTA1P by analyzing the RNA sequencing data of cervical cancer from our previous study and from The Cancer Genome Atlas (TCGA). We then verified SFTA1P expression by qRT-PCR. The cell proliferation and migration capacity of SFTA1P was assessed by using CCK-8, colony formation, transwell and wound healing assays. RNA pull-down, RNA immunoprecipitation (RIP), RNA stability and western blot assays were used to reveal potential mechanisms. Athymic nude mice were used to evaluate tumorigenicity and metastasis in vivo. SFTA1P is upregulated in cervical tumor tissues and its high expression is associated with poor prognosis. Biologically, knockdown of SFTA1P inhibited the proliferation, migration, and invasion of cervical cancer cells in vitro, as well as tumorigenesis and metastasis in vivo. Mechanistically, SFTA1P was shown to interact with polypyrimidine tract binding protein 1 (PTBP1) to regulate the stability of tropomyosin 4 (TPM4) mRNA, thereby resulting in malignant cell phenotypes. TPM4 knockdown could attenuate the suppression of cell progression induced by either SFTA1P or PTBP1 knockdown. Our findings demonstrate that SFTA1P can promote tumor progression by mediating the degradation of TPM4 mRNA through its interaction with PTBP1 protein. This provides a potential therapeutic strategy to target the SFTA1P-PTBP1-TPM4 axis in cervical cancer. Cervical cancer is the most common gynecologic cancers in women, with 604,127 new cases and 341,831 deaths worldwide each year [1, 2]. Its high prevalence (13.3/100000) and mortality (7.3/100000) impose a heavy burden on public health [1]. Nearly 95% of cervical cancers are caused by persistent infection with high-risk human papillomavirus (HPV) [3]. Although the incidence of cervical cancer recently has declined because of widespread vaccination and screening, cervical cancer still causes a serious threat to women’s reproductive health [4]. Whilst primary surgery with radiotherapy is the main treatment for early stage cervical cancer [5], there remains no effective treatment strategy for advanced metastatic cervical cancer. As a result, cervical cancer still accounts for a significant proportion in cancer-related deaths in women [6]. Therefore, further study of the molecular mechanism of cervical carcinogenesis and progression remains of high priority, particularly towards the exploration of new methods for early diagnosis and treatment. Long non-coding RNAs (lncRNAs) are a class of functional RNAs over 200 nucleotides in length with little or no protein-coding potential, accounting for a large percentage of non-coding RNAs [7, 8]. Although lncRNAs had been formerly viewed as background noise from junk DNA, accumulating evidence has been more recently suggesting that lncRNAs are involved in various biological processes including differentiation [9], apoptosis [10], inflammation [11] and especially cancer [12]. Numerous studies have also reported that lncRNAs can regulate cellular viability [13], proliferation [12, 14], migration [15, 16], and angiogenesis [17, 18] in cancers. Overall, it has been widely suggested that lncRNA could regulate genes at epigenetic, transcriptional and translational levels [19–21]. Though some studies have begun to fill in some details, the specific molecular mechanisms of lncRNAs in many cancers remain to be further elucidated. SFTA1P is a novel lncRNA located in Chromosome 10p14 with a full length of 693 bp. Previous studies have demonstrated that SFTA1P is downregulated in lung cancers, with such a downregulation associated with cell migration and invasion [22–24]. In gastric cancer, SFTA1P acts as a tumor suppressor by influencing cell proliferation and migration via down-regulating TP53 [25], while in hepatocellular carcinoma, SFTA1P acts more like an oncogene by down-regulating miR-4766-5p via the PI3K/AKT/mTOR signaling pathway [26]. However, there has been no corresponding studies on SFTA1P in cervical cancer, and any potential roles of SFTA1P in this context remain to be revealed. In the present study, we verified that lncRNA SFTA1P is overexpressed in cervical cancer tissues and is associated with poor prognosis. In vitro and in vivo functional studies showed that SFTA1P promotes cervical cancer cell proliferation and migration. Analysis of its mechanism revealed that lncRNA SFTA1P regulates cervical cancer progression by interacting with PTBP1 protein to facilitate TPM4 decay. Our findings provide a potential biomarker and therapeutic target for cervical cancer. To study the potential role of lncRNAs in cervical cancer, we reanalyzed the RNA-seq data of 90 tumors and 39 adjacent normal tissues from patients with cervical cancer from our previous study [27]. We identified 17,082 lncRNAs, 4063 of which were expressed in more than 25% of the samples with an average FPKM of >0.1. These were retained for subsequent differential expression analysis. In these 1912 lncRNAs showed significant differences in expression between tumor and normal tissues (|t-statistics|>1.96, p-value < 0.05). We then evaluated the association of these significant lncRNAs with the prognosis of cervical cancer patients, as noted in the The Cancer Genome Atlas (TCGA) database. This analysis yielded 500 lncRNAs associated with overall survival of cervical cancer patients (|z-score|>1.96, p-value < 0.05). Forty-six lncRNAs were found to be upregulated in tumors and had a poor prognosis, with SFTA1P showing the most significant association with overall survival (Fig. 1A, B). qRT-PCR analysis of 20 pairs of new cervical cancer and adjacent normal tissues also verified that SFTA1P was highly expressed in cervical cancer tissues in most cases (Fig. 1C). Kapan-Meier survival analysis based on RNA-seq data of cervical cancer from TCGA showed patients with higher expression of SFTA1P had worse prognosis than those with lower expression of SFTA1P (Fig. 1D, E). Use of the Coding Potential Assessment Tool (CPAT) [28] and Coding Potential Calculator 2 (CPC2) [29] further confirmed that SFTA1P is a non-coding RNA with no protein-coding potential (Fig.1 F, G). To investigate the biological functions of SFTA1P in vitro, we investigated the expression of SFTA1P in seven cervical cancer cell lines. The expression level of SFTA1P was relatively high in CaSki, C33A, and C-4 I (Fig. 2A). Thus we knocked down SFTA1P by transfecting these three cell lines with siRNAs targeting SFTA1P (Fig. 2B and Supplementary Fig.S1A). Knockdown of SFTA1P significantly inhibited cell proliferation in CaSki, C-4 I and C33A cell lines and colony formation in CaSki and C-4 I (Fig. 2C-D and Supplementary Fig. S1B). Conversely, overexpression of SFTA1P significantly promoted cell growth in SiHa cells, but only marginally in HeLa (Supplementary Fig. S1C-D). In addition, SFTA1P knockdown could increase the G0/G1 cell proportion and decrease the S phase cell proportion compared with the controls, indicating that depletion of SFTA1P causes G1 arrest (Fig. 2E). To further examine the function of SFTA1P in vivo, we subcutaneously injected CaSki or C-4 I cells stably knocked down SFTA1P into the flanks of nude mice. Nude mice in the Sh-SFTA1P (SFTA1P knockdown) group had significantly smaller tumor volume and weight than the control group (Supplementary Fig. S1E), suggesting SFTA1P as a promoter of tumorigenicity of cervical cancer cells in vivo. To explore the metastatic ability of SFTA1P in cervical cancer cells, we carried out migration and invasion assays. SFTA1P knockdown significantly reduced migration and invasion ability of CaSki and C-4 I cells (Fig. 3A, B). While SFTA1P overexpression could promote cell migration of SiHa and Hela cells (Supplementary Fig. S1F). Wound healing assays were also used to verify the function of SFTA1P knockdown in cervical cancer cells and the results showed the relative migration distances were decreased in the si-SFTA1P group as compared with the control group (Fig. 3C). To further evaluate the effect of SFTA1P on tumor invasion in vivo, athymic nude mice were injected intravenously with C-4 I cells stably transfected with sh-scramble or sh-SFTA1P via the tail vein. It was confirmed that knockdown of SFTA1P could reduce in vivo metastasis of cervical cancer cells (Fig. 3D). To explore the underlying mechanism of SFTA1P, RNA-FISH was first used to determine the subcellular localization of SFTA1P in cervical cancer cells. SFTA1P was mainly localized in the cytoplasm of CaSki and C-4 I cells (Fig. 4A), indicating that SFTA1P may regulate target protein expression at the posttranscriptional level by sponging microRNAs or modulating RBPs [30]. Then, we performed RNA pull down assays in C-4 I cells to identify potential SFTA1P-interacting proteins. Distinct bands between control lacZ and SFTA1P sense, with weights between 55 and 70 kDa, were excised from the gel and then subjected to mass spectrometry analysis (Fig. 4B). Proteins with incorrect molecular weights or non-specifically bound proteins on control lacZ and SFTA1P sense were excluded. PTBP1 was selected as one of the top candidates for follow-up research (Fig. 4C and Supplementary Fig. S2B, C). The physical interaction between SFTA1P and PTBP1 was further validated by western blot with SFTA1P antisense and sense (Fig. 4D) and RIP analysis with PTBP1 antibodies (Fig. 4E). This was consistent with the prediction of the RBPmap database (http://rbpmap.technion.ac.il/) predicting that SFTA1P may bind to PTBP1 (Supplementary Fig. S2A). PTBP1 is an RNA-binding protein with 4 RNA recognition motifs (RRMs) according to Uniprot (https://www.uniprot.org/). To determine structural determinants of the interactions between SFTA1P and PTBP1, we carried out deletion mapping of PTBP1 functional domains. After transfecting plasmids with Flag tag, we examined their ability to bind to SFTA1P by RNA pull-down assay, followed by Flag protein immunoblotting analysis. The interaction of SFTA1P and PTBP1 with either RRM3 domain or RRM4 domain deletion was decreased (Fig. 4F, G), suggesting these two domains may be key structures for PTBP1 to bind SFTA1P. It has been previously reported that PTBP1 plays a tumor-promoting role in cancer progression [31–33] and is associated with cervical lesion progression and carcinogenesis in our former studies [34, 35]. To find related downstream genes, we performed RNA-seq on SFTA1P knockdown cells (upper panel in Fig. 5A) and simultaneously analyzed PTBP1 RNA-seq data downloaded from the GEO database (GSE168907) (lower panel in Fig. 5A). The Venn diagram shows that there were 68 genes co-regulated by SFTA1P and PTBP1 (Fig. 5B). Considering that SFTA1P and PTBP1 have the same tumor-promoting roles in cervical cancer, we focused on genes that were simultaneously up- or down-regulated in SFTA1P knockdown and PTBP knockdown cancer cells. We examined several genes by qPCR and western blot analyses and showed that SFTA1P and PTBP1 knockdown could consistently increase both mRNA and protein levels of TPM4 to a greater extent, suggesting that TPM4 is regulated by SFTA1P and PTBP1. According to our previous RNA-seq data [27], CASP7, EMC6 and PERP were upregulated in cervical tumor tissues, which seemed to conflict with the qPCR results of SFTA1P knockdown (Supplementary Fig. S3A). Therefore these three genes were excluded. BCAP31 was also excluded as it was not significantly different in western blots between si-PTBP1 and control groups (Supplementary Fig. S3B, C). Taken together, we selected TPM4 as the main downstream candidate of SFTA1P and PTBP1 for follow-up studies. Whilst previous studies have reached contradictory conclusions as to whether TPM4 acts as either an oncogene or anti-oncogene in human cancers [36–39], its role in cervical cancer remains unclear. Our RNA-seq data showed that TPM4 is down‐regulated in cervical cancer tissues (Fig. 6A). To investigate the biological functions of TPM4 in cervical cancer, we detected the expression of TPM4 in cervical cancer cell lines and knocked down TPM4 using siRNAs in CaSki and C-4 I cells with relatively high basal TPM4 expression (Fig. 6B and Supplementary Fig. S4A). CCK8 and colony formation indicated that TPM4 knockdown significantly promoted proliferation in cervical cancer cells (Fig. 6C, D). In contrast to SFTA1P knockdown, TPM4 knockdown induced a decrease in the proportion of cells in the G1-phase accompanied by a corresponding increase in the S-phase (Fig. 6E). Results from transwell assays and wound healing also demonstrated that TPM4 knockdown promoted the migration and invasion of cervical cancer cells (Fig. 6F–H). Taken together, these results imply that TPM4 may act as a tumor suppressor in cervical cancer cells. To investigate whether SFTA1P mediates the function of TPM4 in cervical cancer cells, we co-transfected si-SFTA1P and si-TPM4 in CaSki and C-4 I cells (Fig. 7A). Decreased migration induced by SFTA1P knockdown was rescued by co-knockdown of SFTA1P and TPM4 (Fig. 7B). Similarly, we also explored the role of PTBP1 in regulating the function of TPM4 in cervical cancer. Results indicated that the reductions in cell proliferation and migration induced by PTBP1 knockdown was rescued by the co-knockdown of PTBP1 and TPM4 (Fig. 7C–E). Previous studies have shown that PTBP1 can bind to target mRNAs and influence their stability [40, 41]. Therefore, we explored whether PTBP1 could bind to TPM4 mRNA. RBPmap predicted that TPM4 mRNA may contain potential PTBP1 binding sites within its 3’UTR (Supplementary Fig. S5). RNA pull-down and RIP assays also verified binding between PTBP1 and TPM4 mRNA (Fig. 7F, G). It was also demonstrated that SFTA1P knockdown significantly reduced the enrichment of TPM4 mRNA by PTBP1 (Fig. 7G). Finally, an additional RNA stability experiment showed that either SFTA1P or PTBP1 knockdown led to increased TPM4 mRNA stability (Fig. 7H). All of the above suggested that SFTA1P and PTBP1 promote the malignant process of cervical cancer cells by regulating TPM4 mRNA stability. Over recent years, lncRNAs have received increasing attention due to their biological functions in various diseases. Among these, it has been reported that lncRNAs play a critical role in various cancers, such as lung, breast and colorectal cancer [12, 42, 43]. Previous studies have showed that whilst LncRNA SFTA1P is up-regulated in hepatocellular carcinomas it is down-regulated in lung carcinoma and gastric cancer [24–26]. The present study revealed that SFTA1P is upregulated in cervical cancer tissues, its higher expression being highly predictive of worse prognosis. We further demonstrated that SFTA1P acts as an oncogene by promoting cervical cancer cell proliferation, migration, and invasion, both in vitro and in vivo. Increasing evidence suggests that the function of lncRNAs is closely related to their subcellular localization [30]. Our FISH assay demonstrated that SFTA1P is mainly localized in the cytoplasm, indicating that SFTA1P may regulate target protein expression at a posttranscriptional level by sponging microRNAs or modulating RBPs. Thus, we utilized RNA pull-down and mass spectrometry analysis to identify potential proteins interacting with SFTA1P, with PTBP1 being the lead candidate. An RIP assay further verified the physical interaction between SFTA1P and PTBP1. PTBP1 is a member of the heterogeneous nuclear ribonucleoprotein (hnRNP) family, which is involved in splicing regulation, IRES-mediated translation initiation, and mRNA stability [13, 40, 41, 44]. It has been reported to be up-regulated to exert tumor-promoting roles in a variety of cancers including cervical cancer [31–35]. However, the biological functions of PTBP1 in cervical cancer remains to be explored. Considering that SFTA1P and PTBP1 share the same tumor-promoting effect, we focused on genes that were simultaneously up- or down-regulated in SFTA1P RNA-seq and PTBP1 RNA-seq experiments. By qPCR and western blot analyses, we found that TPM4 was the most likely downstream gene of the SFTA1P-PTBP1 complex. Although there are a few studies on the biological function of TPM4 in cancers [36–39], the role of TPM4 in cervical cancer remains unclear. In the present study, we demonstrated that TPM4 knockdown can promote the proliferation and migration of cervical cancer cells, suggesting that TPM4 acts as a tumor suppressor in cervical cancer. Furthermore, TPM4 knockdown rescued the reductions of malignant phenotypes induced by SFTA1P and PTBP1, suggesting that SFTA1P and PTBP1 may function through TPM4. We then explored the underlying mechanism by which SFTA1P and PTBP1 influence TPM4 in cervical carcinogenesis and progression. PTBP1 is a canonical RNA-binding protein which has many functions including alternative splicing, mRNA stability and polyadenylation [45]. We thus investigated the role of SFTA1P and PTBP1 in regulating TPM4 mRNA. RNA pull down and RIP assays showed that PTBP1 could bind with TPM4 mRNA, which was attenuated by SFTA1P knockdown. Knockdown of SFTA1P and PTBP1 increases the stability of TPM4 mRNA, suggesting that PTBP1 can promote the degradation of TPM4 mRNA, and that this degradation will be enhanced in the presence of SFTA1P. Finally, there are some limitations in this study. Whether PTBP1 can act on TPM4 through its other functions, such as alternative splicing, remains unanswered. The clinical therapeutic potential of the SFTA1P-PTBP1-TPM4 axis in cervical cancer also awaits further investigation. In conclusion, the lncRNA SFTA1P is up-regulated and associated with poor prognosis in cervical cancer. SFTA1P can promote cervical carcinogenesis and progression by regulating PTBP1 protein-mediated degradation of TPM4 mRNA. These findings provide a potential therapeutic strategy to target the SFTA1P-PTBP1-TPM4 axis in cases of cervical cancer. To evaluate the expression of SFTA1P, 20 paired cervical cancer tissue samples, with corresponding adjacent normal tissues from the surgical specimen archives of Women’s Hospital of Zhejiang University School of Medicine (Hangzhou, China) were obtained at the time of diagnosis and prior to the initiation of any treatment. RNA was extracted from snap-frozen tissue specimens stored at −80 °C in liquid nitrogen. The diagnosis was confirmed by reviewing H&E slides by a gynecologic pathologist. Research involving human subjects was conducted in accordance with the International Ethical Guidelines for Biomedical Research. All subjects participating in the study provided informed consent. RNA-seq data of 90 tumors and 39 adjacent normal tissues from patients with cervical cancer were obtained from our previous study [27]. After read alignment, transcript assembly and quantification, as previously described, differentially expressed genes and lncRNAs between tumors and adjacent normal tissues were calculated using a Student’s t-test. The Bonferroni correction was used to adjust p-values. Our previous workflow with parameter tuning was followed for the prediction of lncRNAs [46]. Briefly, transcripts with multi exons and >160 bp in length were kept for downstream analysis. PhyloCSF was used to access the protein coding potential of the remaining transcripts by aligning them to genomes from multiple species, including chimpanzee, rhesus monkey, mouse, guinea, pig, cow, horse and dog [47]. Transcripts that met any of the following criteria were discarded: PhyloCSF score >50, complete branch length (CBL) > 0, open reading frame (ORF) > 150 amino acids, or CBL = 0 but OFR > 50. Finally, transcripts with a median E-value greater than 1e−18 by blastx were retained as candidate lncRNAs. RNA-Seq data and clinical data of cervical cancers from TCGA were downloaded from the GDC Data Portal (https://portal.gdc.cancer.gov/) as described in our previous study [27]. The association of each lncRNA with overall survival was calculated using a univariate Cox proportional hazards model. P-values, hazard ratios with a 95% confidence interval and z-scores were calculated. Survival analyses were performed using the R package “survival”. Cervical cancer cell lines C33A, CaSki, C-4 I, SiHa, DOTC2 4510 and HT-3 were purchased from ATCC (Supplementary Figs. S6–S8). HeLa was a gift from other lab and authenticated by STR typing (Supplementary Fig. S9). CaSki was cultured in 1640, C-4 I was in Waymouth’s MB 752/1, other cell lines were in MEM medium, respectively, containing 10% FBS, 100 ng/mL streptomycin, 100 U/mL penicillin and 2 umol/mL in 5% CO2, in a 37 °C cell incubator. The cells were subcultured when the degree of fusion reached 80–90%. RNA extraction and RT-qPCR were conducted for each sample as previously described [34, 48]. All primers are listed in Supplementary Table S1. The C33A, CaSki and C-4 I cells were cultured to 50% confluence in 6-well plates and were transfected by using transfection reagents (SignaGen, Frederick, MD) according to the manufacturer’s instructions. The silencing effect of siRNA interference was detected 24 h after transfection. C33A (4000 cells/well), CaSki (2000cells/well), and C-4 I(4000cells/well) were transfected with siRNAs and then were plated in a 96-well plate. Cell Counting Kit-8 (CCK-8) (Dojindo, Tokyo, Japan) was used to measure cell proliferation at 24, 48, 72 and 96 h after transfection. The absorbance at 450 nm was measured by microplate reader. Colony formation assays were performed to monitor the clonality of cervical cancer cells. Treated CaSki (1000 cells/well) and C-4 I (2000/well) cells were seeded into 6-well plates and cultured for 10 days. Colonies were stained with crystal violet after formaldehyde fixation. The number of visible colonies was counted by ImageJ software (https://imagej.net/). Each experiment was repeated three times. Cell migration and invasion assays were carried out using 24-well transwell plates (Corning Costar, Tewksbury, MA, USA). Cervical cancer cells were transfected with siRNA or negative control for 24 h and then starved for 24 h with serum free medium. 1 × 105 cells for CaSki and 1.5 × 105 cells for C-4 I were plated with 300 μL serum-free media into uncoated or matrigel-coated upper chamber for migration or invasion assay. The lower chambers were filled with medium supplemented with 20% FBS. Plates were incubated in 5% CO2 at 37 °C overnight. Each membrane was photographed and migratory cells were counted under a microscope. The wound healing assays were performed by using culture-inserts (Ibidi GmbH, Munich, Germany) as described previously [34]. Cells were seeded in 70 μL medium at a density of 7 × 105 cells/mL (CaSki) and 10 × 105 cells/mL (C-4 I) on each side of the culture-inserts, into 12-well plate. The inserts were removed after 24 h, and the cells were washed with phosphate-buffered saline (PBS). The wound healing ratio was determined by collecting images at the indicated time points. Cell cycle progression was determined using a Cell Cycle and Apoptosis Analysis Kit (C1052, Beyotime, Shanghai, China) according to the manufacturer’s instructions. The stained cells were analyzed by flow cytometry (Beckman Coulter Cytoflex, Beckman, USA). Cells were lysed using RIPA buffer (Beyotime) containing protease and phosphatase inhibitors. Cellular lysates were centrifuged at 12,000 rpm for 20 min and then denatured in boiling water for 10 min. Total proteins were separated using sodium dodecyl surfate-polyacrylamide gel electrophoresis (SDS-PAGE) and transferred onto polyvinylidene fluoride (PVDF) membranes. Incubation with antibodies was performed after blocking the membrane with 5% skim milk. The antibodies used were as follows:β-actin, Flag, PTBP1, TPM4, BCAP31 and MCCC2 (Supplementary Table S2). For the in vivo tumorigenicity assay, 4–5 weeks old female BALB/c nude mice were randomly divided into two groups. CaSki and C-4 I cells stably transfected with sh-scramble or sh-SFTA1P were dissociated using trypsin and washed twice with sterilized PBS. Then, 100 μL of PBS containing 2 × 106 cells was subcutaneously inoculated into the flank of mice. Tumor growth was measured after 6 days of tumor implantation. Two (CaSki) or three (C-4 I) weeks after inoculation, the mice were sacrificed according to the policy for treating tumor-bearing animals humanely. For the in vivo invasion assay, 2 × 106 C-4 I cells stably transfected with sh-scramble or sh-SFTA1P were injected intravenously into the tail vein of nude mice. C-4 I cells stably transfected with sh-scramble or sh-SFTA1P were injected intravenously into the tail vein of nude mice. Luciferin (Gold Biotech, St Louis, MO, USA) was administered weekly to the mice by intraperitoneal injection. Twenty minutes after each administration, the mice were imaged using IVIS@ Lumina II system (Caliper Life Sciences, Hopkinton, MA, USA). All experiments were performed in accordance with the Guide for the Care and Use of Laboratory Animals (NIH publication 80–23, revised 1996) and the approval of the Zhejiang University animal ethics committee. The RNA pull-down assays were performed using a Pierce™ Magnetic RNA-Protein Pull-down Kit (Thermo Scientific, Irwindale, CA, USA, Catalog # 20164) [49]. Briefly, biotin-labeled DNA probes including anti-sense and sense probes were incubated with streptavidin magnetic beads for 3 h at room temperature. The lysates of the cells were incubated overnight at 4 °C with streptavidin magnetic beads. Proteins bound to magnetic beads were separated using SurePAGE and excised for mass spectrometry analysis (Lumingbio, Shanghai, China). RIP assay was implemented with a Magna RNA immunoprecipitation kit (Millipore, Bedford, MA, USA) according to the manufacturer’s instructions. Briefly, cell suspension was prepared in RIP buffer. Cell suspensions were incubated overnight at 4 °C with anti-PTBP1 antibody (Abcam). After precipitation, RNA was purified and analyzed by qRT-PCR. CaSki cells and C33A cells transfected with si-NC or si-SFTA1P were cultured for 48 h after transfection. Each group prepared three independent assay samples. The total RNA was extracted using Trizol (Invitrogen). A TruSeq RNA Sample Prep Kit (Illumina) was used to prepare DNA libraries. To ensure uniform cluster density, Illumina’s qPCR quantification guide was used to quantify libraries. RNA-seq data of SFTA1P knockdown (Supplementary Table S3) was aligned to the human genome (hg19) using TopHat2 (v 2.0.13) [50]. Transcripts were assembled from RNA-seq alignments using Stringtie2 (v2.1.0) [51]. An evaluation of gene expression was based on the fraction of fragments per kilobase of transcript per million reads mapped (FPKM). Gene expression differences between knockdown and control cells were detected using a linear model with cell line as a covariate using the R statistical package. Cells were transfected with siRNA or negative control for 48 h and added actinomycin-D (5 μg/ml) to block mRNA synthesis. RT-qPCR analysis of total RNA was carried out at different time points. The relative abundance of TPM4 mRNA was calculated using the ΔΔCt method and normalized to GAPDH. At 0 h following actinomycin D treatment, mRNA was arbitrarily set to 1. Data are expressed as the mean ± SD. Differences between groups were examined by either Student’s t test or one- or two-way ANOVA. Statistical significance was defined as p < 0.05. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001. Data analyses were carried out using the GraphPad Prism 8.0 (GraphPad Software). SFTA1P-Supplementary Materials Original Data File Reproducibility checklist

PMC9640682

Paria Bayati,Mahsa Kalantari,Mohammad-Ali Assarehzadegan,Hadi Poormoghim,Nazanin Mojtabavi

MiR-27a as a diagnostic biomarker and potential therapeutic target in systemic sclerosis

07-11-2022

Rheumatic diseases,Skin diseases,Medical research,Rheumatology

Systemic sclerosis (SSc) or scleroderma is a multiorgan rheumatoid disease characterized by skin tightening or organ dysfunction due to fibrosis, vascular damage, and autoimmunity. No specific cause has been discovered for this illness, and hence no effective treatment exists for it. On the other hand, due to the lack of diagnostic biomarkers capable of effectively and specifically differentiating the patients, early diagnosis has not been possible. Due to their potent regulatory roles in molecular pathways, microRNAs are among the novel candidates for the diagnosis and treatment of diseases like SSc. MiR-27a is a microRNA known for its role in the pathogenesis of fibrosis and cancer, both of which employ similar signaling pathways; hence we hypothesized that Mir-27a could be dysregulated in the blood of individuals affected by SSc and it might be useful in the diagnosis or treatment of this disease. Blood was collected from 60 SSc patients (30 limited and 30 diffuse) diagnosed by a rheumatologist according to ACR/AULAR criteria; following RNA isolation and cDNA synthesis; real-time qPCR was performed on the samples using Taq-Man probes and data were analyzed by the ΔΔCT method. Also, potential targets of miR-27a were evaluated using bioinformatics. It was revealed that miR-27a was significantly down-regulated in SSc patients in comparison to healthy individuals, but there was no difference in miR-27 expression between limited and diffused SSc patients. Besides, miR-27a was found to target several contributing factors to SSc. It seems that miR-27a has a protective role in SSc, and its downregulation could result in the disease's onset. Based on bioinformatics analyses, it is speculated that miR-27a likely targets factors contributing to the pathogenesis of SSc, which are elevated upon the downregulation of miR-27a; hence, miR-27a mimics could be considered as potential therapeutic agents for the treatment of SSc in future studies. Since no difference was observed between limited and diffuse patient groups, it is unlikely that this microRNA has a role in disease progression. According to ROC analysis of qPCR data, miR-27a could be employed as a valuable diagnostic biomarker for SSc.

MiR-27a as a diagnostic biomarker and potential therapeutic target in systemic sclerosis Systemic sclerosis (SSc) or scleroderma is a multiorgan rheumatoid disease characterized by skin tightening or organ dysfunction due to fibrosis, vascular damage, and autoimmunity. No specific cause has been discovered for this illness, and hence no effective treatment exists for it. On the other hand, due to the lack of diagnostic biomarkers capable of effectively and specifically differentiating the patients, early diagnosis has not been possible. Due to their potent regulatory roles in molecular pathways, microRNAs are among the novel candidates for the diagnosis and treatment of diseases like SSc. MiR-27a is a microRNA known for its role in the pathogenesis of fibrosis and cancer, both of which employ similar signaling pathways; hence we hypothesized that Mir-27a could be dysregulated in the blood of individuals affected by SSc and it might be useful in the diagnosis or treatment of this disease. Blood was collected from 60 SSc patients (30 limited and 30 diffuse) diagnosed by a rheumatologist according to ACR/AULAR criteria; following RNA isolation and cDNA synthesis; real-time qPCR was performed on the samples using Taq-Man probes and data were analyzed by the ΔΔCT method. Also, potential targets of miR-27a were evaluated using bioinformatics. It was revealed that miR-27a was significantly down-regulated in SSc patients in comparison to healthy individuals, but there was no difference in miR-27 expression between limited and diffused SSc patients. Besides, miR-27a was found to target several contributing factors to SSc. It seems that miR-27a has a protective role in SSc, and its downregulation could result in the disease's onset. Based on bioinformatics analyses, it is speculated that miR-27a likely targets factors contributing to the pathogenesis of SSc, which are elevated upon the downregulation of miR-27a; hence, miR-27a mimics could be considered as potential therapeutic agents for the treatment of SSc in future studies. Since no difference was observed between limited and diffuse patient groups, it is unlikely that this microRNA has a role in disease progression. According to ROC analysis of qPCR data, miR-27a could be employed as a valuable diagnostic biomarker for SSc. Systemic sclerosis (SSc) is an inflammatory disease of connective tissue with the hallmark characteristic of over-accumulation of the extracellular matrix, leading to fibrosis in the skin and different organs like lungs and kidneys; as a result, the affected individuals may experience vasculopathy and subsequently organ failure. The patients are placed into two distinct subcategories of limited and diffused, based on their symptoms and skin fibrosis pattern; with the limited form being the less severe, affecting only distal parts of the body and having less internal organs involvement, and the diffused form which affects the trunk and internal organs too. Patients affected with SSc experience a range of symptoms and complications; they are also prone to develop other autoimmune disorders like lupus and cancer. So far, there is no specific cure for this disease, mostly because there are many unknown aspects of this disease, and treatment approaches are symptom-oriented. Because of the wide range of symptoms that SSc bears, diagnostic approaches for this disease are also challenging and the most accepted criteria stated by the ACR/EULAR are also subject to change from time to time. Accordingly, there are different estimations concerning the prevalence and incidence of SSc; for instance, the prevalence of SSc in North America is reported as wide as 13.5 to 44.3 per 100,000 individuals. Since conventional treatment approaches are not helpful to cease SSc progression and efficiently ameliorate the symptoms, physicians and scientists consider exploring new approaches such as employing microRNAs in the treatment of SSc. Besides, finding an effective diagnostic biomarker that could facilitate diagnosing SSc at early onset, has been a major interest for researchers. Such a biomarker should be capable of identifying most of the patients or in other words, shows a high profile of sensitivity while also demonstrating a high profile of specificity as well. But currently, the majority of accepted diagnosing criteria for SSc are based on the disease complications rather than laboratory biomarkers; this results in the failure of the very early detection of SSc. On the other hand, the available laboratory biomarkers such as auto-antibodies against topoisomeraseI, centromere or RNA polymeraseIII are not found in all SSc patients; besides, they are not much specific and are found in other rheumatoid diseases too. Furthermore, some newer biomarkers such as Lungen-6 (KL-6), surfactant protein-D (SP-D), and CCL18 are limited to the detection of complications such as interstitial lung disease (ILD). As a result, exploring new approaches such as utilizing microRNAs which could detect the disease rather than the associated complications would be very promising for addressing the issues discussed so far. MicroRNAs (miRNAs) are non-coding short sequences of ribonucleotides (about 22 nucleotides) that are capable of binding to other RNAs through their 3' UTR, which results in the blockade of protein synthesis from their targets or degradation of those targets. One of the most crucial features of miRNAs is their secretion from the host cell where they were transcribed and processed in the first place; hence they are present not only in the cells but unlike their mRNA targets, they can be found nearly in all of the body fluids including blood and even urine in a highly stable form since they are protected from RNases due to their interactions with proteins. This class of small RNAs is implicated in regulating different aspects of cellular physiology, including proliferation, apoptosis, and different signaling pathways, making them responsible for various abnormalities in cellular functions leading to diseases such as malignancies. MiR-27, as its name implies is a microRNA, which is known for contributing to the pathogenesis of cancer, and also it is well known for being involved in adipogenesis. One of the key players in fibrosis and associated disorders is TGF-β, which has a central role in the pathogenesis of SSc and almost affects all the aspects of fibrogenesis in different stages as well as different signaling pathways. Accordingly, a previous study has elucidated the role of miR-27 in a mouse model of pulmonary fibrosis as a negative regulator of TGF-βR1 and samd2, which are crucial components of SSc pathogenesis too. It was also demonstrated that TGB-β1 and smad3 are targeted by mir-27 in cervical cancer. Moreover, a different study on lung cancer has reported the concurrent upregulation of miR-27a and downregulation of two significant mediators of the TGF-β signaling pathway, SMAD2, and SMAD4. Among the potent regulators of the TGF-β signaling pathway are PPAR-γ and PTEN, both of which are reported to be targeted by miR-27a. Besides, there are some studies regarding the effect of miR-27 on NF-κB which also acts downstream of the TGF-β pathway. The hallmark cytokine in fibrosis, interleukin-6 and matrix-metalloproteinase 9 and 13, are also reported to be regulated by miR-27. Moreover, it has been shown that miR-27a expression is also reduced in the lungs of IPF patients and bleomycin-induced lung fibrosis in mice. There are common molecular pathways in cancer and fibrosis which is a hallmark of SSc, and since there is impaired adipogenesis in patients with SSc and mice models of this disease; we aimed to evaluate the expression of miR-27a in the whole blood of SSc patients in order to find any deregulations and in case of obtaining a significant result, to test if it is possible to take advantage of miR-27 differential expression as a diagnostic tool. Sixty female SSc patients (30 limited SSc and 30 diffused SSc) presenting to Firouzgar hospital, Tehran, Iran, were enrolled in this study. Male patients were excluded due to their low numbers. The diagnosis of the patients as well as categorizing them into limited and diffused subgroups was done based on ACR/EULAR criteria by an expert rheumatologist. All patients filled out an informed consent form which was confirmed by the ethics committee of the Iran University of medical sciences (all methods were performed in accordance with the relevant guidelines and regulations by the ethics committee of the Iran University of medical sciences; ethic code: IR.IUMS.FMD.REC1396.30675). 20 consented healthy individuals matching the sex and age of the patients' group were also included as controls. Table 1 gives a concise description of the demographic features of the patients. The majority of patients were on a regimen of prednisolone, azathioprine, chloroquine, phosphoesterase inhibitors, along with NSAIDs upon the time blood was drawn. Whole blood was collected from patients each day between 9 am to 12 pm and was immediately added to RNAZOL BD (MRC) (0.5 ml blood with 1 ml RNAZOL), and total RNA was isolated according to the manufacturer's protocol. After evaluating the RNA concentration and purity by NANODROP (Thermofisher); the RNA was converted into cDNA by TaqMan advanced microRNA synthesis kit (Ambion) according to the kit instructions. qPCR was carried out using TaqMan fast advanced microRNA fast master mix (Ambion) and TaqMan advanced microRNA assay (hsa-miR-27a-3p); thermal cycling was performed by RotorGene 6000(Qiagen) as recommended by the manufacturer protocol. hsa-miR-191a assay was employed for data normalization. Data were analyzed using the ΔΔCT method and were compared using ANOVA by GraphPad PRISM software. P-values lower than 0.05 were considered statistically significant. Using TargetScan, mirpath, mirpathDB, and KEGG pathway, we searched for putative targets of miR-27a which are known to be involved in fibrosis and hence SSc pathogenesis, specifically those contributing to the epithelial to the mesenchymal pathway (EMT). All patients filled out an informed consent form which was confirmed by the ethics committee of the Iran University of medical sciences (ethics code: IR.IUMS.FMD.REC1396.30675). All methods were performed in accordance with the relevant guidelines and regulations by the ethics committee of the Iran University of medical sciences; ethics code: IR.IUMS.FMD.REC1396.30675. Realtime qPCR data are indicative of miR-27 presence in the whole blood. Comparison of RealTime qPCR data based on the expression of miR-27 between limited and diffused SSc patients and the healthy controls revealed a significant downregulation of miR-27 in both SSc groups compared with the healthy individuals; However, no difference was observed between the limited and diffused SSc patients (Fig. 1). In order to evaluate the predictive value of miR-27 as a diagnostic marker for systemic sclerosis patients, receiver operative characteristic (ROC) analysis was performed and the resulting ROC curve showed a cutoff of < 9.975 (log2 of relative expression) and a good area under the curve (0.99) which is demonstrative of a good differentiating marker. The corresponding sensitivity and specificity of the selected cutoff were 0.96 and 0.95 respectively (Fig. 2). For better understanding the possible role of miR-27a in the SSc pathogenesis and progression we further conducted a series of analyses in which the expression of miR-27a was compared between groups positive for a specific symptom or a laboratory factor associated with diagnosis or categorization of the disease; it was shown that the downregulation of miR-27a corresponds with the positive SCL70 antibody, while it was associated with negative ACA. Also, the presence of Th/To ribonucleoprotein autoantibody, ILD, PAH, myositis, and digital ulcers are associated with downregulation of miR-27a, but it is associated with lower frequency of telangiectasia. Although the male cases were omitted from the statistical analyses due to their low abundance; here we compared the expression of miR-27a between the female cases and the 5 male cases and it was observed that miR-27a expression is higher in males (Fig. 3). We also conducted statistical analysis to find correlations between miR-27a expression and patient symptoms like FVC, DLCO, mRSS, CRP, RF and …; although we couldn’t find any significant correlation between miR-27a expression and these symptoms, except for TPO which was found to be significantly corelated with miR-27a (Table 2). TargetScan (http://www.targetscan.org) predicted 1613 genes to be possibly targeted by miR-27a, among those genes, we searched for the genes involved in the several signaling pathways working downstream of the TGF-β which nearly regulates all aspects of fibrogenesis during a healing response, including but not limited to endothelial or epithelial to mesenchymal transition. Especially, the PI3K/AKT/mTOR pathway was considered for bioinformatics evaluations, as this pathway modulates many aspects of cell biology, such as proliferation, survival and protein synthesis; all of which are known to be involved in the pathogenesis of cancer and fibrosis. The genes were identified based on previous studies and data from KEGG as well as miRPathDB. It was found that miR-27a could target and regulate the expression of over 67 genes, known to be cardinals of EMT responsible for the transdifferentiation of fibroblasts into myofibroblasts; these cells overproduce high amounts of extracellular matrix components, including collagen and matrix metalloproteinases (MMPs). Table 3 lists the 67 genes predicted to be targeted by miR-27a, and Fig. 4 schematically illustrates these genes and their role in the development of fibrosis in SSc. Fibrosis-associated disorders like systemic sclerosis (SSc) annually cause the morbidity and mortality of thousands of people around the world, yet there is a lot to be understood to unravel the exact mechanisms involved and hence no curative treatment plan exists for diseases like SSc. The broad range of symptoms and complications and also disease severity are among the main causes that this illness is not fully uncovered. Of the new research targets are microRNAs, which are implicated in all aspects of cell biology, and their roles in different diseases are of significant interest to scientists. Based on the previous studies regarding the role of miR-27 in cancer and fibrosis, which share many similar pathologic aspects, and since this microRNA is implicated in the regulation of the TGF-β signaling pathway as a cardinal regulator of fibrosis, we sought to assess the expression of miR-27 in whole blood of the SSc patients to evaluate its usefulness in the early diagnosis of SSc patients. Also, we aimed to evaluate its capability for distinguishing limited and diffused subsets of the disease, if applicable. The role of miRNAs in fibrosis-associated disorders has been widely studied, and it has been shown that miRNAs can target different aspects of fibrotic procedures. In the current study, the expression of miR-27 in the whole blood of SSc patients was investigated by taking advantage of Real-time qPCR; the statistical analyses demonstrated a significant downregulation of miR-27 in the SSc patients compared with the healthy controls. The expression of miR-27a was also slightly lower in the diffuse group compared with the limited group. According to the ROC analysis, a highly specific and sensitive cutoff was assigned to the qPCR data, suggesting an excellent diagnostic value of miR-27a for diagnosing SSc. We had presumed that the relative expression of miR-27 in diffuse SSc patients would be significantly different from the limited ones, but the results were not different significantly. In further analyses, the expression of miR-27a was compared between different subsets of patients based on the presence or absence of a specific condition or a laboratory marker. It was observed that the expression of miR-27a among those patients who were positive for anti-topoisomerase (anti-SCL70) antibody was significantly downregulated compared to the patients negative for this marker. As this marker significantly correlates with disease severity (diffuse form) and pulmonary involvement, this finding could suggest the potential of miR-27a for differentiating patients from healthy ones as well as utilizing it in the assessment of disease progression, although this hypothesis needs further robust examinations in much larger populations of the SSc patients to draw a comprehensive conclusion. Also, the expression of miR-27a was evaluated in subsets of patients positive for ACA and ribonucleoprotein autoantibodies; it was found that the downregulation of miR-27a is associated with negative ACA, which can be explained by the very low frequency of ACA in Iranian SSc patients, also ACA is more prevalent among the limited subtype of the SSc and this is consistent with our data. Besides, it was shown that the downregulation of miR-27a which is found in diffuse patients is also associated with positive anti-ribonucleoprotein antibodies. Disease manifestations such as ILD, PAH, myositis, and digital ulcers were all associated with the downregulation of miR-27a which is in line with previous results, as the downregulation of miR-27a is associated with the diffuse subtype and the complications are more prevalent among this subset. On the other hand, the downregulation of miR-27a was associated with the absence of telangiectasia. Also, it seems that the expression of miR-27a in male patients is higher than the female one. One major limitation of our data is that there were no recently diagnosed patients in our study, and the majority had an average five years history of being diagnosed with SSc. Due to the challenges regarding the diagnosis of these patients in the early stages, we were unable to find a minimum number of newly diagnosed patients required for obtaining statistically significant results. As illustrated in Fig. 4, miR-27a was predicted to play an essential role in the fibrosis process. This microRNA targets various gene transcripts downstream of the TGF-β pathway which are involved in the transdifferentiation of fibroblasts into myofibroblasts; this process serves as the basis for the development of fibrosis-associated complications, including skin thickening, vascular problems, and organ fibrosis. It should be noted that the epithelial-to-mesenchymal transition (EMT) process in cancer refers to the transition of epithelial cells into ECM-secreting mesenchymal cells but in the course of fibrotic procedures, fibroblasts transformation into myofibroblasts is called fibroblast-to-myofibroblast transition or transdifferentiation. In this regard, we found different studies, which confirm the inhibitory effect of miR-27a on the predicted genes. It was shown that miR-27a suppresses inflammation by targeting IL-6 and MAP kinases like P38 and JNK. Besides, miR-27a was found to inhibit the expression of SMAD signaling mediators that act downstream of the TGF-β pathway. Another inhibitory effect of miR-27a on the TGF-β signaling pathway is mediated via direct targeting of the TGF-βRI transcript. Also, major growth factors capable of contributing to fibrosis pathogenesis were found to be downregulated by miR-27a, including IGF-1 and VEGF. All of the aforementioned studies are in line with our results which strongly demonstrate the protective effect of miR-27a against fibrosis and hence SSc. As depicted in Fig. 4, miR-27a also targets and regulates MMP13 which is a matrix metalloproteinase involved in the remodeling of extracellular matrix during fibrotic changes. Recently, Qi Cheng et al. have shown that miR-27a is capable of suppressing fibrosis in vitro by targeting the secreted phosphoprotein 1 (SPP1) as well. MiR-27a has been studied widely in cancer and metabolism, but to our knowledge, this is the first study considering the role of miR-27a in systemic sclerosis, although it needs further functional analyses in order to give a comprehensive and conclusive outcome. The remarkable finding of our study is the downregulation of miR-27a in the whole blood of patients affected with systemic sclerosis (either limited or diffused) in comparison to healthy individuals. Thus, we could propose a role for miR-27a in SSc; it seems likely that miR-27 negatively affects molecular pathways involved in fibrogenesis, such as the TGF-β signaling pathway, and as a result, it could be considered an anti-fibrotic microRNA. In support of our theory Cui H, et al. investigated the therapeutic effect of miR-27a on pulmonary fibrosis by demonstrating the inhibitory effect of miR-27a on Smad2 and Smad4, which are the major mediators of the TGF-β signaling pathway; They have shown that α-smooth muscle actin is directly regulated by miR-27a as well, which is the characteristic feature of myofibroblast differentiation. Also, there are other shreds of evidence regarding the relationship between miR-27a and the TGF-β signaling pathway in other circumstances. Consistent with our theory, Fang Fang, et al. have shown the ability of miR-27a to inhibit the TGF-β signaling pathway through suppression of TGF-βRI, Smad2, and Smad4 expression; as well as reduced phosphorylation of Smad3 in cervical adenocarcinoma; cell proliferation, migration, and invasion which employ the same mechanism of EMT in fibrosis, were also attenuated. Dong-Kyu Chae et al. have shown the negative regulation of SMAD2, and SMAD4 by miR-27a in lung cancer cell lines. Consistently, Qi Xu, et al. have demonstrated that miR-27a suppresses TGF-β-induced expression of SMAD4 in human lymphatic endothelial cells, which supports our hypothesis on the role of miR-27a as a likely anti-fibrotic factor. It is noteworthy to mention that in a model of myocardial ischemia, Zhang Xl, et al. have shown the downregulation of TGFβRI along with the reduction of IL-6, TNF-α, and p-NFκB by miR-27a; all of which are major inflammatory factors contributing to fibrosis pathogenesis. On the contrary, Hui Zhang, et al. have investigated the role of miR-27a in hepatic stellate cell (HSC ) activation and fibrosis and concluded that miR-27a increases in response to TGF-β and contributes to HSC activation and fibrosis; they have demonstrated that the inhibition of miR-27a leads to the suppression of PPARγ, α-SMA and collagen too.Quin Lin, Et al. and Sun Young Jang, et al. in two different but similar studies on adipogenesis, have shown that the overexpression of miR-27a results in the downregulation of PPARγ, which is known as an inhibitor of TGF-β non-canonical pathway. In this regard, Deng K, et al. have shown the negative regulation of PPARγ by miR-27a, too. Besides, in a study conducted by Ji-Hoon Cho, et al. with a systems biology approach, it was found that miR-27a along with miR-23a and miR24 is upregulated in response to TGF-β by the Zeb-1 transcription factor which is a major participant in EMT. It could be suggested that miR-27a elevation in these instances may take place upon over-activation of the TGF-β signaling pathway as a negative feedback mechanism to attenuate its physiological consequences. The other regulator of the TGF-β pathway, PTEN, has also been considered as a target of miR-27a; In an interesting study, though on diabetic mice models, Aiqing Zhang, et al. have shown the ability of miR-23a-mir-27a cluster for downregulation of FoxO1 and PTEN; they have also demonstrated that the expression of TGF-β and pSMAD3/4 are suppressed by miR-27a thereby resulting in the attenuation of renal fibrosis. Collectively we believe that miR-27a has a regulatory role regarding the signaling pathways involved in fibrosis and therefore SSc, and the majority of data support the idea that miR-27a has a protective effect against SSc. Also, it is likely that a genetic deficit or any contributing factor for SSc causes the downregulation of miR-27a thereby leading to the aberrant activation of TGF-β and other signaling pathways contributing to SSc pathogenesis. The major limitation of our study is lack of functional assays such as utilizing miR-27a mimics or anatgomiRs in cultures of cells obtained from the SSc patients and assessing the alterations of the possible targets to further confirm the results of the current srudy, which we hope to be addressed in near future. Based on the statistical analyses, miR-27a could serve as a reliable diagnostic marker for SSc, and given its proposed role in regulating TGF-β and other contributing pathways to SSc, it could be considered as a treatment option both for SSc and its related disorders and complications, which indeed necessitates further investigations.

PMC9640937

Yongzhi Cao,Zhao Wang,Changming Zhang,Yuehong Bian,Xin Zhang,Xin Liu,Wendi Chen,Yueran Zhao

Metformin promotes in vitro maturation of oocytes from aged mice by attenuating mitochondrial oxidative stress via SIRT3-dependent SOD2ac 10.3389/fcell.2022.1028510

25-10-2022

oocytes,ROS,IVM,metformin,aged mice

Human female fecundity decreases irreversibly as chronological age rises, adversely affecting oocyte quality, consequently worsening pregnancy outcomes and increasing the extent of birth defects. The first-line type 2 diabetes treatment metformin has been associated with delayed aging and reduction of oxidative stress; yet it remains unclear if metformin confers any benefits for oocytes from aged mice, particularly in the context of the assisted human reproductive technology (ART) known as in vitro maturation (IVM). Here, we found that adding metformin into the M16 culture medium of oocytes from aged mice significantly improved both oocyte maturation and early embryonic development. This study showed that metformin reduced the extent of meiotic defects and maintained a normal distribution of cortical granules (CGs). RNA-seq analysis of metformin-treated oocytes revealed genes apparently involved in the reduction of mitochondrial ROS. Further, the results supported that the metformin improved mitochondrial function, reduced apoptosis, increased the extent of autophagy, and reduced mitochondrial ROS via SIRT3-mediated acetylation status of SOD2K68 in oocytes from aged mice. Thus, this finding demonstrated a protective effect for metformin against the decreased quality of oocytes from aged mice to potentially improve ART success rates and illustrated a potential strategy to prevent or delay reproductive aging.

Metformin promotes in vitro maturation of oocytes from aged mice by attenuating mitochondrial oxidative stress via SIRT3-dependent SOD2ac 10.3389/fcell.2022.1028510 Human female fecundity decreases irreversibly as chronological age rises, adversely affecting oocyte quality, consequently worsening pregnancy outcomes and increasing the extent of birth defects. The first-line type 2 diabetes treatment metformin has been associated with delayed aging and reduction of oxidative stress; yet it remains unclear if metformin confers any benefits for oocytes from aged mice, particularly in the context of the assisted human reproductive technology (ART) known as in vitro maturation (IVM). Here, we found that adding metformin into the M16 culture medium of oocytes from aged mice significantly improved both oocyte maturation and early embryonic development. This study showed that metformin reduced the extent of meiotic defects and maintained a normal distribution of cortical granules (CGs). RNA-seq analysis of metformin-treated oocytes revealed genes apparently involved in the reduction of mitochondrial ROS. Further, the results supported that the metformin improved mitochondrial function, reduced apoptosis, increased the extent of autophagy, and reduced mitochondrial ROS via SIRT3-mediated acetylation status of SOD2K68 in oocytes from aged mice. Thus, this finding demonstrated a protective effect for metformin against the decreased quality of oocytes from aged mice to potentially improve ART success rates and illustrated a potential strategy to prevent or delay reproductive aging. Female fecundity decreases as chronological age rises, with pronounced decreases after the age of 35 years in humans (Leridon, 2004). However, societal trends like the postponement of marriage and childbearing in women of reproductive age have increased the likelihood of infertility (Herbert et al., 2015). The parallel decline in both the quantity and quality of oocytes contributes to the gradual age-related decline in fertility (Djahanbakhch et al., 2007; Nelson et al., 2013), and at least 50% of the oocytes in women aged >40 are non-viable (Fu et al., 2014; Wang et al., 2021). Age-related declines in oocyte quality are associated with mitochondrial dysfunction, chromosome misalignment, and impaired spindle assembly (Eichenlaub-Ritter et al., 2004; Liu, 2016), leading to lower fertility rates, poor embryonic development, worsened pregnancy outcomes, and higher rates of birth defects (Herbert et al., 2015; Mikwar et al., 2020). In vitro maturation (IVM) is a procedure wherein immature cumulus-oocyte complexes (COCs) are collected from small antral follicles and with the final stages of meiosis completed during in vitro culture (Wang et al., 2021). There are many small immature follicles by means of superovulation that using IVM can serve as embryo resources for in vitro fertilization (IVF), especially for advanced maternal age (Liu et al., 2018). Further, IVM offers substantial cost reductions compared to classic protocols for IVF, making it attractive for patients in developing countries (Coticchio et al., 2015). IVM research has progressed in recent years to significantly improve success rates and to provide evidence of safety in terms of neonatal and childhood outcomes: by 2015, more than 5000 IVM babies were born (Coticchio et al., 2015; Wakim et al., 2017). However, compared to conventional IVF, IVM results in a substantially lowered success rate and reduced oocyte developmental potentiality, and yet further IVM performance decreases have been associated with increased maternal age (Liu et al., 2018). Age-related organ dysfunction is linked to disruption of redox homeostasis (Ruder et al., 2008), resulting from overproduction of reactive oxygen species (ROS) and/or deterioration of antioxidant defenses (Timoteo-Ferreira et al., 2021). Mitochondria are known to be the primary endogenous source of ROS (Hamatani et al., 2004; Liu et al., 2022), and an increase in ROS in oocytes has been shown to cause mitochondrial dysfunction and result in disrupted ATP production (Herbert et al., 2015; Marangos et al., 2015). Thus, it is plausible that scavenging ROS and reducing mitochondrial oxidative stress in oocytes could help alleviate age-related oocyte aging and fertility decline. To do this, researchers have explored supplementation of oocyte IVM medium with various antioxidant compounds, including quercetin, resveratrol, melatonin, coenzyme Q, and so on (Liu et al., 2018; Li et al., 2019a; Cao et al., 2020; Ma et al., 2020). The diabetes drug metformin has been shown to delay aging in several experimental models (Martin-Montalvo et al., 2013; Barzilai et al., 2016; Fang et al., 2018) and to reduce oxidative stress and germ cell loss (Ghasemnejad‐Berenji et al., 2018). During oocyte maturation, mitochondria supply the majority of the cellular ATP via the respiratory chain for the generation of cellular energy (Yu et al., 2010). Metformin has been shown to act via both AMP-activated protein kinase and inhibition of mitochondrial respiration (Rena et al., 2017), yet it remains unclear if metformin confers any benefits for oocytes from aged mice, particularly in the context of the assisted human reproductive technology (ART) known as IVM. In this study, we explored the effects of metformin in promoting IVM and subsequent formation of blastocysts using aged mice models. The results showed that metformin can improve mitochondrial function, reduce meiotic defects, and this study supported a protective mechanism that metformin can reduce mitochondrial ROS via SIRT3-mediated acetylation status of SOD2K68 (SOD2K68ac) in oocytes from aged mice, suggesting the use of metformin as a potential strategy to reduce meiotic structure defects in oocytes from aged mice to potentially extend reproductive capacity. To investigate whether metformin impacts oocytes development from aged mice, 1065 GV (germinal vesicle) -stage oocytes from 9- to 10-month-old female mice were collected and cultured in M16 medium with 10, 20, or 50 μM metformin or control for 16 h (Figure 1A). The 10 μM metformin significantly increased the rate of polar body (PB1) extrusion compared to the control (10 μM metformin = 77.0 ± 4.28%, n = 280 vs. control = 67.9 ± 3.60%, n = 265; p < 0.05); note that the 10 µM concentration outperformed the other treatments (10 μM metformin = 77.0 ± 4.28%, n = 280 vs. 20 μM metformin = 74.60 ± 7.36%, n = 267 vs. 50 μM metformin = 74.65 ± 5.03%, n = 253) (Figure 1B). These results clearly suggest that treating oocytes from aged mice with metformin can significantly promote the IVM rate, and we used a 10 μM metformin concentration for all subsequent experiments, because it was the lower concentration that increased PB1 rate with respect to the control. We next assessed the potential involvement of metformin in early embryonic development from aged mice by performing IVF with or without 10 μM metformin. We found no significant differences in the rate of fertilization between the two groups (metformin = 81.86 ± 12.36%, n = 295 vs. control = 90.79 ± 6.72%, n = 274; p > 0.05). However, the presence of metformin in the culture medium significantly increased by 9.4% (metformin = 74.02 ± 10.71%, n = 295 vs. control = 64.7 ± 5.98%, n = 274; p < 0.05) which developed into blastocysts (p < 0.05) (Figure 1E). These results suggest that metformin treatment of oocytes from aged mice can apparently improve both IVM rates and early embryonic development. It has been widely reported that oocyte quality is determined by spindle morphology and chromosome alignment (Huang et al., 2015; Chen et al., 2016). Further, studies have reported that abnormal spindle morphology and aneuploidy in oocytes from aged mice can lead to decreased fertilization rates, increased risk of miscarriage, and birth defects in children (Martin-Montalvo et al., 2013; Barzilai et al., 2016; Cao et al., 2020). Our confocal microscopy analysis of MII-stage oocytes after IVM from aged mice revealed multiple spindle morphology and chromosome alignment abnormalities, including elongated spindles, an apparent lack of poles, and chromosome misalignment (Figure 2A). However, the metformin treatment group significantly reduced the proportion of abnormal chromosomes and spindles (metformin = 38.19 ± 5.24%, n = 71 vs. control = 64.60 ± 2.44%, n = 47; p < 0.01), as observing orderly aligned chromosomes on the equatorial plate and bipolar spindles (Figure 2C). The CGs is regarded as an informative indicator of oocyte cytoplasmic maturation that can block polyspermy following fertilization (Walls and Hart, 2018). We assessed whether metformin affects the distribution of CGs of oocytes from aged mice using Lens culinaris agglutinin (LCA)-FITC staining and confocal microscopy. As the meiotic maturation is completed, normal distribution of CGs is distributed evenly in the oocyte subcortical region and except the CG-free domain near the chromosomes, whereas the abnormal distribution of CGs is discontinuous and weak signals in the oocyte cortex and without leaving CG-free domains. We found that the proportions of abnormal distribution of CGs were significantly reduced in the metformin treatment group, but more than 50% (metformin = 27.94 ± 4.01%, n = 60 vs. control = 51.98 ± 13.88%, n = 79; p < 0.05) lost this normal localization in the control group. (Figures 2B,D). Altogether, these results suggest that metformin may rescue the meiotic defects and recover the cytoplasmic maturation. We next performed a single-cell transcriptome analysis of oocytes from aged mice, including both metformin-treated and control samples. Compared with controls, the metformin-treated oocytes had 295 up-regulated differentially expressed genes (DEGs) and 155 down-regulated genes (with the following cutoff criterion: adjusted p-values less than 0.05 (Figure 3A). Gene Ontology (GO) analysis of the top-30-ranking DEGs revealed enrichment for functional annotations including oxidative phosphorylation, oxygen binding, oxidoreductase activity, antioxidant activity, oxygen transport, cellular oxidant detoxification, and hydrogen peroxide metabolic process (Figure 3B). The expression trend data for 11 randomly selected genes in each group was verified using qPCR (Figure 3C). Numerous up-regulated genes have annotated functions related to anti-oxidative metabolism (Sod2), oocyte and embryonic development (Bmp15, Gdf9), autophagy (Tomm6), aging (Sirt3), and the mitochondrial function (Uqcrb, Mrps21). Some of the down-regulated genes had annotated functions concerning lipid binding (Golph3l) and metabolic processes (Acadsb) (Figure 3C). Our transcriptome analysis of MII-stage oocytes thus indicates that metformin treatment can mitigate oxidative stress in oocytes from aged mice. ROS is one of the causes of age-related decline in fertility (Appasamy et al., 2008; Igarashi et al., 2015; Becatti et al., 2018), with specific reports of dysregulated mitochondrial redox balance, destabilization of mitochondrial DNA, as well as disrupted oocyte membrane function and deterioration (Droge, 2002; Tamura et al., 2020). We used carboxy-H2DCF diacetate to stain oocytes from aged mice with or without metformin treatment to evaluate whether metformin impacts ROS. Briefly, we found that the ROS level was significantly reduced in the metformin treatment oocytes compared to controls (Figures 4A,G; metformin = 31.78 ± 12.57, n = 98 vs. control = 56.22 ± 45.12, n = 92; p < 0.001). Mitochondria are considered the major site of intracellular ROS production, as more than 90% of total cellular oxygen reduction involves electron carriers of the mitochondrial respiratory chain (Hamatani et al., 2004; Nohl et al., 2005). Given that previous studies of oocytes from aged mice have revealed mitochondrial dysfunctions such as defects in mitochondrial membrane potential (MMP), mitochondrial distribution (MD), and ATP levels (Zeng et al., 2018; Pasquariello et al., 2019; Cao et al., 2020), we focused our assessments of mitochondrial function on these aspects. Pursing the idea that metformin may provide beneficial effect(s) against age-induced mitochondrial dysfunction of oocytes, we investigated the mitochondrial membrane potential (MMP) using a standard immunolabeling protocol with JC-1 wherein increasing membrane potential induces a green-to-red shift (Muhammad et al., 2020). This quantitative analysis confirmed that the red/green ratio was significantly increased in the metformin-treated oocytes compared to controls (Figures 4B,H; metformin = 1.09 ± 0.22, n = 75 vs. control = 1.0 ± 0.28, n = 68; p < 0.05). To further assess mitochondrial function, we used MitoTracker-Red staining to examine the mitochondrial distribution of oocytes from aged mice. Confocal microscopy showed that whereas around 60% (metformin = 41.15 ± 3.18%, n = 67 vs. control = 62.41 ± 8.72%, n = 70) of the oocytes from aged mice without metformin treatment had abnormally distributed mitochondria (i.e., asymmetric clustering), this proportion was significantly decreased by that given metformin treatment (Figures 4C,I). The distribution of mitochondria is known to dynamically impact ATP homeostasis, and mitochondrial disorders frequently lead to reduced ATP production (Wakim et al., 2017). Our analysis showed higher ATP levels in metformin-treated oocytes than in untreated controls (Figure 4J; metformin = 7.18 ± 4.21, n = 143 vs. control = 5.57 ± 2.36, n = 143; p < 0.001). Together, these results show that metformin treatment of oocytes from aged mice mitigates oxidative stress, specifically by reducing ROS levels and promoting mitochondrial function. Given our observations of disrupted mitochondrial distribution, ATP production and MMP, we next evaluated whether metformin treatment influences mitochondrial ultrastructure in oocytes from aged mice. Our transmission electron microscopy (TEM) analysis showed that the metformin-treated oocytes had a significantly reduced extent of abnormal mitochondrial ultrastructure phenotypes (Figure 4K; metformin = 29.52 ± 6.33%, n = 14 vs. control = 36.92 ± 6.20%, n = 12; p < 0.01), which typically exhibit blurred cristae, narrowed inter-membrane spaces (Figures 4D, F1, F2). The normal mitochondrial ultrastructure had defined cristae, clearly visible intact inner and outer membranes, and well-defined intermembrane spaces (Figures 4E, F3, F4). Collectively, these results demonstrate that metformin can somehow improve age-related abnormal ultrastructure phenotypes of oocyte mitochondria. Given the known impacts of oxidative stress in the induction of apoptosis (Chen et al., 2014; Dimozi et al., 2015), we next performed a TUNEL (Terminal deoxynucleotidyl transferase dUTP nick-end labeling) analysis to investigate potential impacts(s) of metformin on oocytes from aged mice. There were significantly fewer apoptotic oocytes in the metformin-treated group compared to the untreated controls (Figures 5A,D; metformin = 27.66 ± 2.34%, n = 54 vs. control = 42.48 ± 4.39%, n = 47; p < 0.01). Further, we found that the extent of caspase3 activation was significantly reduced in the metformin-treated group compared to the untreated controls (Figures 5B,E; metformin = 0.63 ± 0.30, n = 92 vs. control = 1.0 ± 0.32, n = 85; p < 0.05). Previous studies have shown that, during follicular development, oocytes block the induction of apoptosis by employing autophagy as a “survival mechanism” (Pepling, 2012). We conducted immunofluorescence staining of oocytes from aged mice against the autophagy marker protein LC3 (Microtubule-associated protein light chain 3) and found that metformin treatment significantly increased LC3 levels (Figures 5C,F; metformin = 1.14 ± 0.13, n = 71 vs. control = 1.0 ± 0.11, n = 52; p < 0.05). Thus, metformin treatment can both increase autophagy activity and decrease the extent of apoptosis in oocytes from aged mice. SIRT3 (Sirtuin 3) is localized in the mitochondria and regulates the acetylation status of the most mitochondrial proteins (Sultana et al., 2016), often associated with aging (Ansari et al., 2017). Previous studies have shown that SIRT3 can deacetylate SOD2K68, consequently activating SOD2, thereby increasing its mitochondrial anti-oxidative activity (Someya et al., 2010; Liu et al., 2017; Cao et al., 2020), and acetylation levels of SOD2 are negatively associated with its enzymatic activity (Cao et al., 2020). However, whether metformin can mitigate mitochondrial ROS by SIRT3-mediated reduction of SOD2K68ac in IVM from oocytes from aged mice still needs to be investigated. We next assessed whether maternal age affects SIRT3 level in GV oocytes, immunoblotting of oocytes from old (42–45 weeks) and young mice (6–8 weeks) revealed that oocytes from aged mice had reduced SIRT3 expression compared with that in young mice (Figures 6A,E; young = 0.91 ± 0.08, n = 3 vs. aged = 0.62 ± 0.23, n = 3; p = 0.11). We next conducted SOD2K68ac staining using confocal microscopy in the metformin-treated group and the untreated controls from the MII-stage oocytes after IVM, and found that metformin-treated significantly reduced SOD2K68ac levels (Figures 6B,F; metformin = 13.35 ± 4.90, n = 66 vs. control = 15.11 ± 4.48, n = 67; p < 0.05). We further examined whether metformin in the IVM culture medium of oocytes from aged mice may affect the SOD2K68ac by regulating SIRT3, consequently reducing mitochondrial ROS in oocytes from aged mice. Fully grown GV oocytes from old mice were injected with exogenous Sirt3 siRNAs or PBS, as the negative control (Figure 6C). Monitoring ROS levels in live oocytes using carboxy-H2DCF diacetate fluorescent dye with confocal microscopy showed that the ROS accumulation was significantly increased in the siSirt3 oocytes compared with control (PBS) oocytes. Moreover, treatment of siSirt3 oocytes with metformin led to a significant reduction in ROS as compared with the untreated siSirt3 oocytes (Figures 6D,G; siSirt3 = 2.04 ± 0.68, n = 11 vs. control = 1.0 ± 0.19, n = 12; p < 0.01; siSirt3 = 2.04 ± 0.68, n = 11 vs. siSirt3+metformin = 1.29 ± 0.29, n = 10; p < 0.01). Similarly, assessment of SOD2K68ac levels showed that siSirt3 oocytes had significantly increased SOD2K68ac levels compared to the control cells, and also showed that metformin treatment could partially rescue this increased SOD2K68ac phenotype (Figures 6D,H; siSirt3 = 2.60 ± 0.63, n = 22 vs. control = 0.97 ± 0.74, n = 20; p < 0.001; siSirt3 = 2.60 ± 0.63, n = 22 vs. siSirt3+metformin = 2.23 ± 0.81, n = 24; p < 0.05). Thus, metformin can mitigate mitochondrial ROS via SIRT3-induced reduction of SOD2K68ac in oocytes from aged mice. For human ART, improved IVM culture conditions enable some immature oocytes to develop into embryos. Our results in the present study confirm that metformin can improve IVM success rates in oocytes from aged mice. We demonstrated improvement of early embryonic development upon metformin treatment, provided insights about how metformin impacts the meiotic defects of oocytes and contributes to maintaining a normal distribution of cortical granules in oocytes from aged mice. Our results support that metformin improved the quality of oocytes from aged mice by recovering mitochondrial function, which, in turn, reducing the accumulated ROS suppressing apoptosis and promoting autophagy during aging, we show that metformin can mitigate ROS via SIRT3-mediated reduction of SOD2K68ac in oocytes from aged mice. Biological aging primes the development of age-related diseases (López-Otín et al., 2013). The ovary is the first organ to show signs of biological aging in women (usually occurring between the ages of 40 and 58 years), and seriously affects women’s health and quality of life (Fritz and Jindal, 2018; Schach et al., 2021). More than 90% of age-related deterioration of embryo competence is caused by aneuploidy, which arises principally from chromosomal missegregation in meiosis (Song et al., 2016). Calorie restriction (CR) can improve health conditions, increase lifespan, and the extension of maternal fertility (Selesniemi et al., 2011; Zhang et al., 2021), but it has also been reported to cause reduced fertility or even complete infertility (Tilly and Sinclair, 2013; Dou et al., 2017). Previous studies have shown that metformin can repair quality defects of oocytes induced by arecoline or dehydroepiandrosterone in mice (Huang et al., 2015; Li et al., 2020). The present study showed that metformin can attenuate age-related anomalous spindle formation and chromosome alignment in the nucleus and can also help maintain a normal distribution of CGs in the cytoplasm of oocytes. Clinically, improved IVM culture conditions can offer an effective approach to acquiring more high-quality oocytes for ART to improve the fertility of aged females (Miao et al., 2020). Further, IVM has been used to treat patients with a range of fertility-related conditions including fertility preservation, thus reducing the risk of thrombotic events in at-risk patients and those diagnosed with FSH-resistant ovaries (Walls and Hart, 2018). The asynchronous maturation of the cytoplasm and nucleus of the oocyte is a major challenge when seeking to improve the quality of oocytes in IVM, and emerging evidence has suggested that metformin can help to overcome this (Coticchio et al., 2015). We found no significant differences in the rate of fertilization after metformin treatment. However, we observed that metformin improved IVM rate and early embryonic development of oocytes in naturally aging mice (in which fertility is known to decline rapidly). Thus, the present study supports that metformin has potential clinical benefits for women who wish to postpone childbearing, provided a conceptual basis for improving IVM culture conditions and using immature oocytes from aged humans. ROS fundamental in mediating folliculogenesis, meiosis, ovulation, and embryonic development as secondary messengers for cellular signaling (Agarwal et al., 2012), but overabundant ROS by in vitro culture, post-ovulatory aging and other factors will result in oxidative stress and contributors to aging (Finkel and Holbrook, 2000; Lin et al., 2018). Mitochondria are a major site of ROS damage, and are the known induction site for the intrinsic induction pathway of autophagy (Lyamzaev et al., 2018). Mitochondria generate ATP via oxidative metabolism, and mitochondrial activity can be used to assess the quality of oocytes. Previous studies have established requirements for a low ROS index and for a relatively high ATP level in oocytes (Muhammad et al., 2020), and both ATP metabolism and ROS are directly associated with mitochondrial functional which can be assessed through distribution of mitochondria, MMP, ATP level, mitochondrial ultrastructure and gene expression (Zhang et al., 2006; Cao et al., 2020). The increased mitochondrial dysfunction and reduced autophagy can activate the mitochondrial-related apoptotic signaling pathway during the maturation of oocytes (Tatone et al., 2006). Metformin can reduce oxidative stress, increase antioxidant defenses, increased autophagy and reduce the extent of chronic inflammation (Martin-Montalvo et al., 2013; Bharath et al., 2020). Previous studies have shown that metformin can improve oocyte quality and embryo development and reverse ovulation dysfunction in polycystic ovary syndrome mice model by reducing ROS and improving mitochondrial function in oocytes (Huang et al., 2015), and can reduce apoptosis in blastocysts of obese mice (Louden et al., 2014). Emerging evidence strongly suggests that metformin improves the quality of oocytes from aged mice and subsequently promoting both oocyte maturation and early embryonic development in aged mice oocytes by recovering mitochondrial function, which, in turn, reduces the accumulation of ROS to suppress apoptosis and promote autophagy during aging. Our findings are in agreement with studies reporting that metformin protects against apoptosis and senescence in nucleus pulposus cells (Chen et al., 2016). Thus, this work supports that metformin can be used as an effective agent to prevent age-related mitochondrial dysfunction of the oocyte to alleviate the accumulation of excessive ROS levels. Oxidative damage accelerates mouse ovarian aging by decreasing the expression of antioxidant genes (Lim and Luderer, 2011). Sirt3 is an antiaging gene, the main deacetylase that inhibits mitochondrial ROS, and Sirt3 participates in multiple signaling events including regulation of oxidative stress, mitochondrial biogenesis, apoptosis, and metabolic activity (Sultana et al., 2016). Mitochondrial SOD2 is an antioxidant enzyme and plays a crucial role in controlling ROS production (Chen et al., 2011). Emerging evidence shows that SIRT3/SOD2 signaling activation prevents oxidative stress and mitochondrial damage in multiple pathological and pathological conditions (Li et al., 2019b; Cao et al., 2020; Liu et al., 2021), and mitochondrial-ROS stimulated autophagic cell death dependent on the SIRT3/SOD2 pathway (Zhou et al., 2021). Our data confirm previous reports that SIRT3 levels are increased in oocytes from aged mice compared to young animals. Further, we show that metformin can ameliorate the mitochondrial-ROS after knockdown SIRT3 in oocytes from aged mice, leading to SOD2 deacetylation and activation, and yet SIRT3 can help eliminate ROS by transforming SOD2K68ac into SOD2. Hence our study represents a potential strategy for the treatment of reproductive aging. In this study, we discovered that metformin treatment of oocytes from aged mice can attenuate age-related mitochondrial oxidative stress, alleviate meiotic defects, help maintain a normal distribution of CGs, promote oocyte maturation, and promote early embryonic development. We showed that metformin’s effects involved reduced apoptosis, increased extent of autophagy, and reduced mitochondrial-ROS, and demonstrated that metformin treatment can mitigate ROS via SIRT3-mediated reduction of SOD2K68ac in oocytes from aged mice. Thus, metformin represents a promising direction for future clinical applications in preventing reproductive aging and may help efforts to develop and improve IVM culture systems for patients of advanced maternal age. However, it needs to be emphasized that the use of metformin requires rigorous safety evaluations and careful monitoring for unanticipated impacts on pregnancy outcomes. The protocol for this study was reviewed and approved by the Institutional Review Board of Reproductive Medicine, Shandong University [(2021) Ethical Review #33]. Unless otherwise noted, all chemicals and reagents used were purchased from the Sigma Chemical Company (St. Louis, MO, United States). Old ICR (Institute of Cancer Research) female mice from 42- to 45-week-old (when fertility declines rapidly) and young ICR female mice from 6- to 8-week-old were purchased from Beijing Vital River Experimental Animals Centre (Beijing, China). Mice were housed under a 12-h light: 12-h darkness cycle, in a controlled temperature and humidity animal facility, and were provided with water ad libitum and food. To retrieve fully grown GV oocytes, mice were injected intraperitoneally with 10 IU Pregnant Mares Serum Gonadotropin (PMSG) (Ningbo Hormone Product Company, China). After 46–48 h, mice were ultimately sacrificed, cumulus-oocyte complexes were collected by manual rupturing of antral ovarian follicles, cumulus cells were removed by repeatedly pipetting. For IVM, denuded GV oocytes cultured were cultured in the small drops in M16 medium supplemented with or without different concentrations of metformin (10, 20, or 50 μM), subsequently covered with mineral oil were incubated under 6% CO2, 5% O2, and 90% N2 at 37°C for 16 h to determine the PB1 extrusion rate. For IVF, mice were intraperitoneally injected with 10 IU PMSG, followed 48 h later by 10 IU of human chorionic gonadotrophin (hCG) (Ningbo Hormone Product Company, China). After 16 h, mice were ultimately sacrificed, oviductal ampullae were broken to release cumulus-oocyte complexes. Meanwhile, the adult male mice were sacrificed, and sperm was obtained from cauda epididymis via orchidectomy. Subsequently cumulus-oocyte complexes fertilized with adult male sperm (the concentration of sperm was (1–2.5) × 106/ml) in G-IVF medium (Vitrolife, Sweden) supplemented with or without 10 μM metformin. Zygotes covered with mineral oil were incubated under 6% CO2, 5% O2, and 90% N2 at 37°C in G1 medium (Vitrolife, Sweden) to observe embryonic developmental potential with a stereomicroscope (Nikon SMZ1500). For microinjection in knockdown experiments, 10 pL Sirt3-targeting siRNA (10 ng/μL) was injected into GV stage oocytes, and the same amount of RNase-free PBS was injected as a control. Oocytes were arrested at the GV stage in M16 medium containing 2.5 mM milrinone for 20 h to promote the synthesis of new protein. Then, oocytes were cultured in the M16 medium supplemented with (siRNA + metformin) or without (siRNA) 10 μM metformin for 16 h. RNA was obtained from RiboBio (Guangzhou, China) and the sequences used are listed in Supplementary Table S2. For measurement of intracellular ROS levels, oocytes were incubated in M2 medium containing 10 mM carboxy-H2DCF diacetate (Beyotime, China) for 30 min at 37°C. After being washed 3 times in an M2 medium and subsequently mounted on glass slides, oocytes were imaged with a confocal microscope (Dragonfly, Andor Technology, United Kingdom). The fluorescence intensity for each oocyte was measured with ImageJ (National Institutes of Health, United States). ATP measurement was performed using the luciferin–luciferase reaction (Bioluminescent Somatic Cell Assay Kit, Sigma, United States). Firstly, mixed for 5 s before detection, a linear regression of standard curve containing 11 ATP concentrations from 10 fmol to 10 pmol was used to determine oocyte ATP content. Then, ATP concentrations of oocytes were measured on an EnSpire Multimode Plate Reader (PerkinElmer, United States). Immunofluorescence was performed as described previously (Cao et al., 2020). In brief, oocytes were fixed for 30 min with 4% paraformaldehyde, permeabilized for 20 min with 0.1% Triton X-100, and blocked with 1% BSA for 0.5 h. Samples were incubated with primary antibodies in 1% BSA for 1 h at room temperature. The primary antibodies were as follows: anti-active caspase-3 (Abcam, United States), anti-LC3 (Cell Signaling, United States), and anti-SIRT4 (Abcam, United States). After being washed three times for 5 min, samples were incubated with suitable secondary antibodies at room temperature for 1 h. To visualize the spindle, oocytes were probed with an anti-α-tubulin antibody (Sigma, United States). For the distribution of cortical granules staining, oocytes were probed with labeled lens culinaris agglutinin (Vectorlabs, United States). For the TUNEL staining, oocytes were probed with an in situ cell death kit (Roche, Swiss). To evaluate the mitochondrial membrane potential (MMP), oocytes were incubated in 2 μM JC-1 (Invitrogen, United States) for 30 min at 37°C. To detect the mitochondrial distribution, oocytes were incubated in 200 nM MitoTracker-Red (Invitrogen, United States) for 30 min at 37°C. To visualize chromosomes, oocytes were probed with DAPI (Solarbio, China) for 10 min. Oocytes were examined under a laser scanning confocal microscope (Dragonfly, Andor Technology, United Kingdom). Images were acquired by using the same confocal microscope settings within and between experiments. The mean fluorescence intensity of each oocyte was measured with ImageJ (National Institutes of Health, United States). The antibodies used in these experiments are shown in Supplementary Table S1. For RNA sequencing, 15 MII stage oocytes after IVM from three mice were considered one group, and three replicates were assessed per group. The sample library was built with a smart-seq HT Kit (Takara, Japan) at Shanghai Sinomics Corporation and sequenced with an Illumina NovaSeq 6000 instrument (Illumina, United States). Raw data files are publicly available from the Gene Expression Omnibus (GEO) database under accession number GSE201098. Total RNA was extracted from samples using RNeasy Mini Kits (Qiagen, Germany) following the manufacturer’s protocol. Expression levels of mRNA were partially verified by qPCR experiments performed with a Light Cycler 480 (Roche, Swiss). The mRNA levels were normalized to endogenous GAPDH (Glyceraldehyde-3-phosphate dehydrogenase) mRNA levels using calculations performed with Microsoft Excel. Primer sequences are shown in Supplementary Table S2. Oocytes from five mice treated with or without metformin were used for each sample. To analyze mitochondrial ultrastructure, oocytes were prepared for transmission electron microscopy (TEM). Morphometric analysis of mitochondrial ultrastructure was based on electron micrographs at 30,000-fold magnification. For total protein extraction, a total of 100 oocytes were lysed in SDS buffer by boiling for 5 min. The sample was separated by 10% SDS-PAGE and transferred to a PVDF membrane (Bio-Rad), blocked with 5% skim milk diluted in Tries-buffered saline containing 0.05% Tween-20 (TBST) for 1 h at room temperature, and then incubated with primary antibody overnight at 4°C (anti-SIRT3 antibody, 1:500), incubation with HRP-conjugated secondary antibodies for 1 h at room temperature. Immunoreactive bands and molecular weight were detected using the Odyssey Infrared Imaging System (LI-COR Bioscience, United States). The antibodies used in these experiments are shown in Supplementary Table S1. All experiments were replicated three or more times; data are presented as the mean ± SD unless otherwise indicated, and all % data were subjected to an arcsine-square-root transformation before statistical analysis. Differences between the two groups were analyzed for statistical significance using two-tailed unpaired Student’s t-tests. Comparisons between more than two groups were analyzed using a one-way ANOVA (Analysis of Variance) test implemented in GraphPad Prism 7 (GraphPad Software, San Diego, CA, United States). *p < 0.05; **p < 0.01, and ***p < 0.001.

PMC9641119

Yutu Luo,Suwei Hu,Fang Wang,Junjun Yang,Daohui Gong,Wenjing Xu,Xingxiang Xu,Lingfeng Min

miR-137 represses migration and cell motility by targeting COX-2 in non-small cell lung cancer

01-10-2022

miR-137,COX-2,non-small cell lung cancer (NSCLC),Epithelial-Mesenchymal Transition (EMT),migration

Background Lung cancer is a common malignant tumor, with, non-small cell lung cancer (NSCLC) accounting for about 80–85% of cases. This study investigated the expression of miR-137 in NSCLC tissues and cells and its effects on the migration and invasion of NSCLC cells and related mechanisms. Methods We collected the neoplastic and paracancerous tissues of NSCLC patients, detected the expression of miR-137 in NSCLC tissues and cell lines by real-time quantitative polymerase chain reaction (RT-qPCR), and analyzed the correlation between miR-137 expression and the clinicopathological features and survival of NSCLC. Following transfection with miR-137 mimic or inhibitor in NSCLC cell lines (A549 or H1299) to upregulate or downregulate the expression of miR-137, transwell assay was employed to detect the effects of miR-137 on migration or invasion. Online software was employed to predict and analyze the target gene of miR-137, and luciferase reporter gene system was adopted to validate it. The effects of miR-137 on the expressions of COX-2 and Epithelial-Mesenchymal Transition (EMT) related proteins were investigated by Western blot. Results Compared to paracancerous tissues and BEAS-2B cells, the expressions of miR-137 in NSCLC tissues, A549 and H1299 cells were dramatically down-regulated (P<0.01). After transfection with miR-137 mimic or inhibitor in A549 and H1299 cells, the miR-137 expressions were markedly up-regulated or down-regulated (P<0.01), respectively. The number of migrating or invading cells was observably decreased or increased (P<0.01) after transfected with mimic or inhibitor, respectively, while relative luciferase activity was evidently decreased in cells co-transfected with miR-137 mimic and wild type recombined vector of 3’UTR of COX-2. While the expressions of COX-2 and E-cadherin were both substantially reduced in A549 cells treated with miR-137 mimic, that of vimentin was substantially raised. The expression of miR-137 correlated with smoking history, lymph node metastasis, and TNM clinical stage, and patients with high miR-137 expression had apparent longer survival. Conclusions The expression of miR-137 was significantly down-regulated in NSCLC tissues and cells, and correlated with NSCLC progress. miR-137 suppressed the migration and invasion of NSCLC cells through regulating EMT relative proteins by targeting COX-2. miR-137 is expected to become a novel biomarker and therapeutic target of NSCLC.

miR-137 represses migration and cell motility by targeting COX-2 in non-small cell lung cancer Lung cancer is a common malignant tumor, with, non-small cell lung cancer (NSCLC) accounting for about 80–85% of cases. This study investigated the expression of miR-137 in NSCLC tissues and cells and its effects on the migration and invasion of NSCLC cells and related mechanisms. We collected the neoplastic and paracancerous tissues of NSCLC patients, detected the expression of miR-137 in NSCLC tissues and cell lines by real-time quantitative polymerase chain reaction (RT-qPCR), and analyzed the correlation between miR-137 expression and the clinicopathological features and survival of NSCLC. Following transfection with miR-137 mimic or inhibitor in NSCLC cell lines (A549 or H1299) to upregulate or downregulate the expression of miR-137, transwell assay was employed to detect the effects of miR-137 on migration or invasion. Online software was employed to predict and analyze the target gene of miR-137, and luciferase reporter gene system was adopted to validate it. The effects of miR-137 on the expressions of COX-2 and Epithelial-Mesenchymal Transition (EMT) related proteins were investigated by Western blot. Compared to paracancerous tissues and BEAS-2B cells, the expressions of miR-137 in NSCLC tissues, A549 and H1299 cells were dramatically down-regulated (P<0.01). After transfection with miR-137 mimic or inhibitor in A549 and H1299 cells, the miR-137 expressions were markedly up-regulated or down-regulated (P<0.01), respectively. The number of migrating or invading cells was observably decreased or increased (P<0.01) after transfected with mimic or inhibitor, respectively, while relative luciferase activity was evidently decreased in cells co-transfected with miR-137 mimic and wild type recombined vector of 3’UTR of COX-2. While the expressions of COX-2 and E-cadherin were both substantially reduced in A549 cells treated with miR-137 mimic, that of vimentin was substantially raised. The expression of miR-137 correlated with smoking history, lymph node metastasis, and TNM clinical stage, and patients with high miR-137 expression had apparent longer survival. The expression of miR-137 was significantly down-regulated in NSCLC tissues and cells, and correlated with NSCLC progress. miR-137 suppressed the migration and invasion of NSCLC cells through regulating EMT relative proteins by targeting COX-2. miR-137 is expected to become a novel biomarker and therapeutic target of NSCLC. Lung cancer is one of the most common malignant tumors globally, and has become the leading cause of death from malignant tumors in the urban Chinese population (1). According to the pathological characteristics and differentiation degree of cancer cells, lung cancer can be divided into small cell lung cancer (SCLC) (15%) and non-small cell lung cancer (NSCLC) (85%) (2), the latter which includes squamous cell carcinoma (SCC), adenocarcinoma, and large cell carcinoma. Compared with small cell carcinoma, the growth and division of NSCLC cells are slower, and diffusion and metastasis are relatively late (3). About 75% of NSCLC sufferers are diagnosed at an advanced stage, and the 5-year survival rate is very low (4,5). The pathogenesis of lung cancer is complex and understanding its potential molecular mechanisms is of great significance to improve the survival rate of patients and reduce mortality from the disease (6). MicroRNA (miRNA) is a kind of non-coding single stranded RNA molecule with a length of about 22 nucleotides encoded by endogenous genes. miRNAs participate in the regulation of post transcriptional gene expression in animals and plants (7,8), and are both important regulatory factors in tumors and closely related to the occurrence and development of lung cancer (9,10). After abnormal expression, miRNA can be used as carcinogenic miRNA or tumor suppressor miRNA to regulate the expression of signal pathway genes and affect the proliferation, migration, invasion, and metastasis of lung cancer cells (9,11). More than 50% of human miRNAs are in special chromosomal regions, which are amplified, deleted, or translocated during tumor development. miRNAs play an important role in the occurrence and evolution of tumors (12) by regulating the physiological processes of cells such as apoptosis, cell proliferation, cell cycle control, DNA repair, and metabolism, and negatively regulating the expression of genes and proteins as carcinogens or tumor suppressors (9,13,14). miR-137 has been proven to hold a tumor suppressor gene function, and its antitumor effect has been confirmed in a variety of cancers (15,16). In our previous study, its promoter methylation was associated with NSCLC cell migration and prognosis (17). And, the A549 cells were treated with cigarette smoking extract for 16 weeks, miR-137 was one of the miRNAs with significantly different expression. In this study we investigated the expression of miR-137 in NSCLC tissues and cells and its effect on the migration and invasion of NSCLC cells and related mechanisms. We present the following article in accordance with the MDAR reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-22-2177/rc). We obtained the paraffin embedded cancerous and paracancerous tissues of 10 NSCLC patients (four with squamous cell cancer and six with adenocarcinoma) who attended the Subei People’s Hospital in 2012. The tissues of ten patients with benign lung lesions were also collected. To further assay the relationship between miRNA expression and the clinicopathological features of NSCLC, a further 56 NSCLC cancerous tissues embedded with paraffin collected between 2008 to 2009 were also obtained. No patient had received chemotherapy before surgical excision, and the resected tissues were embedded with paraffin after being fixed. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The research was approved by the Ethics Committee of the Subei People’s Hospital of Jiangsu Province, and written informed consent was obtained from all patients prior to participation. Total RNA was extracted and purified from paraffin embedded tissues using an RNeasy FFPE Kit (Univ-Bio Company, Shanghai, China), with Trizol applied to isolate total RNA from cultured cells. cDNA was synthesized using a miRcute miRNA cDNA synthesis kit (Tiangen, Biotech, Beijing, China). Primers were synthesized by the Shenggong Company (Shanghai, China), and their sequences are listed in Table 1. SYBR Green and specific primers of miR-137 and U6 were employed to detect the expression of miR-137, and U6 was applied as internal reference. A transwell chamber with or without matrigel was employed to investigate the effect of miR-137 on the migration or invasion of NSCLC cells. Briefly, the H1299 or A549 cells were digested and subcultured into the upper chamber with or without matrigel after 24 hours of transfection with miR-137 mimic or inhibitor. After a further 24 hours, a cotton swab was adopted to wipe out the cells not passing through polycarbonate membranes, while those passing through pore membranes were fixed with 4% paraformaldehyde and stained with Giemsa for 20 min. The cells were observed with a Leica microscope and analyzed by ImageJ. BEAS-2B (Human bronchial epithelial cells), NCI-H1299 cells, and A549 cells (human NSCLC cell line) were obtained from Procell Life Science & Technology Co., Ltd. (Wuhan, China), and were cultured in a 37 ℃ and 5% CO2 constant-temperature incubator with RPMI-1640 medium containing 10% fetal calf serum (HyClone, ThermoFisher, Shanghai). Every second day, the cells were digested with typsin and subcultured, and during their logarithmic growth period, were transfected according to the following protocol. The miR-137 mimic, mimic negative control, miR-137 inhibitor, inhibitor negative control, COX-2 siRNA, and scramble siRNA were synthesized by RiboBio Co., Ltd (Guangzhou, China). The coding sequence of COX-2 was cloned into lentivirus vector GV492 to obtain the recombined overexpression vector Lv-COX-2, which was provided by Vigen Biotechnology (Zhengjiang, China). The mimic, inhibitor, or siRNA was transfected into H1299 or A549 cells with lipofectamine 2000 (Invitrogen, Shanghai, China). In brief, the cells were digested with typsin and cultured into a 6-well plate with 50,000 cells/well before 24 hours of transfection. The miR-137 mimic, inhibitor (50 pmol), or COX-2 siRNA (100 pmol) were diluted in OPTI-MEM and transfected into NSCLC cells, and Lv-COX-2 was transfected into A549 cells at a multiplicity of infection (MOI) of 5. The online software miRanda (http://www.microrna.org/microrna/getMirnaForm.do) was employed to analyze the target genes of miR-137, and as a predictive result, COX-2 (PTGS2) was a target gene of miR-137. The 3’-UTR of COX-2 with or without miR-137 binding mutation site was cloned into pGL3 vector, and the constructed recombined vector pGL3-PTGS2 (WT) or pGL3-PTGS2 (MT) was co-transfected with miR-137 mimic into the logarithmic growth phase of HEK293 cells. After 48 hours transfection, the luciferase was detected using a Luciferase Assay system (Promega Biotech, Beijing, China). The NSCLC H1299 or A549 cells were collected after 72 hours transfection with miR-137 mimic, COX-2 siRNA, or Lv-COX-2, respectively. RIPA lysis solution was applied to extract total protein, and a BCA assay kit was used to quantify its concentration. Total proteins with 50 µg/lane were separated by SDS-PAGE electrophoresis, then transblotted into PVDF membrane. After blocking, the membrane was incubated with rabbit anti-human primary antibodies, COX-2 (1:2,000) (BioVision, Waltham, MA), vimentin (1:2,000) (Merck, Rockville, MD), E-cadherin (1:5,000) (Abcam, Waltham, MA), and β-actin (1:5,000) (Beyotime Biotechnology, Haimen, China), overnight at 4 ℃, respectively. Membranes were then reacted with goat HRP-conjugated anti-rabbit secondary antibody (sigma, St. Louis, MO) for 1 hour at 37 ℃ following washing with TBST. The bands were visualized by enhanced chemiluminescence (ECL) reagent using a Tanon 5200 image system (Tanon, Shanghai, China) and quantified using ImageJ. All data are presented as mean ± standard deviation (SD). Student’s t two-tailed tests were employed to perform comparison between two groups, and one-way ANOVA with post hoc Holm-Sidak correction was performed for multiple comparisons. GraphPad Prism 8 was employed to analyze the data, Kaplan-Meier and log-rank tests were adopted to analyze the disease-free survival and overall survival effected by miR-137. P<0.05 was determined as statistically significant. We used RT-qPCR to investigate the expression of miR-137 in NSCLC tissues and cells, with BEAS-2B, a human normal bronchial epithelial cells, as control. The results showed the expression of miR-137 was dramatically down-regulated in NSCLC cell lines (H1299 and A549), compared to BEAS-2B (P<0.01) (Figure 1), and as anticipated, was markedly down-regulated in cancerous tissues compared with paracancerous normal tissues and benign lung tissues (P<0.01) (Figure 1B). The expression analysis of miR-137 in ten pairs of cancerous tissues and paracancerous normal tissues is shown in Figure 1C. As stated above, the expression of miR-137 was down-regulated in NSCLC tissues and cells, and we further investigated this relationship in 56 NSCLC cancerous tissues. The results showed the expression of miR-137 was correlated with smoking history, lymph node metastasis, and TNM clinical stage (P=0.032, P=0.01 and P=0.015, respectively), while there was no apparent relationship with gender, age, histological type of tumor, tumor size, or tumor differentiation (P>0.05) (Table 2). To investigate the effect of miR-137 on the migration and invasion of NSCLC cells, the synthesized miR-137 mimic and inhibitor were subjected to transfection. After 48 hours transfection, the analytical results of RT-qPCR showed miR-137 expression was substantially increased or decreased in NSCLC cells transfected with miR-137 mimic or inhibitor (both P<0.01), respectively, compared to the negative control. The synthesized miR-137 mimic and inhibitor were then adopted for application in the following experiments. Metastasis of various organs can occur in the late stage of lung cancer, which often brings great suffering to patients and threatens their lives. Transwell assay was employed to detect the influence of migration or invasion by miR-137 and showed its overexpression by mimic markedly restrained the migration or invasion of A549 and H1299 cells. Conversely, miR-137 expression down-regulated by inhibitor significantly facilitated migration or invasion. These results are shown in Figure 2. miRNAs play important roles in multiple life processes, including tumorigenesis and progress, by regulating target genes. As predicted, COX-2 (PTGS2) was a target gene of miR-137, and there were binding sites in the 3’-UTR of COX-2, as shown in Figure 3A. The luciferase reporter gene system was adopted to verify COX-2 as the target gene of miR-137, and showed relative luciferase activity dramatically declined in cells co-transfected with miR-137 mimic and pGL3-PTGS2 (WT). This result directly validated COX-2 as the target gene of miR-137 (Figure 3B). The effects of miR-137 on COX-2 were then detected by Western blot and showed COX-2 expression was apparently suppressed in H1299 and A549 cells transfected with miR-137 mimic (Figure 3C). Epithelial-Mesenchymal Transition (EMT) plays a crucial role in embryonic development, tissue reconstruction, and cancer metastasis (18), and whether miR-137 suppressed the migration and invasion by regulating EMT was then examined. The expression influences of E-cadherin and vimentin by miR-137 were measured by Western blot, and showed the expression of vimentin was dramatically downregulated in A549 cells alone transfected with miR-137 mimic, while that of E-cadherin was dramatically upregulated. In addition, when A549 cells were co-transfected with Lv-COX-2 and miR-137 mimic, the expression of vimentin was significantly enhanced, and E-cadherin was reduced (Figure 4). Among the recruited 56 patients with NSCLC, one died of postoperative pneumonia, and the follow-up data of eight patients were lost. A Kaplan-Meier survival curve was applied to analyze the correlation between miR-137 expression and survival in the remaining 47 patients, who were divided based on the relative expression of miR137 into two groups. A high expression group containing 23 patients with miR-137 expression ≥ median and a low expression group containing 24 patients with miR-137 expression < median were established, and as shown in Figure 5, those with high miR-137 expression had apparent longer disease-free survival (P=0.01) and overall survival (P=0.04). miRNAs may act as tumor suppressors or carcinogens in lung cancer by targeting and controlling the expression of multiple signal pathway genes and affecting the proliferation, migration, invasion, and other malignant processes of tumor cells (19,20). In recent years, with the deepening of research, miRNAs have increasingly been recognized as lung cancer biomarkers, and the regulatory mechanism of lung cancer-related miRNAs in tumors has been found (21,22). However, the self-regulation mechanism of miRNA is unclear, and whether this affects the occurrence and evolution of tumors requires further research and exploration (23,24). At the same time, although the mechanism of miRNA in the occurrence and development of lung cancer has been widely studied, its precise mechanism in regulating the malignant biological process of lung cancer cells has not been clarified. miRNA has broad clinical application prospects, and is expected to become a key target for molecular targeted therapy of lung cancer (19,25). Studying the potential molecular mechanisms involved is of great significance for the diagnosis, treatment, and prognosis of a variety of malignant tumors, including lung cancer. In this study, miR-137 expression was dramatically down-regulated in NSCLC tissues and cells, and the results indicated it may be a tumor suppressor of the disease. Further research showed miR-137 suppressed the migration and invasion of NSCLC cells, and its expression correlated with the smoking history, lymph node metastasis, and TNM clinical stage of NSCLC patients. Prostaglandin E (PGE) plays an important role in human physiological regulation, involving multiple processes such as inflammation, coagulation, cell growth, tumorigenesis, and development (26,27). Cyclooxygenase (COX), also known as prostaglandin peroxidase, is the rate-limiting enzyme in the process of PGE synthesis, and can metabolize arachidonic acid (AA) to form various prostaglandin (PG) products (28). The human body contains two different COX isoenzyme isomers: Constitutive COX-1 and inducible COX-2. While COX-2 is minimally expressed in normal tissue, this can increase as much as 80 fold, and large amounts can be expressed under the stimulation of inflammatory factors, such as IL-1, TNF-α, lipopolysaccharide (LPS), and cAMP, (29), promoting the synthesis of many PGEs and triggering an inflammatory reaction (30). PGEs produced by COX-2 have a variety of biological activities and can participate in pathophysiological processes through a variety of pathways including tumor cell growth, metastasis, chemoresistance, and tumor stem cell proliferation (31,32). Therefore, the up regulation of COX-2 expression is considered closely related to inflammation, pain, tumorigenesis, and cancer development. COX-2 is highly expressed in gastric cancer, esophageal adenocarcinoma, colorectal cancer, and other gastrointestinal tumors, and can promote tumorigenesis and tumor development by regulating the expression of genes related to tumor cell proliferation and apoptosis (33). Bothe specific and non-specific inhibitors of COX-2, such as nonsteroidal anti-inflammatory drugs (NSAIDs), can induce apoptosis of gastric cancer, colorectal cancer, and pancreatic cancer cells (34). A study on esophageal cancer showed celecoxib could inhibit the expression of COX-2, down regulate the expression of PGE, inhibit the proliferation of esophageal cancer cells, and improve the therapeutic effect of chemotherapy and radiotherapy (35). The biological role of miRNA is to regulate target genes, and in this study, online software and luciferase reporter gene systems were employed to predict and validate COX-2 as a target gene of miR-137, which observably suppressed it. COX-2 has an oncogene function in lung cancer, and we identified miR-137 as playing a tumor suppressor role by regulating it. EMT has been widely studied in malignant tumors and is considered to play a crucial role in chemotherapy tolerance and the migration and invasion of cancer cells (18,36). While during EMT, the expression of epithelial markers (E-cadherin) decreased, interstitial markers (N-cadherin and vimentin) increased in cancer tissues (36,37). EMT is observed in the invasion and metastasis of many malignant tumors, including NSCLC (38). In this study, miR-137 suppressed the migration and invasion of NSCLC cells by inhibiting vimentin and promoting E-cadherin. In summary, the expression of miR-137 was significantly down-regulated in NSCLC tissues and cells. miR-137 suppressed the migration and invasion of NSCLC cells through regulating EMT relative proteins by targeting COX-2, and correlated with the progress of the disease. It is expected to become a novel biomarker and therapeutic target of NSCLC in the future. The article’s supplementary files as 10.21037/tcr-22-2177 10.21037/tcr-22-2177 10.21037/tcr-22-2177

PMC9641240

Rocío Belén Duca,Cintia Massillo,Paula Lucía Farré,Karen Daniela Graña,Juana Moro,Kevin Gardner,Ezequiel Lacunza,Adriana De Siervi

Hsa-miR-133a-3p, miR-1-3p, GOLPH3 and JUP combination results in a good biomarker to distinguish between prostate cancer and non-prostate cancer patients

26-10-2022

prostate cancer,microRNA,high fat diet,methylation,oncogenes

The incidence and mortality of Prostate Cancer (PCa) worldwide correlate with age and bad dietary habits. Previously, we investigated the mRNA/miRNA role on PCa development and progression using high fat diet (HFD) fed mice. Here our main goal was to investigate the effect of HFD on the expression of PCa-related miRNAs and their relevance in PCa patients. We identified 6 up- and 18 down-regulated miRNAs in TRAMP-C1 mice prostate tumors under HFD conditions using miRNA microarrays. Three down-regulated miRNAs: mmu-miR-133a-3p, -1a-3p and -29c-3p were validated in TRAMP-C1 mice prostate tumor by stem-loop RT-qPCR. Hsa-miR-133a-3p/1-3p expression levels were significantly decreased in PCa compared to normal tissues while hsa-miR-133a-3p was found to be further decreased in metastatic prostate cancer tumors compared to non-metastatic PCa. We examined the promoter region of hsa-miR-133a-3p/1-3p genes and compared methylation at these loci with mature miRNA expression. We found that hsa-miR-1-2/miR-133a-1 cluster promoter hypermethylation decreased hsa-miR-133a-3p/1-3p expression in PCa. GOLPH3 and JUP, two hsa-miR-133a-3p and miR-1-3p predicted target genes, were up-regulated in PCa. ROC analysis showed that the combination of hsa-miR-133a-3p, miR-1-3p, GOLPH3 and JUP is a promising panel biomarker to distinguish between PCa and normal adjacent tissue (NAT). These results link PCa aggressiveness to the attenuation of hsa-miR-133a-3p and miR-1-3p expression by promoter hypermethylation. Hsa-miR-133a-3p and miR-1-3p down-regulation may enhance PCa aggressiveness in part by targeting GOLPH3 and JUP.

Hsa-miR-133a-3p, miR-1-3p, GOLPH3 and JUP combination results in a good biomarker to distinguish between prostate cancer and non-prostate cancer patients The incidence and mortality of Prostate Cancer (PCa) worldwide correlate with age and bad dietary habits. Previously, we investigated the mRNA/miRNA role on PCa development and progression using high fat diet (HFD) fed mice. Here our main goal was to investigate the effect of HFD on the expression of PCa-related miRNAs and their relevance in PCa patients. We identified 6 up- and 18 down-regulated miRNAs in TRAMP-C1 mice prostate tumors under HFD conditions using miRNA microarrays. Three down-regulated miRNAs: mmu-miR-133a-3p, -1a-3p and -29c-3p were validated in TRAMP-C1 mice prostate tumor by stem-loop RT-qPCR. Hsa-miR-133a-3p/1-3p expression levels were significantly decreased in PCa compared to normal tissues while hsa-miR-133a-3p was found to be further decreased in metastatic prostate cancer tumors compared to non-metastatic PCa. We examined the promoter region of hsa-miR-133a-3p/1-3p genes and compared methylation at these loci with mature miRNA expression. We found that hsa-miR-1-2/miR-133a-1 cluster promoter hypermethylation decreased hsa-miR-133a-3p/1-3p expression in PCa. GOLPH3 and JUP, two hsa-miR-133a-3p and miR-1-3p predicted target genes, were up-regulated in PCa. ROC analysis showed that the combination of hsa-miR-133a-3p, miR-1-3p, GOLPH3 and JUP is a promising panel biomarker to distinguish between PCa and normal adjacent tissue (NAT). These results link PCa aggressiveness to the attenuation of hsa-miR-133a-3p and miR-1-3p expression by promoter hypermethylation. Hsa-miR-133a-3p and miR-1-3p down-regulation may enhance PCa aggressiveness in part by targeting GOLPH3 and JUP. Prostate cancer (PCa) is currently the most commonly diagnosed type of cancer and the fifth leading cause of cancer deaths among men over the age of 50 years worldwide (https://gco.iarc.fr/). Although PCa is a multifactorial disease, different epidemiological studies suggested that lifestyle and environmental factors influence the development and progression of this disease (1). Dietary fats and obesity have the potential to cause PCa initiation, promotion and progression (2). The proposed mechanisms for PCa induced by dietary fats are divided into growth factor signaling, lipid metabolism, inflammation and hormonal modulation among others (2). However, the underlying molecular mechanisms responsible for the effect of high fat diet (HFD) on PCa development and progression remain unknown. Previously, we generated several preclinical mice models to investigate the impact of HFD on PCa development and progression. We reported that C-terminal binding protein 1 (CTBP1) depletion in androgen-insensitive PCa xenografts from HFD-fed mice modulated the expression of mRNAs and microRNAs (miRNAs) involved in cancer related pathways which impacts on PCa proliferation and invasion (3–5). Additionally, recent androgen-sensitive PCa allografts and HFD mice model demonstrated that high fat intake significantly increased tumor growth. Tumors developed in HFD fed mice showed overexpression of oncogenes and oncomiRs compared to control diet (CD) (6–8). MiRNAs are endogenous small non-coding RNAs (18-22 nucleotides) that regulate gene expression. Compelling evidence have demonstrated that miRNA expression is deregulated in several human cancer types through numerous mechanisms, including amplification or deletion of miRNA genes, abnormal miRNAs transcription, deregulated epigenetic changes and defects in the miRNA biogenesis machinery (9). MiRNAs can function either as oncogenes (oncomiRs) or tumor suppressors (tsmiRs) under certain conditions. The deregulated miRNAs have been shown to affect several hallmarks of cancer, including sustaining proliferative signaling, evading growth suppressors, resisting cell death, activating invasion and metastasis, and inducing angiogenesis (9). In PCa, several miRNAs have been proposed to regulate cell proliferation, cell cycle, apoptosis, as well as invasion and adhesion processes (9). In addition, diet and lifestyle factors are involved in the regulation of miRNA expression in different tissues and pathologies, including cancer (10–12). Finally, an increasing number of studies have identified miRNAs as potential biomarkers for PCa diagnosis, prognosis and therapy. Here our main goal was to investigate the effect of HFD on the expression of cancer-related miRNAs in prostate tumors and their relevance in PCa patients. Starting from microarray data from prostate tumors obtained from CD or HFD fed mice we focused on the role of two miRNAs (miR-133a-3p/1-3p) and their target genes. Using PCa patient samples from public databases, we examined the expression levels of hsa-miR-133a-3p/1-3p. The biological roles of hsa-miR-133a-3p/1-3p and their relevant target genes were investigated by bioinformatics approaches. Finally, the promoter methylation of hsa-miR-133a-3p/1-3p host genes and their correlation with mature miRNA expression was evaluated. Figure 1 summarizes the steps of the methodology we followed in this study. TRAMP-C1 cell line (ATCC: CRL-2730, Manassas, VA, USA) was cultured in DMEM medium (GIBCO, Thermo Scientific, Massachusetts, USA) supplemented with 10% of fetal bovine serum, antibiotics and 0.25 IU/μl of human recombinant insulin in a 5% CO2 humidified incubator at 37°C. Six-weeks-old C57BL/6J male mice (N=12) were housed under pathogen-free conditions following the IBYME’s animal care guidelines. Mice were randomized into two dietary groups and fed ad libitum during 20 weeks with CD (3,120 kcal/kg, 5% fat) or HFD (4,520 kcal/kg, 37% fat). After 12 weeks of diet, 5 × 106 TRAMP-C1 cells were subcutaneously injected. Animals were sacrificed in the 20th week and tumor samples were collected. The metabolic state of the animals and tumor volume were analyzed as previously described (6). For microarray analysis, total RNA from CD or HFD allografts was isolated using TriReagent (Molecular Research Center) and hybridized with GeneChip® miRNA 4.0 Array (Affymetrix) (N=3 per group). For miRNA expression analysis, we employed the Limma and pd.mirna.4.0 packages in the R/Bioconductor environment. For differential expression analysis we used the Rank Product Method for two class unpaired data and a fold discovery rate (FDR) < 0.05 (13). Total RNA from allografts and plasma was isolated using TriReagent (Molecular Research Center). For plasma samples, cel-miR-39 synthetic miRNA (20 fmol) was spiked in before RNA isolation. miRNAs were retrotranscribed using the stem-loop method as previously described (4, 14, 15). Briefly, 100 ng from allografts or 4 μl of total RNA from plasma and 0.07 μM of stem-loop primer were preheated (70°C, 5min). Retrotranscription (RT) was performed using M-MLV reverse transcriptase (Promega) and incubated in MyGenie96 Thermal Block (Bioneer) (30min 16°C, 60min 42°C, 2min 70°C). qPCRs were run in 10 μl with 0.1 μM of each primer and 5 μl of PowerUp™ SYBR™ Green Master Mix (Thermo Fisher), in StepOne Plus Real Time PCR (Applied Biosystems) (50°C 2min, 95°C 10 min, 40 cycles: 95°C 15s, annealing temperature 15s, 60°C 1min and 95°C 15s) as previously described (7). All reactions were run in duplicate. The expression levels of miRNAs were calculated using ΔΔCT method normalizing to hsa-miR-103a-3p and hsa-miR-191-5p levels and control. The expression levels of plasma miRNAs were normalized to cel-miR-39. Statistical analysis was performed using Mann-Whitney test. Primer sequences for miRNA stem-loop RT-qPCR are listed in Table S2 . MiRNA and gene expression of prostate tumors of patients was obtained from TCGA Prostate Cancer (PRAD) cohort available in the UCSC Xena resource (16) (https://xenabrowser.net/). PCa samples (n=497) and normal adjacent tissue (NAT) (n=52) with expression data of miRNA mature strand [miRNA-Seq (IlluminaHiSeq_miRNASeq)] and genes [RNAseq (IlluminaHiSeq)] as log2 (RPM+1) values were included in the present study. Additionally, miRNA expression profile in metastatic PCa patients was analyzed in a cohort of 19 patients with metastases diagnosis after radical prostatectomy (MPCA (metastatic prostate cancer)) and 19 patients without evidence of disease recurrence (NMPCA (non-metastatic prostate cancer)), using next generation whole miRNome sequencing results from Nam’s work available from GEO dataset (GSE117674) (17). GSE117674 was generated by Ion Torrent S5 XL (Homo sapiens) high-throughput sequencing. MiRNA expression levels in tumors of patients with PCa and castrate-resistant prostate cancer (CRPC) were obtained from the Sequence Read Archive (SRA) data (PRJDB5095) available in miTED bioinformatics tool (https://dianalab.e-ce.uth.gr/mited/#/expressions). Three PCa and three CRPC samples were included in the present study, whose data was downloaded as log2 (RPM) values. Finally, circulating miRNA expression profile was analyzed in the peripheral blood of a cohort of patients with type 2 diabetes (n=6) and healthy donors (HD) (n=4) using microarrays data available from GSE27645. Normalized signal intensity data was downloaded from the Gene Expression Omnibus (GEO) public functional genomics data repository (https://www.ncbi.nlm.nih.gov/geo/). GSE27645 was generated by miRCURY LNA microRNA Array, v.11.0. Data normalization and homogeneity of variances was assessed using Shapiro–Wilk test, and boxplot or Levene test, respectively. To compare the expression between PCa and NAT samples (N=52 per group) Paired Sample t-test or Sign Median test (using the signmedian.test R package) were applied. Also, Student’s t test or Mann-Whitney test were used to analyze the statistical differences between NMPCA and MPCA (n=19 per group), and between PCa and CRPC (n=3 per group). Predicted target genes of hsa-miR-133a-3p and hsa-miR-1-3p were obtained from microT-CDS resource. To identify common target genes, we used Venn diagrams (http://bioinformatics.psb.ugent.be/webtools/Venn/). To the obtained gene list, publicly available log2(norm_count+1) gene expression values for TCGA Prostate Cancer (PRAD) and the GTEx project patient samples were downloaded from UCSC’s Xena Browser (http://xena.ucsc.edu/) (16). PCA was performed to determine samples distribution based on the expression of the target genes identified from the downregulated miRNAs. We included in the analysis normal prostates (n=100) (GTEx), normal adjacent tissue (NAT) (n=52) and prostate tumors (n=495) (TCGA-PRAD) samples. For PCA plots, the R function “prcomp” from stats package (version 4.0.2) was used. To investigate the functional role of selected miRNAs, we performed Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Gene Ontology (GO) annotation using DIANA-miRPath v3 tool (http://snf-515788.vm.okeanos.grnet.gr/), from a list of predicted target genes derived from microT-CDS. The top 10 of the statistically significant terms (p-Value <0.05) were selected. For dot plot, ggplot2 package was used. Expression levels of selected miRNAs and target genes were obtained from TCGA-PRAD patient cohort data available in UCSC Xena. Expression data of miRNA mature strand [miRNA-Seq (IlluminaHiSeq_miRNASeq)] and genes [RNAseq (IlluminaHiSeq)] were downloaded as log2 (RPM+1) values. PCa samples from 495 patients were included in the present analysis. We generated a correlation matrix between hsa-miR-133a-3p, hsa-miR-1-3p and the selected target genes, applying the Spearman correlation coefficient using the Hmisc R package. For the selected miRNAs, target genes with a negative correlation coefficient rho plus p-Value <0.05 were selected for further analysis. Also, for correlation matrix graphical representation, the R function “chart.Correlation” from PerformanceAnalytics package (version 4.0.2) was used. To evaluate the power of hsa-miR-133a-3p, miR-1-3p, GOLPH3 and JUP to distinguish between tumor and NAT, we performed a receiver-operating characteristic (ROC) analysis using the expression data available in the TCGA-PRAD dataset. The ROC curve, the area under the curve (AUC), sensitivity, specificity and the optimal point were plotted and calculated for the miRNAs, genes and their combination using the R function “roc” from pROC package (version 4.0.5) Hsa-miR-1-2/miR-133a-1 and miR-1-1/miR-133a-2 promoter methylation was evaluated in 52 paired PCa and NAT samples from the TCGA-PRAD cohort. DNA methylation data (Illumina Infinium HumanMethylation450) was downloaded as beta value using UCSC Xena resource. The Ensembl browser (http://www.ensembl.org) was employed to identify the coding genes of the mentioned miRNAs: MIR1-2: chr18:19,408,965-19,409,049, MIR133A1: chr18:19,405,659-19,405,746, MIR1-1: chr20:62,554,306-62,554,376, MIR133A2: chr20:62,564,912-62,565,013. Methylation probes targeting miRNA promoter regions were identified by mapping 5000 base pairs upstream of miRNA TSS (hsa-miR-1-2/miR-133a-1 promoter: chr18:19,409,049-19,414,049, hsa-miR-1-1/miR-133a-2 promoter: chr20:62,554,306-5000-62,544,306). The correlation between promoter methylation and mature miRNA expression was assessed by a Spearman correlation analysis in 497 PCa samples using GraphPad Prism 8.0.1. Previously, we reported that HFD significantly increased prostate tumor growth, oncogenes and oncomiRs expression in male mice (4–7). In this work, we investigated the impact of HFD on TRAMP-C1 tumor growth developed in C57BL/6J male mice ( Supplemental Figure 1A ). In addition, miRNA expression profile from these tumors was determined using a high-throughput platform. GeneChiP miRNA 4.0 Affymetrix was hybridized with total RNA isolated from CD or HFD TRAMP-C1 tumors. After data normalization, we selected differentially expressed miRNAs with a FDR<0.05 and identified 18 down- and 6 up-regulated miRNAs in HFD tumors compared to CD group ( Figure 2A and Table S1 ). Since most of the reported works study the role of oncomiRs, in this work we focused on the role of tumor suppressor microRNAs (tsmiRs) in prostate cancer associated to HFD. To validate these results, the expression levels of 6 down-regulated selected miRNAs (mmu-miR-133a-3p, 1a-3p, 29c-3p, 18a-3p, 148a-3p and 223-3p) in TRAMP-C1 tumors from HFD and CD mice were evaluated by RT-qPCR. The results showed a significant decrease in the expression of mmu-miR-133a-3p, 1a-3p and 29c-3p in HFD-tumor compared to CD mice ( Figure 2B ). No changes were detected in mmu-miR-18a-3p, 148a-3p and 223-3p expression levels ( Figure 2B ). We further determined the expression profile of the selected miRNAs in mice bloodstream. We found that mmu-miR-1a-3p was significantly down-regulated in plasma from HFD mice compared to CD fed animals ( Supplemental Figure 2A ). Mmu-miR-133a-3p, miR-29c-3p, miR-18a-3p and miR-223-3p showed no changes in the levels of their plasma circulation ( Supplemental Figure 2A ). To further evaluate the expression profile of the selected miRNAs between CD and HFD tumors in prostate tumors from patients, we performed a bioinformatic analysis using the TCGA-PRAD, GSE117674 and PRJDB5095 datasets. First, we analyzed their expression in prostate primary tumors in comparison to NAT. As shown in Figure 3A , hsa-miR-133a-3p and 1a-3p were significantly decreased in PCa compared to NAT (TCGA-PRAD cohort). No significant differences were found regarding the expression of hsa-miR-29c-3p, miR-18a-3p, miR-148a-3p and miR-223-3p. Since miRNA expression can be altered during prostate cancer progression, the expression profile of the selected miRNAs was also evaluated in metastatic prostate cancer (MPCA) and tumors derived from patients without metastasis. Thus, we explored the miRNome sequencing data from Nam´s study (GSE117674) (17). Hsa-miR-133a-3p was found decreased in tumors from metastatic patients (after surgery) compared to tumors from non-metastatic patients, while hsa-miR-29c-3p was found increased in MPCA ( Figure 3B ). There were no differences in the expression of hsa-miR-18a-3p, miR-148a-3p and miR-223-3p, while hsa-miR-1-3p was not detected in this cohort of patients ( Figure 3B ). Additionally, we used the miTED tool to evaluate the expression of the miRNAs in tumors from patients with castration-resistant prostate cancer (CRPC). Hsa-miR-133a-3p and hsa-miR-1-3p showed a non-significant diminution in tumors from patients with CRPC compared to PCa ( Figure 3C ). It is important to mention that the number of cases assessed was low (n=3), therefore, in the future, it is necessary to increase the number of patients in order to find statistically significant differences between the groups. Overall, results suggest that hsa-miR-133a-3p and miR-1-3p might act as tumor suppressors in PCa. As mentioned above, mmu-miR-1a-3p was found to be decreased in plasma from HFD fed mice ( Supplemental Figure 2A ). In addition, these mice showed elevated fasting blood glucose and cholesterol levels ( Supplemental Figure 1C ), but no changes were found in their body weight ( Supplemental Figure 1B ). Therefore, we investigated the relationship between hyperglycemia and the levels of these miRNAs in plasma. We analyzed the levels of hsa-miR-133a-3p, miR-1-3p, miR-29c-3p, miR-18a-3p, miR-148a-3p and miR-223-3p in blood samples of patients with type 2 diabetes (T2DM) (n=6) and healthy donors (HD) (n=4) (GSE27645). No differences were found in circulating levels of the 6 miRNAs ( Supplemental Figure 2B ). Due to no validated target gene data was found for the miRNAs included in this study, we obtained a list of predicted target genes for hsa-miR-133a-3p and miR-1-3p, using microT-CDS resource. To identify common target genes, we used Venn diagrams ( Figure 4A ). We found 608 target genes for hsa-miR-133a-3p, while 634 target genes for hsa-miR-1-3p. There were 38 target genes in common between hsa-mR-133a-3p and miR-1-3p ( Figures 4A, B ). Additionally, to determine the relevance of down-regulated miRNAs-target genes in human samples, we performed a PCA of the 38 common target genes using the normalized expression data from normal prostate (GTEX), NAT and prostate tumors samples (TCGA-PRAD). The 2-dimensional scatterplot of the first two principal components revealed marked differences in overall gene expression between normal prostate and PCa samples ( Figure 4C ). To explore the functional role of the selected miRNAs and pathways, we used DIANA-miRPath v3 tool (http://snf-515788.vm.okeanos.grnet.gr/). With all the predicted target genes, we performed a KEGG pathway enrichment analysis using a p-value <0.05. This analysis revealed that target genes of hsa-miR-133a-3p and miR-1-3p were associated with processes such as Transcriptional misregulation in cancer and ECM−receptor interaction ( Figure 5A and Table S4 ). Also, using DIANA-miRPath v3, a GO analysis was performed at three levels: biological processes, cellular components and molecular function. The top 10 of the statistically significant terms (p-value <0.05) are shown in Figure 5B . Target genes hsa-miR-133a-3p and miR-1-3p were enriched in processes associated with Nucleic acid binding transcription factor activity, Gene expression and Protein binding transcription factor activity ( Figure 5B and Table S5 ). Therefore, hsa-miR-133a-3p and miR-1-3p modulated specific cancer related pathways. In order to find relevant target genes for the 2 selected miRNAs, we performed a correlation matrix, using the expression data of hsa-miR-133a-3p and miR-1-3p and the 26 target genes involved in the processes Transcriptional misregulation in cancer ( Table S4 ) of prostate tumors from patients available in TCGA-PRAD (UCSC Xena). From this analysis, we selected the target genes that showed a negative Spearman correlation coefficient rho and a p-Value <0.05 with the 2 miRNAs ( Figure 6A ). We found 3 target genes (GOLPH3, H3F3A and JUP) with a significant negative correlation with the selected miRNAs ( Figure 6A ). Also, hsa-miR-133a-3p and miR-1-3p showed a strong positive correlation between them with a significant p-value ( Figure 6A ). Then, we analyzed the expression pattern of the three target genes in prostate tumors and normal samples from TCGA-PRAD. As shown in Figure 6B , we found that GOLPH3 and JUP expression was significantly increased in primary prostate tumors compared to NAT. There were no differences in the expression of H3F3A. Up to date, digital rectal examination (DRE) and serum prostate-specific antigen (PSA) monitoring are the standard methods of PCa screening (18). However, PSA is organ- but not tumor-specific biomarker with low specificity and high false-positive rate in patients with benign prostatic hyperplasia (BPH) (18, 19).Therefore, we studied weather hsa-miR-133a-3p, miR-1-3p, GOLPH3, JUP or a combination of them, resulted in a good biomarker to distinguish between PCa and non-PCa patients. To do this, we performed a receiver-operating characteristic (ROC) analysis. As shown in Figures 7A, B, D poor predictive power to discriminate between tumor tissue and NAT was obtained when we calculated the area under the curve (AUC) for hsa-miR-133a-3p, miR-1-3p and JUP in (0.659, 0.706 and 0.655, respectively). AUC for GOLPH3 was 0.845 ( Figure 7C ), which shows that these gene is useful to distinguish between tumor tissue and NAT. However, a gene panel might be used in the clinic instead of a single diagnostic biomarker. For this reason, we analyzed a combination of genes and miRNAs (hsa-miR-133a-3p, miR-1-3p, GOLPH3 plus JUP) as a possible biomarker panel to distinguish between tumor tissue and NAT. It was shown that this combination improved the performance of GOLPH3, obtaining an AUC of 0.867, 86.5% sensitivity and 78.8% specificity ( Figure 7E ). Therefore, this combination resulted in a good diagnostic biomarker panel able to discriminate between tumor tissue and non-tumor tissue. The hsa-miR-1/133 family is located at three different loci (as clustered miRNAs) at chromosomes 18q11.2 (miR-1-2/miR-133a-1), 20q13.33 (miR-1-1/miR-133a-2), and 6p12.2 (miR-206/miR-133b) (20). Here, we analyzed the methylation status at miR-1-2/miR-133a-1 and miR-1-1/miR-133a-2 promoters. Based upon the Ensembl and UCSC Xena resources, two and thirteen methylation probes were identified targeting the hsa-miR-1-2/miR-133a-1 and miR-1-1/miR-133a-2 promoters, respectively ( Figure 8 ). We compared the methylation status of two cluster promoters (miR-1-2/miR-133a-1 and miR-1-1/miR-133a-2) in 52 paired PCa and NAT samples. As shown in Figure 8A , one probe (cg17106157) targeting miR-1-2/133a-1 showed significantly higher beta values in PCa compared to NAT. Also, ten and one probes targeting miR-1-1/133a-2 promoter showed lower and higher beta values in PCa vs NAT respectively ( Figure 8B ). Then, a correlation analysis between promoter methylation and mature miRNA expression levels was performed in 497 PCa samples. As presented in Table S3 , Spearman correlation analysis showed that hsa-miR-133a-3p and hsa-miR-1-3p expression negatively correlated with cg17106157 (chromosome 18) probe and with cg05898333 (chromosome 20) probe. Moreover, hsa-miR-133a-3p and hsa-miR-1-3p expression positively correlated with cg15580304, cg14523475, cg08148458 and cg22617703 probes targeting the miR-1-1/miR-133a-2 promoter. Therefore, the decreased expression observed in hsa-miR-1-3p/miR-133a-3p in prostate tumors might be due to a hsa-miR-1-2/miR-133a-1 cluster promoter hypermethylation. Here we demonstrated that HFD markedly reduces the expression of potential tsmiRs in TRAMP-C1 tumors developed as allograft in C57BL/6J mice. Target genes modulated by these tsmiRs regulate processes mainly associated to cancer related pathways. Among these, mmu-miR-133a-3p and miR-1a-3p were dramatically diminished in tumors by HFD. Additionally, human orthologous miRNAs were significantly down-regulated in human prostate tumors compared to NAT and normal prostates. Moreover, we found that hsa-miR-133a-3p and miR-1-3p were significantly decreased in prostate tumors of metastatic patients compared to tumors of non-metastatic patients. The hsa-miR-1, -133 and -206 family are located at three different loci at chromosomes 20q13.33 (miR-1-1/miR-133a-2), 18q11.2 (miR-1-2/miR-133a-1) and 6p12.2 (miR-206/miR-133b) (20). The mature miR-133 isomers (A and B) are highly similar, differing only at the 3′-terminal base, with miR-133a-1/2 terminating G-3′ and miR-133b with A-3′, respectively (21). Due to their close locations at distinct loci, miR-1/133a, miR-206/133b are constituted as clustered miRNAs (21). Recent studies showed miR-1, -133 and -206 family deregulation in cancer, in which typically they function as tumor suppressors (22, 23). Hsa-miR-133a-3p is probably the most studied miRNA of this family and has been extensively reported as down-regulated in several types of cancer and predicted a poor prognosis (24–29). In PCa, a low expression of hsa-miR-133a-3p has been associated with the recurrence and distant metastasis of PCa (30–33). Likewise, a recent study from Tang et al. demonstrated that hsa-miR-133a-3p expression is reduced in PCa tissues compared with the NAT and benign prostate lesion tissues, particularly in bone metastatic PCa tissues. Also, low expression of miR-133a-3p is significantly correlated with advanced clinicopathological characteristics and shorter bone metastasis-free survival in PCa patients (34). As referred to hsa-miR-1, different studies demonstrated that is the most down-regulated miRNA in PCa compared to non-cancerous prostate tissues and significantly decreased in recurrent PCa specimens in comparison to non-recurrent PCa samples (35–37). MiR-1 is further down-regulated in cancer progression and alone can predict disease recurrence (36). Also, miR-1 has sufficient power to distinguish recurrent PCa specimens from non-recurrent (35, 36, 38). Furthermore, it’s has been reported to target the oncogenic function of purine nucleoside phosphorylase (PNP) in PCa (38). Thus, hsa-miR-1 may exert similar tumor suppressor activities and coordinately regulate the expression of oncogenes controlling PCa initiation and progression. Based on our and other groups’ findings, we propose hsa-miR-133a-3p and miR-1-3p as promising miRNAs to be studied as potential biomarkers for PCa diagnosis and prognosis. In this work, we also aim to find relevant target genes for hsa-miR-133a-3p and miR-1-3p. We performed a correlation analysis using data from PCa patients available in public algorithms. This analysis allowed us to find three relevant target genes for the two miRNAs with a significant negative correlation. Only two of the three target genes, GOLPH and JUP were significantly increased in primary prostate tumors compared to NAT. In addition, ROC analysis showed that the combination of hsa-miR-133a-3p, miR-1-3p, GOLPH3 and JUP is a promising panel biomarker to distinguish between PCa and NAT. In addition, we performed a bioinformatic analysis to search experimentally validated target genes of hsa-miR-133a-3p and miR-1-3p, involved in the androgen receptor (AR) pathway. Although the AR gene is not a target of these miRNAs, we found that several AR co-regulator genes (MNAT1, GTF2H1, RAN) were direct targets of hsa-miR-133a-3p and miR-1-3p. In turn, we found that genes involved in the MAPK signaling pathway (F2RL1, HRAS), a major oncogenic and AR crosstalk pathway in PCa, were experimentally validated targets of hsa-miR-1-3p. We suggest that hsa-miR-133a-3p and miR-1-3p act as tumor suppressor miRNAs in PCa because they interfere in the AR signaling pathway, the central pathway for PCa initiation and growth. In particular, these miRNAs would be involved in the castration resistance process, since they have several AR co-regulators as direct targets. Furthermore, both miRNAs showed a tendency to be decreased in more aggressive prostate tumors. Therefore, hsa-miR-133a-3p and miR-1-3p emerge as potential tsmiRs whose attenuation would increase PCa aggressiveness. MiRNAs may be epigenetically silenced by DNA methylation of their encoding genes (9). DNA hypermethylation was found to down-regulate several tsmiRNAs, whereas DNA hypomethylation was reported to up-regulate oncomiRs (39). In this study, we analyzed the methylation status at miR-1-2/miR-133a-1 and miR-1-1/miR-133a-2 promoters in PCa and normal samples and examined the correlation between promoter methylation and mature miRNA expression. This bioinformatics analysis suggests that hypermethylation of hsa-miR-1-2/miR-133a-1 cluster promoter might decrease hsa-miR-1-3p/miR-133a-3p expression in prostate tumors. Therefore, epigenetic repression of the hsa-miR-1-2/miR-133a-1 cluster may play a critical role in PCa aggressiveness by activating GOLPH3 and JUP. Golgi phosphoprotein 3 (GOLPH3) is an oncogene involved in the development of carcinoma in a number of organs and a candidate metastasis gene in human cancer (40, 41).Different studies have investigated the role of GOLPH3 in PCa. Independent reports demonstrated that GOLPH3 overexpression in PCa tissues is linked to higher Gleason grade, advanced pathological tumor stage, the presence of metastasis, worst overall survival and the state of the lymph nodes (40, 42, 43). Overexpression of GOLPH3 is associated with the transition of hormone sensitive to hormone refractory PCa (43). Li and Guo reported that GOLPH3 silencing inhibited cell proliferation and arrested the cell cycle at the G2/M phase (44). Also, GOLPH3 silencing activated P21 expression but suppressed the expression of CDK1/2 and cyclinB1 protein together with the phosphorylation of AKT and mTOR (44). Further revealed that silencing of GOLPH3 reduced cell migration and invasion ability (41). Finally, in vitro studies demonstrated that GOLPH3 regulates cell size, enhances growth factor-induced mTOR signaling in human cancer cells and modulates the response to rapamycin (45). The role of junction plakoglobin (JUP) during cancer progression is still controversial. Spethmann et al., demonstrated that the opposing biological roles of JUP were reflected by antagonistic prognostic effects in different molecular subtypes. High expression of JUP was associated with adverse tumor stage, high Gleason grade lymph node metastases in a subset of PCa patients without TMPRSS2:ERG fusion (46). Also, Overexpression of JUP was linked to strong androgen receptor expression, high cell proliferation, and PTEN and FOXP1 deletion (46). On the other hand, it was found that SOX4 interacts with JUP in a Wnt-dependent manner in LNCaP cells and this complex may inhibit Wnt signaling (47). Therefore, based on our and previous studies, GOLPH3 and JUP have a critical role in PCa pathogenesis and progression. In conclusion, our results demonstrated that HFD dramatically reduces the expression of tsmiRs in androgen-sensitive prostate tumors. Additionally, the expression of hsa-miR-133a-3p and miR-1-3p negatively correlates with GOLPH3 and JUP, two PCa driver oncogenes. Besides, hsa-miR-133a-3p and miR-1-3p are epigenetically silenced by promoter hypermethylation and functions as tsmiRs in PCa. Although evaluations of the two miRNAs and their target genes expression in larger populations are still needed, our results indicate that hsa-miR-133a-3p, miR-1-3p, GOLPH3 and JUP are functional drivers of PCa and may be their combination is a promising diagnostic biomarker panel for prostate cancer. In conclusion, our results demonstrated that HFD modulates the expression of a substantial number of miRNAs in PCa. Attenuation of hsa-miR-133a-3p and miR-1-3p expression by promoter methylation in prostate tumors may enhance PCa development, in part by targeting GOLPH3 and JUP. Hsa-miR-133a-3p, miR-1-3p, GOLPH3 and JUP are functional drivers of PCa and may be their combination is a promising diagnostic biomarker panel for prostate cancer. The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE181350. The animal study was reviewed and approved by Comité Institucional para el Cuidado y Uso de Animales de Laboratorio-IBYME. RD and AD designed the study. RD and CM carried out the experiments with contributions of PF, KDG and JM. RD and EL performed bioinformatics analyses. RD, CM, EL, and ADS interpreted the data. RD, CM and ADS wrote the manuscript with contributions of KG and approval from all authors. All authors contributed to the article and approved the submitted version. This research was supported by the Argentinean Agency of Science and Technology (ANPCyT PICT 2014-324; PICT 2015-1345, PICT 2018-1304, PICT START UP-2019-21), National Cancer Institute (Argentina) (INC 2020) and Williams Foundation (Argentina). This work was part of Ph.D. thesis of RD supported by the CONICET fellowship from Argentina. The authors thank National Cancer Institute (Argentina), Williams Foundation (Argentina) and Argentinean Agency of Science and Technology for their support. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

PMC9641317

Jiahui Liu,Mi Jiang,Jinlei Guan,Yuan Wang,Wenjuan Yu,Yuanping Hu,Xin Zhang,Jie Yang

LncRNA KCNQ1OT1 enhances the radioresistance of lung squamous cell carcinoma by targeting the miR-491-5p/TPX2-RNF2 axis

01-10-2022

Lung squamous cell carcinoma (LUSC),long non-coding RNA KCNQ1OT1 (lncRNA KCNQ1OT1),miR-491-5p,Xenopus kinesin-like protein 2 (TPX2),RING finger protein 2 (RNF2)

Background Lung cancer, especially lung squamous cell carcinoma (LUSC), is one of the most common malignant tumors worldwide. Currently, radiosensitization research is a vital direction for the improvement of LUSC therapy. Long non-coding RNAs (lncRNAs) can be novel biomarkers due to their multiple functions in cancers. However, the function and mechanism of lncRNA KCNQ1OT1 in the radioresistance of LUSC remain to be elucidated. Methods The clonogenic assay was employed to determine the radioresistance of SK-MES-1R and NCI-H226R cells. Real-time quantitative polymerase chain reaction (RT-qPCR) and Western blot were conducted for the detection of gene expression. Cell proliferation was determined by the methyl thiazolyl tetrazolium (MTT) assay, colony formation assay, and 5-ethynyl-2'-deoxyuridine (EdU) staining, and cell apoptosis was assessed by flow cytometry. The relationships between genes were also evaluated by applying the luciferase reporter and radioimmunoprecipitation (RIP) assays. Results Radioresistant LUSC cells (SK-MES-1R and NCI-H226R) had strong resistance to X-ray irradiation, and lncRNA KCNQ1OT1 was highly expressed in SK-MES-1R and NCI-H226R cells. Moreover, knockdown of lncRNA KCNQ1OT1 prominently suppressed proliferation, attenuated radioresistance, and accelerated the apoptosis of SK-MES-1R and NCI-H226R cells. More importantly, we verified that miR-491-5p was a regulatory target of lncRNA KCNQ1OT1, and Xenopus kinesin-like protein 2 (TPX2) and RING finger protein 2 (RNF2) were the target genes of miR-491-5p. The rescue experiment results also demonstrated that miR-491-5p was involved in the inhibition of cell proliferation and the downregulation of TPX2 and RNF2 expression mediated by lncRNA KCNQ1OT1 knockdown in SK-MES-1R and NCI-H226R cells. Conclusions LncRNA KCNQ1OT1 was associated with the radioresistance of radioresistant LUSC cells, and the lncRNA KCNQ1OT1/miR-491-5p/TPX2-RNF2 axis might be used as a therapeutic target to enhance the radiosensitivity of radioresistant LUSC cells.

LncRNA KCNQ1OT1 enhances the radioresistance of lung squamous cell carcinoma by targeting the miR-491-5p/TPX2-RNF2 axis Lung cancer, especially lung squamous cell carcinoma (LUSC), is one of the most common malignant tumors worldwide. Currently, radiosensitization research is a vital direction for the improvement of LUSC therapy. Long non-coding RNAs (lncRNAs) can be novel biomarkers due to their multiple functions in cancers. However, the function and mechanism of lncRNA KCNQ1OT1 in the radioresistance of LUSC remain to be elucidated. The clonogenic assay was employed to determine the radioresistance of SK-MES-1R and NCI-H226R cells. Real-time quantitative polymerase chain reaction (RT-qPCR) and Western blot were conducted for the detection of gene expression. Cell proliferation was determined by the methyl thiazolyl tetrazolium (MTT) assay, colony formation assay, and 5-ethynyl-2'-deoxyuridine (EdU) staining, and cell apoptosis was assessed by flow cytometry. The relationships between genes were also evaluated by applying the luciferase reporter and radioimmunoprecipitation (RIP) assays. Radioresistant LUSC cells (SK-MES-1R and NCI-H226R) had strong resistance to X-ray irradiation, and lncRNA KCNQ1OT1 was highly expressed in SK-MES-1R and NCI-H226R cells. Moreover, knockdown of lncRNA KCNQ1OT1 prominently suppressed proliferation, attenuated radioresistance, and accelerated the apoptosis of SK-MES-1R and NCI-H226R cells. More importantly, we verified that miR-491-5p was a regulatory target of lncRNA KCNQ1OT1, and Xenopus kinesin-like protein 2 (TPX2) and RING finger protein 2 (RNF2) were the target genes of miR-491-5p. The rescue experiment results also demonstrated that miR-491-5p was involved in the inhibition of cell proliferation and the downregulation of TPX2 and RNF2 expression mediated by lncRNA KCNQ1OT1 knockdown in SK-MES-1R and NCI-H226R cells. LncRNA KCNQ1OT1 was associated with the radioresistance of radioresistant LUSC cells, and the lncRNA KCNQ1OT1/miR-491-5p/TPX2-RNF2 axis might be used as a therapeutic target to enhance the radiosensitivity of radioresistant LUSC cells. Lung cancer is still a common malignant tumor worldwide, with high morbidity and mortality (1). Non-small cell lung cancer (NSCLC) accounts for 80–85% of lung cancer cases (2). According to histopathological classification, NSCLC can be further divided into lung adenocarcinoma, lung squamous cell carcinoma (LUSC), and large cell carcinoma (3). Currently, more than half of newly diagnosed cancer patients require radiation therapy, which can be combined with surgery, chemotherapy, or molecular targeted therapies (4). Radiation therapy has played a vital role in the treatment of patients with metastatic disease (5). It has been proven that the biological basis of radiation therapy is the effect of ionizing radiation on biological cells (6). Radiation-sensitive patients means that when radiation doses are limited to levels that maximize therapeutic effects, reduce normal tissue cell death, prevent excessive inflammation, and preserve stem cell populations, the irradiated normal tissues of patients can resist permanent damage and do not exhibit clinically relevant adverse effects (7). However, according to statistics, 60–70% of NSCLC patients have received radiotherapy, while the radiotherapy effect and prognosis of NSCLC are still poor due to the constraints of radiation resistance and other factors (8). It is worth noting that the exact mechanism of LUSC cell radioresistance has not been elucidated. Therefore, how to improve the radioresistance of LUSC cells has become a new approach and strategy for LUSC treatment. Long non-coding RNAs (lncRNAs) are a class of linear RNA molecules that have no transcriptional function or protein coding potential, and are mainly produced by RNA polymerase II/I (9). LncRNAs have been confirmed to regulate gene expression through genomic imprinting, transcriptional regulation, and chromatin modification (10,11). Studies have also demonstrated that lncRNAs can be involved in cell proliferation, apoptosis, differentiation, chromatin remodeling, migration, and invasion, among other processes (12,13). At present, lncRNAs have been proven to be crucial in almost all diseases, such as tumors (12), nervous system disorders (14), and cardiovascular system diseases (15). Therefore, lncRNAs can be applied as potential diagnostic markers and new drug targets for diseases. Recent research revealed that lncRNA KCNQ1OT1 was closely associated with the processes of multiple diseases including cancers (16-18), ischemia reperfusion (19), and diabetic nephropathy (20), among others. Interestingly, It was discovered that lncRNA KCNQ1OT1 was upregulated in lung cancer compared with the normal tissues and the upregulation of lncRNA KCNQ1OT1 reduced the survival rate of NSCLC patients (18). Besides, lncRNA KCNQ1OT1 has also been revealed to regulate the cisplatin resistance of cancer (21). However, the role and mechanism of lncRNA KCNQ1OT1 in radiation therapy for LUSC remain unclear. MicroRNAs (miRNAs) are a class of small non-coding RNAs that can mediate mRNA transcription and degradation through binding to the complementary 3'-untranslated region (3'-UTR) (22,23). Research has shown that miRNAs are involved in cell proliferation, cell cycle, apoptosis, oncogenesis, and differentiation (24,25). In cancer tissues, miRNAs play the roles of oncogenes or tumor suppressor genes (26). In the diagnosis and therapy of clinical tumors, miRNAs can be used as early diagnostic indicators, effective prognostic indicators, and new treatment targets for lung cancer (27,28). Recent research has demonstrated that lncRNAs can competitively bind miRNAs with the targeted regulatory genes to induce cancer cell progression (29). However, the potential miRNAs and target genes of lncRNA KCNQ1OT1 have not been explored in radiation therapy for LUSC. In this study, we generated radioresistant cells (SK-MES-1R and NCI-H226R cells) through X-ray irradiation, and further verified the correlation of lncRNA KCNQ1OT1 with the radioresistance of LUSC cells. Moreover, we disclosed the potential role and regulatory mechanism of lncRNA KCNQ1OT1 in the radioresistance of radioresistant LUSC cells. We present the following article in accordance with the MDAR reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-22-1261/rc). LUSC cell lines (SK-MES-1 and NCI-H226) and HEK293 cells were purchased from American Type Culture Collection (Manassas, USA). SK-MES-1 cells were incubated in minimum Eagle’s medium (MEM, Gibco; Thermo Fisher Scientific, Inc.; Shanghai, China), NCI-H226 cells were grown in RPMI-1640 medium (Gibco), and HEK293 cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM, Life Technologies; Thermo Fisher Scientific, Inc.; Shanghai, China). All the media were supplemented with 10% fetal bovine serum (FBS, HyClone; Logan, USA) and all cells were incubated at 37 ℃ with 5% CO2. SK-MES-1 and NCI-H226 cells were cultured to achieve 90% confluence and then exposed to 0, 2, 4, 6, 8, and 10 Gy 6 Mv X-rays at room temperature with a radiation distance of 100 cm and a radiation area of 10 cm × 10 cm for 4 h. After cell passage, the procedure was repeated. The surviving cells were the radioresistant cells (SK-MES-1R and NCI-H226R). After 8 passages, cells were used for the subsequent experiments. Control, lncRNA KCNQ1OT1 siRNAs (si#1 and si#2), miR-491-5p mimics, and anti-miR-491-5p were obtained from Shanghai Integrated Biotech Solutions Co., Ltd. (Shanghai, China). SK-MES-1R and NCI-H226R cells were transfected with above recombinants at a concentration of 40 nM using Lipofectamine 3000 (Invitrogen; Thermo Fisher Scientific, Inc.; Shanghai, China) in line with the experimental instructions. The 4 kinds of cell lines were inoculated in 6-well plates with 200, 200, 400, 800, 1,600, 2,000, and 4,000 cells per well, respectively. Then, cells were exposed to 0, 2, 4, 6, 8, and 10 Gy 6 Mv X-rays. After 14 days, 4% paraformaldehyde was applied to fix the cells and Wright-Giemsa (Shanghai yuan Mu Biotechnology Co., Ltd.; Shanghai, China) was adopted to stain the cells. Cell clones were observed and counted with at least 50 cells. Based on previous research (30), the surviving fraction was also counted. According to the kit instructions, total RNA was extracted by the TRIzol method (Invitrogen), and cDNA was obtained by reverse transcription using the BestarTM qPCR RT kit (DBI Bioscience, Shanghai, China). The levels of lncRNA KCNQ1OT1 and miR-491-5p in cells were examined by RT-qPCR analysis with SYBR green master mix (Thermo Fisher Scientific). Primer sequences are exhibited in Table 1. The treated SK-MES-1R and NCI-H226R cells (4,000 cells/well) were administered in 96-well plates and cultured at 37 ℃ for 0, 12, 24, 48, and 72 h. At the set time point, 20 µL MTT reagent (cat. no. M5655) was added to each well and the cells were incubated for an additional 4 h. After dissolution with dimethyl sulfoxide (DMSO, cat. no. D8418), the 96-well plates were placed under an automatic microplate reader to detect the optical density (OD) at 490 nm. After digestion and counting, the treated SK-MES-1R and NCI-H226R cells were seeded in 6-well plates with 400 cells/well. After incubation for 12 days, cells were fixed using methanol for 10 min and dyed with crystal violet for 10 min. After washing, the cells were air dried, and the number of clones was taken and counted. Cell proliferation was monitored by the EdU assay kit (Life Technologies). EdU solution (10 µM) was added to the cells in 24-well plates, and cells were cultured for 2 h at 37 ℃. After fixation using 4% formaldehyde for 20 min, the EdU-stained cells were examined. Next, the cells were treated with Hoechst 33342 for 20 min, and the results were visualized under a fluorescence microscope (Olympus, Tokyo, Japan). The treated SK-MES-1R and NCI-H226R cells were collected and washed using phosphate-buffered saline (PBS). The cell suspension was then added with Annexin-V-fluorescein isothiocyanate (FITC) and propidium iodide (PI) (BD Biosciences, San Jose, USA) for 15 min. Apoptotic cells were determined using a FACS Calibur Flow cytometer (BD Bioscience). The treated SK-MES-1R and NCI-H226R cells were harvested, and total proteins were extracted using RIPA buffer. The protein concentration was determined by the BCA method (Beyotime Biotechnology, China). Protein samples (40 µg) in each group were subjected to electrophoresis for 2 h and transmembrane treatment for 1 h, and then sealed in 5% non-fat milk for 2 h. Subsequently, the membranes were cultivated with primary antibodies at 4 ℃ overnight, followed by the cultivation with horseradish peroxidase (HRP)-conjugated secondary antibodies (1:1,000, cat. no. ab6802, Abcam, Shanghai, China) for 1 h. The immunochemical detection was conducted using the ECL system (Thermo Fisher Scientific). The primary antibodies were Bax (1:1,000, cat. no. 5023, Cell Signaling Technology, Shanghai, China), Bcl2 (1:1,000, cat. no. ab196495, Abcam), cleaved caspase-3 (1:1,000, cat. no. 9664, Cell Signaling Technology), Xenopus kinesin-like protein 2 (TPX2; 1:1,000, cat. no. ab32795, Abcam), RING finger protein 2 (RNF2; 1:1,000, cat. no. ab101273, Abcam), and β-actin (1:5,000, cat. no. ab179467, Abcam). As reported by a previous study (31), we also used the Magna RIP RNA-Binding Protein Immunoprecipitation kit (Millipore, Billerica, USA) to conduct the anti-AGO2 RIP assay. Extracts of the treated SK-MES-1R and NCI-H226R cells in RIP buffer were incubated with normal rabbit IgG (Proteintech Group, Inc.; Wuhan, China) and AGO2 antibodies (Cell Signaling Technology), which were combined with magnetic beads. We isolated the immunoprecipitated RNAs and examined genes using RT-qPCR. We constructed the wild-type and mutant lncRNA KCNQ1OT1, TPX2, and RNF2 with potential miR-491-5p binding sites using the pMIR-REPORT plasmids (Promega Biotechnology Co., Ltd.; Beijing, China). HEK293T cells (1×105 cells/well) were inoculated in a 24-well plate and co-transfected with luciferase plasmids, miR-491-5p mimics, and miRNA control for 48 h. Luciferase activity was confirmed using a dual luciferase reporter assay system (Promega). Measurement data was presented as mean ± standard deviation (SD) from 3 replications. The statistical significance was confirmed using SPSS software 21.0 (SPSS Inc., Chicago, USA) with Student’s t-test. P<0.05 indicated a significant difference. All experiments were independently repeated in triplicate and all experimental data were biologically repeated in triplicate. To explore the possible relationship between LUSC cell radiosensitivity and lncRNA KCNQ1OT1, the parental LUSC cells (SK-MES-1 and NCI-H226) and the radioresistant LUSC cells (SK-MES-1R and NCI-H226R) were exposed to different levels of X-ray irradiation. The results from the clonogenic assay showed that SK-MES-1R and NCI-H226R cells displayed greater resistance to X-ray exposure than their parental cells (Figure 1A,1B). Subsequently, our data from RT-qPCR demonstrated that lncRNA KCNQ1OT1 expression was notably increased in SK-MES-1R and NCI-H226R cells relative to their respective parental cells (Figure 1C). Furthermore, we demonstrated that lncRNA KCNQ1OT1 was significantly expressed in the cytoplasm of SK-MES-1R and NCI-H226R cells, as well as in the nucleus (Figure 1D,1E). Consequently, we validated that lncRNA KCNQ1OT1 was prominently upregulated in the SK-MES-1R and NCI-H226R cells, especially in the nucleus. To further elucidate the impact of lncRNA KCNQ1OT1 on radioresistant LUSC cells, lncRNA KCNQ1OT1 expression was knocked down by siRNAs in SK-MES-1R, NCI-H226R, and the parental cells. As presented in Figure 2A, lncRNA KCNQ1OT1 was significantly downregulated in the knockdown group versus the control group, indicating the effective transfection of siRNAs in each group. Next, cell proliferation was examined by conducting the MTT assay, colony formation assay, and EdU staining. The MTT results demonstrated that the proliferation capacities of SK-MES-1R and NCI-H226R cells were dramatically weakened in the knockdown group compared to the control group (Figure 2B,2C). Also, the experimental results of the colony formation assay showed that silencing lncRNA KCNQ1OT1 resulted in a significant reduction in the proliferation of SK-MES-1R and NCI-H226R cells (Figure 2D). Similarly, the results of EdU staining also revealed that the proliferation of SK-MES-1R and NCI-H226R cells could be significantly inhibited by lncRNA KCNQ1OT1 knockdown (Figure 2E). On the whole, we demonstrated that silencing lncRNA KCNQ1OT1 has a significant inhibitory effect on radioresistant LUSC cell proliferation. Furthermore, we adopted MTT assay to assess the role of lncRNA KCNQ1OT1 silencing in the radioresistance of SK-MES-1R and NCI-H226R cells. The results showed that silencing of lncRNA KCNQ1OT1 significantly repressed cell growth in SK-MES-1R and NCI-H226R cells, and X-ray irradiation could also lead to a decrease in the survival rate of SK-MES-1R and NCI-H226R cells (Figure 3A,3B). Additionally, the flow cytometry data showed that silencing of lncRNA KCNQ1OT1 could significantly increase the number of apoptotic cells after treatment with 4 Gy X-ray irradiation (Figure 3C,3D). Meanwhile, we also found that in X-ray-treated SK-MES-1R and NCI-H226R cells, the levels of Bax and cleaved caspase-3 were dramatically elevated, and the level of Bcl2 was markedly lowered in the lncRNA KCNQ1OT1 silencing group compared to the control group (Figure 3E,3F). Overall, we uncovered that lncRNA KCNQ1OT1 knockdown markedly reduced the radioresistance of radioresistant SK-MES-1R and NCI-H226R cells. More and more evidence has shown that lncRNAs may play the role of miRNA sponges, regulating the binding of miRNAs to the target mRNAs (32,33). We used a bioinformatics tool (LncBase Predicted v.2) to predict the target miRNAs of lncRNA KCNQ1OT1, and screened 4 potential target miRNAs including miR-491-5p, miR-133b, miR-15a, and miR-7. Firstly, we discovered that only miR-491-5p was prominently downregulated in SK-MES-1R and NCI-H226R cells compared with their respective parental cells (Figure 4A,4B). Secondly, we performed the anti-AGO2 RIP assay to monitor whether lncRNA KCNQ1OT1 could directly interact with these 4 miRNAs. The data indicated that lncRNA KCNQ1OT1 could be specifically enriched in miR-491-5p-overexpressed SK-MES-1R and NCI-H226R cells (Figure 4C,4D). In addition, we constructed the wild-type and mutant lncRNA KCNQ1OT1 plasmids, which were co-transfected into HEK-293 cells. The results uncovered that the luciferase activity was substantially reduced in HEK-293 cells with the co-transfection of miR-491-5p and wild-type lncRNA KCNQ1OT1, while the luciferase activity was not affected in the lncRNA KCNQ1OT1 mutant co-transfection group (Figure 4E). We also found that silencing of lncRNA KCNQ1OT1 could notably upregulate miR-491-5p in SK-MES-1R and NCI-H226R cells (Figure 4F). As a whole, miR-491-5p was an inhibitory target of lncRNA KCNQ1OT1. We further predicted the target genes of miR-491-5p using bioinformatics analysis, and discovered that TPX2 and RNF2 might be the potential target genes of miR-491-5p. The luciferase assay showed that miR-491-5p reduced only the luciferase activity of wild-type TPX2, while the luciferase activity of mutant TPX2 was not responsive to miR-491-5p overexpression (Figure 5A). We also demonstrated that miR-491-5p significantly attenuated the luciferase activity of wild-type RNF2, but did not affect the luciferase activity of mutant RNF2 (Figure 5B). Furthermore, the colony formation assay elucidated that overexpression of miR-491-5p led to the inhibition of proliferation in SK-MES-1R and NCI-H226R cells (Figure 5C). Similarly, inhibition of miR-491-5p could result in the enhancement of proliferation in SK-MES-1R and NCI-H226R cells (Figure 5D). Western blotting analysis demonstrated that overexpression of miR-491-5p significantly lowered TPX2 and RNF2 expression, and inhibition of miR-491-5p markedly elevated TPX2 and RNF2 expression in SK-MES-1R and NCI-H226R cells (Figure 5E,5F). Overall, we demonstrated that miR-491-5p could significantly prevent proliferation and reduce TPX2 and RNF2 expression in SK-MES-1R and NCI-H226R cells. Subsequently, we performed rescue experiments to explore the impact of lncRNA KCNQ1OT1/miR-491-5p on the proliferation or TPX2 and RNF2 expression in radioresistant LUSC cells. LncRNA KCNQ1OT1 siRNAs and anti-miR-491-5p were adopted to transfect SK-MES-1R and NCI-H226R cells. The colony formation assay revealed that lncRNA KCNQ1OT1 knockdown dramatically repressed proliferation, and inhibition of miR-491-5p markedly accelerated proliferation. Meanwhile, co-transfection of lncRNA KCNQ1OT1 siRNAs and anti-miR-491-5p offset this effect in SK-MES-1R and NCI-H226R cells (Figure 6A). Western blot results demonstrated that lncRNA KCNQ1OT1 knockdown significantly downregulated TPX2 and RNF2 expression, inhibition of miR-491-5p notably upregulated TPX2 and RNF2 expression, and co-transfection of lncRNA KCNQ1OT1 siRNAs and anti-miR-491-5p further enhanced the expression of TPX2 and RNF2 in SK-MES-1R and NCI-H226R cells (Figure 6B). Collectively, these data demonstrated that lncRNA KCNQ1OT1 knockdown prevented the proliferation of SK-MES-1R and NCI-H226R cells by miR-491-5p to regulate TPX2 and RNF2. Lung cancer has been considered to be one of the biggest contributors to cancer-related deaths worldwide (34). As a vital subtype of lung cancer, the incidence of LUSC is also increasing yearly (35). At present, radiotherapy has become one of the crucial methods of LUSC treatment because it can inhibit tumor growth and induce cell apoptosis. Increasing the radiosensitivity of tumor cells has become the most effective strategy for LUSC radiotherapy (36). However, the progression of LUSC is a multifactorial and multi-step process, and the carcinogenesis mechanism is still not very clear. At present, there is no effective biological target to increase radiosensitivity. In recent years, more and more evidence has confirmed that lncRNAs can participate in tumor progression through multiple pathways (37,38). Over the past decade, significant efforts have been made to bring ncRNA-based therapies to clinical use, some of which have received FDA approval. However, trial results to date have been contradictory, with some studies reporting effective results and others showing limited efficacy or toxicity (39,40). More important, studies have found that lncRNAs are involved in regulating the radiosensitivity of tumors (41,42). For instance, LINC02532 contributes to the radiosensitivity of clear cell renal cell carcinoma (43). LINC00958 could suppress radiosensitivity in colorectal cancer (44), and lncRNAs, such as PVT1 (45) and TUG1 (46), can also enhance radiosensitivity by promoting the apoptosis of tumor cells. Therefore, investigating the mechanism of lncRNAs in LUSC can lay the foundation for the improvement of radiosensitivity. According to the literature, lncRNA KCNQ1OT1 has also been proven to be involved in the progression of multiple diseases, such as osteolysis (47), fracture healing (48), myocardial ischemia/reperfusion injury (49), atrial fibrillation (50), and diabetic cardiomyopathy (51). Furthermore, research has shown that lncRNA KCNQ1OT1 is also related to the progression of acute promyelocytic leukemia (52), ovarian cancer (17), bladder cancer (53), and NSCLC (18). Moreover, studies demonstrated that lncRNA KCNQ1OT1 was involved in oxaliplatin-resistant colon cancer (16), methotrexate-resistant colorectal cancer (54), and cisplatin-resistant tongue cancer (21). In our study, we established radioresistant cells (SK-MES-1R and NCI-H226R cells) through X-ray irradiation. LncRNA KCNQ1OT1 was notably upregulated in SK-MES-1R and NCI-H226R cells. Moreover, we verified that knockdown of lncRNA KCNQ1OT1 has a significant inhibitory effect on the proliferation and radioresistance of radioresistant LUSC cells. Knockdown of lncRNA KCNQ1OT1 also had a significant promotive effect on the apoptosis of radioresistant LUSC cells. Therefore, we demonstrated that lncRNA KCNQ1OT1 could dramatically enhance the radioresistance and induce the malignant behaviors of LUSC. MiRNAs are a group of crucial regulatory factors in cancers, and are closely related to the progression of NSCLC (55). Studies have discovered that lncRNAs can serve as “sponges” of miRNAs to reduce miRNA abundance, thus alleviating the inhibition effect of miRNAs on the downstream target genes (56,57). Research has shown that the lncRNA/miRNA/mRNA axis can play vital roles in regulating the biological behaviors and radiosensitivity of various cancer cells, such as hepatocellular carcinoma (58), pancreatic cancer (59), lung adenocarcinoma (60), nasopharyngeal carcinoma (61), and ovarian cancer (62), among others. We also predicted the target miRNAs of lncRNA KCNQ1OT1 through a bioinformatics tool, and miR-491-5p, miR-133b, miR-15a, and miR-7 were screened as the potential target miRNAs of lncRNA KCNQ1OT1. Several studies have confirmed that miR-491-5p can function as a tumor suppressor in many types of cancers, such as osteosarcoma (63), colorectal cancer (64), gastric cancer (65), prostate cancer (66), nasopharyngeal carcinoma (67), and NSCLC (68). After experimental verification, we also discovered that miR-491-5p was a regulatory target of lncRNA KCNQ1OT1, and miR-491-5p could also be markedly upregulated by lncRNA KCNQ1OT1 knockdown. Furthermore, through bioinformatics and experimental validation, we proved that TPX2 and RNF2 might be the potential target genes of miR-491-5p. As a microtubule-associated protein, TPX2 is essential for microtubule formation and can regulate many crucial biological processes (69,70). The aberrant expression of TPX2 has a vital relationship with the progression of human malignant tumors (71,72). RNF2, a member of the polycomb gene family, is a ubiquitin ligase with ring structure (73). Recent research has also confirmed that RNF2 is highly expressed in human malignant tumors, and is associated with the proliferation, invasion, and prognosis of tumors (74,75). Another study showed that silencing of RNF2 could promote the radiosensitivity of NSCLC (76). In our study, we also revealed that miR-491-5p could markedly downregulate TPX2 and RNF2 expression in radioresistant LUSC cells. In addition, knockdown of lncRNA KCNQ1OT1 could also markedly inhibit the proliferation of LUSC through miR-491-5p to regulate TPX2 and RNF2. We demonstrated that lncRNA KCNQ1OT1 could markedly induce the radioresistance of LUSC by directly targeting miR-491-5p to reduce TPX2 and RNF2 expression. Therefore, we uncovered that the lncRNA KCNQ1OT1/miR-491-5p/TPX2 or RNF2 axis is correlated with the radioresistance of LUSC cells. In the future, this conclusion will be further validated in clinical and in vivo samples. The article’s supplementary files as 10.21037/jtd-22-1261 10.21037/jtd-22-1261 10.21037/jtd-22-1261

PMC9641602

Erick C. Castelli,Mateus V. de Castro,Michel S. Naslavsky,Marilia O. Scliar,Nayane S. B. Silva,Raphaela N. Pereira,Viviane A. O. Ciriaco,Camila F. B. Castro,Celso T. Mendes-Junior,Etiele de S. Silveira,Iuri M. de Oliveira,Eduardo C. Antonio,Gustavo F. Vieira,Diogo Meyer,Kelly Nunes,Larissa R. B. Matos,Monize V. R. Silva,Jaqueline Y. T. Wang,Joyce Esposito,Vivian R. Cória,Jhosiene Y. Magawa,Keity S. Santos,Edecio Cunha-Neto,Jorge Kalil,Raul H. Bortolin,Mário Hiroyuki Hirata,Luiz P. Dell’Aquila,Alvaro Razuk-Filho,Pedro B. Batista-Júnior,Amaro N. Duarte-Neto,Marisa Dolhnikoff,Paulo H. N. Saldiva,Maria Rita Passos-Bueno,Mayana Zatz

MUC22, HLA-A, and HLA-DOB variants and COVID-19 in resilient super-agers from Brazil

25-10-2022

human leukocyte antigens,immune response,major histocompatibility complex (MCH),HLA,SARS-CoV-2,MUC22,COVID-19,resistant genetic variants

Background Although aging correlates with a worse prognosis for Covid-19, super elderly still unvaccinated individuals presenting mild or no symptoms have been reported worldwide. Most of the reported genetic variants responsible for increased disease susceptibility are associated with immune response, involving type I IFN immunity and modulation; HLA cluster genes; inflammasome activation; genes of interleukins; and chemokines receptors. On the other hand, little is known about the resistance mechanisms against SARS-CoV-2 infection. Here, we addressed polymorphisms in the MHC region associated with Covid-19 outcome in super elderly resilient patients as compared to younger patients with a severe outcome. Methods SARS-CoV-2 infection was confirmed by RT-PCR test. Aiming to identify candidate genes associated with host resistance, we investigated 87 individuals older than 90 years who recovered from Covid-19 with mild symptoms or who remained asymptomatic following positive test for SARS-CoV-2 as compared to 55 individuals younger than 60 years who had a severe disease or died due to Covid-19, as well as to the general elderly population from the same city. Whole-exome sequencing and an in-depth analysis of the MHC region was performed. All samples were collected in early 2020 and before the local vaccination programs started. Results We found that the resilient super elderly group displayed a higher frequency of some missense variants in the MUC22 gene (a member of the mucins’ family) as one of the strongest signals in the MHC region as compared to the severe Covid-19 group and the general elderly control population. For example, the missense variant rs62399430 at MUC22 is two times more frequent among the resilient super elderly (p = 0.00002, OR = 2.24). Conclusion Since the pro-inflammatory basal state in the elderly may enhance the susceptibility to severe Covid-19, we hypothesized that MUC22 might play an important protective role against severe Covid-19, by reducing overactive immune responses in the senior population.

MUC22, HLA-A, and HLA-DOB variants and COVID-19 in resilient super-agers from Brazil Although aging correlates with a worse prognosis for Covid-19, super elderly still unvaccinated individuals presenting mild or no symptoms have been reported worldwide. Most of the reported genetic variants responsible for increased disease susceptibility are associated with immune response, involving type I IFN immunity and modulation; HLA cluster genes; inflammasome activation; genes of interleukins; and chemokines receptors. On the other hand, little is known about the resistance mechanisms against SARS-CoV-2 infection. Here, we addressed polymorphisms in the MHC region associated with Covid-19 outcome in super elderly resilient patients as compared to younger patients with a severe outcome. SARS-CoV-2 infection was confirmed by RT-PCR test. Aiming to identify candidate genes associated with host resistance, we investigated 87 individuals older than 90 years who recovered from Covid-19 with mild symptoms or who remained asymptomatic following positive test for SARS-CoV-2 as compared to 55 individuals younger than 60 years who had a severe disease or died due to Covid-19, as well as to the general elderly population from the same city. Whole-exome sequencing and an in-depth analysis of the MHC region was performed. All samples were collected in early 2020 and before the local vaccination programs started. We found that the resilient super elderly group displayed a higher frequency of some missense variants in the MUC22 gene (a member of the mucins’ family) as one of the strongest signals in the MHC region as compared to the severe Covid-19 group and the general elderly control population. For example, the missense variant rs62399430 at MUC22 is two times more frequent among the resilient super elderly (p = 0.00002, OR = 2.24). Since the pro-inflammatory basal state in the elderly may enhance the susceptibility to severe Covid-19, we hypothesized that MUC22 might play an important protective role against severe Covid-19, by reducing overactive immune responses in the senior population. Although a diverse clinical spectrum has been described among patients with Covid-19, robust data show that increasing age correlates with more severe disease and a higher frequency of deaths worldwide (1). While the elderly have a higher prevalence of comorbidities such as cardiovascular diseases, diabetes, and cancer - which are also independently associated with a higher risk of severe Covid-19 (2), increasing age is still the most significant risk factor for Covid-19 mortality (3). To yield clues about this infection susceptibility, the comparison of extremely older people presenting mild symptoms and young adults with a very severe outcome may contribute with relevant observations. Covid-19 severity among the elderly may be related to immunosenescence, changes in cytokine patterns, activation of inflammatory pathways, and impaired innate and adaptive immune responses (4–6). Also, comorbidities in older individuals are strongly associated with an increased risk of Covid-19 complications (7, 8). Alternatively, older individuals are more likely to have been exposed to other corona and influenza viruses during their lifespan, even by vaccination, increasing their odds of defeating SARS-CoV-2 (9). For instance, centenarians exposed to the 1918 H1N1 influenza virus might present some protection against SARS-CoV-2 infection (10). The MHC (Major Histocompatibility Complex) region contains more than 200 genes, many of them related to immunity. Therefore, it is a natural candidate for influencing infectious disease susceptibility and severity. MHC genes influence different levels of the immune response against viruses, such as genes encoding cytokines and molecules of the complement system, which may influence Covid-19 severity and cytokine storm (11, 12), genes encoding membrane-associated mucins (MUC22) (13), and genes encoding molecules that mediate NK cell responses (HLA-G, HLA-E, MICA, and MICB) (14–17). Some of the MHC genes are particularly important to the antigen presentation pathway. HLA-A, HLA-B, and HLA-C encode the heavy chain of the MHC-I molecule, responsible for binding the intracellular antigens and presenting them on the cell surface to the T cell receptor (TCR) of CD8 T lymphocytes. Likewise, HLA-DRA, HLA-DRB1, HLA-DQA1, HLA-DQB1, HLA-DPA1, HLA-DPB1, and others, encode the MHC-II molecule, responsible for binding the exogenous antigens, usually internalized by antigen-presenting cells such as macrophages, and presenting them on the cell surface to CD4 T lymphocytes. These genes are highly polymorphic, with hundreds to thousands of alleles for each locus (18). Such diversity influences antigen presentation since different MHC molecules may present a different subset of antigens (19). Because of the unusually high polymorphism and extensive paralogy of the MHC region, particularly at the HLA classical class I and II genes, the MHC region requires specialized tools to align short reads correctly and, thus, call genotypes and haplotypes properly (20, 21). Additionally, allele frequencies vary across populations, as clearly documented in previous studies focusing on HLA genes and Covid-19 (22–26). Associations between HLA genotype and disease severity extend to other unrelated viruses, such as HIV and dengue (27, 28). Some MHC variants have already been reported to be associated with Covid-19 severity. HLA-G variant rs9380142 was associated with Covid-19 critical illness (29) and HLA-E allele E*01:01 with Covid-19 severity, particularly in patients requiring intensive care (30). The CCHCR1 locus was associated with critical illness in Covid-19 (29). We hypothesized that differences in MHC can predispose to a severe or mild clinical course in Covid-19 despite age. Therefore, aiming to verify the existence of MHC differences between extreme opposed outcomes for Covid-19, we have analyzed exomes of Brazilian convalescents by SARS-COV-2 grouped according to Covid-19 clinical status and age: a group of super elderly patients (>= 94 yo) recovered from Covid-19 with mild to moderate symptoms (without ventilation support) as compared to younger adults (mean age <= 52) with severe disease (with ventilation support). We also compared patients with a previously whole-genome sequenced (WGS) census-based sample of elderly individuals from the same city (São Paulo, Brazil), sampled before the current pandemic (31). Brazilians are a highly admixed population composed of tri-hybrid proportions of European (average 73%), African (18%), and Native American (9%) ancestries (31). This was taken into account and therefore global genomic ancestry was controlled when performing the association study. This survey included 225 patients with Covid-19, as illustrated in Figure 1 . Diagnostic tests (RT-PCR) confirmed the positive SARS-CoV-2 infection in all individuals. The samples were collected between June and October 2020, before new SARS-CoV-2 variants were reported in Brazil (especially Gamma) and before the onset of the Brazilian vaccination program against Covid-19. Covid-19 severity was classified according to the clinical spectrum of the World Health Organization’s updated guideline for Covid-19 treatment (https://www.covid19treatmentguidelines.nih.gov/overview/clinical-spectrum/). Patients that were asymptomatic or presented mild symptoms were grouped into MILD Covid-19; deceased and/or hospitalized in ICU requiring ventilation support were grouped into SEVERE Covid-19 ( Figure 1 ). The SEVERE group is significantly younger than the MILD group ( Supplementary Table S1 , p< 10-5). We also considered a special group named super elderly (>= 94 yo) who recovered from Covid-19, with 72 super elderly with mild Covid-19 and 15 with moderate symptoms. We retrieved clinical data regarding the progression of Covi-19 and diagnostic test results. These Covid-19 groups were compared with MHC data from a previously whole-genome sequenced (WGS) sample of Brazilian elderly individuals (>= 65 yo), known as the SABE cohort (31), collected before the SARS-CoV-2 outbreak and representative of the general elderly population in the same city. Age, sex, and mean genetic ancestry distributions for each group are displayed in Supplementary Table S1 and were used as co-variables in regression models. We obtained full exomes from DNA extracted from patients’ samples with SARS-CoV-2 infection. We used the Nextera Rapid Capture Custom Enrichment Kit or the Nextera Flex Kit (Illumina, San Diego, CA, USA) for library preparation and the IDT xgen-V1 kit for capture following manufacturer protocols. Whole-exome sequencing was performed on the NovaSeq 6000 equipment (Illumina, USA) with a 150-base paired-end dual index read format. Reads were aligned to the human reference GRCh38 using Burrow–Wheeler Aligner (BWA), algorithm MEM (https://github.com/lh3/bwa/tree/master/bwakit). We also called genotypes using GATK HaplotypeCaller (version 4.0.9). We used the genotypes obtained in this step to infer the genetic ancestry. The pipeline used for alignment, variant calling, variant refinement, and genetic ancestry assessment is detailed elsewhere (22). For the general elderly population (SABE), whole-genome sequencing was performed previously (31). Although SARS-Cov-2 infection is unknown for this cohort, this data provides a baseline for the frequency of each polymorphism in the general elderly population from São Paulo. The MHC region is prone to genotyping errors because of alignment bias in paralogous and highly polymorphic genes (20, 21, 32). The HLA classical class I and II genes are the most impacted ones by alignment bias, and conventional NGS analysis workflows are not suitable for genotyping them. We used a customized workflow to circumvent this issue and get reliable genotypes and haplotypes in the MHC region. We used HLA-mapper (version 4) (20) to optimize read alignment along the MHC region ( Supplementary Figure S1 ). The input for HLA-mapper is the BAM file obtained in the previous step (for exomes or whole-genomes). After applying HLA-mapper to correct the alignments, we called genotypes using GATK 4 HaplotypeCaller. Then, we selected only the variants that overlapped the region captured by the Exome, with no more than 5% of missing alleles in the Exomes. After, we refined the variants using the standard Variant Quality Score Recalibration (VQSR) supplemented with known variants from HLA genes. To obtain phased variants for each gene, we first phased closely located variants using WhatsHap (33). Then, we combined phase sets using Shapeit 4 (34). The final product is a phased VCF with SNPs throughout the MHC. For HLA-A, HLA-B, HLA-C, HLA-E, HLA-F, HLA-G, MICA, MICB, HLA-DOA, HLA-DOB, HLA-DMA, HLA-DMB, HLA-DRA, HLA-DPA1, HLA-DPB1, TAP1, and TAP2, we obtained the complete exonic sequences for each individual by converting the phased VCF obtained in the previous step into complete CDS sequences using vcfx transcript (www.castelli-lab.net/apps/vcfx). We also translated these sequences into protein sequences (the allotypes) using Emboss transeq. We called HLA alleles (3-field and 2-field resolution) directly from these CDS and predicted protein sequences, comparing them with the ones reported in the IPD-IMGT/HLA database (35). Because of exome probe-capturing bias in some MHC regions, we imputed HLA-DRB1, HLA-DQA1, and HLA-DQB1 2-field alleles instead of calling alleles directly from the VCF data, as discussed in the next section. For exomes, we noticed a probe-capturing bias in some MHC regions, which is not an unexpected issue, especially for HLA genes (32, 36). This bias is quite strong for HLA-DRB1, HLA-DQA1, and HLA-DQB1, as illustrated in Supplementary Figure S2 . Although the HLA-mapper optimization corrects most of the alignment errors in HLA genes, the absence of sequences from one chromosome (the capture bias) led to genotyping bias and allele call errors. This is particularly problematic when comparing exomes and whole genomes because this error occurs only in the former. To circumvent this issue, we only selected from the exomes the variants with an average proportion of reads among alleles in heterozygous sites (i.e., the allele balance) over 0.3, represented as a red line in Supplementary Figure S2 . This procedure might have eliminated some important variants, but it allowed us to compare exomes and whole genomes by avoiding variants prone to genotyping errors. In addition, we selected only variants with a frequency of at least 1%, either among patients or among the general elderly population. Accordingly, 2,346 SNPs were selected across the MHC and were considered for all subsequent analyses. The capture bias discussed above prevented the direct call of HLA alleles for HLA-DRB1, HLA-DQA1, and HLA-DQB1. We applied an imputation method with HIBAG 1.5 (37) to call 2-field resolution alleles ( Supplementary Figure S1 ). First, we built a reference panel based on whole-genome data from Brazilians, the 1000Genomes, and HGDP datasets, using the same pipeline as presented in Supplementary Figure S1 . The selected variants for the imputation model are bi-allelic variants present in both the reference panel and exomes and show an average proportion of reads among alleles in heterozygous sites over 0.30 in exomes ( Supplementary Figure S2 ). After imputation, the incompatibility between imputed HLA alleles and direct calls was as follows: HLA-A (4.1%), HLA-B (2.8%), HLA-C (2.2%), HLA-DRB1 (20.9%), HLA-DQA1 (23%), and HLA-DQB1 (28.4%). Because of that, we opted to consider only the direct calls for HLA-A, HLA-B, and HLA-C, and only the imputed alleles from HLA-DRB1, HLA-DQA1, and HLA-DQB1. The usual threshold for genome-wide significance in GWAS studies is P< 10-8, defined based on the average number of segregation blocks in European genomes. However, here, we are focusing on 2,346 SNPs across the MHC (not the full genome), in a different population (admixed Brazilians), and in a region with different LD patterns than the rest of the genome. Therefore, we calculated the number of different segregation blocks observed across our data by using Haploview and the confidence intervals algorithm (38). We detected exactly 100 segregation blocks with all bi-allelic markers with minimum allele frequencies of 2%. Therefore, we set alpha = 0.05/100 = 0.0005 as the threshold for detecting an associated variant despite multiple tests (the red lines in the Manhattan plots). We also report candidates that reach a 10-fold higher threshold, alpha < 0.005 (the blue line in the Manhattan plots) to avoid missing potential variants associated with Covid 19 severity. We used plink2 to fit a logistic regression that considers each variable site, allotype, or amino acid residue as an independent marker. We created a plink-format table file containing a column for each allele of a SNP, every allotype, each amino acid in a specific position, and the dosage observed for the samples (from 0 to 2). The regression analysis, performed in R, considered sex and genetic ancestry as covariables in all comparisons. We did not include age as covariable to adjust P-values because of the super elderly. To evaluate the amino acid residues, we first aligned the predicted protein sequence of all individuals. While we considered all SNPs that have passed the filter described above, for the allotype and amino acid residues, we considered only the following genes: HLA-A, HLA-B, HLA-C, HLA-E, HLA-F, HLA-G, MICA, MICB, HLA-DRA, HLA-DRB1, HLA-DQA1, HLA-DQB1, HLA-DPA1, HLA-DPB1, HLA-DOA, HLA-DOB, HLA-DMA, HLA-DMB, TAP1, and TAP2. Aiming to enhance our understanding of underlying mechanisms for HLA alleles associated with disease severity and the mechanisms underlying the associations, we have predicted the HLA molecule structure and the SARS-CoV-2 peptides that can bind to these HLA versions. The detailed methods are in the supplementary material. Supplementary Table S1 presents demographic data and mean genome-wide genetic ancestry for each group. The mean age for the MILD group (66.9 years) is significantly higher than the SEVERE group (51.3 years), p < 10-5. There are more women in the MILD group (58.3%) and among the super elderly (74.7%) than in the SEVERE Covid-19 (43.6%). All groups present similar proportions of genetic ancestry, except the SEVERE group which has a greater African and Native American ancestry than the MILD or the general elderly population from the same city ( Supplementary Table S1 ). On one hand, ancestry might be related to Covid-19 severity, as observed for some Covid-19 comorbidities. For example, diabetes is a risk factor for severe Covid-19 (39) and is more frequent in individuals with higher African and Native-American ancestry in some populations (40). On the other hand, the lower socioeconomic status of a Brazilian citizen is correlated with a higher African and Native American ancestry. Most of the severe cases came from public hospitals, which, on average, are of poorer quality and lower efficiency, and then enriched for individuals with lower socioeconomic status (41). Nevertheless, the reported association results were controlled for genetic ancestry, which allowed adjustment to some extent for the socioeconomic scores. The comparison between the SEVERE and MILD groups revealed three missense candidate variants that are 2-3 times more frequent in the SEVERE group than in MILD, coinciding with genes HLA-A, HLA-DOB, and TAP2 ( Figure 2 and Table 1 ). The HLA-A variant is also significantly less frequent in the MILD group than in the general elderly population (p = 0.0066), and HLA-DOB and TAP2 variants are also significantly overrepresented in the SEVERE group compared to the general elderly population (p = 0.0009 and p = 0.0064, respectively), according with Table 1 . Probably because of the relatively small and different sample size of both the MILD and SEVERE groups, some variants are highlighted when compared to the general elderly population but not when comparing the Covid-19 groups, as, the susceptibility missense variants rs1264457 at HLA-E and rs2228111 at TAP1, among others. On the other hand, the protective missense variants at MUC22 are significantly overrepresented among patients with MILD Covid-19 ( Figure 2 and Table 1 ). We also evaluated whether variants are associated with Covid-19 severity in younger patients by removing the super elderly from the MILD group. The resulting smaller sample size did not allow the identification of candidate variants in this comparison. However, the pattern observed in Table 1 was maintained. We explored the allotypes and amino acid frequencies in different groups ( Table 2 ). DOB*01:02 and the amino acid that defines this allotype, 18Q, are overrepresented in the SEVERE group compared to the MILD and the general elderly population. This amino acid exchange is related to the rs2071554 variant described in Table 1 . HLA-E*01:03 and the main amino acid exchange that composes this allotype, 128G, is significantly overrepresented in the SEVERE group when compared to the general elderly population (p < 0.005) and the MILD group (p < 0.05). This amino acid exchange is related to rs1264457 ( Table 1 ). All variants that define the allotype MICA*008 are overrepresented in the MILD group when compared to the general population ( Table 2 ). Two amino acid residues at HLA-A, 86R and 87N (full-length protein) or 62R and 63N (mature protein), are significantly overrepresented in the SEVERE group when compared to the MILD group (p < 0.005), and overrepresented in the general elderly population when compared to the MILD group (p < 0.01). Likewise, HLA-A residue 156/W is less frequent in the MILD group compared to the SEVERE (p < 0.05) and the general elderly population (p < 0.005). Residues 62R and 63N are associated with many HLA-A allotypes, including A*25, A*26, A*33, A*34, A*66, A*68, and A*69. Residue 156/W is associated with allotypes A*25, A*26, A*34, A*43, A*66, and A*68. The linkage disequilibrium (LD) pattern in Figure S7 indicates that most Covid-19-associated MUC22 polymorphisms are in strong LD. Likewise, the MICA variants (all associated with MICA*008) are in strong LD. MUC22 and MICA are independent signals. The signals from HLA-DOB and TAP2 might not be independent. There is no LD between the HLA-A variant and other relevant variants across the MHC. The strongest signal coincides with gene MUC22, rs62399430, two times more frequent among the super elderly than in the SEVERE group (p = 0.0057, OR=0.30). Moreover, when super elderly are compared to the general elderly population from the same city, we detected candidate variants in two genes, MUC22 and PSORS1C1/CDSN ( Figure 3 ). Most of the signals coincide with gene MUC22. Variants rs62399430, rs11753789, and rs12110785 (p < 0.0002, OR > 2.0) are, in general, two times more frequent among super elderly recovered from Covid-19. Two of these are missense variants. Most MUC22 variants are in Linkage Disequilibrium (r2> 0.8, Figure S7 ). These variants are also overrepresented in the MILD Covid-19 group, which includes most of the super elderly patients and younger patients with mild Covid-19. Another candidate variant for protection is rs145583110, an intronic variant from PSORS1C1 or exonic for CDSN, which is 3 times more frequent among the super elderly than in the general population (p = 0.0018, OR = 3.85). The allotype and amino acid residue frequencies reveal no relevant association when comparing super elderly with MILD Covid-19 and younger patients with SEVERE Covid-19, or when super elderly are compared to the general elderly population. Here we investigated the polymorphisms across the MHC region associated with Covid-19 disease severity in patients with extreme phenotypes: young adults with severe Covid-19, and super elderly individuals with mild Covid-19. All the samples were collected between June and October 2020, before new SARS-CoV-2 variants were reported in Brazil (especially Gamma) and before the onset of the Brazilian vaccination program against Covid-19. This data may shed some light on the mechanisms underlying SARS-CoV-2 resistance, particularly for the earlier SARS-CoV-2 strains in unvaccinated individuals. All samples from the control elderly Brazilian population were collected before the SARS-CoV-2 outbreak. We applied a bioinformatics pipeline to correct alignments and call reliable genotypes and HLA alleles and detected some associated and candidate variants that influence infection severity, some related to genes from the antigen presentation pathway and others from different pathways. This section will focus on the strongest hits and the frequent candidate variants associated with the phenotypes, particularly MUC22, HLA-A, and HLA-DOB. We detected missense MUC22 variants associated with mild Covid-19 when evaluating all patients with mild symptoms, and the super elderly recovered from Covid-19. MUC genes encode mucins, and 16 different mucins have been identified in the lung (42). Mucins are high-molecular-weight glycoproteins that can be secreted or anchored to the cell membrane (transmembrane mucins) (42). MUC22 is a member of the mucins’ family. It encodes transmembrane mucins expressed in the bronchi of the lungs and participates in the inflammatory and innate immune response (43, 44). Airway mucus comprises water, antimicrobial proteins, serum protein transudates, and mucus glycoproteins. It protects and lubricates the respiratory tract. However, excessive mucus production is related to inflammatory lung diseases (45), which are found in severe cases of Covid-19. Overexpression of MUC1 and MUC5AC mucins, for instance, play a key role in Covid-19 symptoms and may contribute to the high viscosity of airway mucus, leading to airflow obstruction and respiratory distress (46). MUC22 is up-regulated in infections with respiratory syncytial vírus (47). Some studies have been pointing to mucin´s role in Covid-19 (48, 49). The outcome of SARS-CoV-2 infection may be correlated with a signature of shed mucins in circulation from infected lung or respiratory tract epithelial cells (48). MUC22 polymorphisms have been associated with diffuse panbronchiolitis (43) and asthma in Latinos (50). One may argue that these variants are related to longevity and not with protection against severe Covid-19. However, this variant is not correlated with longevity because (a) the SABE sample refers to a census-based cohort of elderly Brazilians with an average age of 75, and thus variant associated with longevity would already be more frequent, and (b) their frequency in SABE is very similar to frequencies observed in European and Latin American populations (https://www.ncbi.nlm.nih.gov/snp/rs62399430#frequency_tab). Except for rs146685560, all MUC22 protective variants are correlated with higher expression of miR-6891 in many tissues, including the esophagus and lung (gtexportal.org). miR-6891 is co-expressed with MUC22 in the alveolus (pneumocyte type II). Interestingly, miR-6891 is encoded in the MHC, and targets the ORF3a gene from SARS-CoV-2 (51), which encodes a sodium or calcium ion channel protein involved in replication and pathogenesis (52). Most importantly, miR-6891-5p is upregulated in Calu3 cells infected with SARS-CoV-2 (53). During the early stages of SARS-CoV-2 infection, ORF3a directs the host’s immune response (54), may induce lysosomal evasion (55, 56) and could promote cytokine storms by activating the NF-kB signaling and NLRP3 inflammasomes pathways (57). Therefore, we can hypothesize that a higher expression of miR-6891-5p, associated with all MUC22 protective variants, may contribute to less severe symptoms during SARS-CoV-2 infection. One possible explanation for higher miR-6891-5p expression is linkage with HLA-B since the MIR6891 gene coincides with an HLA-B intron. In the present study, there is a clear association between the protective variant rs62399430/T and some HLA-B alleles, such as B*35:01, B*35:03, B*48:02, and B*51:01. All of these HLA-B alleles are listed as high mRNA expressing alleles (58–60). Thus, higher HLA-B mRNA expression might be correlated with higher miR-6891 expression, and MUC22 variants are tagging this phenotype. Also, MUC22 is approximately 100 kb from CCHCR1 gene, the most important signal in the MHC region for Covid-19 susceptibility, according to the Covid-19 Host Genetics Consortium (Covid19hg - https://app.covid19hg.org/). All CCHCR1 variants associated with Covid-19 (29, 61) are intronic and were not captured by our genotyping method (whole-exome sequencing). The exomic CCHCR1 variants included in our survey do not correlate with Covid-19 severity. This is expected since the meta-analysis provided by Covid19hg indicates that CCHCR1 is associated with Covid-19 susceptibility but not with severity when comparing hospitalized and non-hospitalized patients (as performed here). Moreover, studying LD among the most relevant variants from MUC22 and CCHCR1, we detected a weak LD (r2 < 0.3, D’ < 0.7). Covid19hg effort detected no relevant signal from MUC22, including the SNPs described here. Although our MUC22 findings are not cross-validated by the Covid19hg, we must consider that we are evaluating a very different cohort, aged and super elderly individuals with mild Covid-19 and younger patients with severe Covid-19. The signal from MUC22 is much stronger in the super elderly group. The frequency of rs62399430 among the super elderly with mild Covid-19 is 2x higher than the observed among Europeans and in the general elderly populations and 3.8x higher than in Africa. Therefore, considering study designs and cohort ancestries, the MUC22 signal might be ancestry-specific and independent of CCHCR1. The HLA-DO molecule is a heterodimer formed by two heavy chains, HLA-DOA and HLA-DOB. HLA-DO is a non-classical MHC-II molecule that does not present peptides on the cell surface, but it is required to efficiently load endosomal peptides onto MHC-II molecules (62–66). Thus, modifications in HLA-DOB may directly influence antigen presentation in the MHC class II pathway. The HLA-DOB*01:02 allele has also been associated with an increased risk of death in patients with non-small cell lung cancer; reduced median survival time (67, 68), with type 1 diabetes (69), which is a major comorbidity related to severe Covid-19 (39), and with resistance to SARs-CoV-2 infection (22). DOB*01:02 has a different signal peptide, which may influence cellular localization and trafficking of the protein (15), possibly leading to inadequate antigen presentation. HLA-DOB*01:02 is rare (0.6%) among SARs-CoV-2 resistant individuals (22), has similar frequencies of around 6% among patients with mild Covid-19 or the control elderly population, and reaches 13.6% in patients with severe Covid-19 ( Table 1 ). The frequency of DOB*01:02 among the super elderly is similar to the general elderly population. DOB*01:02 is frequent in Africa (reaching the same frequency as the SEVERE group) and less frequent in Europe. Although the percentage of Native-American and African ancestries are much higher in the SEVERE group than others, the associations described here are adjusted for genetic ancestry and sex. Because DOB*01:02 frequencies vary among different biogeographic regions, other studies are unlikely to find similar results unless addressing African or admixed populations comparable to the Brazilian one. We detected three different associations involving genes from the class I antigen presentation pathway. These associations involve TAP1 and TAP2, which participate in the peptide pumping from the cytoplasm to the endoplasmic reticulum, and gene HLA-A, which will present these peptides on the cell surface to T CD8 lymphocytes ( Tables 1 , 2 ). High expression levels of TAP1 and TAP2 are correlated with the amount of virus in lung tissue (70). The TAP2 signal might be a hitchhiking association due to linkage with HLA-DOB*01:02 ( Figure S7 ). For HLA-A, the amino acid residues at positions 62 and 63 (mature protein) depend on the haplotype formed by four different variants, rs1059455, rs1064588, rs2230991, and rs199474424. Their presence is associated with severe Covid-19. The fact that (a) this combination of amino acids only occurs when there is a specific haplotype, (b) this haplotype occurs in many different HLA-A alleles, and (c) allele frequencies vary in different populations, may explain why we did not detect any association between HLA-A allotypes with Covid-19 severity and why previous surveys described different results or no association (23, 71–77). One possible mechanism for these associations is different binding affinities to SARs-CoV-2 peptides coupled with the subset of peptides pumped by TAP. The higher frequency of 62R-63N in the SEVERE group is related to alleles A*33:03, A*68:01, and A*68:02. A*68 alleles are among the strong binders for SARs-CoV-2 peptides (78, 79) and among the best binders for respiratory viruses (70). To investigate whether residues 62R/63N could be interfering with the subset of antigens presented by HLA-A, we performed an in silico prediction of the antigen processing pathway impact on the presentation ability of the alleles carrying 62R/63N. Since the set of MHC alleles that an individual presents defines the ligandome on its cell surface, we predicted the peptidome of inspected alleles and compared them with the corresponding immunogenic regions from the SARS-CoV-2 spike protein. The comparison did not provide evidence that the investigated alleles lack the potential to present T cell epitopes already described for SARS-CoV-2 sequences ( Supplementary Figures S3 , S4 ). Additionally, we extracted the frequency of response for each predicted peptide from each 62R/63N sample alleles to compare their average values of immunogenicity frequencies with other alleles associated with good and bad outcomes in Covid-19. Again, the alleles carrying 62R/63N and overrepresented in the severe group exhibited similar numbers, indicating that the presentation ability was not responsible for the impaired response ( Supplementary Figure S5 ). To infer if the mutations could be interfering with immunogenicity triggering, we also looked for alterations present in the 62R/63N alleles that could impact the TCR interaction surface of the MHC cleft. The HLA structural models were screened, looking for shared features in regions usually contacted by complementarity-determining region 3 (CDR3) loops ( Supplementary Figure S6 ). Hierarchical clustering analysis showed that four out of eight investigated alleles presented an electrostatic potential distribution fingerprint in the probed area. This physicochemical element was already described as pivotal to cytotoxicity elicitation, as described previously (80). Interestingly, the clustered alleles were HLA-A*33:01, A*33:03, A*68:01, and A*68:02, the ones with higher frequencies among patients with Severe Covid-19 ( Figure 4 ). These analyses emphasize the need for a deeper investigation when we are dealing with HLA alleles and their involvement with immunogenic issues. Only looking for ligandome predictions can underestimate the whole importance of these structures in T cell stimulation. Among the alleles with increased frequency in the SEVERE group, A*68:02 is particularly frequent in Africa, A*62:01 among Native Americans, and A*33:03 in Asia (www.allelefrequencies.net). Although the SEVERE group from Brazil presents a higher African and Native American ancestry than the MILD group ( Supplementary Table S1 ), the associations presented here (62R/63N, p = 0.0017, OR=2.7) are adjusted for ancestry and sex. This p-value is much lower, 0.0003, and significant even after correction for multiple tests within the HLA-A locus when not adjusted for ancestry. Therefore, population stratification should be ruled out as the main issue leading to these results. Since the associated alleles are most frequent in non-European populations, particularly Native Americans and Africans, it is unlikely that any study addressing European ancestry samples (23, 25, 74, 75, 77, 81–83) would find similar results. In fact, most of the previous studies evaluate European patients and find different results than the one presented here. Nevertheless, a frequency analysis of HLA alleles among Covid-19 infected patients from Saudi Arabia found HLA-A*68 among the most common alleles associated with severe disease (84), and A*68 is relatively common in Saudi Arabia (www.allelefrequencies.net). These results demonstrate the importance of addressing admixed populations such as Brazilians and other less-studied population samples. In short, here, we performed an in-depth analysis of the MHC region in a cohort of unvaccinated super elderly individuals with Covid-19 that presented mild, or no symptoms compared with a group of younger patients with severe Covid-19 and/or a lethal outcome. We used a method to call genotypes and haplotypes in the MHC that minimizes alignment and genotyping errors. Interestingly, the strongest signals in the MHC region for candidate variants protecting against severe Covid-19 coincide with gene MUC22. Missense variants at MUC22 are more frequent among the super elderly and in the MILD group than in the SEVERE group and the general elderly population. We hypothesized that MUC22 might play an important protective role against severe Covid-19. Functional studies must be placed to evaluate the true impact of such variants on MUC22 function. The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: European Genome-phenome Archive (EGA), under accession number EGAS0000100637. This study was approved by the Committee for Ethics in Research of the Institute of Biosciences at the University of São Paulo (CAAE 34786620.2.0000.5464). The patients/participants provided their written informed consent to participate in this study. EC, MC, MN, and MZ contributed to the conceptualization. MC, LM, and MVRS contributed to data curation. EC, MN, MOS, NS, KN, IO, EA, ES, and GV contributed to the formal analysis. MZ contributed to funding acquisition. MC, MN, MOS, EC-N, and KS contributed to the investigation. EC, MN, MOS, and KN contributed to the methodology. MZ contributed to the project administration. EC, MN, and MOS contributed the software. EC, MN, MOS, and KN contributed to visualization. EC, MZ, MC, MN, KS, and MOS contributed to writing–original draft. NS, RP, VAOC, CC, CM-J, GV, DM, LM, MVRS, JW, JE, VRC, JM, EN, JK, RB, MH, LD’A, AR-F, PB, AD, MD, PS, MP-B, and MZ contributed to writing–review, and editing. All authors contributed to the article and approved the submitted version. This work was supported by the São Paulo Research Foundation (FAPESP/Brazil) (grant numbers 2013/08028-1, 2014/50931-3, 2019/19998-8, and 2020/09702-1), the National Council for Scientific and Technological Development (CNPq) (grant number 465355/2014-5), and JBS S.A. (grant number 69004). FAPESP/Brazil (Grant numbers 2013/17084-0 and 2017/19223-0) and the United States National Institutes of Health (NIH) (R01 GM075091) supported the development of the HLA and KIR pipeline and the genetic ancestry approach. This study was also supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil (CAPES)-Finance Code 001 and Fleury Group (Project NP-565). The funders were not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication. The authors are extremely grateful to all volunteers for their participation and collaboration, the nurses for sample collection, the technical team for the material process and data analysis, and the Fleury Laboratory for serology tests. Special thanks to Brazilian Senator Mara Gabrilli for financial support and to JBS S.A. for the additional funding. The funders were not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

PMC9641630

Ghada T. Terana,Mohamed M. Abd-Alhaseeb,Gamal A. Omran,Tarek M. Okda

Quercetin potentiates 5-fluorouracil effects in human colon cancer cells through targeting the Wnt/β-catenin signalling pathway: the role of miR-27a

24-10-2022

colorectal cancer,5-fluorouracil,quercetin,miR-27a,Wnt/β- catenin

Introduction 5-fluorouracil (5-FU) is the most widely used chemotherapeutic drug in treating colorectal cancer. However, its toxicity to normal tissues and tumour resistance are the main hurdles to efficient cancer treatment. MiR27-a promotes the proliferation of colon cancer cells by stimulating the Wnt/β-catenin pathway. The present study was conducted to examine whether quercetin (Q) combined with 5-FU improves the anti-proliferative effect of 5-FU on HCT-116 and Caco-2 cell lines through detection of the miR-27a/Wnt/β-catenin signalling pathway. Material and methods Cell viability in HCT-116 and Caco-2 cell lines following quercetin and 5-FU treatment alone and in combination for 48 hours was determined using the MTT assay. The flow cytometry, quantitative real-time polymerase chain reaction, and ELISA techniques were used. Results Our results showed that combination of quercetin and 5-FU exhibited greater cytotoxic efficacy than did 5-FU alone. Co-administration of both drugs either in combination 1 (1 : 1 Q: 5-FU) or in combination 2 (1 : 0.5 Q: 5-FU) enhanced apoptosis in HCT-116 and Caco-2 cells compared with 5-FU alone and significantly inhibited the expression of miR-27a, leading to upregulation of secreted frizzled-related protein 1 and suppression of Wnt/β-catenin signalling, which was confirmed by a significant decrease in cyclin D1 expression. Conclusions Quercetin strongly enhanced 5-FU sensitivity via suppression of the miR-27a/Wnt/β-catenin signalling pathway in CRC, which advocates further research of this combination with the lower dose of 5-FU.

Quercetin potentiates 5-fluorouracil effects in human colon cancer cells through targeting the Wnt/β-catenin signalling pathway: the role of miR-27a 5-fluorouracil (5-FU) is the most widely used chemotherapeutic drug in treating colorectal cancer. However, its toxicity to normal tissues and tumour resistance are the main hurdles to efficient cancer treatment. MiR27-a promotes the proliferation of colon cancer cells by stimulating the Wnt/β-catenin pathway. The present study was conducted to examine whether quercetin (Q) combined with 5-FU improves the anti-proliferative effect of 5-FU on HCT-116 and Caco-2 cell lines through detection of the miR-27a/Wnt/β-catenin signalling pathway. Cell viability in HCT-116 and Caco-2 cell lines following quercetin and 5-FU treatment alone and in combination for 48 hours was determined using the MTT assay. The flow cytometry, quantitative real-time polymerase chain reaction, and ELISA techniques were used. Our results showed that combination of quercetin and 5-FU exhibited greater cytotoxic efficacy than did 5-FU alone. Co-administration of both drugs either in combination 1 (1 : 1 Q: 5-FU) or in combination 2 (1 : 0.5 Q: 5-FU) enhanced apoptosis in HCT-116 and Caco-2 cells compared with 5-FU alone and significantly inhibited the expression of miR-27a, leading to upregulation of secreted frizzled-related protein 1 and suppression of Wnt/β-catenin signalling, which was confirmed by a significant decrease in cyclin D1 expression. Quercetin strongly enhanced 5-FU sensitivity via suppression of the miR-27a/Wnt/β-catenin signalling pathway in CRC, which advocates further research of this combination with the lower dose of 5-FU. Colorectal cancer (CRC) is the second leading cause of cancer-related deaths and the third most common malignancy worldwide [1]. Currently, chemotherapy is the most widely used protocol for treating CRC patients [2]. However, its clinical applications are limited due to its toxic effect on normal cells, in addition to the emergence of drug resistance. It was shown that the Wnt/β-catenin pathway plays an important role in oncogenesis, tumour progression, and chemoresistance [3, 4]. Development, tissue homeostasis, cell differentiation, and cell proliferation are all significantly influenced by the family of glycosylated lipid-modified proteins known as Wnt-secreted proteins [5]. Numerous types of human malignancies, including CRC, have been linked to the pathophysiology of inappropriate Wnt signalling pathway activation [6]. An intracellular transcriptional coactivator known as β-catenin is stabilized as a result of a complex signalling cascade that is started when Wnt proteins connect to frizzled transmembrane receptors. After β-catenin translocation into the nucleus, β-catenin is able to couple with T cell factor or lymphoid enhancing factor (TCF/LEF) transcription factors activating Wnt target gene transcription. Secreted frizzled-related proteins (SFRPs), a class of secreted proteins, have recently been recognized as extracellular regulators of the Wnt signalling pathway [6]. SFRPs have a cysteine-rich domain homologous to the frizzled receptors. Recent studies have demonstrated down-regulation of secreted frizzled-related protein 1 (SFRP1) in CRC [7, 8]. Furthermore, deregulated β-catenin activation has been linked to the development of drug resistance to conventional chemotherapy [9, 10]. Several studies have shown that inhibiting or silencing Wnt/β-catenin signalling in drug-resistant cancer cells reduces p-glycoprotein levels and reverses microb drug resist to chemotherapeutic agents [11–13]. MiRNAs are small, single-stranded, non-coding RNAs (18–22 nucleotides) that negatively regulate target gene expression by interfering with transcription and translation [14]. They contribute to carcinogenesis as tumour suppressors or oncogenes through the regulation of their target genes [15–18]. It was shown that miRNAs can exhibit crosstalk with key cellular signalling networks including the Wnt/β-catenin cascade [19, 20]. For example, miR-27a, located on chromosome 19 [21], has been shown to function as an oncogene in several human cancers [22–25]. According to recent research, miR-27a can directly target SFRPs, a tumour suppressor protein and Wnt signalling pathway regulator, resulting in potentiation of epithelial-mesenchymal transition in oral squamous carcinoma stem cells and the invasion of human osteosarcoma cells [26, 27]. It was shown that miR-27a induces chemoresistance to tamoxifen in human breast cancer cell lines [28]. Recently, there has been increased interest in the clinical utilization of herbal remedies and natural products as a safe, effective, and low-cost alternative to conventional therapeutics [29–31]. Quercetin (Q) is a bioactive component belonging to the flavonol subclass of flavonoids, which is ubiquitous in various foods [32, 33]. Quercetin exhibits a variety of pharmacologic effects such as antiviral, antibacterial, antioxidant, anticancer, and anti-inflammatory [34]. Several studies have demonstrated that the anticancer effects of Q are mediated through induction of cell cycle arrest, apoptosis, and suppression of proliferation in leukaemia, melanoma, breast cancer, ovarian cancer, lung cancer, and colon cancer cell lines [35, 36]. Quercetin has been reported to inhibit cell growth through the inhibition of miR-27a in renal cancer cells [37] and colon cancer cells [38]. Moreover, miR-27a was shown to stimulate the proliferation and invasiveness of HCT-116 colon cancer cells by targeting SFRP1 through the Wnt/β-catenin signalling pathway [39]. The goal of this study was to examine whether Q combined with 5-fluorouracil (5-FU) would improve the antiproliferative effects of chemotherapy in HCT-116 and Caco-2 cells compared with 5-FU alone through the detection of the Wnt/β-catenin signalling pathway. In addition, in light of what was previously published about the effect of Q on oncogenic miR-27a, the present study further examined the potential effect of miR-27a targeting on enhancing the chemotherapeutic effect of 5-FU. Quercetin (#SLBV2993), 5-FU (#MKBX3795V), foetal bovine serum (FBS), 3-(4,5-dimethylthiazol-2-yl)-2,5- diphenyltetrazolium bromide (MTT), and dimethyl sulfoxide (DMSO) were purchased from Sigma-Aldrich (St. Louis, MO, USA). Trypsin, Dulbecco’s Modified Eagle’s Medium (DMEM), phosphate-buffered saline (PBS), and penicillin/streptomycin antibiotics were obtained from GIBCO (New York, USA). L-glutamine (2%) was purchased from Invitrogen (New York, USA). Ethanol was obtained from the El Nasr Pharmaceutical Chemicals Co. (Cairo, Egypt). Two human colon cancer cell lines, HCT-116 and Caco-2, were procured from the American Type Culture Collection (ATTC, Manassas, VA, USA). The cells were cultured in DMEM supplemented with 10% FBS and 1% penicillin/streptomycin and were incubated in a 5% carbon dioxide and 95% air atmosphere at 37°C (Thermo Electron Co., Waltham, Massachusetts, USA). Stock solutions of Q and 5-FU were prepared by dissolving in DMSO and diluting with DMEM to the appropriate concentration [40]. Dulbecco’s Modified Eagle’s Medium containing equivalent amounts of DMSO was used as a control for each cell line. The effect of Q and/or 5-FU on cell viability was evaluated by MTT assay as described by Van Meerloo et al. [41]. Cells were seeded in 96-well plates (1 × 104 cells/well) and maintained overnight at 37°C, old media was aspirated, and 100 µl of treatment media containing Q concentrations (0, 3.125, 6.25, 12.5, 25, 50, and 100 µg/ml) and/or 5-FU (0, 15.6, 31.25, 62.5, 125, 250, and 500 µg/ml) were added and incubated for 48 hours. The cells were then treated with MTT (5 mg/ml) for 4 hour, and the resulting formazan crystals were dissolved in 100 µl DMSO. The optical density (OD) was recorded at 570 nm using a microplate reader. Experiments were performed in triplicate in 3 independent experiments [42–44]. The growth inhibition rate was expressed as: growth inhibition (%) = (1-OD of treated/OD of control) × 100. The linear regression method was used to calculate the IC50 values of both Q and 5-FU. Cells were divided into the following 5 groups: Group I (control group), cells were treated with 1% DMSO; Group II, cells were treated with 5-FU at its IC50 value; Group III, cells were treated with Q at its IC50 value; Group IV combination 1 (QFH), cells were treated with a combination of Q and 5-FU at concentrations based on IC50 values (1 : 1 Q: 5-FU); and Group V combination 2 (QFL), cells were treated with a combination of Q and 5-FU at concentrations based on IC50 values (1 : 0.5 Q: 5-FU). Experiments were performed in triplicate in 3 independent experiments. All treatments were done using HCT-116 and Caco-2 cells at 70–80% confluence. The cells were incubated in a CO2 incubator for 48 hours, trypsinized, and immediately prepared for molecular analysis. Supernatants were kept at –80ºC for cyclin D1 detection by ELISA. Apoptotic cells were quantified using an Annexin V-FITC-propidium iodide (PI) double staining kit (Cat. No. 556547 BD Pharmingen, San Jose, CA, USA) according to the manufacturer’s instructions. Each of the HCT-116 and Caco-2 cells was plated in a 24-well plate (5 ´ 105 cells/well) and then incubated for 24 hours for attachment, and then the cells were treated with Q and/or 5-FU, as in the experimental design, for 48 hours. After cell collection by trypsinization, HCT-116 and Caco-2 cells were washed with PBS and stained by annexin V/propidium (BD Biosciences) based on the manufacturer’s instructions for 25 minutes at room temperature in a dark place. The stained cells were analysed using an Attune flow cytometer (Applied Bio-system, USA). Experiments were performed in triplicate. Total RNA was extracted from HCT-116 and Caco-2 cells using the miRNA Easy Kit (Cat. No. 217004 Qiagen Strasse, Hilden, Germany), and the RNA concentration and purity were measured using a NanoDrop 1000 Spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA). The miScript miRNA assay system (Qiagen Strasse, Hilden, Germany) was used to measure miR-27a-3p expression with U6 snRNA as the internal control. For the measurement of WNT1, SFRP1, and β-catenin mRNA expression, RNA was reverse-transcribed into cDNA using the HiSenScript™ RH (–) cDNA Synthesis Kit (iNtRON Biotechnology, Inc., Korea), and quantitative real-time PCR analysis was done using TOPreal™ qPCR 2X PreMix (Cat. No. RT500S Enzynomics, Korea) along with specific primers (Thermo Fisher Scientific, UK), as shown in Table 1. Glyceraldehyde-3-phosphate dehydrogenase was used as an internal control. Calculation of relative expression was done as described by Livak et al. [45]. The ELISA technique was used to determine the cyclin D1 levels. A specific antibody for cyclin D1 (Cat. No. LS-F21523, LifeSpan BioSciences, Inc., USA) was precoated separately onto microplates. The standard and samples were then added to the wells to form an immobilized antibody complex. Biotin-conjugated antibody was added, followed by avidin-conjugated horseradish peroxidase. A substrate solution was then added to the wells for colour development. A sulphuric acid stop solution was added to terminate colour development, and the OD was measured for each well at a wavelength of 450 nm. The colour intensity was measured relative to the quantity of cyclin D1. Data are expressed as the mean ± standard deviation (SD). Results were analysed by a one-way ANOVA test. A post hoc Tukey’s multiple comparison test was used for multiple comparison analysis. Significant differences among the means were considered at p < 0.05. Statistical analysis and graphical presentation of the data were done using the Graph Pad Prism® software package version 8.0.2 (GraphPad Software Inc., CA, USA). The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of the Faculty of Pharmacy’s Ethics Committee, Damanhour University. Figure 1 shows the cell viability of HCT-116 and Caco-2 cells treated with different concentrations of Q and/or 5-FU for 48 hours. Both Q and 5-FU decreased the viability of the 2 cell lines depending on the dosage when used alone. Q and 5-FU showed IC50 = 12.36 µg/ml and 125 µg/ml, respectively, on HCT-116, and showed IC50 = 15 µg/ml and 133 µg/ml, respectively, on Caco-2 cells. Furthermore, the combination of Q and 5-FU showed higher antiproliferative activity compared with 5-FU alone in both cell lines. As the pharmacodynamic endpoint of cancer therapy, an apoptosis assay was performed to investigate the Q enhancing effect on 5-FU-induced apoptosis. Figures 2 and 3 show the apoptotic effects of the 5-FU, Q, QFH, and QFL. The percentages of total apoptosis (early apoptosis plus late apoptosis) following treatment with 5-FU, Q, QFH, and QFL, respectively, were 18.25%, 18.1%, 49%, and 52.9% on HCT-116 cells. Compared to the 5-FU group, Q/5-FU combinations significantly (p < 0.001) increased apoptosis (Fig. 2). The Caco-2 cell apoptosis percentages using 5-FU, Q, QFH, and QFL application, respectively, were 21.12%, 42.26%, 44.44%, and 42.38%. Compared to the 5-FU group, Q/5-FU combinations significantly (p < 0.001) increased apoptosis (Fig. 3). As shown in Figures 4A and B, treatment with Q resulted in a significant downregulation of miR-27a expression compared with the control group in both cell lines. Quercetin and 5-FU co-treatment (combinations 1 and 2) resulted in greater downregulation of miR-27a expression compared with the 5-FU group in both cell lines. Secreted frizzled-related protein 1 is a negative regulator of Wnt/β-catenin signalling. Secreted frizzled-related protein 1 was significantly upregulated by Q in both cell lines compared with the control groups. Overexpression of SFRP1 was observed following combined treatment with Q and 5-FU (combination 1, 2) compared with the 5-FU group in both cell lines (Figs. 5 A, B). In HCT-116 cells, Q had no significant effect on WNT1 and β-catenin gene expressions compared with the control group. Upon 5-FU treatment, there was a significant upregulation of WNT1 and β-catenin expression compared with the control group. Q and 5-FU co-treatment (combinations 1 and 2) resulted in a significant downregulation of WNT1 and β-catenin expression compared with the 5-FU group (Figs. 6A, B). In the Caco-2 cell line, the quantitative real-time polymerase chain reaction assay showed significant upregulation of β-catenin expression following treatment with Q and 5-FU alone compared with the control group. Q and 5-FU co-treatment (combinations 1 and 2) resulted in a significant downregulation of β-catenin expression compared with 5-FU alone (Fig. 6 C). In Caco-2 cells, Q demonstrated no significant effect on WNT1 expression compared with the control group. Following combined treatment with Q and 5-FU (combinations 1 and 2) there was significant downregulation of WNT1 expression compared with the 5-FU group (Fig. 6 D). The highest level of cyclin D1 was observed in the control groups in both HCT-116 and Caco-2 cell lines. There was a significant reduction in cyclin D1 expression following treatment with Q and 5-FU alone compared with the control group in both cell lines. Significant inhibition of cyclin D1 was observed in the groups treated with QFH and QFL compared with 5-FU alone in both cell lines (p < 0.01) (Figs. 7A, B). Colorectal cancer is one of the most frequently occurring malignancies worldwide. 5-fluorouracil is the cornerstone drug used for treating CRC; however, tumour cell resistance and cytotoxicity to normal cells are obstacles to successful treatment [46, 47]. Therefore, new therapeutic strategies are needed to circumvent resistance, alleviate adverse effects, and reduce the dose of chemotherapy [48]. Using natural products in combination with chemotherapy represents a fruitful strategy to increase the sensitivity of cancer cells to chemotherapy (chemo-sensitizers) and reduce the negative effects of chemotherapy (chemo- protectors) [49, 50]. Quercetin either alone or in combination was shown to attenuate the progression of colon cancer through several mechanisms, including cell cycle arrest, decreased cell viability, modulation of oncogenic signalling pathways, induction of apoptosis and autophagy, and inhibition of metastasis. To the best of our knowledge, this is the first study to examine the effect of Q on enhancing the anti-proliferative effects of 5-FU concerning the Wnt/β-catenin signalling pathway through miR-27a modulation in HCT-116 and Caco-2 cell lines. Our study revealed that a combination of Q ad 5-FU resulted in a higher cytotoxic effect on HCT-116 and Caco-2 cells compared with 5-FU alone. This was shown by a higher apoptotic rate upon treatment with combination 1 and combination 2 compared with 5-FU alone. Our findings are consistent with previous studies. A recent study revealed that a combination of 5-FU and Q enhanced the cytotoxic and apoptotic effects of 5-FU on MCF-7 cells compared to 5-FU alone [51]. As previously reported, 5-FU in combination with Q and melatonin in human liver and colon cancer cells enhances its effect on apoptosis and growth suppression compared to 5-FU alone [52–54]. Curcumin combined with 5-FU stimulated apoptosis and decreased Bcl-2 protein levels in cancer cells [55]. MiR-27a is located on chromosome 19 [21] and has been reported to function as a tumour promoter in different types of human cancers, including breast cancer, gastric adenocarcinoma, HCC, and pancreatic cancer [22–25, 56]. Our results indicated that combination 1 and combination 2 resulted in more significant downregulation in the gene expression of miR-27a than either agent alone. Our findings are in line with previous research examining the effects of a combination of resveratrol and Q (5–20 µg/ml) on cell growth inhibition via the miR-27a reduction in colon cancer cells [38]. Li et al. reported that the combination of Q and hyperoside 5 to 20 µg/ml inhibited miR-27a expression level in 786-O renal cancer cells [37]. The Wnt/β-catenin signalling pathway is one of the main dysregulated pathways in CRC, and it potentiates cell proliferation and drug resistance [57]. Secreted frizzled-related protein 1 is an antagonist of the frizzled receptors and Wnt pathway activation. Previous studies have demonstrated the downregulation of SFRP1 in CRC [7]. Moreover, according to recent research, miR-27a could directly target SFRP1 through Wnt/β-catenin signalling pathway in HCT-116 cells [39]. The present study showed that combinations 1 and 2 inhibited Wnt/β-catenin signalling by significantly upregulating SFRP1 that was a result of higher downregulation of miR-27a expression in both cell lines more than each drug alone. Consistent with previous reports, it was reported that Q increases chemotherapeutic drug sensitivity of K562 and K562R cells by suppressing the Wnt/β-catenin signalling pathway [58]. Another study demonstrated that Q increased the sensitivity of human HCC cells to chemotherapeutic drugs through the FZD7/β-catenin signalling pathway [59]. Another study reported that cardamonin, a natural chalcone, enhanced the antiproliferative effect of 5-FU in gastric cancer cells by targeting the Wnt/β-catenin signalling pathway [60]. Cyclin D1, a target of the β-catenin pathway, is overexpressed in several tumour types and mediates cell cycle progression from the G1 to the S phase [61]. The synergistic effect of combinations 1 and 2 of Q and 5-FU was confirmed by a more significant decline in cyclin D1 levels in both combination groups compared with 5-FU alone. This result is consistent with a previous study that demonstrated that amla extract, which is rich in Q, inhibited human colon cancer stem cells (HCCSC) by targeting the Wnt/β-catenin signalling pathway, resulting in a decrease in cyclin D1 expression [62]. It was noted that the combination of 5-FU and chloroquine caused G1 arrest as well as CDK2 and cyclin D1 downregulation [63]. It was demonstrated that Q combined with 5-FU enhanced the antiproliferative effects of chemotherapy agents in HCT-116 and Caco-2 cells by targeting SFRP1 through the Wnt/β-catenin signalling pathway via miR-27a downregulation. Further studies are recommended for the use of this combination with a reduced dose of chemotherapy.

PMC9641661

36189829

Ximena Soto,Joshua Burton,Cerys S. Manning,Thomas Minchington,Robert Lea,Jessica Lee,Jochen Kursawe,Magnus Rattray,Nancy Papalopulu

Sequential and additive expression of miR-9 precursors control timing of neurogenesis

03-10-2022

pri-mir-9,miR-9,Neurogenesis,Zebrafish,Temporal control

ABSTRACT MicroRNAs (miRs) have an important role in tuning dynamic gene expression. However, the mechanism by which they are quantitatively controlled is unknown. We show that the amount of mature miR-9, a key regulator of neuronal development, increases during zebrafish neurogenesis in a sharp stepwise manner. We characterize the spatiotemporal profile of seven distinct microRNA primary transcripts (pri-mir)-9s that produce the same mature miR-9 and show that they are sequentially expressed during hindbrain neurogenesis. Expression of late-onset pri-mir-9-1 is added on to, rather than replacing, the expression of early onset pri-mir-9-4 and -9-5 in single cells. CRISPR/Cas9 mutation of the late-onset pri-mir-9-1 prevents the developmental increase of mature miR-9, reduces late neuronal differentiation and fails to downregulate Her6 at late stages. Mathematical modelling shows that an adaptive network containing Her6 is insensitive to linear increases in miR-9 but responds to stepwise increases of miR-9. We suggest that a sharp stepwise increase of mature miR-9 is created by sequential and additive temporal activation of distinct loci. This may be a strategy to overcome adaptation and facilitate a transition of Her6 to a new dynamic regime or steady state.

Sequential and additive expression of miR-9 precursors control timing of neurogenesis MicroRNAs (miRs) have an important role in tuning dynamic gene expression. However, the mechanism by which they are quantitatively controlled is unknown. We show that the amount of mature miR-9, a key regulator of neuronal development, increases during zebrafish neurogenesis in a sharp stepwise manner. We characterize the spatiotemporal profile of seven distinct microRNA primary transcripts (pri-mir)-9s that produce the same mature miR-9 and show that they are sequentially expressed during hindbrain neurogenesis. Expression of late-onset pri-mir-9-1 is added on to, rather than replacing, the expression of early onset pri-mir-9-4 and -9-5 in single cells. CRISPR/Cas9 mutation of the late-onset pri-mir-9-1 prevents the developmental increase of mature miR-9, reduces late neuronal differentiation and fails to downregulate Her6 at late stages. Mathematical modelling shows that an adaptive network containing Her6 is insensitive to linear increases in miR-9 but responds to stepwise increases of miR-9. We suggest that a sharp stepwise increase of mature miR-9 is created by sequential and additive temporal activation of distinct loci. This may be a strategy to overcome adaptation and facilitate a transition of Her6 to a new dynamic regime or steady state. MicroRNAs (miRs) are a class of small (∼22 nt) regulatory non-coding RNAs, which regulate gene expression at the post-transcriptional level. These small RNAs are processed from large microRNA primary transcripts (pri-mir) into 70∼90 nt precursors (pre-mir) before further splicing into ∼22 nt mature miR. miR-9 is a highly conserved miR that is expressed predominantly in the central nervous system (CNS) of vertebrates and plays a crucial role during CNS development. Specifically, previous work in Xenopus, zebrafish and mice has shown that miR-9 is essential for cell fate transitions during neurogenesis (Shibata et al., 2011; Coolen et al., 2013; Bonev et al., 2011, 2012). miR-9 post-transcriptionally targets many transcription factors that are involved in neural development such as FoxG1 (Shibata et al., 2008), Tlx (also known as Nr2e1; Zhao et al., 2009) and members of the Hes/Her helix-loop-helix family of transcription factors, including Hes1 in mouse and Xenopus (Bonev et al., 2011, 2012) and Her6/Her9 in zebrafish (Coolen et al., 2012; Galant et al., 2016; Soto et al., 2020; Leucht et al., 2008). The Hes/Her family of proteins is expressed dynamically in an oscillatory manner at the ultradian timescale (Hirata et al., 2002; Shimojo et al., 2008). Hes/Her oscillations are achieved by a negative feedback loop, whereby Hes/Her proteins inhibit their own transcription coupled with a rapid turnover of protein and mRNA. Instability of both protein and mRNA allows for levels of the protein to fall, de-repression to occur and expression to resume, generating a cyclic pattern (Hirata et al., 2002; Novak and Tyson, 2008). Indeed, both mRNAs and proteins of Hes family genes are unstable: for example, in mice, the half-life of Hes1 mRNA is ∼24 min, the Hes1 protein half-life is in the order of 22 min (Hirata et al., 2002) and the Her6 (Hes1 zebrafish orthologue) protein half-life is ∼12 min (Soto et al., 2020). Instability of mRNA, as well as translation of protein, are partly controlled by miRs. Indeed, our previous work revealed that miR-9 regulation is important for controlling Hes1 mRNA stability and allowing the oscillatory expression of Hes1 to emerge (Bonev et al., 2012; Goodfellow et al., 2014). We have recently shown that in zebrafish, the dynamics of Her6 protein expression switch from noisy to oscillatory and then to downregulation, and that these changes coincide temporally with the onset of miR-9 expression in the hindbrain (Soto et al., 2020). When the influence of miR-9 on her6 is removed experimentally, Her6 expression does not evolve away from the ‘noisy’ regime and is not downregulated with a consequent reduction in progenitor differentiation. We have interpreted this to mean that the miR-9 input is necessary to constrain gene expression noise, enabling oscillations to occur and to be decoded by downstream genes, which in turn participate in downregulating Her6 as cells differentiate (Soto et al., 2020). However, not only the presence of miR-9 but also the amount of miR-9 present is important, as too much or too little miR-9 can lead to dampening of Hes1 oscillations (Bonev et al., 2012; Goodfellow et al., 2014). Indeed, mathematical modelling showed that increasing miR-9 over time drives the Hes1 expression into different states (oscillatory or stable high/low) and that the amount of miR-9 present in the cell determines the length of time for which Hes1 oscillates, effectively timing the transition to differentiation (Phillips et al., 2016; Goodfellow et al., 2014). Together these findings support that Hes/Her dynamics and downregulation are sensitive to the amount of mature miR-9 present in the cell; however, the mechanism by which the miR-9 level is controlled is not known. This question is complicated by the observation that vertebrates (and some invertebrates) possess multiple copies of the miR-9 gene at distinct loci, which are all capable of producing the same mature miR. For example, both human and mouse contain three copies of miR-9 (Rodriguez-Otero et al., 2011; Shibata et al., 2011) and frogs have four (Walker and Harland, 2008). Due to an additional round of whole-genome duplication (WGD) in teleost fish (Amores et al., 1998; Jaillon et al., 2004), zebrafish have seven paralogues of miR-9 (pri-mir-9-1 to pri-mir-9-7) (Chen et al., 2005). One possibility is that different genomic loci contribute to miR-9 regulation in a qualitative way, with differential temporal and spatial specificity of mature miR-9 expression. Indeed, there is some limited evidence that these discrete copies of miR-9 are expressed differentially during development both temporally and spatially (Nepal et al., 2016; Tambalo et al., 2020). Another, and yet unexplored, possibility is that transcription from different loci may serve to control miR-9 quantitatively, that is to increase the amount of miR-9 in the cell and perhaps do so in a temporally controlled manner, thus contributing to the change of miR-9 levels that is necessary to drive a change in the dynamics of Hes/Her targets. Here, we undertake a systematic study of pri-mir-9 expression in zebrafish that aims to address the likelihood of these distinct scenarios, with special attention to the possibility of a quantitative control mechanism. We show by in situ hybridization that the expression of miR-9 spreads from the forebrain to the hindbrain and increases quantitatively in the hindbrain between 24 and 48 h post-fertilisation (hpf). A detailed time course of the expression of all seven pri-mir-9 paralogues shows that they are all transcriptionally active, but exhibit subtle, yet distinct, temporal and spatial profiles. Focusing on a set of early- and late-expressed pri-mir-9s in the hindbrain (pri-mir-9-1, pri-mir-9-4 and pri-mir-9-5) by quantitative single molecule fluorescent in situ hybridisation at single cell level, we found that, surprisingly, in many cells, early and late pri-mir-9s were concurrently transcriptionally active such that the expression from late-activated pri-mir-9s is added on to the early ones. This is functionally significant as the specific mutation of the late pri-mir-9-1 selectively reduces neurons that normally differentiate late. Our mathematical modelling suggests that the sharp quantitative increase afforded by the deployment of additional transcriptional units, may facilitate the downregulation of Her6 at late time points. We found this to be consistent with a subtle but reproducible failure to downregulate Her6 at late stages when pri-mir-9-1 was specifically mutated. Taken together, although both quantitative and qualitative mechanisms may contribute to the decoding function of mature miR-9s, we found a previously unappreciated quantitative component in the deployment of pri-mir-9s, which is temporally controlled and in turn controls the evolution of Her6 dynamic expression over time. miRs are derived from a duplex precursor and the -5p strand (‘guide’) is preferentially incorporated into an RNA-induced silencing complex (RISC) to exert its regulatory functions, while the complementary -3p strand (‘passenger’) is thought to be rapidly degraded. Indeed, for the mature miR-9 the miR-9-5p is designated as the ‘guide’ strand and its annotation is derived from the mature miR sequence being embedded in the 5′ stem of the miR-9 precursor. To investigate the expression of the mature miR-9 (-5p strand) in zebrafish embryos, we first performed a whole-mount in situ hybridization (WM-ISH) for the mature miR-9 using a locked nucleic acid (LNA) probe. Mature miR-9 was detected only in the forebrain at 24 hpf (Fig. 1A), but at 30 hpf miR-9 was weakly observed in the midbrain and rhombomere (r) 1 of the hindbrain, maintaining high expression in the forebrain (Fig. 1A, 30 hpf). As development progressed, miR-9 expression increased in the hindbrain with steady high levels in the forebrain (Fig. 1A, 35-38 hpf; blue arrow). Later in development, levels in the hindbrain were further increased, while those in the forebrain were decreased (Fig. 1A, 48 hpf; blue arrow, hindbrain; green arrow, forebrain). These results show a temporally controlled increase of miR-9 expression along the brain/hindbrain axis as previously described in Soto et al. (2020). To characterize the increase of expression in the hindbrain in a quantitative manner, we used quantitative real-time PCR (RT-qPCR) on dissected hindbrains from stages 25 hpf to 48 hpf. This analysis confirmed that there is upregulation in the time frame analysed. An initial low level of expression at 30 hpf was followed by a sharp upregulation at 37 hpf, which was maintained through to 42 hpf, undergoing a second sharp increase at 48 hpf (Fig. 1B). In zebrafish, the mature miR-9 can be produced from seven paralogues of miR-9. The miR-9 paralogues occupy seven unique loci across the genome (GRCz11; Genome Reference Consortium Zebrafish Build 11) (Yates et al., 2020). With the exception of miR-9-3, which is located upstream of a long intergenic noncoding RNA (lincRNA), all miR-9 genes are intragenic, overlapping annotations of lincRNAs or proteins (Yates et al., 2020) (Fig. S1A,B). Our in silico analysis of previously published RNA-seq data shows differential temporal expression of six of the seven miR-9 paralogues hosts (White et al., 2017). It is also clear that upregulation of miR-9 host genes (and hence miR-9) coincides with a gradual decline in the expression of Her/Hes family gene expression, consistent with the idea that Her/Hes genes are major targets of miR-9 (Fig. S1C) (Bonev et al., 2011). Previous work has revealed that the seven miR-9 zebrafish paralogues are expressed in the forebrain at early stages of neurogenesis; however, toward the end of embryonic neuronal differentiation they are also expressed in the hindbrain (Nepal et al., 2016). Little is known about the period spanning the peak of neurogenesis, when miR-9 controls downstream targets such as the ultradian oscillator Her6. To characterize the expression in greater spatiotemporal detail, particularly over regions of the hindbrain area in which Hes/Her target genes are expressed, we investigated the expression of all seven primary transcripts over a time period spanning the peak of neurogenesis, which occurs at 33 hpf (Lyons et al., 2003), using specific probes for each pri-mir-9 (Fig. S2; Materials and Methods, Molecular cloning). We observed that all pri-mir-9s were first expressed in the forebrain (24 hpf) in a regional specific manner, which is not further characterised here. At 48 hpf they are all also expressed in the hindbrain (Fig. 1C, 24 and 48 hpf; Fig. S3A-C) consistent with previously described results (Nepal et al., 2016). Differential expression was evident in the intermediate stages. Specifically, pri-mir-9-3, -9-4 and -9-5 were expressed ahead of the others in the hindbrain (Fig. 1C, 30-31 hpf; blue arrowhead). At the peak of hindbrain neurogenesis (34-36 hpf), pri-mir-9-2 and -9-7 were upregulated, joining most of the pri-mir-9s that were highly expressed at this stage (Fig. 1C, 34-36 hpf). Pri-mir-9-1 and -9-6 were temporally delayed, showing hindbrain expression at 48 hpf, at which point all pri-mir-9 were fully expressed. Quantifying the expression with RT-qPCR confirmed that pri-mir-9-4 and -9-5 were expressed early and that expression of pri-mir-9-1 commenced relatively late, at 42 hpf (Fig. 1D,E). At 48 hpf, all pri-mir-9s had lower level of expression, although pri-mir-9-1 continued to be relatively high compared with the other pri-mir-9s (Fig. 1D,E). Overall, every pri-mir-9 was expressed in the CNS and exhibited a temporal progression. To achieve a more detailed characterisation of expression, we selected three different primary transcripts based on: (1) the onset of their hindbrain temporal expression during development, earliest or latest; and (2) a phylogenetic analysis of sequence based on vertebrate evolutionary relationship performed by Alwin Prem Anand et al. (2018) to select representatives that are widely distributed in the phylogenetic tree. Thus, pri-mir-9-5 was selected as the earliest to be expressed in the hindbrain and belonging to clade I/subgroup I, pri-mir-9-4 as the earliest and belonging to clade II, and pri-mir-9-1 as the latest and belonging to clade I/subgroup II (Alwin Prem Anand et al., 2018) (Fig. S3D). Double whole-mount fluorescent in situ hybridization (WM-FISH) for pri-mir-9-1/pri-mir-9-4 and pri-mir-9-1/pri-mir-9-5 performed on stage 30-32 hpf embryos revealed expression of pri-mir-9-4 and pri-mir-9-5 along the anterior-posterior (A-P) hindbrain axis, whereas the expression of pri-mir-9-1 at this early stage was limited to the region of the anterior hindbrain corresponding to r1 (Fig. 2A; red arrowhead). A transverse view at mid-hindbrain (r4) reveals expression of pri-mir-9-4 and pri-mir-9-5 within the ventricular zone (VZ; Fig. 2A,B,E), indicating that pri-mir-9s are expressed in the region where most of the progenitors are found (Lyons et al., 2003; Tambalo et al., 2020). Pri-mir-9-1 staining shows an artefactual surface expression, as indicated with white arrow in transverse view at 30-32 hpf (Fig. 2A,B); this is because of the WM-FISH detection method. We repeated this analysis at 48 hpf to examine whether the late expression of pri-mir-9-1 is cumulative with pri-mir-9-4 and pri-mir-9-5 or spatially distinct. Double WM-FISH of pri-mir-9-1 with pri-mir-9-4 or pri-mir-9-5 revealed overlapping expression of the primary transcripts in both longitudinal and transverse views (Fig. 2C,D). In addition, some distinct expression was observed in transverse views in that pri-mir-9-1 was more broadly expressed toward the dorsal progenitor region (Fig. 2C-E) when compared with pri-mir-9-4 and pri-mir-9-5. For overlapping expression to contribute to the total levels of mature miR-9 in a cell, early and late pri-mir-9s would need to be expressed in the same cells. Thus, we investigated pri-mir-9 expression at the single-cell level, using triple WM-smiFISH for pri-mir-9-1, -9-4 smiFISH (single-molecule inexpensive fluorescent in situ hybridization) and -9-5 to detect nascent transcription sites, and Phalloidin staining to reveal cell boundaries. At 30 hpf we observed that most cells expressed only one miR-9 primary transcript, pri-mir-9-4 or pri-mir-9-5, while a small proportion expressed both and none expressed pri-mir-9-1 (Fig. 3A,D-F). By contrast, at 36-37 hpf and 48 hpf (Fig. 3B,C), the number of cells that expressed one pri-mir-9 decreased and, correspondingly, the number that expressed two or three pri-mir-9s increased. The most striking increase was observed in the number of cells that co-express three pri-mir-9s at 36-37 hpf, which was because of the onset of transcription of pri-mir-9-1 in the same cells that expressed pri-mir-9-4 and -9-5. This finding suggests that, in many hindbrain cells, the late expression of pri-mir-9-1 is added to the earlier expression of pri-mir-9-4 and -9-5. Based on the smiFISH data presented above, we created a map that depicts transcription in single cells in transverse sections of the hindbrain over development (Fig. 4A-C). From left to right we observe: (i) the whole transverse section obtained from the Phalloidin staining, (ii) the region in which cells transcribe pri-mir-9-5, (iii) the cells with overlapping transcription for pri-mir-9-5/9-4 and (iv) the cells in which the three primary transcripts are transcribed (Fig. 4A-C). At 30 hpf, pri-mir-9-4 and -9-5 are co-expressed in many cells of the VZ (Fig. 4A). At 36-37 hpf, pri-mir-9-1 is transcriptionally activated in most, but not all, neural progenitors that already express pri-mir-9-4 and -9-5 (Fig. 4B). At 48 hpf the pattern of triple pri-mir-9 co-expression is similar to that seen in 36-37 hpf (Fig. 4C). This result supports the concurrent expression of pri-mir-9s at late stages but also shows their expression in the neural progenitor area, which thins during development as cells differentiate (Fig. 4D). All three paralogues are switched off in differentiating cells located in the marginal zone, suggesting that they are involved in the decision to differentiate rather than in maintaining the differentiated state (Fig. 4A-C). To explore the identity of the triple pri-mir-9 expressing progenitors, we turned our attention to the dorso-ventral (D-V) progenitor axis of the VZ. The everted structure of the zebrafish hindbrain means that dorsal progenitors are located more laterally than medial or ventral ones (Fig. 4D). We compared the expression to neurog1, ascl1 and atoh1, which are markers for ventral, medial and dorsal progenitors, respectively (Fig. 4E) (Tambalo et al., 2020). Remarkably, at early stages of development the cells expressing two primary transcripts were mostly localized in the ventral progenitor region of the VZ (Fig. 4A), whereas at later stages the cells with three primary transcripts excluded the ventral-most domain (Fig. 4B,C), suggesting that miR-9 high levels are required in medial and dorsal progenitors. Pri-mir-9-5 was expressed throughout the everted D-V axis (Fig. 4B,D,F) and was co-expressed with her6 and her9, both of which were expressed in the progenitor domain (mainly medial and some dorsal) and are downregulated as cells differentiate. We have previously described Her6 protein expression also in ventral progenitors, which is, however, extremely weak at late development (36-37 hpf) and has not been detected by smiFISH here (Soto et al., 2020). Both her6 and her9 contain miR-9 binding sites and are candidates for dynamic regulation by miR-9 (Fig. 4F) (Coolen et al., 2013; Leucht et al., 2008; Soto et al., 2020). The spatial analysis above showed that the expression of pri-mir-9-1 is added onto to pre-existing pri-mir-9-4 and -9-5 expression in medial and dorsal progenitors. To find out whether there is any specificity in deleting pri-mir-9-1, we designed a CRISPR/Cas9-based knockdown with guides that were specific to pri-mir-9-1 (Fig. 5A; Fig. S4A-C). This resulted in reduction of mature miR-9 and pri-mir-9-1 from 37 hpf onwards when the endogenous locus was transcribed (Fig. 5B,C). RT-qPCR was also performed to pri-mir-9-3, -9-4 and -9-5 under mutation of pri-mir-9-1. Some reduction (with high variability between samples) was also observed in pri-mir-9-4 and -9-5, but it was not maintained at later stages of development (Fig. S4E,F, 48 hpf). Pri-mir-9-3 was not affected (Fig. S4D). Exploring the potential defect further we used a panel of differentiation markers spanning the D-V axis (Fig. 5D). Injected fish did not show overt abnormalities; however, RT-qPCR analysis at 3 days post-fertilisation (dpf) showed that the differentiation marker elavl4 was reduced (Fig. 5E). This analysis also showed that, within the her6 domain, there was a reduction of noradrenergic neurons (NAN) derived from medial progenitors (dmbx1a; Fig. 5G) and adjacent GABAergic interneurons (pax2a; Fig. 5H), while the more ventral neuronal markers tal1 and isl1 (isl1a) were not significantly different, neither was the most dorsal marker (barhl1a) outside the her6 domain (Fig. 5F,I-K). Medial/dorsal progenitors differentiate later in vertebrate development than ventral ones (Delile et al., 2019), therefore our findings suggest that the late increase of miR-9, afforded by the additional deployment of pri-mir-9-1, is needed for cells to adopt a late neuronal fate. Having shown that the increase in miR-9 in development is functionally important for differentiation, we wanted to explore whether the shape of the increase is also important. In other words, whether the way that miR-9 increases in steps can be decoded. This was motivated by the biological evidence obtained from smiFISH and RT-qPCR experiments in which we observed a stepwise sharp increase of the primary transcripts (Fig. 6A) and the mature miR-9 (Fig. 1B) over time. We used a mathematical model to ask whether a simple network of gene interactions can differentially respond to a stepwise increase of miR-9 rather than a gradual increase. Biological systems need to be robust to stochastic fluctuations that are due to low copy numbers, or to random perturbations in the surrounding environment. This is referred to as adaptation in the context of a particular output of interest, making the biological system resistant to changes of the input. However, some changes of signals are not simply due to noise or environmental fluctuations, and adaptive systems may therefore also have to respond to specific signals under changing conditions, especially during development, in order to move into a new state. Thus, we explored whether a stepwise change in gene expression can allow a system to move out of an adaptively stable state. As incoherent feed-forward loops (IFFL) are common in biology (Goentoro et al., 2009; Shen-Orr et al., 2002) and have been shown to enable adaptation (Khammash, 2021), we hypothesized the existence of such a network centred around miR-9 as the input and Her6 as the output (Fig. S5A; Materials and Methods, Mathematical modelling) in which miR-9 affects Her6 negatively (directly) but also positively (indirectly) via repressing a repressor, X. Here, miR-9 directly reduces the rate of production of Her6 protein as well as the rate of production of an intermediate (unknown) species X. Similarly, the production of Her6 is repressed by X (Fig. S5A; parameter values, Table S14). Mathematically, we say that Her6 adapts perfectly to changes in miR-9 as, in this model, the steady state of Her6 is independent of miR-9 (Materials and Methods, Mathematical modelling, Steady state calculation of Her6 in the perfect adaptation model). The speed of this adaptation is controlled by the difference in reaction speed of the direct and indirect interactions between miR-9 and Her6. If the direct interaction is much faster than the indirect interaction, Her6 returns to steady state slowly after miR-9 copy numbers are perturbed. However, if the indirect interaction is faster, adaptation occurs quickly. Such ‘perfect adaptation’ is beneficial because it allows stable mean expression of Her6 in the presence of fluctuations of miR-9 expression (Fig. S5B,C). Conversely, no changes in miR-9, i.e. linear or stepwise, can lead to persistent downregulation of Her6. As Her6 is downregulated in response to increasing miR-9 levels during development, there would need to be an additional mechanism that enables the controlled escape from perfect adaptation. To investigate this, we extended our model to include such a potential mechanism (Fig. 6B). Specifically, we introduced a downstream target of Her6, named Y, which self-activates and interacts with Her6 through mutual repression [as we have already previously hypothesised in Soto et al. (2020)]. The different behaviours of this system can be seen in Fig. 6C,D. A linear increase in miR-9 leads to an initial repression of Her6, which then proceeds to return to its unperturbed steady state, due to the perfect adaptation (Fig. 6C; Fig. S6A). However, following a sharp increase of miR-9, the concentration of Her6 decreases more strongly. This is sufficient for Y to overcome the repression from Her6, so that it can self-activate and in turn repress Her6 into a new, lower steady state (Fig. 6D; Fig. S6B). Hence, this extended motif can indeed overcome the built-in adaptation. Importantly, the escape from adaptation is triggered by a step-like change in miR-9 expression and cannot be achieved through gradual changes in miR-9 expression, or small-scale fluctuations. The qualitative behaviour of the model is not sensitive to different values of the parameter p1, which regulates the strength of repression of Her6 by Y. The precise choice of p1 simply modulates the level of the lower state of Her6 expression (Fig. 6D versus Fig. S6B). The prediction from the mathematical model is that a stepwise increase of miR-9 is needed for Her6 protein to transition to a new gene expression state. We have previously shown that, during development, Her6 expression undergoes a transition from noisy to oscillatory to downregulation (Soto et al., 2020). Therefore, we performed her6 smiFISH in transverse sections of the hindbrain to quantify the percentage of her6-positive cells in a wild-type and in a pri-mir-9-1 homozygous mutant (pri-mir-9-1−/−) stable fish line (Fig. S7A-C). In the pri-mir-9-1 mutants, at 48 hpf her6 was not downregulated in medial progenitors (Fig. 7A,B) nor in ventral progenitors where the expression is normally lower (Soto et al., 2020). The lack of her6 downregulation was confirmed with WM-ISH (Fig. S7D) and with live imaging of homozygous Her6::Venus knock-in zebrafish (Soto et al., 2020), which were also heterozygous for the pri-mir-9-1 mutation (Fig. S7E, pri-mir-9-1+/−). Therefore, our findings suggest that the late activation of pri-mir-9-1 contributes to the increase of miR-9 needed to downregulate Her6/Her9 in late neural progenitors so that they can give rise to a spatiotemporally appropriate neuronal fate. miR-9 is expressed from several genomic loci which, after transcription and processing, produce the same 5′ mature form of miR-9 that targets the key neural progenitor transcription factors, Her/Hes. How common is this multi-locus organisation? In humans, only 6.3% of mature miR arms are identical across two or more loci (Kozomara et al., 2019): it is thus not very common, but it is not unique to miR-9. In zebrafish this number rises to around 32.3% (Kozomara et al., 2019). The higher number of miR expressed from multiple loci is possibly due to the teleost-specific WGD. Evidence from rainbow trout also shows that, following the salmonid-specific extra round of WGD, miRs appear to be retained at higher levels than protein-coding genes (Berthelot et al., 2014). This may suggest that extra copies of miR are evolutionarily advantageous. Here, we propose that retention of multiple miR loci could have specific functional advantages for regulatory control of target gene expression of an organism. By examining in detail the temporal and spatial expression at a single cell level of three selected early and late pri-mir-9s, from across their phylogenetic tree, we offer two possible, not-mutually exclusive, explanations for this multi-site organisation or primary transcripts. The first explanation involves a qualitative mechanism. In this scenario, distinct pri-mir-9s have a different spatial expression, which allows them to target different, i.e., region-specific, gene expression. Some differences in the spatial expression of pri-mir-9s are easily discernible at low resolution (e.g. differential expression in the forebrain), whereas others are subtle and require post-hybridisation sectioning to document, as we have done here. An example of the latter is the expression of pri-mir-9-1 which extends more dorsally in the hindbrain than pri-mir-9-4 at a late stage of development. This correlates well with the expression of Her6 and Her9, which are both miR-9 targets but are expressed adjacent to each other along the D-V axis (Soto et al., 2020). The second explanation favours a quantitative mechanism. In this scenario, the differential temporal expression, where some primary transcripts commence their expression early while others are only expressed late, results in the simultaneous expression of both (or more) transcriptional loci in the same cells at a particular time in development. In support of this scenario, we have shown using smiFISH that pri-mir-9-1, a late onset primary transcript, is co-expressed in the same cells as the earlier onset pri-mir-9-4 or -9-5. This co-expression may be a strategy to increase the amount of miR-9 available to the cell to a level more than that possible with transcription from one locus alone. Why would an increase in mature miR-9 over time be needed? One possibility is raised by the recent work from Amin et al. (2021), who demonstrated that miR-dependent phenotypes emerge at particular dose ranges because of hidden regulatory inflection points of their underlying gene networks. This indicates that the miR cellular dose is a major determinant of in vivo neuronal mRNA target selection. A complementary scenario is supported by our previous work where we have shown that the dynamical profile of Hes1 (i.e. oscillatory expression to stable expression of different levels), as well as the amount of time that Hes1 oscillates for, depends on the amount of miR-9 in the cell (Goodfellow et al., 2014; Phillips et al., 2016; Bonev et al., 2012). More recently, we have also shown, using in vivo manipulations, that the input of miR-9 changes the dynamic expression of Her6 from noisy to oscillatory and then to decreasing (Soto et al., 2020). Taken together, these findings suggest that variations in the dose level of a single miR achieved by additive transcription can exert regulatory effects either by targeting different downstream gene products or by modifying the dynamic expression of the same targets. Both scenarios are compatible with experimental results, whereby mutating the late-onset pri-mir-9-1 preferentially reduced the appearance of markers for neurons that differentiate late. This suggests that the late miR-9 increase is important for late cell fate choices. This is further supported by our previous work reporting a complete repression of neurogenesis along the D-V axis of the hindbrain (Bonev et al., 2011) when total miR-9 is knocked out, whereas in this article pri-mir-9-1 knockout (KO) has more specific effect. In other cases where multiple paralogues of an miR have been described, differential and non-mutually exclusive qualitative and quantitative regulation may also take place. For example, a recent study found that miR-196 paralogues show both unique and overlapping expression in the mouse (Wong et al., 2015). In this study, single KOs showed some unique phenotypes (qualitative mechanisms) but combinatorial KOs showed better penetrance and additional defects, suggesting an additive role of miR-196 paralogues in establishing vertebral number (quantitative mechanism). A salient finding from our analysis is that the increase in the amount of miR-9 present in the cell is sharp, as one would perhaps expect by the onset of transcription from additional loci. An exciting possibility, supported by our mathematical modelling, is the existence of gene network motifs that do not respond to slow increases of miR-9 because they are designed to show adaptation, that is, to have steady output in spite of external perturbations. Such network motifs often involve IFFLs, which in turn are very common in biological systems because of their multiple advantages, including fold-change detection and robustness of output (Goentoro et al., 2009; Khammash, 2021). However, in development, cells also need to transition from one state to another in order to diversify cell fates, which is essential for the development of multicellular organisms. Thus, despite the usefulness of adaptation for robustness and homeostasis (Khammash, 2021), a mechanism must exist to be able to over-ride it. We suggest that, in the case of miR-9, a sharp, non-linear increase may be needed to push a dynamical system into a new state and this may be associated with a cell fate change. In our case, we suggest that the increase of miR-9 during development serves to drive the dynamics of Her6 (and other targets) from one state to another, which may include temporal downregulation, and which in turn is important for the sequential acquisition of cell fates. At present, our computational model is qualitative, rather than quantitative, and the identity of some interacting genes in the network motif are not known. For example, we postulate the existence of a gene X that lies between miR-9 and Her6. Interestingly, a preliminary bioinformatic screen using transcription factor binding profiles from JASPAR (Castro-Mondragon et al., 2022) and miR target predictions for miR-9 from TargetScanFish (Release8.0) (McGeary et al., 2019) has identified Onecut, among others, as a potential candidate for factor X, which is a predicted regulator of Her6 and a direct target of miR-9. This is encouraging because Onecut is expressed in the zebrafish hindbrain, is a validated miR-9 target (Madelaine et al., 2017; Bonev et al., 2011) and a temporal factor for mammalian neurogenesis (Sagner et al., 2021). Despite these limitations, this model was conceptually useful to illustrate the existence of a system that can decode and distinguish between specific upstream signalling profiles. Interestingly, miRs are very commonly involved in transcription factor network motifs, including IFFLs (Tsang et al., 2007). However, the regulation of each pri-mir-9 is presently unknown, but miRs are often involved in reciprocal interactions with transcription factors (Minchington et al., 2020). A fully parameterized model based on experimental evidence and identification of the unknown components/genes would be needed before it can be tested further. In conclusion, by providing evidence for both a quantitative and qualitative mechanism, we have shed light on the possible roles of organising pri-mir-9s in several distinct genomic loci, which may have led to their evolutionary conservation. An added benefit of our work is that the detailed characterisation we have described here will enable the selection of the correct genomic locus for genetic manipulation of miR-9 production, depending on the precise spatio-temporal expression. It would be interesting to see whether the same mechanism is observed in mammalian species that have three distinct primary miR-9s. Animal experiments were performed under UK Home Office project licences (PFDA14F2D) within the conditions of the Animal (Scientific Procedures) Act 1986. Animals were only handled by personal licence holders. miRs and total mRNA were extracted from a pool of ten zebrafish hindbrains using the miRVana miRNA Isolation kit and gDNA removed using DNase1 (New England Biolabs). Reverse transcription was performed with either TaqMan MicroRNA Reverse Transcription kit (Applied Biosystems) for mature miR-9 or SuperScript III (Invitrogen) with random hexamers for pri-mirs. Each qPCR reaction was prepared in triplicate in a 96-well plate with the relevant TaqMan MicroRNA assay or using POWER SYBR Green Mastermix (Thermo Fisher Scientific), 0.2 μM each forward and reverse primer (see Table S3 for respective primers) and 50 ng cDNA. Reactions were run on Step One Plus Real-time PCR System (Applied Biosystems) alongside negative controls. The data for each sample were normalized to the expression level of U6 snRNA for mature miR-9 or b-actin for pri-mir-9s and analysed by the 2−ΔΔCt method. For each primer pair, the PCR product was examined by gel electrophoresis and its melting curve to ensure a single fragment of the predicted molecular weight. RNA probes for pri-mir-9-1, pri-mir-9-2, pri-mir-9-4, pri-mir-9-5 and pri-mir-9-7 were PCR amplified and cloned into pCRII vector using primers described in Table S1. Except for pri-mir-9-2 probe, they were designed to distinguish the primary transcripts by including sequences, intron and exon, before and after each miR processing, while also covering the sequence corresponding to mature miR-9 (Fig. S2). As the mature miR-9 sequence is conserved between paralogues, to avoid any cross-binding of probes to this sequence we mutated it on each probe using QuikChange II XL Site-Directed Mutagenesis assay. This allowed us introduce deletions and single nucleotide exchange in specific regions of the mature miR-9 sequence (Table S2; Fig. S2, sequence highlighted in red). pri-mir-9-3 and pri-mir-9-6 probes were generated from plasmids kindly gifted by Laure Bally-Cuif (Nepal et al., 2016). Chromogenic in situ hybridisation was performed as previously described (Thisse and Thisse, 2008) using specific probes for each pri-mir-9 (described in the Materials and Methods, Molecular cloning) and her6 (previously used in Soto et al., 2020). The antibody used to detect the riboprobes was AP-anti-DIG (Roche, 11093274910, 1:1000). Multicolour fluorescence in situ hybridisation was modified from Lea et al. (2012) by developing with tyramide amplification (Perkin Elmer) after addition of antisense RNA probes and antibodies conjugated to horseradish peroxidase, anti-DIG-POD (Roche, 11207733910, 1:1000) and anti-FITC-POD (Roche, 11426346910, 1:500) (Lea et al., 2012). Transverse sections were obtained as described in Dubaissi et al. (2012) with modifications. Embryos were embedded in 25% fish gelatine and 30% sucrose for a minimum of 24 h. We collected 18 µm thickness hindbrain sections and transferred them onto superfrost glass slides. The slides were air dried overnight under the fume hood and mounted with Prolong Diamond Antifade. Chromogenic in situs were imaged using a Leica M165FC with a DFC7000T camera. Fluorescent in situ sections were imaged using Leica TCS SP5 upright confocal with HCX PL APO LU-V-I 20×0.5 water UV lens or Olympus FLUOVIEW FV1000 confocal with UPLSAPO 20× NA:0.75 lens. The smiFISH probes were designed using the probe design tool at http://www.biosearchtech.com/stellarisdesigner/. The software can assign varied size of probes, 18-22 nt, therefore we gave a size of 20 nt for all designed probes with the maximum masking level available for zebrafish. Using the respective pri-mir-9 sequence we designed 36 probes for pri-mir-9-1, 35 probes for pri-mir-9-4 and 35 probes for pri-mir-9-5 (Tables S5-S7, respectively). Using the respective gene mature mRNA sequence, we designed 29 probes for her6, 33 probes for her9, 40 probes for neurog1, 39 probes for atoh1a and 40 probes for ascl1a (Tables S8-S12, respectively). The designed probes were X-FLAP tagged (5′-CCTCCTAAGTTTCGAGCTGGACTCAGTG-3′) at the 5′ of each gene-specific sequence. The gene-specific probes were ordered from Integrated DNA Technologies (IDT) in a 96-well format in nuclease-free water, 100 µM concentration. Upon arrival, we combined 100 µl of the gene-specific probes together, mixed, split into 100 µl aliquots and stored at −20°C. In addition, we ordered fluo-FLAP sequences (5′-CACTGAGTCCAGCTCGAAACTTAGGAGG-3′) from either IDT or Biosearch Technology. These were labelled with either Atto-550, CalFluor-610 or AlexaFluor-647. Each gene-specific probe mix was labelled by mixing 2 µl of the gene-specific X-FLAP probe mix (100 µM), 2.5 µl of fluo-FLAP (100 µM) and 5 µl of 10× NEBuffer 3 (New England Biolabs) in a final volume of 50 µl. The hybridisation cycle was 85°C for 3 min, 65°C for 3 min and 25°C for 3 min. The labelled probe was stored at −20°C. The whole-mount smiFISH protocol for zebrafish embryos was developed by adapting smiFISH protocol from Marra et al. (2019). Embryos were fixed in 4% formaldehyde in 1× PBS. After smiFISH protocol, embryos were stained with Phalloidin-Alexa Fluor 488 (400× dilution in PBS 1× Tween 0.1%) for 1 h at room temperature and followed by three washes with PBS-Tween. Embryos were embedded in 4% low melting point agarose (Sigma-Aldrich) to collect 250 µm thickness hindbrain transverse sections. smiFISH images were collected using a Leica TCS SP8 upright confocal with HC APO L U-V-I 63×/0.9 water lens, magnification 0.75×. We acquired three-dimensional stacks of 1024×1024 pixels and z-size 0.63 µm, magnification 0.75×, 16 bits per pixel, pinhole of 1 airy unit and scan speed of 200. Channels were sequentially imaged. smiFISH images were collected with frame accuracy 3 and line average 6. To quantify her6-positive cells from smiFISH images we acquired three-dimensional stacks of 1024×1024 pixels and z-stacks 43-51, covering a total of 27-32 μm, that is approximately the size of half to one rhombomere (voxel size x:0.229, y:0.229, z:0.63 µm). Deconvolution of confocal images was performed using Huygens Professional Software. As pre-processing steps, the images were adjusted for ‘microscopic parameters’ and ‘object stabilizer’ as additional restoration, the latter was used to adjust for any drift during imaging. Following this, we used the deconvolution Wizard tool, the two main factors to adjust during deconvolution were the background values and the signal-to-noise ratio. Background was manually measured for every image and channel, and the optimal signal-to-noise ratio identified for the images was value 3. After deconvolution the images were analysed using Imaris 9.5. Segmentation was performed using Phalloidin-AlexaFluor 488 as membrane marker. Using Imaris 9.5 software we selected the ‘Cells tool’ from which ‘Cells only’ was used as detection type and ‘Cell boundary’ was selected as cell detection type. Automated segmentation was performed, followed by manual curation to identify for cells incorrectly segmented. To quantify pri-mir-9 nascent transcriptional sites and her6 transcripts we used the ‘Spot tool’. The estimated spot diameter size was xy 1 µm and z 2 µm. We used the default parameters to identify the nascent transcriptional sites and further manual curation was performed to correct for minimal errors carried out by the software. Further on, spots were imported into the segmented cells to identify the cells that contained one, two or three pri-mir-9s. Following membrane segmentation and quantification of her6 transcripts, the percentage of her6-positive cells was calculated over the total number of cells segmented from the hindbrain transverse section (covering 27-32 μm). For the in silico analysis of the miR host gene expression we downloaded the time course RNA-seq data (TPM) from White et al. (2017). Here, we used the overlapping host genes as a proxy for the expression of the miR. miR would not show up in standard RNA-seq analysis and there is no current miR time course data. Host genes were identified as those with overlapping annotations with the miR-9 genes. The host genes for each pri-mir-9 are in Table S4. Pri-mir-9-7 has no overlapping annotation at this time and is thus not reported on in these data. We filtered the RNA-seq data removing genes which were neither the host genes of the miR or members of the Her family. Three repeats for each stage of development are included in the data and we averaged the expression across the three repeats for each stage. The stages reported in the data are based on standard embryonic stages in zebrafish development. However, we wanted to visualize the expression in terms of hours and the stages were converted accordingly. Finally, before plotting, these data were z-scored to normalize the expression of each of the genes so that we could compare changes in expression over time rather than absolute levels. These data were then plotted using the heatmap.3 package in R. For pre-mir-9-1 deletion using CRISPR/Cas9, sgRNA target sites were identified using the CRISPRdirect (http://crispr.dbcls.jp/) and Target Finder (Feng Zhang lab; http://crispr.mit.edu/). sgRNAs were generated following CRISPRscan protocol (Moreno-Mateos et al., 2015) using the oligonucleotides described in Table S13. Transcription of sgRNA was carried out using MEGAshortscript T7 kit (Ambion/Invitrogen) with 100-400 ng of purified DNA following the manufacturer's instructions. After transcription sgRNA was purified using MEGAclear™ Transcription Clean-Up Kit. The Cas9nls protein was obtained from New England Biolabs (M0646T). One-cell stage wild-type embryos were injected with ∼1 nl of a solution containing 185 ng/μl Cas9nls protein, 125 ng/μl sgRNA, 40 ng/μl caax-mRFP mRNA in 0.05% Phenol Red. To evaluate if each sgRNA was generating mutation, genomic DNA was extracted from 3-4 dpf embryos using 50 μl NP lysis buffer per embryo [10 mM Tris (pH 8), 1 mM EDTA, 80 mM KCl, 0.3% NP40 and 0.3% Tween] and 0.5 μg/μl Proteinase K (Roche) for 3-4 h at 55°C, 15 min at 95°C and then stored at 4°C. Then, High Resolution Melt (HRM) was performed (Fig. S4A) using the Melt Doc kit (Applied Biosystems) following the manufacturer's instructions. Specific primers were designed to generate an amplicon of 395 bp in wild-type conditions: forward primer 5′-ACAGTTGACTTTCTAATTACAACCC-3′ and reverse primer 5′-AGCAGGAGGAGATAATCACAGC-3′. To analyse the effect of pre-mir-9 deletion in F0 embryos we combined three different sgRNA flanking the region of the mature miR-9 region and they were microinjected as described above. We chose to use three sgRNAs to increase our probability of deleting the mature miR-9 sequence. The embryos were injected with 125 ng/µl of each sgRNA: this low concentration of sgRNA was used to not have overt phenotype at the macroscopic level during the experimental period (24hpf-72 hpf), minimizing the chances of non-specific toxicity. Further on, the amplicons with deletion were identified by agarose gel and sequencing (Fig. S4B,C), as described below. To identify F1 progeny with germ line transmission (GLT), 3-5 dpf embryos were fin clipped following the protocol described by Robert Wilkinson (Wilkinson et al., 2013) with modifications. Sylgard (Sigma-Aldrich, 761028)-coated 10 cm dishes were prepared for dissections. Embryos were placed into Sylgard-coated dishes containing L15 medium with 0.1% Tricaine (Sigma-Aldrich, UK) and 5% fetal bovine serum (Sigma-Aldrich). Once fin clipped, the embryo was rinsed in E3 medium and transferred to a fresh well; the biopsy was transferred to a PCR tube for genomic extraction. Genomic extraction was carried out in 10 μl volume using Phire Animal Tissue Direct PCR kit (Thermo Fisher Scientific, F-140WH). PCR reaction was carried out with 1 μl of the genomic extraction and primers used for HRM. An amplicon of 395 bp indicates a wild-type band and 275 bp indicates a pri-mir-9-1 mutant band. To evaluate the region deleted in the F1 pri-mir-9-1 mutants, PCR was performed per embryo, the amplicon obtained was cloned into pCRII and transformed into bacteria Top10. Three bacterial colonies were miniprepped and sequenced. The F1 adult animals were kept as Her6::Venus+/−;pri-mir-9-1+/− and were inbred to obtain and compare offspring such as Her6::Venus+/+;pri-mir-9-1+/− or −/− with Her6::Venus+/+;pri-mir-9-1+/+. To perform a comparative analysis of overall Her6 expression during hindbrain development on the mixed genotype population, a pool of ten embryos were laterally mounted in 1% low-melting agarose on glass-bottom dishes (MatTek Corporation P50G-1.5-14-F) and imaged using a Zeiss LSM 880 fast Airyscan microscope, followed by genotyping. Only pairs (wild type and mutant) that were found within the same pool were analysed, to allow comparison between similar developmental stages. Parameters used were ×1 zoom; image size x: 425 m, y: 425 mm, z: 150 mm. Images were subject to 2D maximum projection in FIJI. The perfect adaptation model can be described by a set of differential equations (Fig. S5A; Table S14):where h is Her6, m is miR-9 and αh, μh, αX and μX are positive real constants which represent the production and degradation rates of Her6 and X, respectively. The negative interaction between each of these model components is given by an arbitrary function G. To identify a possible shape of G, we consider the steady state of Eqns (1) and (2), which leads to:which combine to give:if G is defined by the negative interaction, G(m)=1/m. Hence, in our chosen model the steady state of Her6, h*, is independent of miR-9. To achieve this, we made the assumption that G is a nonlinear negative interaction, which agrees with previous models of miR-9 interactions (Goodfellow et al., 2014). In order to explore the adaptation properties of this network, we made certain simplifications over previous models (Goodfellow et al., 2014) such as omitting the her6 autorepression, transcriptional delays and noise. Thus, this simplified Her6 network does not reproduce the oscillatory expression of Her6 but instead explores the transition between different stable steady states. The extended system can be described by the following set of differential equations (Fig. 6A; Table S14): We pre-define the profile of miR-9 expression over time to interrogate both stepwise and linear expression, and then solve the system for h, X and Y. The parameters αh, αX and αY represent the basal production rates of h, X and Y, respectively. Similarly, μh, μX and μY represent the degradation rates of h, X and Y, respectively, and βY represents the production rate of Y under self-activation. The hi and pi are Hill coefficients and repression thresholds, respectively, for each of the Hill functions. For the activating Hill function with arbitrary input parameter p, Hill coefficient n and repression threshold p0, as p grows much larger than p0, tends to 1, and as p goes to 0, tends to 0. For the limits are reversed, i.e. is equal to 1 for small values of p and goes to 0 for p≫p0. The Hill coefficient n determines the sensitivity of the function to changes in p, i.e. larger n corresponds to higher sensitivity. All parameters introduced here are constants, and their values are listed in Table S14. These parameters are chosen such that Y is repressed and has no effect on the system when Her6 is at its high steady state h*. Statistical tests were performed in GraphPad Prism 9. Data were tested for normality with D'Agostino–Pearson test. Discrete scatter plots show median with 95% confidence interval where multiple independent experiments are analysed. Statistical significance between two datasets was tested with Mann–Whitney test (non-parametric). For paired experiments the data was tested for normality with Kolmogorov–Smirnov test followed by a one-tailed paired t-test. Sample sizes, experiment numbers and P-values <0.05 are reported in each figure legend. Click here for additional data file. 10.1242/develop.200474_sup1 Click here for additional data file.

PMC9641762

Ying Du,Gang Geng,Chunquan Zhao,Tian Gao,Bin Wei

LncRNA MEG3 promotes cisplatin sensitivity of cervical cancer cells by regulating the miR-21/PTEN axis

07-11-2022

Cervical cancer,MEG3,miR-21,PTEN,Cisplatin; chemosensitivity

Background Cervical cancer (CC) is a common gynecological malignancy worldwide. Some patients perform serious resistance after chemotherapy, and long-stranded non-coding RNA MEG3 is reported to be involved in the regulation of chemoresistance in many solid tumors. However, its involvement in cervical adenocarcinoma has not been reported. Methods Hela cell lines, cisplatin-resistant cell lines (Hela-CR) and nude mice were used in this study. After MEG3 was overexpressed or knocked down in cells by the lentivirus vector, cell growth was detected by the CCK-8 assay, and cell migration was evaluated using Transwell assay. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to examine the expression of MEG3, miR-21 and PTEN mRNA. Apoptosis was detected by flow cytometry. The targeting relationship between mRNAs was predicted and verified using dual-luciferase reporter gene experiments. Western blot was executed to examine Bax, cleaved-caspase 3, Bcl-2, PTEN and GAPDH expression. Cells were injected into the mice to form xenograft tumors to compare tumorigenesis capacity. Results We demonstrated that MEG3 was down-regulated in cervical cancer by analyzing the TCGA database. Moreover, knockdown of MEG3 promoted CC cell proliferation, migration and inhibited the apoptosis. These changes of CC cells were more pronounced under cisplatin treatment. Further studies showed that the MEG3/miR-21/PTEN axis affected cisplatin sensitivity in cervical cancer cells, and these results of recue assay were used to confirm this conclusion. Conclusions MEG3 performing as ceRNA promotes cisplatin sensitivity in CC cells through the miR-21/PTEN axis. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-022-10188-0.

LncRNA MEG3 promotes cisplatin sensitivity of cervical cancer cells by regulating the miR-21/PTEN axis Cervical cancer (CC) is a common gynecological malignancy worldwide. Some patients perform serious resistance after chemotherapy, and long-stranded non-coding RNA MEG3 is reported to be involved in the regulation of chemoresistance in many solid tumors. However, its involvement in cervical adenocarcinoma has not been reported. Hela cell lines, cisplatin-resistant cell lines (Hela-CR) and nude mice were used in this study. After MEG3 was overexpressed or knocked down in cells by the lentivirus vector, cell growth was detected by the CCK-8 assay, and cell migration was evaluated using Transwell assay. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to examine the expression of MEG3, miR-21 and PTEN mRNA. Apoptosis was detected by flow cytometry. The targeting relationship between mRNAs was predicted and verified using dual-luciferase reporter gene experiments. Western blot was executed to examine Bax, cleaved-caspase 3, Bcl-2, PTEN and GAPDH expression. Cells were injected into the mice to form xenograft tumors to compare tumorigenesis capacity. We demonstrated that MEG3 was down-regulated in cervical cancer by analyzing the TCGA database. Moreover, knockdown of MEG3 promoted CC cell proliferation, migration and inhibited the apoptosis. These changes of CC cells were more pronounced under cisplatin treatment. Further studies showed that the MEG3/miR-21/PTEN axis affected cisplatin sensitivity in cervical cancer cells, and these results of recue assay were used to confirm this conclusion. MEG3 performing as ceRNA promotes cisplatin sensitivity in CC cells through the miR-21/PTEN axis. The online version contains supplementary material available at 10.1186/s12885-022-10188-0. Cervical cancer (CC) is one of the common gynaecological cancers and its incidence rate is the third highest among female cancers worldwide [1]. In China, the incidence of cervical cancer is about 96,000 new cases in 2015, accounting for about 18% of new cases of cervical cancer in the world, including about 26,000 deaths [2]. CC has become a great threat to women’s health. Cervical adenocarcinoma accounts for about 20–25% of the different pathological types, and its incidence is increasing year by year, and the incidence age of CC patients gradually gets younger [3]. Chemotherapy is one of the most important treatments for patients with large localized lesions or systemic metastases [4]. Chemotherapy can improve patients’ prognosis, reduce localized lesions, facilitate subsequent treatment, and inhibit distant metastases [5]. And paclitaxel in combination with cisplatin is the first-line chemotherapy regimen for cervical adenocarcinoma, however, some patients have poor outcome due to chemoresistance, mainly cisplatin resistance, leading to dismal prognosis [6]. Therefore, it is important to investigate the mechanism of cisplatin resistance in cervical adenocarcinoma and improve cisplatin sensitivity in the treatment of cervical adenocarcinoma. Numerous studies have confirmed that long non-coding RNA (lncRNA) is involved in chemoresistance in a variety of malignancies and plays a key regulatory role [7–10]. The human maternally expressed gene 3 (MEG3) is localized in the MEG3 imprinted region of 14q32.3 delta-like 1 homologue (DLK1) and contains multiple imprinted genes that are widely distributed in brain, adrenal, placenta, ovary, spleen, breast. MEG3 is significantly downregulated in primary human cancers such as lung cancer, liver cancer, gallbladder cancer, pituitary tumors and different cancer cell lines, and its expression level is significantly related with tumor grade, metastasis and poor prognosis [11]. Furthermore, the molecular mechanism investigation revealed that reduced expression of MEG3 resulted in increased drug resistance in a variety of malignancies [12, 13]. Exogenous overexpression of MEG3 was reported to lead to tumor cells to regain drug sensitivity, suggesting that MEG3 may regulate the biological activity of tumor cells by modulating their drug sensitivity [14]. However, whether Lnc RNA MEG3 is involved in chemoresistance in cervical adenocarcinoma has not been reported. In this study, we investigated the effect and mechanism of MEG3 on cisplatin sensitivity in CC cells to provide a theoretical basis for using MEG3 as a target for CC therapy. The CC cell line Hela was purchased from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China). HeLa cell were cultured in increasing concentrations of cisplatin for over 6 months to establish cisplatin‐resistant (CR) cell lines (Hela-CR). Cells were cultured in DMEM with 10% fetal bovine serum, 100 U/ml penicillin and 100 U/ml streptomycin at 37 °C in a 5% CO2 incubator. The lentivirus vector containing MEG3 knockdown or MEG3 overexpression lentivirus or the corresponding negative control were purchased from Shanghai Gene Pro Technology Co. Lentiviral transfection was performed strictly according to the instructions. Briefly, 3 × 103 cells were inoculated in 6-well culture plates and after the degree of cell fusion reached 50%, the virus and 5 µg/ml Polybrene were added to the complete medium, the plates were put back into the cell incubator and the culture was continued for 24 h and then replaced with fresh medium and the culture was continued. Cell lines with stable transfection were obtained. mimic NC (B04001, GenePharma, China), miR-21 mimic (B01001, GenePharma, China), inhibitor NC (B04003, GenePharma, China), miR-21 inhibitor (B03001, GenePharma, China), siNC (A06001, GenePharma, China), siPTEN (A01004, GenePharma, China), pcDNA3.1 empty vector and pcDNA3.1-PTEN overexpression plasmid were purchased from Shanghai GenePharma Technology Co. The targeted sequence of PTEN siRNA is 5’-AACAGTAGAGGAGCCGTCAAA-3’. Lipofectamine 3000 transfection reagent (Invitrogen) was used for all necessary transfections and transfections were performed strictly according to the manufacturer's instructions. The CCK-8 assay was used to determine cell activity. Cells were inoculated at a concentration of 3 × 103 cells per well in a 96-well plate and incubated overnight with different concentrations of cisplatin for 2 days. The optical density was measured at 450 nm using a microplate reader. The IC50 of HeLa and Hela-CR cells were calculated by GraphPad software according to CCK-8 results. Total RNA was extracted using RNAiso Plus (9108, Takara, Japan). mRNA was reverse transcribed into complementary cDNA using PrimeScript RT reagent Kit (RR047A, Takara, Japan). LncRNA was reverse transcribed into complementary cDNA using LnRcute lncRNA cDNA Strand Synthesis Kit (KR202, TIANGEN, China). miRNA was reverse transcribed into complementary cDNA using miRNA 1st Strand cDNA Synthesis Kit (MR201, Vazyme, China). Before reverse transcription, the whole genome DNA was removed according to the kit instructions. The relative expression of MEG3, miR-21 and PTEN were detected by TB Green® Premix Ex Taq™ II (RR820Q, Takara, Japan) to detect the level of MEG3, miR-21 and PTEN mRNAs. GAPDH or U6 was used as an internal control. Relative expression levels were calculated using the 2−ΔΔCt method. Primers are shown in Table 1. The cells were inoculated at a density of 500 cells/dish into 6-well plates and cultured in medium containing 10% fetal bovine serum, which was renewed every 3 days. Cells were cultured at 37 °C in a 5% CO2 incubator for approximately 2 weeks. The medium was washed off with PBS, fixed in 4% paraformaldehyde for 15 min, stained with 0.1% crystal violet for 30 min, photographed and counted using Image J software. Apoptosis was detected by flow cytometry. The treated cells were collected from each group and used to detect apoptosis according to the instructions of PE Annexin V Apoptosis Detection Kit (BD Pharmingen, USA). Apoptosis was also detected using a flow cytometer (EPICS, XL-4, Beckman, USA). Total cell protein was extracted by RIPA Lysis Buffer (P0013B, Beyotime, China). Protein concentrations were measured by BCA Protein Assay Kit (P0010, Beyotime, China). Protein extracts (30 μg) were loaded and separated by SDS–polyacrylamide gel and transferred to PVDF membranes. According to the molecular weight of the target protein, the PVDF membrane was cut along the marker and incubated with primary antibodies. Primary antibodies specific against Bax (1:2000, ab32503, Abcam, USA), cleaved-caspase 3 (1:500, ab32042, Abcam, USA), Bcl-2 (1:1000, ab32124, Abcam, USA), PTEN (1:1000, ab267787, Abcam, USA) and GAPDH (1:10,000, ab8245, Abcam, USA) were used to incubate membranes overnight at 4 °C. HRP-conjugated Goat Anti-Rabbit (1:10,000, ab6721, Abcam, USA) or HRP-conjugated Goat Anti-Mouse (1:10,000, ab6789, Abcam, USA) secondary antibodies were used for 1 h at room temperature. The signal was visualized using BeyoECL Plus (P0018S, Beyotime, China) and a gel imaging system, and Image J was used to calculate the gray values of the images. The cell migration was detected by transwell assay. 2 × 104 number of cells were transferred to the top chamber of a noncoated membrane chamber in DMEM medium containing 5% fetal calf serum. DMEM containing 20% fetal calf serum was added to the lower chamber to function as a chemoattractant. After incubation for 24 h, nonmigrating cells were removed from the upper well, and the remaining migration cells were fixed with 4% paraformaldehyde and stained with crystal violet. The results were observed and photographed using a light microscope. Use ENCORI (https://rna.sysu.edu.cn/encori/index.php) target prediction tool to prediction potential binding sites between miR-21 and PTEN or MEG3. A wild-type (MEG3 3' UTR-WT, PTEN 3' UTR-WT) and mutant (MEG3 3' UTR-MUT, PTEN 3' UTR-MUT) luciferase reporter gene plasmid containing the binding site were purchased from GenePharma (Shanghai, China). After cell plating, cell density was reached to approximately 50% and the WT or MUT luciferase plasmids and renilla luciferase plasmid were co-transfected with miR-21 mimic or miR NC in Hela-CR cells. 24 h after transfection, Luciferase activity was measured using the Dual-Glo Luciferase Assay System (E2920, Promega, USA) and GloMax® 20/20 Luminometer (E5311, Promega, USA) to detect firefly and renilla luciferase activity. Six-week-old, female, nude mice were purchased from Beijing Biocytogen Co., Ltd (Beijing, China). The procedures for the handling and care of the mice were approved by the Animal Experimentation Ethics Committee of China Medical University (IACUC. No.2019388). Hela cells with knocked down MEG3 and control and overexpressed MEG3 Hela-CR cells were prepared, 1 × 107 cells of each group in Matrigel (BD Biosciences, USA) were injected into the right flanks of mice to form xenograft tumors. As the tumor volumes reached about 100 mm3, mice were daily injected intraperitoneally with cisplatin (25 mg/kg) for 7 days. Meanwhile, 10 nmol [15] antagomiR-21, antagomiR-NC, agomiR-21 or agomiR-NC were injected intratumorally in a multisite injection manner every 3 days for 2 weeks. The average volume of the tumor was measured three times every 3 days. Tumor volumes were calculated using the formula: (Long x Wide2)/2. Paraffin-embedded tumor tissue was cut into 4 μm thick sections and dewaxed in dimethylbenzene, hydrated in gradient alcohol, washed in distilled water, then immersed in hematoxylin for 15 min, washed to remove excess stain, fractionated in 1% ethanol hydrochloride for 10 s and washed in running water for 15 min before the tissue was stained with 0.5% eosin for 5 min. Xylene was clear and gradient alcohol was dehydrated. Neutral gum was used to seal the slices, which were observed under light microscopy and photographed. After euthanasia of mice, tumors were excised from these mice, fixed in 4% paraformaldehyde, and embedded in paraffin. Tissue specimens were incubated with antibodies against PTEN and a biotin-conjugated secondary antibody and then incubated with an avidin–biotin–peroxidase complex. Visualization was performed using amino-ethyl carbazole chromogen. Slides were analyzed using the Olympus BX43 microscope system (Olympus, Japan). Results were expressed as mean ± standard deviation (SD) of three independent experiments unless otherwise specified. Data were analyzed using Graph Prism 8.2 software (GraphPad Prism, USA). Student's t-test were used to analyze differences between groups. One-way analysis of variance (ANOVA) with Tukey's multiple comparison post hoc test were used to compare in more than two groups, and p < 0.05 was considered as significant difference. MEG3 expression levels in cervical cancer samples from the TCGA database were analyzed by GEPIA (http://gepia2.cancer-pku.cn). Obviously, MEG3 was downregulated in cervical cancer tissues compare with normal cervical tissues. In addition, compared with normal cervical tissues, the expression of MEG3 in EMT, Hormone, and PI3K-AKT subtypes of cervical cancer tissues was significantly lower (Fig. 1A). To investigate the effect of MEG3 on cisplatin resistance in cervical cancer cells, we established Hela-CR, which IC50 values were significantly higher (Fig. 1B) and the MEG3 levels were significantly lower than Hela cells (Fig. 1C). Next, Hela cells were transfected with lentivirus containing MEG3 knockdown to establish a Hela cell line with stable knockdown of MEG3 (Fig. 1D), and conversely, Hela-CR cells were transfected with MEG3 overexpression lentivirus to establish a Hela-CR cell line with stable high expression of MEG3 (Fig. 1E). As expected, knockdown of MEG3 increased the IC50 value of Hela cells (Fig. 1F) and overexpression of MEG3 decreased the IC50 value of Hela-CR cells (Fig. 1G). The above findings suggest that MEG3 is a potential biological marker of cisplatin resistance in cervical cancer. In view of the potential oncogenic effect of MEG3, we examined the biological functions of Hela cells treated with 1 μg/ml of cisplatin and Hela-CR cells treated with 3 μg/ml of cisplatin under untreated or treated conditions. The results showed that cisplatin treatment inhibited the proliferation, migration ability and decreased the apoptosis rate of Hela cells, and knockdown of MEG3 reversed the effect of cisplatin (Fig. 2A, 2C, 2E and 2G). Overexpression of MEG3 inhibited the proliferation, migration ability and increased the apoptosis rate of Hela-CR cells, and the effect was more pronounced after cisplatin treatment (Fig. 2B, 2D, 2F and 2H). In summary, we reported that MEG3 could promote the sensitivity of CC cells to cisplatin. In a variety of cancers, lncRNAs act as ceRNA sponging miRNAs, increasing the expression of potential target mRNAs. We found that the miR-21 expression in Hela-CR cells was significantly higher than that in Hela cells (Fig. 3A), and overexpression of MEG3 decreased the miR-21 expression in Hela-CR cells (Fig. 3B), and conversely, knockdown of MEG3 increased miR-21 expression in Hela cells (Fig. 3C). The potential binding sites between MEG3 and miR-21 were predicted by bioinformatics analysis (Fig. 3D) and verified by dual luciferase reporter assay (Fig. 3E). The next test was whether miR-21 affected MEG3 expression. We found that transfection of miR-21 mimic decreased MEG3 expression in Hela-CR cells (Fig. 3F) and conversely, transfection of miR-21 inhibitor increased MEG3 expression in Hela cells (Fig. 3G). In conclusion, MEG3 acts as a sponge of miR-21. It has been reported that miR-21 can reduce cisplatin sensitivity in cervical cancer cells by targeting PTEN [16]. We hypothesized that the miR-21/PTEN axis could interact with MEG3 through a ceRNA pattern. Potential binding sites between miR-21 and PTEN were predicted by bioinformatic analysis (Fig. 4A) and verified by dual luciferase reporter assay (Fig. 4B). Next, the results showed that the expression levels of PTEN mRNA (Fig. 4C) and protein (Fig. 4D) were significantly lower in Hela-CR cells than in Hela cells. Overexpression of MEG3 increased PTEN mRNA (Fig. 4E) and protein (Fig. 4F) expression levels in Hela-CR cells, while transfection with miR-21 mimic reversed the effect of MEG3 overexpression. Conversely, knockdown of MEG3 decreased PTEN mRNA (Fig. 4G) and protein (Fig. 4H) expression levels in Hela cells, while transfection with miR-21 inhibitor reversed the effect of knockdown of MEG3. These results suggest that MEG3 positively regulates PTEN expression by sponging miR-21. Further evidence of the function of the MEG3/miR-21/PTEN ceRNA pathway was demonstrated. Firstly, the miR-21 inhibitor reduced the IC50 values of Hela cells with knockdown of MEG3, while silencing PTEN reversed the effect of miR-21 inhibitor (Fig. 5A). Conversely, miR-21 mimic increased the IC50 values of Hela-CR cells overexpressing MEG3, while overexpression of PTEN reversed the effect of miR-21 mimic (Fig. 5B). Subsequently, the miR-21 inhibitor increased the apoptosis rate of Hela cells with knockdown of MEG3, while silencing PTEN reversed the effect of miR-21 inhibitor (Fig. 5C, E). Conversely, miR-21 mimic decreased the apoptosis rate of Hela-CR cells overexpressing MEG3, while overexpression of PTEN reversed the effect of miR-21 mimic (Fig. 5D, F). The above results indicated that MEG3 promoted the sensitivity of cervical cancer cells to cisplatin through the MEG3/miR-21/PTEN axis. To investigate the role of MEG3/miR-21/PTEN axis in cisplatin resistance in CC in vivo, the xenograft tumor model of nude mice was established. After cisplatin treatment, the tumor volume, weight and cell necrosis of Hela cells containing knockdown of MEG3 were significantly increased compared to Hela cell, and the administration of antagomiR-21 reversed the effect of knockdown of MEG3 (Fig. 7A-D). Conversely, the tumor volume, weight and cell necrosis of Hela-CR cells overexpressing MEG3 was significantly reduced after cisplatin treatment compared to those Hela-CR cells without MEG3 overexpressed, and the effect of MEG3 overexpression was reversed by the administration of agomiR-21 (Fig. 7A-D). Furthermore, the results of TUNEL assay showed that the level of apoptosis in mice with Hela cells knockdown MEG3 was reduced after cisplatin treatment, and the administration of antagomiR-21 reversed the effect of knockdown MEG3 (Fig. 6E). Conversely, apoptosis was significantly increased in Hela-CR cells overexpressing MEG3 after cisplatin treatment, and administration of agomiR-21 reversed the effect of overexpression of MEG3 (Fig. 7E). The results also showed that knockdown of MEG3 decreased PTEN expression, and administration of antagomiR-21 increased PTEN expression in Hela cell xenograft (Fig. 6F-G). Conversely, overexpression of MEG3 increased PTEN expression, and administration of agomiR-21 decreased the PTEN expression in Hela-CR cell xenograft (Fig. 7F-G). The above results confirmed that MEG3 promotes the sensitivity of CC cells to cisplatin through the MEG3/miR-21/PTEN axis in vivo. As an important gynecological malignancy, cervical cancer has a high mortality rate and a poor prognosis. Although various chemotherapeutic agents have been developed for the treatment of cervical cancer, resistance to these agents is an important factor in the poor prognosis of patients with cervical cancer. Cisplatin is a common compound and chemotherapy with paclitaxel in combination with cisplatin is the first-line chemotherapy regimen for cervical adenocarcinoma. However, as the tumour progresses, the cancer cells become less sensitive to cisplatin and thus develop resistance to cisplatin. Therefore, there is an urgent need to investigate the specific molecular mechanisms that promote cisplatin resistance. Both inhibition of apoptosis and promotion of cell proliferation have been suggested as possible mechanisms of cisplatin resistance in cancer cells. Many evidence suggests that lncRNAs affect cisplatin resistance in cancer cells by inhibiting apoptosis and promoting cell proliferation [17–19]. In this study, we analyzed MEG3 expression levels in cervical cancer from the TCGA database by using the GEPIA visual online analysis tool, and the results showed that MEG3 expression levels were significantly lower in cervical cancer than in normal tissues. Moreover, MEG3 expression was significantly lower in cervical adenocarcinoma cisplatin-resistant cell lines. Functional analysis experiments indicated that MEG3 promoted the sensitivity of cervical adenocarcinoma cells and drug-resistant cells to cisplatin. LncRNAs have been widely reported to sponge with miRNAs and thus promote cisplatin resistance in various cancers, for example, lncRNA FGD5-AS1 enhances cisplatin resistance in lung adenocarcinoma by suppressing miR-142 expression [18]. miR-21, a microRNA, is expressed at high levels in a variety of solid tumors and affects biological functions such as adhesion, metastasis, and invasion of tumor cells [20]. miR-21 has been found to play a key regulatory role in drug resistance in a variety of tumors, for example, inhibition of miR-21 in hepatocellular carcinoma cells resulted in reduced cell growth, metastasis, invasiveness, and drug resistance [21, 22]. In contrast, transfection of normal tumor cells with miR-21 resulted in a substantial increase in cell metastasis accompanied by increased drug resistance [23]. The above reports suggest that miR-21 plays an important regulatory role in tumor chemoresistance. Several studies have shown that miR-21 can modulate the expression of PTEN by binding to its 3'UTR, thereby regulating its tumor resistance. For example, miR-21 reduced the sensitivity of K562 cell line to adriamycin by inhibiting PTEN expression [24]. The anti-tumor effect of curcumin analogue CDF in gemcitabine-resistant pancreatic cancer cells was mainly achieved by down-regulating miR-21 and thus up-regulating PTEN expression [25]. In this study, we verified through a dual luciferase reporter gene assay the binding of MEG3, PTEN and miR-21 and the expression of MEG3 and PTEN were negatively regulated by miR-21. Furthermore, the different assays showed that MEG3 promoted cisplatin sensitivity in CC cells by sponging miR-21 and increasing PTEN expression. This result was confirmed in vivo using xenograft tumor model of nude mice. In conclusion, this study shows that MEG3 enhances cisplatin sensitivity in cervical cancer cells by regulating the miR-21/PTEN axis, and that MEG3 acts as a ceRNA of the miR-21/PTEN axis to exert oncogenic effects and influence cisplatin resistance in cervical cancer. This study may contribute to reduce chemoresistance in cervical cancer and providing new therapeutic targets for cervical cancer. Additional file 1.

PMC9641828

Wenyi Shen,Juan Pu,Shanye Gu,Jing Sun,Lili Wang,Bin Tan,Jianmeng Chen,Yangsong Zuo

LINC01635, a long non-coding RNA with a cancer/testis expression pattern, promotes lung cancer progression by sponging miR-455-5p

21-10-2022

long non-coding RNA,cancer/testis long non-coding RNAs,long intergenic non-protein coding RNA 1635,microRNA-455-5p,non-small cell lung carcinoma

Long non-coding RNAs (lncRNAs) have been reported to play vital roles in human lung cancer. In recent years, cancer/testis (CT) lncRNAs have been characterized as a novel class of lncRNA. However, this class of lncRNA remains to be thoroughly investigated. The present study identified long intergenic non-protein coding RNA 1635 (LINC01635), which was highly expressed in testis and in a broad range of human cancer types. Next, it was confirmed that LINC01635 was upregulated significantly in samples from patients with lung cancer and in non-small cell lung carcinoma (NSCLC) cell lines. Silencing LINC01635 suppressed the proliferation and metastasis of NSCLC cells in vitro and in vivo. Furthermore, it was found that LINC01635 could bind to microRNA (miRNA or miR)-455-5p and regulate the expression of a series of miR-455-5p-targeting tumor-related genes. Knockdown of miR-455-5p partially rescued the progression of lung cancer cells that was suppressed by LINC01635 silencing. Together, the current results demonstrated that LINC01635 may play important roles in NSCLC progression by targeting miR-455-5p, and that it could be a biomarker and therapeutic target for lung cancer.

LINC01635, a long non-coding RNA with a cancer/testis expression pattern, promotes lung cancer progression by sponging miR-455-5p Long non-coding RNAs (lncRNAs) have been reported to play vital roles in human lung cancer. In recent years, cancer/testis (CT) lncRNAs have been characterized as a novel class of lncRNA. However, this class of lncRNA remains to be thoroughly investigated. The present study identified long intergenic non-protein coding RNA 1635 (LINC01635), which was highly expressed in testis and in a broad range of human cancer types. Next, it was confirmed that LINC01635 was upregulated significantly in samples from patients with lung cancer and in non-small cell lung carcinoma (NSCLC) cell lines. Silencing LINC01635 suppressed the proliferation and metastasis of NSCLC cells in vitro and in vivo. Furthermore, it was found that LINC01635 could bind to microRNA (miRNA or miR)-455-5p and regulate the expression of a series of miR-455-5p-targeting tumor-related genes. Knockdown of miR-455-5p partially rescued the progression of lung cancer cells that was suppressed by LINC01635 silencing. Together, the current results demonstrated that LINC01635 may play important roles in NSCLC progression by targeting miR-455-5p, and that it could be a biomarker and therapeutic target for lung cancer. Lung cancer is one of the most malignant tumors and is the leading cause of cancer-associated mortality worldwide (1,2). Among patients with lung cancer, non-small cell lung carcinoma (NSCLC) accounts for ~85% of all cases (3,4). Despite the development of novel therapies in the past few decades, the 5-year survival rate (4–17% depending on stage and regional differences) of patients with NSCLC remains markedly low (5); this is due to the difficulty in early diagnosis, the lack of targeted therapies, drug resistance and frequent relapses. Therefore, identifying new biomarkers for early diagnosis and new therapies is essential for the clinical treatment of NSCLC. Bioinformatics analysis of the human genome showed that <2% of the genome sequence corresponds to protein-coding genes, whereas >70% is transcribed into non-coding RNAs (ncRNAs) (6). Long ncRNAs (lncRNAs) are an important type of ncRNAs, with a length of >200 nucleotides. lncRNAs have been reported to play important roles in various tumor types, such as lung, colorectal and breast cancer, through multiple mechanisms (7,8). By comparative analysis of microarray and next-generation sequencing data of NSCLC and normal tissues, thousands of lncRNAs were identified to be differentially expressed in NSCLC samples (9–12). Although a few of studies have shown that several lncRNAs could promote or suppress the progression of NSCLC (13), identifying novel oncogenic lncRNAs remains critical for NSCLC diagnosis and treatment. Cancer/testis (CT) antigens are the protein products of genes frequently expressed in multiple human cancer types and in the normal testis (14,15). To date, only a few lncRNAs have been reported to be CT genes (16–19). The present study identified long intergenic non-protein coding RNA 1635 (LINC01635) as a potential novel CT lncRNA. The expression of LINC01635 in lung cancer was investigated, and it was found that LINC01635 was highly expressed in samples from patients with lung cancer and in NSCLC cell lines. Functional studies showed that LINC01635 regulated the proliferation and metastasis of NSCLC cells in vitro and in vivo. Furthermore, it was also found that LINC01635 could bind to microRNA (miRNA/miR)-455-5p in vitro and that it regulated the expression of miR-455-5p-targeting tumor-related genes in NSCLC cells, which demonstrated its functional mechanism in lung cancer. Microarray dataset GSE113852 (20) was downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/), and LINC01635 expression profiling in normal tissue was analyzed according to the data of the RNA-sequencing (RNA-seq) of normal tissues in the Human Protein Atlas (https://www.ncbi.nlm.nih.gov/). The coding potential of LINC01635 was assessed using the CPAT and CNCI online tools (https://lncar.renlab.org/), and the expression level of LINC01635 was also confirmed by analyzing another microarray dataset (GSE101929) (21) using online tools (https://lncar.renlab.org/) (22). LINC01635 expression profiling in different tumors and normal tissues was analyzed online by Gene Expression Profiling Interactive Analysis using RNA-seq data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases (http://gepia.cancer-pku.cn/) (23). A total of 14 paired human NSCLC and adjacent normal lung tissues were obtained from patients who underwent surgery or biopsy at Lianshui County People's Hospital, Kangda College of Nanjing Medical University (Lianshui, China) (mean age, 69 years; range, 50–80 years) between December 2018 and December 2019. All patients were diagnosed with NSCLC according to histopathological evaluation. No radiotherapy or chemotherapy was performed prior to sample collection. Tissue samples were immediately stored in RNAlater solution (Invitrogen; Thermo Fisher Scientific, Inc.) at −80°C. Written informed consents was obtained from all the enrolled patients with NSCLC. The study protocol was approved by the Research Ethics Committee of Lianshui County People's Hospital, Kangda College of Nanjing Medical University (approval no. 2021602-2). The human NSCLC A549, H1299, H1975 and PC9 cell lines, and the human bronchial epithelial HBE135-E6E7 cell line (HBE135) were obtained from the Institute of Biochemistry and Cell Biology of Chinese Academy of Sciences. A549, H1975 and HBE135 cells were cultured in RPMI-1640 medium (Gibco; Thermo Fisher Scientific, Inc.), while H1299 and PC9 cells were cultured in DMEM (Gibco; Thermo Fisher Scientific, Inc.). Both media were supplemented with 10% fetal bovine serum (FBS; Gibco; Thermo Fisher Scientific, Inc.), 100 U/ml penicillin and 100 mg/ml streptomycin. All cell lines were maintained in a humidified air atmosphere with 5% CO2 at 37°C. Total RNA was extracted from patient tissues and cultured cells using TRIzol® reagent (Ambion; Thermo Fisher Scientific, Inc.). A total of 1 µg of RNA was reversed transcribed to cDNA with the PrimeScript RT Reagent Kit (Takara Biotechnology Co., Ltd.) by using 6-mer random primers or a miR-455-5p specific primer (5′-GTTGGCTCTGGTGCAGGGTCCGAGGTATTCGCACCAGAGCCAACCGATGT-3′) based on the stem-loop primer method (24). The reaction conditions were as follows: 42°C for 2 min, 25°C for 5 min, 42°C for 30 min and 85°C for 5 min. qPCR was then performed using SYBR-Green Master Mix (Takara Biotechnology Co., Ltd.) in a StepOnePlus™ Real-Time PCR System (Applied Biosystems; Thermo Fisher Scientific, Inc.) according to the manufacturer's instructions. The thermocycling conditions for qPCR were as follows: 95°C for 5 min for 1 cycle, and 95°C for 10 sec and 60°C for 30 sec for 40 cycles. The endogenous control was GAPDH or U6 (for miR-455-5p), and the expression levels were analyzed by the 2−∆∆Cq method (25). The specific primers were designed with Primer-Blast (National Center for Biotechnology; National Institutes of Health), and the primer sequences are listed in Table I. All primers were synthesized by General Biosystems, Inc. Primers were designed according to the prediction of LINC01635 transcripts in the Ensembl website (https://www.ensembl.org/) and the human mRNA clone of LINC01635 (GenBank ID: BC039533), and LINC01635 was cloned using the cDNA of A549 cell lines by ExTaq (Takara Biotechnology Co., Ltd.) in a Mastercycler® Nexus X2 (Eppendorf). The following thermocycling conditions were used: 94°C for 5 min for 1 cycle, and 94°C for 30 sec, 60°C for 30 sec and 72°C for 2 min for 35 cycles. The primer sequences were as follows: 5′-TTACCGTGCGGAGTTTTGGA-3′ (forward) and 5′-TTATGTGCCTATGAAATTGGAGTTG-3′ (reverse). LINC01635 small interfering (si)RNA (si-LINC01635), negative control (NC) siRNA, miR-455-5p inhibitor and NC inhibitor were purchased from General Biosystems and used for transiently downregulating the expression of LINC01635 or miR-455-5p. The sequences of the synthesized siRNAs and miRNA inhibitors were as follows: 5′-GGAGUUUUGGAUACAUUCU-3′ (si-LINC01635), 5′-UUCUCCGAACGUGUCACGU-3′ (NC siRNA), 5′-UAUGUGCCUUUGGACUACAUCG-3′ (miR-455-5p inhibitor) and 5′-UCUACUCUUUCUAGGAGGUUGUGA-3′ (NC inhibitor). miR-455-5p mimic and NC miRNA mimic were also purchased from General Biosystems and used for transiently upregulating the expression of miR-455-5p. The sequences of the synthesized miRNA mimics were as follows: 5′-UAUGUGCCUUUGGACUACAUCG-3′ (miR-455-5p mimic) and 5′-UCACAACCUCCUAGAAAGAGUAGA-3′ (NC mimic). A549 and H1975 cells (1×105) were seeded in 6-well plates and cultured at 37°C overnight. Next, the cells were transfected with siRNAs (50 µM), miRNA mimics (50 µM) or miRNA inhibitors (100 µM) for 24 h at 37°C using Lipofectamine® 2000 reagent (Invitrogen; Thermo Fisher Scientific, Inc.) according to the manufacturer's instructions. At 24 h post-transfection, the knockdown efficiency was detected by RT-qPCR. A cell proliferation assay was performed using Cell Counting Kit-8 (CCK-8; Dojindo Laboratories, Inc.). A549 and H1975 cells transfected with siRNA-LINC01635 and NC siRNA were seeded at 2×103 cells/well in 96-well plates. Next, 10 µl CCK-8 reagent was added to each well, which contained 100 µl culture medium. After 2 h, cell proliferation was monitored by measuring the optical density at 450 nm on a microplate reader (BioTek Elx800; BioTek Instruments, Inc.) every 24 h from 0 to 96 h according to the manufacturer's instructions. Cell migration was analyzed by Transwell assay. A549 and H1975 cells were transfected with si-LINC01635, NC siRNA, miR-455-5p inhibitor, NC inhibitor or siRNA + inhibitor, and then re-suspended with 200 µl serum-free medium to a total of 2–5×104 A549 cells or 5×104 H1975 cells. Transwell chambers (8-µm pore size) were placed into 24-well plates, and 800 µl medium containing 10% FBS was added to the lower chamber. After 24 h at 37°C, the upper chambers were fixed with methanol for 30 min at room temperature, and then stained with 0.1% crystal violet for 20 min at room temperature. Next, the upper surface of the membrane was removed with cotton swabs. The Transwell inserts were imaged under an optical inverted microscope. Total protein from transfected lung cancer cells was extracted with RIPA lysis buffer (Beyotime Institute of Biotechnology). The protein concentration was measured with a BCA kit (Beyotime Institute of Biotechnology). The protein samples (50 µg per lane) were separated on 10% gels using SDS-PAGE and then were electro-transferred onto polyvinylidene fluoride membranes (MilliporeSigma), which were blocked with 5% skimmed milk for 1 h at room temperature. Subsequently, the membranes were incubated with primary antibodies against MMP2 (1:1,000; cat. no. A00286), MMP9 (1:1,000; cat. no. PB0709) or GAPDH (internal control; 1:10,000; cat. no. A00227) (all Boster Biological Technology) at 4°C overnight. Next, the membranes were incubated with HRP-conjugated AffiniPure mouse anti-rabbit IgG (H+L) (1:5,000; cat. no. BM2006; Boster Biological Technology) for 1 h at room temperature. After washing for 5 times (each for 5 min) using PBST (0.05% Tween-20) at room temperature, the proteins were visualized with the BeyoECL plus kit (Beyotime Institute of Biotechnology). Zebrafish were maintained in a fish culture system (Haisheng Instruments, Inc.) at 28°C under a light-dark cycle of 10–14 h. Approximately 100 2-days post-fertilization (dpf) transgenic Tg(fli1a:EGFP) zebrafish larvae (China Zebrafish Resource Center) were used for cell injection, and the endothelial cells of these transgenic zebrafish larvae were labeled with EGFP (26). At 24 h post-transfection of si-LINC01635 or NC siRNA (control) in cultured cells, including A549 and H1975 cell lines, the cells were collected and stained for 5 min at 37°C and 15 min at 4°C with a fluorescent dye (CM-DiI; Thermo Fisher Scientific, Inc.) for injection. The stained cells were examined under a fluorescence microscope before injection. A total of 300–400 cells labeled with CM-DiI were transplanted into the perivitelline space (PVS) of 48-h-post-fertilization zebrafish larvae under a pressure systems for ejection (Picosprizer III; Parker Hannifin Corporation). At 1 day post-injection (dpi), the injected larvae with similar tumor size of CM-DiI-positive cells were selected and cultured at 34°C until the end of experiments. At 4 dpi, the intact zebrafish larvae were mounted using 1.2% low-melting gel, and the yolk and trunk were imaged via a stereomicroscope (MVX10; Olympus Corporation) or a confocal microscope (FLUOVIEW FV3000; Olympus Corporation) using a 20X water-immersion objective directly. The spatial resolution of the images was 1,600×1,200 pixels for the MVX10 or 1,024×1,024 pixels for the FLUOVIEW FV3000. After the imaging experiments, zebrafish larvae were anesthetized with alcohol and then sacrificed by hypothermia (−20°C). miRNAs that could potentially bind to LINC01635 were predicted by the online tools miRDB (http://mirdb.org/) and LncBase (http://www.microrna.gr/LncBase). The miRNAs that were predicted by both tools were considered as candidates. miRNA-targeting genes were predicted by the online tools miRDB (http://mirdb.org/) and TargetScan (https://www.targetscan.org/). The tumor-related targeted genes of interest were screened out individually by scientific literature search using the key words ‘tumor’ and the name of each miRNA in PubMed (https://pubmed.ncbi.nlm.nih.gov/). A total of 1×107 A549 cells were collected, and their cytoplasmic and nuclear RNA were extracted respectively with the PARIS™ Kit (Invitrogen; Thermo Fisher Scientific, Inc.) according to the manufacturer's instructions. Next, the cytoplasmic and nuclear RNA were reverse-transcribed, respectively. Then, the expression levels of cytoplasmic and nuclear RNA were detected by RT-qPCR, which represented the localization of RNA. U6 was used for the positive control of nuclear RNA, and GAPDH was used for the positive control of cytoplasmic RNA. The total sequence of LINC01635 transcript 1# was cloned into a pmirGLO Dual-Luciferase miRNA Target Expression Vector (E1330; Promega Corporation) to generate the reporter plasmid. To construct a LINC01635 mutant reporter plasmid, the putative binding site of miR-455-5p in LINC01635 was mutated by PCR. The plasmids and miR-455-5p mimic or NC mimic were co-transfected into 2×104 293T cells (Saihongrui Biotechnology Co., Ltd.) cultured in DMEM with 10% FBS) at 37°C for 48 h using Lipofectamine® 2000 reagent (Invitrogen; Thermo Fisher Scientific, Inc.) according to the manufacturer's instructions. The luciferase activity in co-transfected cells was detected using the Dual-Luciferase Reporter Assay System (Promega Corporation) and GloMax® Explorer Multimode Microplate Reader (Promega Corporation). Firefly luciferase activity was used as the main reporter activity, and Renilla luciferase activity was used as the control for normalization. Data are presented as the mean ± SEM from at least three repetitions. Unpaired Student's t-test was used to perform statistical analysis of two unpaired groups, while paired Student's t-test was used to perform statistical analysis of two paired groups (Microsoft Excel 2010; Microsoft Corporation). In addition, one-way ANOVA was used to perform statistical analysis of multiple groups (GraphPad Prism 8; GraphPad Software, Inc.). P<0.05 was considered to indicate a statistically significant difference. To screen for differentially expressed lncRNAs in lung cancer, the gene expression profile of lung adenocarcinoma and normal lung tissue was obtained from GEO dataset GSE113852, which contained the expression data of 27 lung tumor samples and 27 normal lung samples. With Log2FC>2 as the screen threshold, 168 genes were highly expressed in lung adenocarcinoma compared with those in normal lung tissues (Fig. 1A). The expression of these genes in physiological conditions was assessed by analyzing the HPA RNA-seq normal tissue database, and only LINC01635 (ENSG00000228397; Log2FC=2.36; P=8.58×10−8) was found to be highly expressed in the testis (Fig. 1B). LINC01635 was located on chromosome 1p36.12, upstream of human cell division cycle 42 (CDC42) in the human genome, but its transcriptional direction is opposite to that of CDC42. The lncRNA property of LINC01635 was confirmed by evaluating its coding potential (CPAT and CNCI tools) and predicting the secondary structure (Fig. 1B). Furthermore, a high LINC01635 expression level was also confirmed in lung cancer samples by analyzing another GEO dataset using lnCAR online tools (GSE101929; Fig. 1D). In addition, the expression level of LINC01635 was analyzed in multiple cancer types compared with corresponding normal tissues (TCGA and GTEx data), and LINC01635 was highly expressed in multiple cancer types and testis tissues (Fig. 1E). To analyze the common high expression pattern of LINC01635 in different human cancer types, its expression level in all cancer types was compared with that in all normal tissues excluding testis. LINC01635 was found to be significantly upregulated in cancer (Fig. 1E). To validate the data from the bioinformatics analysis, the expression of LINC01635 was firstly checked in samples from human patients with NSCLC (Table II). All patients were diagnosed with LAD NSCLC according to histopathological evaluation. No radiotherapy or chemotherapy was performed before the surgery or puncture. The ΔCq values (CqLINC01636-CqGAPDH) in the lung cancer tissues were lower than those in the normal lung tissues, which showed that the expression level of LINC01635 was significantly higher in the 14 NSCLC tissues than in the corresponding normal lung tissues (P<0.05; Fig. 2A). Next, the expression level of LINC01635 was examined in four human lung cancer cell lines (A549, H1299, H1975 and PC9), and LINC01635 was found to be overexpressed in the A549, H1299 and H1975 cell lines by 3.1- to 3.9-fold compared with that in human bronchial epithelial HBE135 cell line, but was reduced to 21% in the PC9 cell line compared with that in the HBE135 cell line (Fig. 2B). Next, according to the prediction of full-length LINC01635 transcript sequences in the Ensembl website, primers were designed to clone LINC01635 in the A549 cell line and two bands of PCR products were found (Fig. 2C). Next, the PCR products were cloned, and the sequencing results showed that LINC01635 in the A549 cell line had three transcripts (Figs. 2D and S1). These results suggested that LINC01635 could be a CT-lncRNA. To investigate the function of LINC01635 in NSCLC cell lines, LINC01635 was knocked down in A549 and H1975 cell lines by transfection with siRNA, which targeted the common sequence of the three transcripts. After 24 h of transfection, the knockdown efficiency of si-LINC01635 was 70.7% in the A549 cell line and 53.5% in the H1975 cell line, compared with transfection with NC siRNA (Fig. 3A and B). It is worth noting that the designed siRNA also targeted LINC00339, which was partially overlapped but transcriptionally opposite to LINC01635. The expression of LINC00339 was examined in the lung cell lines, as well as the effect of the siRNA on LINC00339. LINC00339 was also highly expressed in the A549 and H1975 cell lines, but the designed siRNA could not efficiently downregulate the expression of LINC00339 in either cell line (Fig. S2). Next, the role of LINC01635 in the proliferation of NSCLC cells was examined using CCK-8 assays. The results indicated that knockdown of LINC01635 decreased the proliferation in the A549 after 72 h post-transfection and in H1975 cell lines after 48 h post-transfection (Fig. 3C and D). Furthermore, Transwell assays were performed and found that knockdown of LINC01635 also significantly suppressed cell migration in both the A549 and H1975 cell lines (Fig. 3E and F). The expression levels of MMP2 and MMP9, migration-related markers, were also assessed and were shown to be downregulated both at the transcriptional and translational levels when LINC01635 was knocked down (Fig. 3G-I). These results demonstrated that LINC01635 plays important roles in the proliferation and migration of NSCLC cell lines. To verify whether LINC01635 could regulate the progression of lung cancer in vivo, zebrafish xenograft models were used to examine proliferation and metastasis simultaneously. The PVS of zebrafish larvae were implanted with A549 or H1975 cells that were transfected with si-LINC01635 and labeled by CM-DiI. At 1 dpi, the zebrafish larvae with a similar tumor size at the PVS and no CM-DiI signal at other sites were selected according to the CM-DiI-positive area (Fig. S3), and then cultured for further analysis. At 4 dpi, the yolk and trunk of the injected zebrafish larvae were imaged to assess the cell proliferation and metastasis, respectively (27,28). Compared with that for NC siRNA transfection, the CM-DiI-positive region was significantly smaller both in the yolk and trunk when LINC01635 was silenced in A549 cells (Fig. 4A-F). Similar results were also obtained in zebrafish xenograft using H1975 cells (Fig. 4G-L). These results demonstrated that LINC01635 regulates the proliferation and metastasis of lung cancer in vivo. To explore the functional mechanism of LINC01635, the subcellular location of LINC01635 was first studied and found to be both in the nucleus and cytoplasm (Fig. 5A). As LINC01635 is located both in the nucleus and cytoplasm of lung cancer cells, it might regulate the downstream genes at the transcriptional and/or post-transcriptional levels. To examine whether LINC01635 could function at the post-transcriptional level by binding miRNAs, its miRNA binging sites were predicted by miRDB (http://mirdb.org/) (29) and LncBase (30), with 9 microRNA binding sites predicted by both softwares in total. Among these miRNAs, miR-455-5p could bind with all of the transcripts of LINC01635 with highest potential (rank 1; Fig. 5B) (31). To confirm the binding possibility between LINC01635 and miR-455-5p, dual-luciferase reporter plasmids were constructed that contained the wild-type or mutant binding site of LINC01635 (Fig. 5C). The dual-luciferase assay revealed that overexpression of miR-455-5p reduced the luciferase activity of the reporter plasmid containing the LINC01635 sequence, but not that of the reporter plasmid containing the LINC01635 mutant sequence (Fig. 5D). To confirm whether the LINC01635 could regulate a series of tumor-related genes through miR-455-5p, several miR-455-5p-targeting genes (CPEB1, TMED2, HDAC4, STK24, CDKN1B, UBE2V1, USP3, RAB18 and SOX11) that are involved in multiple cancer types were examined (32–40). The majority of these genes were downregulated when knocking down LINC01635 in lung cancer cells, and similar regulation was also observed when overexpressing miR-455-5p (Fig. 5E). These results imply that LINC01635 could regulate the expression of tumor-related genes by targeting miR-455-5p. To study the roles of miR-455-5p in lung cancer cells, the expression of miR-455-5p was first downregulated by transfection with the miR-455-5p inhibitor (Fig. 6A). After transfection, the results of CCK-8 and Transwell assays showed that miR-455-5p inhibition promoted the proliferation and migration of the A549 cells (Fig. 6B and C). To examine whether miR-455-5p mediates the roles of LINC01635 in the progression of lung cancer cells, si-LINC01635 and miR-455-5p inhibitor were cotransfected into A549 cells simultaneously. The knockdown of LINC01635 suppressed the growth and migration of lung cancer cells, but miR-455-5p inhibitor transfection partially counteracted the suppressive effects of LINC01635 knockdown both in terms of the proliferation and migration of the lung cancer cells (Fig. 6D and E). These results indicate that LINC01635 could promote the progression of lung cancer cells via the miR-455-5p-mediated pathway. An increasing number of studies have reported that lncRNAs play important roles in the development and progression of NSCLC through different signal pathways (13,41–43). The present study found that LINC01635 was upregulated in lung cancer tissues and cell lines. Functional experiments showed that LINC01635 promoted the proliferation and metastasis of NSCLC cells in vitro and in vivo. Moreover, LINC01635 was able to bind miR-455-5p and regulated the expression of a series of miR-455-5p-targeting tumor-related genes. These findings suggest that LINC01635 could regulate NSCLC progression though binding with miR-455-5p. CT genes are a diverse group of genes that are restrictively expressed in the testis under normal conditions, but they are also expressed in ~40% of different types of cancer (44). Similar to tumors, the testes exhibit abundant cell division, migration and immortalization (45), and lots of testicular genes are considered as promising cancer biomarkers and treatment targets (14). Moreover, the similarities in cellular processes between gametogenesis and tumorigenesis also provide valuable insights into understanding the mechanism of tumorigenesis. CT-lncRNA represents a new direction for the study of lncRNA mechanisms in tumor biology. For example, Hosono et al (16) demonstrated that THOR is a conserved CT-lncRNA and that it promotes the progression of lung cancer through interaction with IGF2BP1 (16). Tan et al (19) showed that CT-lncRNA PACT6 facilitates the malignant phenotype of ovarian cancer through binding with miR-143-3p (19). In the present study, by screening the GEO database of lung cancer tissues and the HPA RNA-seq normal tissue database, LINC01635 was revealed to be a novel CT-lncRNA that promotes lung cancer progression by binding with miR-455-5p. However, in the present study, mainly the basic cellular functions of LINC01635 in lung cancer cells were assessed through the examination of the migration and invasion biomarkers MMP2 and MMP9 in cultured cell lines, and the detailed molecular mechanism of LINC01635 shall be analyzed by examining different biomarkers in future studies. It has been reported that lncRNAs play important roles in proliferation, differentiation, metastasis, metabolism and apoptosis in cancer progression by regulating their target genes at the transcriptional, post-transcriptional and epigenetic levels (7,8). Different regulatory functions depend on the subcellular locations and interactions with specific molecules (46). In the nucleus, lncRNAs generally function by modulating transcriptional programs through chromatin interactions and remodeling by formatting the scaffolding complex (47,48). In the cytoplasm, lncRNAs function by regulating translational and/or post-transcriptional programs to affect gene expression levels. In addition, numerous lncRNAs have been identified as competing endogenous RNAs (ceRNAs) that can regulate the expression of target genes at the post-transcriptional level by competitive binding with miRNAs in the cytoplasm (49). The present study found LINC01635 located in both the nucleus and cytoplasm, which implies that LINC01635 may play regulatory roles at both the transcriptional and post-transcriptional levels. To examine whether LINC01635 could function as a ceRNA, the binding sites of miRNAs in LINC01635 were predicted by cross-comparison analysis between the miRDB and LncBase database, and miR-455-5p was screened out with the highest potential. Furthermore, the present data not only demonstrated that miR-455-5p could bind with LINC01635 directly in vitro, but also showed that miR-455-5p inhibition could partially rescue the suppression effects caused by LINC01635 knockdown, which implies that LINC01635/miR-455-5p may function as a ceRNA network. The present study also examined the expression of a series of miR-455-5p-targeting tumor-related genes following the knockdown of LINC01635 or the overexpression of miR-455-5p, and found that the changes in the expression of some genes was associated. These genes may regulate tumor progression through different pathways. CDKN1B can shift from cyclin-dependent kinase inhibitor to oncogene by regulating the cell cycle in a cyclin-dependent kinase-dependent or -independent manner (36,50). SOX11, a transcription factor, mainly regulates progenitor and stem cell behavior during embryogenesis, and it also expressed in a wide variety of cancer types, such as neck cancer, malignant glioma, ovarian cancer and breast cancer (40,51). RAB18 is a member of the Ras oncogene superfamily, which promotes cell invasion and inhibits cell apoptosis in various cancer types, such as hepatocellular carcinoma and gastric cancer (39,52). UBE2V1 is a member of the ubiquitin-conjugating E2 enzyme variant proteins, and it has been reported as an oncogene that acts via ubiquitination and degradation of SIRT1 (37). USP3 is a deubiquitinase that accelerates tumor proliferation and epithelial-to-mesenchymal transition (EMT) via deubiquitinating KLF5 (38). According to the association in expression between LINC01635 and these genes, our future studies shall focus on CPEB1, TMED2 and HDAC4, which might reveal the novel mechanism of LINC01635/miR-455-5p regulatory pathways. Zebrafish xenografts have been demonstrated as effective models for tumor research (25,26). Compared with mouse models, zebrafish xenografts have obvious advantages. First, the zebrafish xenograft model offers a fast in vivo evaluation method of tumor proliferation and metastasis by using same group of transplanted zebrafish larvae. When using mouse xenograft models, it requires two separate models for evaluating the proliferation and metastasis of the tumor cells, respectively. Second, with the help of transparent larvae, the zebrafish xenograft model supplies intuitive studies at the cellular level. The zebrafish xenograft model can be used to assess proliferation and metastasis in only 1 week by transiently transfecting siRNAs, instead of 2–4 weeks in mouse xenograft models, which have to construct stable cell lines using shRNA plasmids. Third, by combining different types of transgenic lines that label different cell types, zebrafish xenograft can be used for studying the tumor microenvironment in vivo, including angiogenesis and immune reactions. In mouse xenograft models, it usually requires additional staining steps for the quantification in vitro. The results of the zebrafish xenograft model experiments in the present study showed that the silencing of LINC01635 decreased the proliferation and metastasis of the NSCLC cells, which was consistent with the data from the cultured cells, suggesting that a zebrafish xenograft is a good alternative in vivo model for examining tumor biology. In summary, the present study demonstrated LINC01635 is a novel CT-lncRNA and that it promotes the proliferation and metastasis of lung cancer by regulating miR-455-5p-targeting tumor-related genes. These findings indicate that LINC01635 could be a potential biomarker and treatment target for lung cancer.

PMC9643795

Meysam Yazdani,Javad Zamani,Seyed Safa-Ali Fatemi

Identification of a potent dual-function inhibitor for hIMPDH isoforms by computer-aided drug discovery approaches 10.3389/fphar.2022.977568

26-10-2022

drug discovery,hIMPDH,immunosuppressant,virtual screening,transplant rejection

Inosine monophosphate dehydrogenase (IMPDH) is a key enzyme in de novo biosynthesis of purine nucleotides. Due to this important role, it is a great target to drug discovery for a wide range of activities, especially immunosuppressant in heart and kidney transplantation. Both human IMPDH isoforms are expressed in stimulated lymphocytes. In addition to the side effects of existing drugs, previous studies have mainly focused on the type II isoform. In this study, virtual screening and computer-aided approaches were employed to identify potential drugs with simultaneous inhibitory effects on both human IMPDH isoforms. After Re-docking, Double-step docking, and identification of virtual hits based on the PLANTS scoring function, drug-likeness and ADME-Tox assessments of the topmost ligands were performed. Following further evaluation, the best ligand was selected and, in complex with both isoforms, simulated in monomeric and tetrameric forms using molecular dynamics to evaluate its stability and binding pattern. The results showed a potential drug candidate [(S)-N-(3-hydroxy-1-(4-hydroxyphenyl) propyl)-2-(3-methyl-2,4-dioxo-3,4-dihydropyrimidin-1(2H)-yl) acetamide] with a high inhibitory effect on the two human IMPDH isoforms. This drug-like inhibitor could potentially serve as an immunosuppressant to prevent transplant rejection response by inhibiting B- and T-lymphocyte proliferation. In addition, its effect can be evaluated in various therapeutic targets in which IMPDH is known as a therapeutic target, especially in Covid-19 patients.

Identification of a potent dual-function inhibitor for hIMPDH isoforms by computer-aided drug discovery approaches 10.3389/fphar.2022.977568 Inosine monophosphate dehydrogenase (IMPDH) is a key enzyme in de novo biosynthesis of purine nucleotides. Due to this important role, it is a great target to drug discovery for a wide range of activities, especially immunosuppressant in heart and kidney transplantation. Both human IMPDH isoforms are expressed in stimulated lymphocytes. In addition to the side effects of existing drugs, previous studies have mainly focused on the type II isoform. In this study, virtual screening and computer-aided approaches were employed to identify potential drugs with simultaneous inhibitory effects on both human IMPDH isoforms. After Re-docking, Double-step docking, and identification of virtual hits based on the PLANTS scoring function, drug-likeness and ADME-Tox assessments of the topmost ligands were performed. Following further evaluation, the best ligand was selected and, in complex with both isoforms, simulated in monomeric and tetrameric forms using molecular dynamics to evaluate its stability and binding pattern. The results showed a potential drug candidate [(S)-N-(3-hydroxy-1-(4-hydroxyphenyl) propyl)-2-(3-methyl-2,4-dioxo-3,4-dihydropyrimidin-1(2H)-yl) acetamide] with a high inhibitory effect on the two human IMPDH isoforms. This drug-like inhibitor could potentially serve as an immunosuppressant to prevent transplant rejection response by inhibiting B- and T-lymphocyte proliferation. In addition, its effect can be evaluated in various therapeutic targets in which IMPDH is known as a therapeutic target, especially in Covid-19 patients. Inosine monophosphate dehydrogenase is one of the most important therapeutic targets in recent years and has been used in the discovery of antiviral (Dunham et al., 2018), antibacterial (Juvale et al., 2019), antiangiogenic (Naffouje et al., 2019), and immunosuppressive (Glander et al., 2021) drugs. IMPDH catalyzes inosine monophosphate to xanthosine monophosphate in the presence of nicotinamide adenine dinucleotide (NAD), and is the rate-determining enzyme in de novo guanine nucleotide biosynthesis (Hedstrom, 2009). Humans have two IMPDH isoforms: I and II (Natsumeda et al., 1990). These enzymes are expressed in different ratios in most tissues and cells. The type I isoform is highly expressed in peripheral blood mononuclear cells and expressed at low levels in the thymus. Whereas IMPDH type II is least expressed in the spleen and peripheral blood mononuclear cells (Jain et al., 2004). Both isoforms are significantly expressed in stimulated human lymphocytes (Dayton et al., 1994; Senda and Natsumeda, 1994). Each isoform will gain more importance based on its therapeutic applications. The two isoforms have approximately 84% sequence identity and 92% similarity in kinetic properties such as substrate affinities, catalytic activities, and Ki values, as well as contains 514 residues (Konno et al., 1991; Saunders and Raybuck, 2000). These enzymes are usually homotetrameric and are stable in this state. Each IMPDH monomer consists of two domains: the catalytic and cystathionine beta-synthase (CBS). The catalytic domain is a (β/α) 8 barrel and harbors an active-site loop located at the end of the β-sheet C-terminal. The most important amino acid in this loop is catalytic cysteine 331 (Cys331), which interacts along other amino acids with IMP, and among IMPDHs is highly conserved (Sintchak et al., 1996; Hedstrom, 2009; Cuny et al., 2017). The CBS subdomain, also known as Bateman domain, appears to play a role in the binding of IMPDH to DNA and suggested by mediating interactions have a function in translation regulation (McLEAN et al., 2004; Mortimer et al., 2008). CBS domains can bind to adenosine derivatives, regulate the activity of proteins and also act as internal inhibitors (Anashkin et al., 2017). IMPDH inhibitors based on their activities are divided into three groups. The first and second groups occupy the binding positions of IMP and NAD sites, respectively. Finally, the third group ligands binds to allosteric-site that is far from the IMP and NAD pockets (Shu and Nair, 2008). The most important IMPDH inhibitors are Mycophenolic acid (MPA), Mizoribine, Ribavirin (RBV) and Tiazofurin adenine dinucleotide (TAD). All of these drugs suppress the human immune system and exhibit a wide range of antiviral activities. For example, RBV approved for the treatment of infections caused by hepatitis C virus and TAD is the active metabolite of Tiazofurin that is an anticancer and it also has antiviral activity (Ishikawa, 1999; Herrmann et al., 2003; Pankiewicz et al., 2004; Leyssen et al., 2005). MPA is a potent immunosuppressive drug that inhibits the division and proliferation of B-and T-lymphocytes. This natural product has been approved by the FDA for the prevention of acute rejection of heart and kidney transplantation (Kobashigawa et al., 1998; Johnson et al., 1999). MPA is mostly an uncompetitive inhibitor of both IMP and NAD, and is sometimes considered a non-competitive inhibitor at low NAD concentrations (Allison and Eugui, 1996; Link and Straub, 1996; Gan et al., 2002). Despite the application of MPA, it is easily converted to MPA-7-O-glucuronide, which reduces its efficacy, and also its side effects have been reported (Franklin et al., 1995; Davies et al., 2007). In silico methods have been developed to the investigation and identification of novel drugs (Yazdani et al., 2021). Computational screening of chemical libraries to identify small molecules that bind to a target such as an enzyme or protein receptor, known as virtual screening (Shoichet, 2004; Rester, 2008). Molecular dynamics (MD) simulation methods can be applied at each stage of drug discovery and have a variety of applications (Durrant and McCammon, 2011). After screening, MD widely used to confirm and refine the docking solutions (Salsbury Jr, 2010; Lin, 2011). In addition to the constraints of existing inhibitors, most of inhibitor development plans have focused on one of the human IMPDH isoforms (Hedstrom, 2009). Considering the great similarity and identity between these two isoforms and their expression in stimulated lymphocytes, a docking-based virtual screening protocol was conducted to introduce a new dual-function ligand that inhibits both IMPDH isoforms. By the end of 2021, 18 X-ray crystallographic structures have been reported for the IMPDH isoform type II, whereas only one has been reported for type I. Three-dimensional (3D) structures of the IMPDH isoforms; 1NF7 (type II) and 1JCN (type I) at 2.65 and 2.50 Å resolution, were retrieved respectively from the RCSB Protein Data Bank (RCSB PDB) (Risal et al., 2003; Risel et al., 2004). Both structures have two protomers (chains A and B). Because the missing residues at two chains of each structure were the same and equal, chains A were selected as two structures representative. All additional cofactors and co-crystallized ligands in the structures were removed. Molegro Virtual Docker (MVD) version 6.0 includes four search algorithms and four scoring functions, that from their combination, various docking protocols it will be obtained (Thomsen and Christensen, 2006). Search algorithms are used to detect ligand orientations into the related conformational space (poses) and to assess and rate these poses to choose the best pose, the scoring function has been applied (Leach, 2001). In this research, two search algorithms, MolDock Optimizer and MolDock Simplex Evolution (MolDock SE), with two scoring functions, PLANTS score and PLANTS score Grid, were used. The accuracy of these protocols was evaluated by re-docking to enhance the success of the molecular docking procedure. To obtain the crystallized ligand position among the four created protocols and select the best protocol, re-docking was performed. For each docking protocol, 1,000 poses were generated, and the lowest score in each protocol was considered as the best pose. The best poses of docking simulation protocols with the co-crystallized ligand position were compared using root mean square deviation (RMSD). Finally, RMSD was calculated by UCSF Chimera (Pettersen et al., 2004), and the lowest RMSD was recognized as the best and most reliable protocol. All re-docking processes were performed using the X-ray crystallographic structure of type II human IMPDH in complex with the RVB ligand (1NF7). The ZINC 15 database (http://zinc15.docking.org) which is encompasses more than 120 million compounds, including drugs, natural products, metabolites, and annotated compounds was used to select ligands for virtual screening (Sterling and Irwin, 2015). The IMP ligand was used as a reference for the initial screening of the ligand library reconstruction. Initial screening was performed based on the partition coefficient (logP) and molecular weight (Mwt) of the IMP. Predefined subsets were set to drug-like, and the compounds were filtered according to molecular charge, pH range, and reactivity criteria. Finally, the selected ligands were downloaded in 3D conformations in the mol2 format for virtual high-throughput screening. After preparing the target proteins and screening library, the best docking protocol obtained from re-docking was implemented using MVD. In the first docking, the drug-like candidates were docked to the active site of the type II IMPDH crystallographic structure (1NF7). Subsequently, 10% of the best results based on PLANT score were selected for the next step. Next docking was carried out against active site of the crystal structure of type I IMPDH protein PDB 1JCN. After second docking, top twelve ranked ligands were determined and compared with IMP as the main substrate and MPA as an important inhibitor of IMPDH. The parameter settings for all dockings were set to the default MVD. The scoring function was set to an affinity grid resolution of 0.3 Å. Ten runs were performed for each ligand with a threshold energy of 100.0 kcal/mol for pose generation. After re- and double-step docking, ADME (Absorption, Distribution, Metabolism and Excretion) and, bioactivity computational prediction, and toxicity analysis were accomplished. The ADMET predictions is used to understand the pharmacokinetic profiles of the chemical compounds. ADME properties including blood-brain barrier, human intestinal absorption, plasma protein binding (PPB), aqueous solubility, intestinal epithelium cell line biological simulations, and toxicity prediction tests such as the Ames test, carcinogenicity, and rat acute toxicity (LD50) were tested by PreADMET and admetSAR servers. The drug-likeness properties were checked using DruLiTo software and SwissADME tool (Daina et al., 2017). Open Bable GUI tools software was used to obtain all the required formats from available mol2 format. MD was used to predict the sustainability and estimate the kinetics and thermodynamics of binding ligand-protein complexes obtained from double-step virtual screening. MD simulations was carried out using the GROMACS 4.6.5. GROMOS 54A7 was used to create proper topologies. The systems were placed at a distance of 2 nm from the cubic box to the protein surface and solvated using the TIP3P model of water. Na+ or Cl-ions were added to neutralize of the system. After solvation and neutralization, the selected docked complexes were subjected to energy minimization using the steepest descent algorithm in 5,000 steps for each simulation. Equilibration of the systems at a temperature of 300 K and pressure of 1 bar was carried out under the NVT and NPT ensembles. To compute the electrostatic interactions and constraints of the bond lengths, the PME method and LINCS algorithm were used, respectively (Zamani Amirzakaria et al., 2021). Eventually, MD runs were performed separately during 50 ns for two complexes in monomeric form. To validate the results, two complexes with tetrameric form were also subjected to a 500 ns large-scale MD simulation. In addition, the MM-PBSA method (Kumari et al., 2014) was used to evaluate the MD trajectory data in order to calculate the binding free energies of the ligand-receptor complexes. The IMP sites of the proteins were identified using MVD program (Figure 1). The main residues at this site were as follows: Ser68, Pro69, Met70, Asp71, Thr72, Val73, Thr74, Asp274, Ser276, Gln277, Asn303, Val304, Arg322, Val323, Gly326, Ser327 (2hIMPDH), Cys327 (1hIMPDH), Gly328, Ser329, Ile330, Cys331, Ile332, Thr333, Gln334, Glu335, Val336, Met337, Asp364, Gly365, Gly366, Ile367, Gln368, Met385, Met386, Gly387, Ser388, Leu389, Leu390, Tyr411, and Arg412, Met414, Gly415. These residues are relatively conserved among IMPDH enzymes of different species (Nair and Shu, 2007). Re-docking is a docking validation procedure that was used to determine which molecular docking algorithms can better predict the co-crystallized ligand position. Based on re-docking results, the best docking protocol was determined based on RMSD results (Figure 2). Comparing the position of the docked ligand with the four mentioned protocols against the co-crystallized ligand position, the MolDock SE search algorithm with the PLANTS SCORE scoring function protocol showed the lowest RMSD (Figure 2). Therefore, choosing this docking protocol appears to be more logical and reliable. A ligand screening library was constructed by applying certain parameters among millions of compounds. Initially, these ligands were docked to the type II hIMPDH isoform. The best results of the first docking stage were considered as the screening libraries for the second docking stage. This step was performed against the type I isoform of this enzyme and with the presence of the top ten percent of the first step docking results. The top ligands in terms of binding energy were determined based on the PLANTS scoring function during double-step docking (Table 1). These ligands have high affinity to both hIMPDH isoforms and can be potential inhibitors. Among these compounds, Zinc355749373 showed a higher affinity for both isoforms than the other ligands. The physicochemical characteristics of these ligands are given in Table 2. According to the initial screening for the construction of the ligand library from the Zinc database, all the ligands were subjected to Lipinski’s rule of five (Ro5). This rule examines five important physicochemical parameters of a compound to assess its pharmacological ability, which leads to filtration of low-absorption ligands (Lipinski et al., 1997). In accordance with Ro5, all the top 12 selected ligands in terms of binding energy had a molecular weight of less than 500 Da, hydrogen bond donors and acceptors were less than 5 and 10, respectively, and their logP did not exceed 5. The hydrogen bonds between various atoms of the top ligands and both isoforms are shown in Table 3. The length of the hydrogen bonds formed and the number of these bonds significantly affect the binding energies of the ligands. However, the importance of electrostatic and steric interactions between the ligand and the protein should not be overlooked. To choose a ligand as a drug, in addition to having a high affinity for the target, a series of regulations must also be considered. Some of these regulations, such as Lipinsky’s rules, were applied when the ligand library was construct. Filters and other rules such as drug-likeness, ADME and toxicity tests were also reviewed for the top ligands selected from docking. For drug-likeness, Ro5 was investigated as mentioned above, and no violations of this rule were observed for top ligands. Other rules such as BBB Lilkeness, CMC, and MDDR-like rule and filters including Veber (GSK) (Veber et al., 2002), Muegge (Bayer) (Muegge et al., 2001), Ghose (Amgen) (Ghose et al., 1999), and Egan (Pharmacia) (Egan et al., 2000) for top ligands were evaluated. None of the top ligands selected with these considerations showed more than one violation (Table 4), which could be a pleasant result for the selected ligands. To obtain parameters such as BBB, CaCo2, HIA, and CYP of the twelve top ligands, the ADME test was performed. This computational test predicts the absorption, distribution, metabolism, and excretion of compounds, which are very important for the final approval of potential ligands as drugs. To better understand these analyses, IMP as the main substrate and MPA as an approved drug were used as the controls. HIA indicates the intestinal absorption levels in humans. HIA’s high score is important for oral administration of the drug, and compounds with high scores can be easily absorbed by the gastrointestinal tract. Among the top ligands, Zinc573536990, with a full score, showed a high intestinal absorption potential. Most of the compounds with a high probability showed intestinal absorption (Table 5). Ligands with high BBB also indicate high absorption by the blood-brain barrier. This difference in BBB values was due to the different hydrophobicity of the ligands. Caco-2 cells are also a criterion for evaluating cellular interactions, absorption, or transfer from the intestinal epithelial barrier. It was predicted that not all top ligands would cross the Caco-2 cell line. The efflux prediction of pharmacological compounds is done through P-glycoprotein (P-gp) metabolism by the microsomal enzyme family that called cytochrome P450 (CYP450). CYPs are responsible for a large part of drug’s metabolism. It was found that 11 of the 12 top ligands, similar to controls could act as Noninhibitors and Nonsubstrate for CYP450 (Table 5). This means that these ligands cannot disrupt the biotransformation of drug compounds by CYP450 and are not metabolized by this enzyme. The toxicity of the top ligands was investigated using the following three parameters: AMES, carcinogenesis, and LD50 tests (Table 5). The Ames was used to determine mutagenic ligands. Results revealed that Zinc573536990 is mutagen only. MPA which is used as a control in the Ames test, also showed mutagenic activity. Carcinogenicity analysis did not show any carcinogenic ligands. The higher scores of LD50 for ligands compared to the IMP, revealed that all of them are suitable and non-lethal. RMSD profiles obtained from MD simulations over a period of 50 ns were analyzed to evaluate the stability of the ligand-receptor complexes. Figure 3 shows the deviation of the backbone of the initial structure during the simulation period of time. The RMSD values during the simulations of both complexes ranged approximately from 0.07 to 0.75 nm. The RMSD values of both complexes reached to 0.55 nm after 13 ns and no significant fluctuation was observed after that (Figure 3). Root mean square fluctuation (RMSF) plots were used to assess the flexibility and dynamism of the structures. The high peaks marked (Figures 4A,B) in both structures are residues that mainly located in the loop regions. These residues are far from the inhibitor binding site and do not interact with the inhibitor, and their flexibility is expected to not have a significant effect on the stability of the complexes. The areas with lower RMSF values shown in the diagrams, are residues that have a hydrogen bond with the inhibitor and reduced fluctuation. The hydrogen bonds of the best hit ligand with both isoforms during MD simulation are shown in Figure 5. As a result, RMSD and RMSF profiles validated the stability of the inhibitor-proteins complexes and the docking results. The large-scale MD simulations for tetrameric state which is the functional form of IMPDH isoforms, were performed to validate the monomeric state with more accurate results. The tetrameric forms of 1hIMPDH-Zinc355749373 and 2hIMPDH-Zinc355749373 complexes (Figure 6) were generated using the best docking protocol identified in the re-docking. The RMSD values of tetrameric forms during 500 ns simulations, validated the results obtained from simulations of the monomeric forms (Figure 7). The two complexes in tetrameric form stabilized after 100 ns, and this state continued until the end of the simulations. Given that the proteins are homotetramers, no significant differences were observed in the chains simulation results. The binding free energy analysis of 1hIMPDH-Zinc355749373 and 2hIMPDH-Zinc355749373 complexes was calculated using g_mmpbsa tool. The binding free energy values and related energies, such as electrostatic interactions, van der Waals forces, polar solvation, and SASA energies for each chain, were obtained by the MM-PBSA method (Figure 8). The mean binding free energies of 1hIMPDH-Zinc355749373 (−121.23 kJ/mol) and 2hIMPDH-Zinc355749373 (−126.46 kJ/mol) tetrameric complexes were in accordance with the docking scores (Table 1) and indicated a high and almost equal affinity of the inhibitor to both isoforms. The de novo biosynthesis of guanine nucleotides has a particular importance for stimulated cell proliferation, because the salvage pathway alone may not be sufficient (Cuny et al., 2017). IMPDH is a rate-determining enzyme in de novo guanine nucleotide biosynthesis (Hedstrom, 2009). For this reasons, IMPDH is a potential therapeutic target for a range of diseases including organ transplant rejection, cancer, and viral infections. The two human IMPDH isoforms have different expression levels in different cells of the body. Despite various reports, the mRNA expression of both isoforms increases when lymphocytes and immune responses are stimulated (Dayton et al., 1994). Therefore, inhibition of both isoforms is important for suppressing the immune system. Our in silico studies have shown that the Zinc355749373 ligand could potentially inhibit both hIMPDH isoforms and is a potential drug candidate for a variety of purposes, especially to suppress the immune system. Each known drug or inhibitor of IMPDH acts through a different mechanism. The Zinc355749373 inhibitor identified in this study could act as a competitive inhibitor due to screening among ligands similar to IMP (main substrate). Comparison of the binding energy between two ligands display Zinc355749373 has a higher affinity for both isoforms than the IMP. Zinc355749373 competes with the IMP, binds with a higher affinity to IMPDH, occupies IMP positions, and finally inhibits enzyme activity. This type of binding is reversible and the main substrate can replace the nhibitor at higher concentrations. Based on the available information from zinc 15 database, no activity has been reported for this ligand thus far, and it seems to be a good alternative to MPA. To confirm the results of this research, it is necessary to evaluate this inhibitor in vitro and in vivo studies. Table 6 lists the several human IMPDH inhibitors. Increased IMPDH activity in virus-infected cells due to the high need for viral replication in the nucleotide pool highlights the importance of this enzyme as a therapeutic target for viral infections (Nair and Shu, 2007). Therefore, inhibition of IMPDH and reduction of guanine nucleotide levels in infected cells leads to antiproliferative and antiviral effects. Previously, antiviral effects have been reported for some IMPDH inhibitory compounds, such as MPA (Chan et al., 2013), Ribavirin (Koren et al., 2003) and Mizoribine (Saijo et al., 2005) against some members of the coronavirus family, such as SARS-CoV-1 and MERS-CoV. Therefore, IMPDH may be considered as a possible therapeutic target for COVID-19 patients. In a recent study examining the proteome profiling of COVID-19-infected cells, nucleic acid metabolism was identified as one of the metabolic pathways for the major cluster (Bojkova et al., 2020). This finding underscores the limitation COVID-19 proliferation under IMPDH inhibition, which limits the purine biosynthesis. For as much as the replication of coronaviruses depends on the host cellular nucleotide pools. Based on these interpretations, the Zinc355749373 Ligand, which in this bioinformatics study clearly identified the drug potential and its inhibitory effect on both human isoforms of IMPDH, can be evaluated as a potential drug for the treatment of COVID-19 patients. Since the inhibitory effect of Merimepodib, an IMPDH inhibitor, on COVID-19 replication in vitro has recently been identified (Bukreyeva et al., 2020). This study aimed to identify the potential inhibitors of both human IMPDH isoforms. In addition to side effects and other problems, previous inhibitors generally have a greater inhibitory effect on one isoform. Therefore, an urgent need for newer, safer, and more orally bioavailable IMPDH inhibitors is strongly felt. Furthermore, in patients with acute transplant rejection, inhibition of both isoforms of this enzyme to suppress the immune system can be associated with better results. The initial results of this study were associated with the introduction of inhibitors of both isoforms in terms of binding energy. Then, by applying various filters and tests, the Zinc355749373 [(S)-N-(3-hydroxy-1-(4-hydroxyphenyl) propyl)-2-(3-methyl-2,4-dioxo-3,4-dihydropyrimidin-1(2H)-yl) acetamide] ligand showed the characteristics of a potential drug ligand. Also, the MD simulation of this ligand in the complex with both isoforms confirmed the docking results. This potential drug inhibitor can be used in clinical assessments for further verification. In addition to evaluating of this dual-function inhibitor as an immunosuppressant, its anticancer and antiviral activities can be appraised in vitro, given the current conditions, especially in patients with Covid-19.

PMC9643864

36386661

Adele Costabile,Giulia Corona,Kittiwadee Sarnsamak,Daphna Atar-Zwillenberg,Chesda Yit,Aileen J. King,David Vauzour,Monica Barone,Silvia Turroni,Patrizia Brigidi,Astrid C. Hauge-Evans

Wholegrain fermentation affects gut microbiota composition, phenolic acid metabolism and pancreatic beta cell function in a rodent model of type 2 diabetes

26-10-2022

wholegrain,microbiota,polyphenols,pancreatic beta cells,type 2 diabetes

The intestinal microbiota plays an important role in host metabolism via production of dietary metabolites. Microbiota imbalances are linked to type 2 diabetes (T2D), but dietary modification of the microbiota may promote glycemic control. Using a rodent model of T2D and an in vitro gut model system, this study investigated whether differences in gut microbiota between control mice and mice fed a high-fat, high-fructose (HFHFr) diet influenced the production of phenolic acid metabolites following fermentation of wholegrain (WW) and control wheat (CW). In addition, the study assessed whether changes in metabolite profiles affected pancreatic beta cell function. Fecal samples from control or HFHFr-fed mice were fermented in vitro with 0.1% (w/v) WW or CW for 0, 6, and 24 h. Microbiota composition was determined by bacterial 16S rRNA sequencing and phenolic acid (PA) profiles by UPLC-MS/MS. Cell viability, apoptosis and insulin release from pancreatic MIN6 beta cells and primary mouse islets were assessed in response to fermentation supernatants and selected PAs. HFHFr mice exhibited an overall dysbiotic microbiota with an increase in abundance of proteobacterial taxa (particularly Oxalobacteraceae) and Lachnospiraceae, and a decrease in Lactobacillus. A trend toward restoration of diversity and compositional reorganization was observed following WW fermentation at 6 h, although after 24 h, the HFHFr microbiota was monodominated by Cupriavidus. In parallel, the PA profile was significantly altered in the HFHFr group compared to controls with decreased levels of 3-OH-benzoic acid, 4-OH-benzoic acid, isoferulic acid and ferulic acid at 6 h of WW fermentation. In pancreatic beta cells, exposure to pre-fermentation supernatants led to inhibition of insulin release, which was reversed over fermentation time. We conclude that HFHFr mice as a model of T2D are characterized by a dysbiotic microbiota, which is modulated by the in vitro fermentation of WW. The differences in microbiota composition have implications for PA profile dynamics and for the secretory capacity of pancreatic beta cells.

Wholegrain fermentation affects gut microbiota composition, phenolic acid metabolism and pancreatic beta cell function in a rodent model of type 2 diabetes The intestinal microbiota plays an important role in host metabolism via production of dietary metabolites. Microbiota imbalances are linked to type 2 diabetes (T2D), but dietary modification of the microbiota may promote glycemic control. Using a rodent model of T2D and an in vitro gut model system, this study investigated whether differences in gut microbiota between control mice and mice fed a high-fat, high-fructose (HFHFr) diet influenced the production of phenolic acid metabolites following fermentation of wholegrain (WW) and control wheat (CW). In addition, the study assessed whether changes in metabolite profiles affected pancreatic beta cell function. Fecal samples from control or HFHFr-fed mice were fermented in vitro with 0.1% (w/v) WW or CW for 0, 6, and 24 h. Microbiota composition was determined by bacterial 16S rRNA sequencing and phenolic acid (PA) profiles by UPLC-MS/MS. Cell viability, apoptosis and insulin release from pancreatic MIN6 beta cells and primary mouse islets were assessed in response to fermentation supernatants and selected PAs. HFHFr mice exhibited an overall dysbiotic microbiota with an increase in abundance of proteobacterial taxa (particularly Oxalobacteraceae) and Lachnospiraceae, and a decrease in Lactobacillus. A trend toward restoration of diversity and compositional reorganization was observed following WW fermentation at 6 h, although after 24 h, the HFHFr microbiota was monodominated by Cupriavidus. In parallel, the PA profile was significantly altered in the HFHFr group compared to controls with decreased levels of 3-OH-benzoic acid, 4-OH-benzoic acid, isoferulic acid and ferulic acid at 6 h of WW fermentation. In pancreatic beta cells, exposure to pre-fermentation supernatants led to inhibition of insulin release, which was reversed over fermentation time. We conclude that HFHFr mice as a model of T2D are characterized by a dysbiotic microbiota, which is modulated by the in vitro fermentation of WW. The differences in microbiota composition have implications for PA profile dynamics and for the secretory capacity of pancreatic beta cells. The gut microbiota produces a vast range of metabolites that have the potential to regulate various aspects of host physiology, from metabolism to immune and central nervous system function (Tremaroli and Backhed, 2012; Turroni et al., 2018). In humans, the composition of this community is shaped by intrinsic and extrinsic factors, such as to a smaller extent genetic background and, to a larger extent, exposome-related variables, including diet, lifestyle, and drug use (Arora and Backhed, 2016; Vujkovic-Cvijin et al., 2020). Imbalances in the gut microbiota have been linked to countless pathological conditions, including metabolic disorders such as obesity, cardiovascular disease, and diabetes (Brahe et al., 2016). In particular, individuals with type 2 diabetes (T2D) typically exhibit a low-diversity microbiota with enrichment in opportunistic pathogens or pathobionts (e.g., Collinsella), along with depletion of health-associated taxa, mainly short-chain fatty acid (SCFA) producers belonging to the Lachnospiraceae and Ruminococcaceae families, as well as other microbes linked to metabolic health, such as Akkermansia (Zhao et al., 2018, 2019; Hoang et al., 2020). Similar outcomes have also been observed in T2D rodent models (Ley et al., 2005; Serino et al., 2012; Ishioka et al., 2017; Pierantonelli et al., 2017). Although there are still doubts about the actual causal role of the gut microbiota in T2D (Gurung et al., 2020), accumulating findings support its potential to predict related metabolic outcomes (Rothschild et al., 2018; Aasmets et al., 2021) and indicate that its manipulation in T2D patients or animal models has positive implications for a range of metabolic markers including blood glucose (Cani et al., 2008; Everard et al., 2011; Murphy et al., 2013; Kreznar et al., 2017), with cascading impacts on liver, adipose tissue and muscle (Zhao et al., 2018; Adeshirlarijaney and Gewirtz, 2020). Direct modulation of pancreatic beta cell function may furthermore play a significant role in the central regulation of glucose metabolism, either by gut metabolites directly affecting insulin release from beta cells in the pancreatic islet or by enhancing beta cell survival in response to glucolipotoxicity and inflammation linked to the development of T2D. Polyphenols are a group of diverse phytochemicals found in a wide range of fruit, vegetables and grains. Whole-grain cereals, although investigated to date primarily for their high fiber content, have received considerable attention in the last several decades due to the presence of a unique blend of bioactive phenolic acid (PA) components like ferulic acid and other hydroxycinnamic acids and hydroxybenzoic acids (Li et al., 2008), which could help explain their beneficial effects on metabolic control as observed in several clinical trials involving participants with either increased risk of T2D or presenting with overt T2D (Rave et al., 2007; Aune et al., 2013; Hou et al., 2015; He et al., 2016; Marventano et al., 2017). Polyphenols are in fact known to have anti-inflammatory, antiproliferative, vasodilatory and strong antioxidant properties important for cardiometabolic outcomes (Zhu and Sang, 2017; Ruskovska et al., 2021). These effects are mediated by a bidirectional interaction between polyphenols and gut microbiota, with the former having a prebiotic potential (Gibson et al., 2017) and the latter influencing the bioavailability and bioactivity of polyphenolic metabolites (Ruskovska et al., 2021; Plamada and Vodnar, 2022). However, less is known about the direct impact of polyphenols and their conjugates on pancreatic islet function following microbial modification, although some studies suggest a cytoprotective action and improved secretory function following exposure to some polyphenolic metabolites (Adisakwattana et al., 2008; Son et al., 2011; Fernández-Millán et al., 2014; Sompong et al., 2017; Marrano et al., 2021; Nie and Cooper, 2021; Papuc et al., 2022). In this context, we hypothesize that the gut microbiota of animals with T2D has a different impact on the polyphenolic profile of metabolites produced from wholegrains compared to the microbiota of healthy controls. This is turn will result in distinct effects on physiological functions, including that of pancreatic islets. Specifically, using an in vitro gut fermentation model, we here investigate how 24-h fermentation of stool samples from control mice or mice fed a high-fat, high-fructose (HFHFr) diet (a well-established model for T2D, Im et al., 2021), in the presence of a wholegrain wheat substrate impacts microbiota composition and PA profile. We further assess the direct effect of both fermentation supernatants and selected PAs on pancreatic beta cell function including viability, apoptosis, and insulin release. Fecal samples were obtained from a previous study, in which 6-week-old C57Bl/6J mice were fed a control or a HFHFr diet for 16 weeks (Vauzour et al., 2017), see Supplementary Table 1 for nutritional composition of diets. At the end of the study, stool samples were collected, immediately snap frozen in liquid nitrogen and stored at −80°C until analysis. Vanillic acid, isovanillic acid, syringic acid, homovanillic acid, caffeic acid, hippuric acid, p-coumaric acid, 3,4-dihydroxybenzoic acid, 2,5-dihydroxybenzoic acid, 2,4-dihydroxy benzoic acid, Vanillin, 4-hydroxybenzoic acid, 4-hydroxybenzaldehyde, sinapic acid, 3,5-dihydroxy benzoic acid, hydroferulic acid, ferulic acid, isoferulic acid, syringaldehyde, salicylic acid, 3,5-dichloro-4-hydroxybenzoic acid, 3-(3,4-dihydroxyphenyl)propanoic acid, 3,4-dihydroxyhydrocinnamic acid, o-coumaric acid, 2-hydroxyphenylacetic acid, 3-hydroxybenzaldehyde, 3-hydroxyphenylacetic acid, gallic acid, 3-hydroxybenzoic acid and 3,5-dichloro-4-hydroxybenzoic acid were obtained from Sigma–Aldrich (Gillingham, UK). Methanol, water, acetonitrile, and formic acid were all LC-MS grade and were obtained from Fisher Scientific (Loughborough, UK). The bread samples Wholegrain Wheat (WW), containing 7.6 g/100 g fiber, and Control Wheat (CW), containing 2.3 g/100 g fiber, were purchased in a local grocery shop (phenolic composition reported in Supplementary Table 2). The samples were subjected to the simulated gastrointestinal digestion procedure as previously described (Corona et al., 2016). Briefly, this method consists of two sequential stages: gastric digestion and small intestinal digestion. Samples (10 g) were dissolved in 30 ml of acidified water (pH = 2), and pepsin (320 U/ml) was added. Samples were incubated at 37°C for 2 h on a shaker covered with foil to exclude light. The pH was adjusted to 7.5 by adding a few drops of 6 M NaOH, and pancreatin (4 mg/ml) and bile extracts (25 mg/ml) were added. The samples were again incubated at 37°C for 2 h on a shaker. Digested samples were freeze-dried and stored at −20°C. Aliquots of digested samples were used as substrates for in vitro batch culture fermentations and to assess PA content. An aliquot of 300 mg of dried stool samples was diluted in anaerobic PBS (0.1 M phosphate buffer solution, pH 7.4, w/w) to 1% final concentration and homogenized (Stomacher 400, Seward, West Sussex, UK) for 2 min at 240 paddle beats per min. Samples were added to anaerobic fermenters within 15 min of voiding. All physicochemical conditions in the distal colon were replicated in a microscale pH-controlled fermentation system (5-ml working volume) as described (Onumpai et al., 2011). Batch culture fermentations were set up in parallel, each using fecal slurries from control and HFHFr mice (3 controls vs. 3 HFHFr). Culture media and temperature conditions were described by Onumpai et al. (2011). Fructo-oligosaccharides [1% (w/v), Beneo P95; Orafti, Tienen, Belgium] and fecal slurry without any substrate addition were fermented in parallel with the two different pre-digested breads as positive and negative controls, respectively. The different pre-digested breads, WW and CW, were added to the respective fermentation vessels just prior to the addition of the fecal slurry, at the concentration of 1% (w/w dry solid/total dietary fiber). Each vessel was inoculated with 500 μl of fresh fecal slurry (1/10 w/w) for both control and HFHFr. The batch cultures (n = 3) were run over a period of 24 h and samples were obtained from each vessel at 0, 6, and 24 h for gut microbiota profiling, phenolic metabolites analyses and analysis of pancreatic beta cell function. Soluble and bound phenolic fractions were extracted from substrate samples (freeze-dried, digested, and fermented) using an established extraction procedure adapted from Li et al. (2008) and Dall’Asta et al. (2016), similar to that previously described (Schär et al., 2018). 3,5-Dichloro-4-hydroxybenzoic acid in 80:20 EtOH/H2O was used as an internal standard (IS). Samples and IS (50 μg/ml, 5 μl) were aliquoted and extracted with 1 ml of 80/20 EtOH/H2O. The solution was vortexed, sonicated for 10 mins and centrifuged for 15 min at 13,200 rpm. The supernatants were collected, and a second extraction was performed. The combined supernatants were evaporated to dryness and used for soluble PA extraction, while extraction pellets were used for bound PA extraction. Following extraction of soluble PAs, fermented samples were subjected to solid phase extraction (SPE) as follows: SPE Cartridges Strata-X 33 μm Polymeric Reversed Phase (Phenomenex, Torrance, California, USA) were mounted on a vacuum manifold and preconditioned with 3 ml of acidified methanol and 3 ml of acidified water. Subsequently, 0.5 ml of acidified water was added to the cartridges followed by the samples. The cartridges were rinsed twice with 0.5 ml of acidified methanol and washed with 6 ml of LC-MS water, and vacuum was applied to dry the cartridges. Samples were eluted with 3.75 ml of acidified methanol, and vacuum was applied to facilitate full elution. The eluted extracts were evaporated under nitrogen stream and dissolved in 100 μl of water acidified with formic acid (0.1%), vortexed and transferred into a vial in preparation for UPLC-MS/MS analysis. The UPLC-electrospray ionization-MS/MS system consisted of an Aquity UPLC H class (Waters Inc., USA) coupled to a Xevo TQ-S micro electrospray ionization mass spectrometer (Waters Inc., California, USA) operated using MassLynx software (V4.1, Waters Inc., California, USA). Compound separation was achieved with the multiple reaction monitoring (MRM) method previously described (Schär et al., 2018) using an Aquity UPLC HSS T3 1.8-μm column (2.1 × 100 mm) attached to a Van guard pre-column of the same material and pore size, maintained at 45°C with a flow of 0.65 ml/min and a sample injection volume of 2 μl. The mobile phase consisted of 0.1/99.9 v/v formic acid/water (A) and 0.1/99.9 v/v formic acid/acetonitrile (B); the mobile phase gradient consisted of: 1% B at 0 min, 1% B at 1 min, 30% B at 10 min, 95% B at 12 min, 95% B at 13 min, 1% B at 13.10 min, 1% B at 16 min. Calibration curves were prepared by injecting analytical standards (0.001–100 μg/ml) with R2 > 0.995 for all compounds. The limit of detection (LOD) and limit of qualification (LOQ) were determined as the signal-to-noise ratio of 3 and 10, respectively. Target lynx was used to automate data acquisition. Microbial DNA was extracted from 250 mg of fermentation samples using the QIAamp DNA Stool Mini Kit (QIAGEN, Hilden, Germany) according to the manufacturer’s instructions. 16S rRNA gene sequencing was performed according to the method described by Corona et al. (2020). In brief, for each sample, the hypervariable V3-V4 regions of the 16S rRNA gene were PCR-amplified using the S-D-Bact-0341-b-S-17/S-D-Bact-0785-a-A-21 primers (Klindworth et al., 2013) with Illumina overhang adapter sequences, according to the manufacturer’s guidelines. PCR products were purified using a magnetic bead-based system (Agencourt AMPure XP; Beckman Coulter, Brea, CA, USA), indexed by limited-cycle PCR using Nextera technology, and further purified as described above. Final libraries were pooled at equimolar concentration (4 nM), denatured with 0.2 N NaOH, and diluted to 6 pM before loading onto the MiSeq flow cell. Sequencing was performed on an Illumina MiSeq platform with a 2 × 250 bp paired-end protocol, according to the manufacturer’s instructions (Illumina, San Diego, CA, USA). Sequencing reads were deposited in the National Center for Biotechnology Information Sequence Read Archive (Bioproject ID PRJNA885427). Raw sequences were processed using a pipeline combining PANDAseq (Masella et al., 2012) and QIIME 2 (Bolyen et al., 2019). After length and quality filtering, reads were binned into amplicon sequence variants (ASVs) using DADA2 (Callahan et al., 2016). Taxonomy was assigned via the VSEARCH algorithm (Rognes et al., 2016), using the Greengenes database as a reference. Alpha diversity was measured using the Shannon and inverse Simpson index. Beta diversity was computed based on Bray-Curtis distances and visualized on a Principal Coordinates Analysis (PCoA) plot. The MIN6 beta cell line was kindly provided by Professor J-I Miyazaki, University of Tokyo, Japan. Cells (passages 30–37) were maintained in Dulbecco’s Modified Essential Medium (DMEM, 10% v/v fetal bovine serum (FSB), 2 mM L-glutamine, 100 U/ml penicillin 0.1 mg/ml streptomycin, 25 mM glucose) and incubated at 37°C (5% CO2). When 70–80% confluent, cells were passaged or seeded into 96-well plates for experiments the following day. They were routinely tested for mycoplasma contamination. Culture reagents were from Sigma-Aldrich unless otherwise stated. Prior to islet isolations, male ICR mice (30 g, Charles River Laboratories, Margate, UK) were housed on a 12-h light/dark cycle with access to food and water ad libitum in accordance with the UK Home Office Regulations. Mouse islets were isolated by collagenase digestion (1 mg/ml, type XI) and separated from exocrine pancreatic tissue on a histopaque gradient, as described (Hauge-Evans et al., 2009). Islets were incubated overnight at 37°C (5% CO2) in Roswell Park Memorial Institute (RPMI) 1640 medium (10% v/v FBS, 2 mM glutamine, 100 U/ml penicillin/0.1 mg/ml streptomycin, 11 mM glucose) prior to experiments. MIN6 cells (10–15,000 cells/well) were incubated for 20 h in supplemented DMEM (10% FBS) with or without 1% (v/v) fermentation supernatants or 1% (v/v) fermentation media (control without slurry) as indicated. Cell viability was assessed by measurement of cellular ATP content following treatment using a CellTiter-Glo Luminescent Cell Viability Assay (Promega, Southampton, UK). In a separate set of experiments, apoptosis was induced in MIN6 cells by 20-h exposure to a combination of palmitate (0.25 mM, 0.95% BSA) and cytokines (25 U/ml IL-1β and 500 U/ml TNFα, PeproTech EC Ltd, London, UK) in the presence or absence of 1% (v/v) fermentation supernatants in supplemented DMEM (2% FBS). Caspase 3/7 activity was measured as an indication of apoptosis using a Caspase-Glo assay (Promega, Southampton, UK) according to manufacturer’s instructions. Plates for both viability and apoptosis assessment were read on a GloMax Navigator luminometer (Promega). Insulin release was assessed in static incubation experiments. Islets (batches of 3) or cells (20,000/well) were pre-incubated for 1 or 2 h, respectively, at 37°C in a bicarbonate-buffered physiological salt solution (Gey and Gey, 1936) containing 2 mM glucose followed by 60-min incubation with 1% (v/v) fermentation supernatants or PAs (100 nM–1 μM) in 0.4- or 0.2-ml salt solution as indicated. Glucose and the pharmacological agents, phorbol myristate acetate (PMA), forskolin (FSK) and 3-isobutyl-1-methylxanthine (IBMX), were routinely included as internal controls for secretory responsiveness. The hormone content of the incubation buffer was assessed by radioimmunoassay (RIA) using an in-house insulin assay as described (Hauge-Evans et al., 2009). Statistical analyses were performed using GraphPad Prism 5 and 8. Matlab was used to plot the Principal Component Analysis (PCA) of the metabolite dataset. For the microbiota data, statistics were performed using RStudio 1.0.44 on R software version 3.3.2 implemented with the packages stats, made4 (Culhane et al., 2005) and vegan. The significance of data separation in the PCoA plot was tested by a permutation test with pseudo-F ratio using the adonis function in vegan. Kruskal–Wallis or Friedman tests and Wilcoxon tests (paired or unpaired as needed) were applied as appropriate (for alpha diversity and relative abundances of taxa). P-values were corrected for multiple comparisons using the Benjamini–Hochberg or false discovery rate (FDR) method. For analyses of PA profiles and beta cell function, data were analyzed statistically using one or two-way ANOVA and Bonferroni’s or Sidak’s multiple comparison test as appropriate. Kendall rank correlation test was used to assess associations between genus- level relative abundances and PA profiles. Only statistically significant correlations with core genera (with relative abundance ≥ 20%) and absolute Kendall’s tau ≥ 0.3 were considered. Overall, differences between groups were considered statistically significant at P < 0.05; 0.05 ≤ P ≤ 0.1 was considered a tendency. First, the baseline microbiota of fecal samples from control and HFHFr mice was profiled. As expected, several differences were found, including greater higher alpha diversity in HFHFr mice (P ≤ 0.004, Wilcoxon test) and significant segregation between groups in the PCoA plot (P = 0.002, permutation test with pseudo-F ratio, Figures 1A,B). Taxonomic profiles were also markedly distinct, with notably lower relative abundances of Lactobacillus, and greater proportions of Oxalobacteraceae and Lachnospiraceae in the HFHFr group (P ≤ 0.03, Wilcoxon test) (Figure 1C and Supplementary Figure 1). After the addition of WW and CW pre-digested substrates to the fermentation process, no significant differences in alpha diversity were observed, although it tended to decrease in the HFHFr group in the presence of WW to values close to those of the control already after 6 h (Figure 2A). Bray-Curtis-based PCoA showed significant separation between the fecal-derived microbial communities of HFHFr and control mice, regardless of experimental condition and time point (P = 1 × 10–4, permutation test with pseudo-F ratio, Figure 2B). Although no significant compositional differences were observed over time, some trends are noteworthy (Supplementary Figure 2). In particular, already at 6 h, the Coriobacteriaceae family and its genus Adlercreutzia showed a contrasting trend depending on the bread sample added, i.e., they tended to increase further in the HFHFr samples added with CW while they decreased (by more than half) in those added with WW. Similar behavior was observed for Pseudomonas and Dialister. Even at 24 h it was possible to note a differential impact of WW and CW on microbial communities. In particular, in the HFHFr samples added with WW, the dominant genus was Cupriavidus (relative abundance at 24 h, 71.5%) followed by Enterococcus (19.8%) and Lactobacillus (3.8%), while in those added with CW, Cupriavidus and Enterococcus shared similar proportions (43.8 and 41.6%, respectively) with Lactobacillus accounting for 7.9%. On the other hand, in the control samples added with WW, Lactobacillus, Enterococcus and Clostridium dominated the ecosystem (cumulative relative abundance, approx. 90%) while in those added with CW, the genus Clostridium was far underrepresented and replaced by Enterobacteriaceae members. The supernatants of the fermentation cultures were analyzed for PA content either initially present from the digest or generated by microbial metabolism of the wheat substrates over time. Supplementary Table 2 shows the soluble, bound and total PAs of the WW and CW bread substrate pre-and post-digestion, prior to fermentation. As expected, WW showed a higher total PA content (395.13 ± 12.40 mg/g DW) than CW (72.18 ± 22.95 mg/g DW), with ferulic (FA) and isoferulic (IFA) acids being the most predominant PAs in both substrates. Overall, the WW digest contained 45.89 ± 0.91 mg total PAs/g DW compared to 20.86 ± 2.38 mg/g DW in the CW digest. Consistently, the total PA content of the fermentation supernatant at 0 h was higher in WW than in CW samples (Figure 3), with similar levels in the control (0.86 ± 0.21 μg/ml) and HFHFr group (0.76 ± 0.17 μg/ml). The phenolic profile of the WW samples was then evaluated for the control and HFHFr groups at specific time points along the fermentation process. Notably, after 6 h, the total PA content of WW supernatants in the HFHFr group was significantly lower than in the control group (P < 0.05, Figure 3), while this difference was no longer significant at 24 h (P > 0.05). When analyzing a panel of 25 individual PAs by two-way ANOVA (Supplementary Table 3), we found a significant effect of both PAs (P < 0.001) and time (P < 0.01), and no interaction (P > 0.05). The fermentation profile of the 10 most abundant PAs in WW is presented in Figure 4, and the differences between control and HFHFr at each time point are shown in the three panels. Multiple comparison analysis (simple effects for each PA) revealed lower levels of PAs in the HFHFr group compared to the control, which were significant for 3-hydroxybenzoic acid (3HBA, P < 0.001), 4-hydroxybenzoic acid (4HBA, P < 0.05), FA (P < 0.05), and IFA (P < 0.001) at 6 h (Figures 5A–D). In addition, 4-hydroxybenzaldhehyde (4-HBAldh) decreased significantly over time (Figure 5E, P < 0.05), while caffeic acid levels followed a trend to increase, albeit to a greater extent in the control group (Figure 5F). Correlations between the PA content and the relative abundances of bacterial taxa were next specifically sought (Supplementary Figure 3). Interestingly, isovanillic acid negatively correlated with the well-known probiotic genus, Lactobacillus (P = 5.0 × 10–4; tau = -0.468, Kendall rank correlation test). A negative correlation was also found between Cupriavidus and 4OH-benzoic acid and salicylic acid (respectively, P = 0.01 and 0.02; tau = -0.327 and -0.308). We hypothesized that WW metabolites produced by bacterial fermentation in the gut may directly affect beta cell function, particularly cell viability, apoptosis and insulin secretion, with potential implications for cellular health under normal conditions and during development of T2D. Specifically, two approaches were selected: (i) pancreatic MIN6 beta cells were exposed in vitro to fermentation supernatants from control or HFHFr samples; and (ii) cells and primary islets were exposed to selected PAs as identified above. First, we assessed whether fermentation supernatants affected MIN6 beta cell viability in our research model in a time- and substrate-dependent manner and modulated proliferative and/or anti-apoptotic properties of the cells. A 1% dilution of fermentation supernatant was used as this had no negative effects on MIN6 viability (data not shown). Based on the results shown in Figure 3, the diluted supernatant had a total soluble PA content of approximately 5–10 ng/ml, which would be equivalent to circulating levels in the nanomolar range depending on type of PAs (Manach et al., 2005). It was found that incubation with WW fermentation supernatants from both control and HFHFr groups did not affect cell viability compared to negative controls (slurry only, no WW substrate) at all time points (0, 6, and 24 h, Figure 6A). The direct effect of fermentation supernatants on apoptosis was then assessed in response to cellular stressors typical of the cellular environment in the development of T2D. Exposure for 20 h to the saturated fatty acid palmitate (0.25 mM) combined with pro-inflammatory cytokines (25 U/ml IL1β + 500 U/ml TNFα) induced apoptosis in MIN6 cells (P < 0.01), which was not modified by co-culture with 1% fermentation supernatants from all time points of either group (P > 0.2 vs. negative control, Figure 6B). Finally, insulin secretion was assessed in MIN6 cells exposed to 20 mM glucose and 500 nM phorbol myristate acetate (PMA, Figure 7). Negative controls from both control and HFHFr samples inhibited secretion at 0 h compared to secretagogues only (P < 0.001). This effect was absent after 6 and 24 h. Surprisingly, insulin secretion was further inhibited at 0 h by exposure to WW and CW supernatants from the control, but not the HFHFr group (P < 0.05 vs. negative control, Figure 7A). The inhibitory effect was fully reverted at 24 h (Figures 7B,C). To establish whether the observed effects on secretion were due to the production or breakdown of specific polyphenolic compounds or other bacterial metabolites present in the supernatant, we tested the effect of the most abundant PAs present in the WW digest prior to and following fermentation (see Supplementary Table 3). Despite their high concentration in the pre-fermentation WW digest compared to other PAs, 100 nM FA and IFA did not individually modulate insulin secretion by MIN6 cells (Figure 8A) nor did a broader range of PAs from the phenolic profile (Figure 4) alone or combined, even at the highest concentration of 1 μM (Figure 8B). These findings were confirmed in experiments with primary islets isolated from mice (Figure 8C). The purpose of this study was to evaluate whether differences in microbiota composition in a model of T2D compared to controls translated into differences in PA profiles and dynamics following in vitro gut fermentation of WW. Circulating, wheat-derived bioactive PAs have the potential for modulating physiological properties of a range of organ systems in a metabolite-dependent manner (Luca et al., 2020; Wan et al., 2021), and studies to date have focused on the GI tract, liver, muscle and adipose tissue in the context of glucose metabolism in T2D (Vrieze et al., 2012; Khan et al., 2014; Herrema et al., 2017; Zhao et al., 2018; Adeshirlarijaney and Gewirtz, 2020; Wang et al., 2021). As information related to the pancreas is still sparse, we also investigated implications of the availability of bioactive metabolites for the functionality of insulin-producing pancreatic beta cells. Initial assessment confirmed that the microbiota composition in the HFHFr fecal samples was distinct from that of the controls, with an increase in proteobacterial taxa (particularly Oxalobacteraceae) and Lachnospiraceae, and a decrease in Lactobacillus. Reports in individuals with T2D have repeatedly shown a state of dysbiosis especially with an increase in pathobionts (Candela et al., 2016; Barone et al., 2021). Notably, our findings are consistent with those of other mouse models of high-fat, high-glucose or high-fructose diets, in which metabolic dysfunction was associated with proportionally higher levels of proteobacteria and decreased lactobacilli, which are one of the most abundant taxa in the mouse gut microbiota (Nguyen et al., 2015; Do et al., 2018). Regarding the Lachnospiraceae family, it should be noted that it is typically believed to be associated with health, due to its ability to produce SCFAs (Koh et al., 2016), but some members have been related to various intra- and extraintestinal diseases, including T2D, in both humans and mouse models, thus stressing the need for further investigation of their impact on host physiology (Kameyama and Itoh, 2014; Vacca et al., 2020). Consistent with the known responsiveness of the gut microbiota to diet (Zmora et al., 2019), WW fermentation by HFHFr samples in our in vitro gut system resulted in some interesting trends as early as 6 h, namely a restoration of diversity to values similar to those of the controls and some compositional variations. As for the latter, it is interesting to note the decrease in the proportions of potentially harmful bacteria, including Pseudomonas, Dialister, and Coriobacteriaceae. In particular, Dialister is a succinate-consuming genus that has previously been related to a number of metabolic abnormalities, such as increased levels of glycated hemoglobin and insulin resistance (Vals-Delgado et al., 2021). Coriobacteriaceae members are also generally overrepresented in obesity and related comorbidities, where they likely contribute to impaired intestinal cholesterol absorption, increased triglyceride synthesis and metabolic endotoxemia, so much so that they have been proposed as a target for microbiome-based interventions (Frost et al., 2014; Gomez-Arango et al., 2018). However, it should be noted that after 24 h of WW fermentation, the HFHFr microbiota tended to be monodominated by Cupriavidus, which has previously been associated with infections in immunocompromised patients (Massip et al., 2020). On the other hand, Cupriavidus spp. are also known for their ability to synthesize polyhydroxyalkanoates from fructose (Przybylski et al., 2019), which could explain their bloom in HFHFr samples. In line with the microbiota-related findings, changes were observed in the PA profile following the fermentation of the wheat substrates over time. In the CW group, an increased level of PAs was seen after 6 h, which could indicate fermentation of bound phenolics, with a subsequent reduction of PA levels at 24 h. Maybe surprisingly, this increase was not observed in the HFHFr group, where no change was observed in overall measured levels of soluble PAs from CW over the fermentation time, either suggesting that microbial activity in the samples did not lead to the conversion of bound to soluble PAs at the selected fermentation times, or alternatively, that microbial conversion of bound phenolics was masked by the further breakdown of soluble PAs into other end products not targeted in the assay (Possemiers et al., 2011). In contrast, the total PA content of fermented supernatants was significantly lower in the HFHFr WW group compared to controls after 6 h, while this difference was no longer seen at 24 h. In addition, a significant decrease in specific PAs was observed, in particular 3OH-benzoic acid, 4OH-benzoic acid, FA, IFA, and 4OH-benzaldehyde, with levels at 6 h significantly reduced in samples from HFHFr mice compared to controls, as well as a delayed appearance of caffeic acid, suggesting a lag in the production and/or release of key metabolites in this group. Consistently, 4OH-benzoic acid levels were negatively associated with the relative abundance of Cupriavidus, a bacterial genus predominant in the microbiota of HFHFr mice and previously reported to interfere in the conversion of propionic acid to 4OH-benzoic acid (Basu et al., 2018). Furthermore, the relative abundance of Lactobacillus, dramatically reduced in HFHFr samples, was negatively correlated with levels of isovanillic acid, a phenolic product known to promote muscle uptake of glucose in differentiated human myoblasts, suggesting a direct involvement in stimulating glucose uptake and metabolism, both of which are critical in the context of T2D (Houghton et al., 2019). Measurement of insulin secretion revealed a clear inhibitory effect on stimulated hormone release when pancreatic beta cells were exposed to pre-fermentation samples containing slurry but no substrate. This was further exacerbated in the control group with the addition of CW and WW digests. Fermentation negated this effect with time, implicating a protective role of the fermentation process in neutralizing harmful components of both the fecal slurry alone and the substrate digest, possibly due to conversion to other, less harmful metabolites by microbial metabolism. It is unclear why this response was enhanced in the control but not the HFHFr group prior to fermentation of wheat substrate. This probably suggests that the observed difference in microbiota composition between the two groups prior to fermentation translates into different bioactive contents of the fermentation media, which have a different impact on the secretory function of pancreatic beta cells. On the other hand, we did not observe differences in the pre-fermentation PA profile between control and HFHFr, nor did individual or combined exposure to the most abundant PAs present in the WW digest lead to a reduction in insulin secretion, suggesting that the observed inhibition was unlikely to be due to acute detrimental effects of these polyphenolic metabolites. Short-chain fatty acids is another group of key metabolites produced from WW in the fermentation process (Silva et al., 2020). As fermentation products they are unlikely to be present at high levels in the substrate digest prior to fermentation (0 h) and are therefore unlikely candidates mediating the inhibition on secretion by samples collected at 0 h (Jardon et al., 2022). Rather, their production over time may be influential in the neutralization of harmful agents in the fermentation media and therefore in the reversal of insulin release. It is in fact known that acetate and propionate stimulate insulin secretion in in vitro settings although there are reports of either little or inhibitory effects of SCFAs on secretory function (McNelis et al., 2015; Priyadarshini et al., 2015; Pingitore et al., 2017; Ørgaard et al., 2019; Bolognini et al., 2021). However, in this study SCFA production was not assessed due to limited sample availability. Finally, it is worth highlighting that beta cell viability and apoptosis were not altered following exposure to metabolites present in the supernatant either before or after fermentation of WW substrate, suggesting that the bioactive components acting on acute insulin secretion did not negatively affect cellular pathways involved in cell viability and survival. The lack of effects could also be due to the relatively low concentration of PAs in the fermentation media (0.5–1 μg/ml, equivalent to nanomolar range). Other studies assessing the impact of selected PAs on beta cell viability and apoptosis have used significantly higher micromolar concentrations and in some cases report beneficial protection (Sompong et al., 2017). However, whereas that is of relevance when assessing the pharmacological potential of these bioactives, here we aimed to used physiologically relevant concentrations (Manach et al., 2005). The present study used an in vitro gut model to assess the direct impact of WW fermentation on PA and microbiota profiles and evaluate pancreatic beta cell function in response to fermentation supernatants and selected PAs. Despite the inherent limitations of the model (primarily, lack of interaction with endothelial enterocytes, local neuronal network, or immune cells), we can conclude that HFHFr mice as a T2D model are characterized by a dysbiotic microbiota, which is modulated by the WW fermentation process in vitro, although a reversal to a profile similar to that of the control is not achieved. The differences in microbiota composition are likely to be implicated in the secretory capacity of pancreatic beta cells. In particular, the fermentation-related cancelation of the initial inhibitory effect of slurry and wheat digest on pancreatic beta cell function suggests an indirect protective role of the microbiota. Importantly, these findings were not linked to acute effects of individual PAs. Our studies thus highlight the existence of complex interactions between the microbiota and the production of phenolic metabolites from wholegrain wheat during fermentation. They furthermore suggest that these dynamics are altered in type 2 diabetes with the potential for beneficial effects on microbiota composition and pancreatic beta cell function. Further research, including animal studies, are required to identify the underlying mechanisms and translate these findings into clinically relevant settings. The data presented in this study are deposited in the National Center for Biotechnology Information Sequence Read Archive (Bioproject ID: PRJNA885427). AC, GC, and AH-E conceived and designed the experiments. AC, GC, AH-E, KS, DA-Z, CY, MB, and AK carried out the experimental work and data collection. AC, GC, AH-E, and MB performed the data analysis and interpretation. AH-E, AC, GC, DV, ST, and PB wrote and/or reviewed the manuscript. All authors critically reviewed the manuscript and approved the submitted version.

PMC9644064

Aditya Sen,Rachel T. Cox

Loss of Drosophila Clueless differentially affects the mitochondrial proteome compared to loss of Sod2 and Pink1 10.3389/fphys.2022.1004099

26-10-2022

mitochondria,Clueless,SOD2,PINK1,mitochondrial proteome,drosophila,respiratory chain complexes

Mitochondria contain their own DNA, mitochondrial DNA, which encodes thirteen proteins. However, mitochondria require thousands of proteins encoded in the nucleus to carry out their many functions. Identifying the definitive mitochondrial proteome has been challenging as methods isolating mitochondrial proteins differ and different tissues and organisms may have specialized proteomes. Mitochondrial diseases arising from single gene mutations in nucleus encoded genes could affect the mitochondrial proteome, but deciphering which effects are due to loss of specific pathways or to accumulated general mitochondrial damage is difficult. To identify specific versus general effects, we have taken advantage of mutations in three Drosophila genes, clueless, Sod2, and Pink1, which are required for mitochondrial function through different pathways. We measured changes in each mutant’s mitochondrial proteome using quantitative tandem mass tag mass spectrometry. Our analysis identified protein classes that are unique to each mutant and those shared between them, suggesting that some changes in the mitochondrial proteome are due to general mitochondrial damage whereas others are gene specific. For example, clueless mutants had the greatest number of less and more abundant mitochondrial proteins whereas loss of all three genes increased stress and metabolism proteins. This study is the first to directly compare in vivo steady state levels of mitochondrial proteins by examining loss of three pathways critical for mitochondrial function. These data could be useful to understand disease etiology, and how mutations in genes critical for mitochondrial function cause specific mitochondrial proteomic changes as opposed to changes due to generalized mitochondrial damage.

Loss of Drosophila Clueless differentially affects the mitochondrial proteome compared to loss of Sod2 and Pink1 10.3389/fphys.2022.1004099 Mitochondria contain their own DNA, mitochondrial DNA, which encodes thirteen proteins. However, mitochondria require thousands of proteins encoded in the nucleus to carry out their many functions. Identifying the definitive mitochondrial proteome has been challenging as methods isolating mitochondrial proteins differ and different tissues and organisms may have specialized proteomes. Mitochondrial diseases arising from single gene mutations in nucleus encoded genes could affect the mitochondrial proteome, but deciphering which effects are due to loss of specific pathways or to accumulated general mitochondrial damage is difficult. To identify specific versus general effects, we have taken advantage of mutations in three Drosophila genes, clueless, Sod2, and Pink1, which are required for mitochondrial function through different pathways. We measured changes in each mutant’s mitochondrial proteome using quantitative tandem mass tag mass spectrometry. Our analysis identified protein classes that are unique to each mutant and those shared between them, suggesting that some changes in the mitochondrial proteome are due to general mitochondrial damage whereas others are gene specific. For example, clueless mutants had the greatest number of less and more abundant mitochondrial proteins whereas loss of all three genes increased stress and metabolism proteins. This study is the first to directly compare in vivo steady state levels of mitochondrial proteins by examining loss of three pathways critical for mitochondrial function. These data could be useful to understand disease etiology, and how mutations in genes critical for mitochondrial function cause specific mitochondrial proteomic changes as opposed to changes due to generalized mitochondrial damage. Mitochondria are highly dynamic and multifunctional organelles, responsible for producing the majority of cellular ATP through oxidative phosphorylation. Due to their symbiotic origin of evolution, mitochondria contain their own genome and cannot persist without sufficient nuclear contribution (Margulis 1970; Andersson et al., 1998). Mitochondrial DNA (mtDNA) encodes 13 polypeptides and 2 rRNAs and 22 tRNAs required for the translation of the 13 polypeptides. Mitochondria are involved in many processes, such as metabolism, mitochondrial protein import, fission/fusion, and steroid biosynthesis. To carry out these pathways, mitochondria require over one thousand nucleus-encoded proteins for normal function (Area-Gomez and Schon 2014). Recent studies analyzing the mitochondrial proteome in vertebrates and invertebrates have revealed that the number of so called ‘mitochondrial proteins’ is variable with the composition and numbers varying depending on the source of material and the isolation methods. In fact, it is challenging to define the dynamic nature of the mitochondrial proteome in normal and diseased tissue using different computational algorithms and molecular techniques (Calvo and Mootha 2010). Mitochondrial diseases often arise from single gene mutations affecting specific processes, but these mutations could also affect protein levels, protein import, or protein turnover in unexpected ways. Symptoms and severity of mitochondrial diseases are complex and sometimes hard to diagnose (Grier et al., 2018; Forny et al., 2021). For researchers modeling mitochondrial disease, parsing the direct effects of a mutation versus indirect global effects due to general mitochondrial dysfunction can be challenging. The relatively simple genetics of Drosophila, combined with its usefulness as a model for mitochondrial disease, presents an opportunity to compare how single gene mutations affect mitochondrial proteomes (Sen and Cox 2017). We have taken advantage of mutations in three well-described genes critical for mitochondrial function, clueless (clu), Superoxide dismutase 2 (Sod2) and PTEN-induced putative kinase 1 (Pink1), to analyze how loss of each pathway affects the mitochondrial proteome. Clueless (Clu) is a nucleus-encoded ribonucleoprotein that forms mitochondrion-associated particles (Cox and Spradling 2009; Sheard et al., 2020). clu mutant flies are sick, sterile, short lived and have Parkinsonism-like phenotypes (Cox and Spradling 2009; Sen et al., 2013). CluH-Knockout (Cluh-KO) mutant mice die shortly after birth (Schatton et al., 2017). In addition, clu mutant flies and Cluh-KO mice have reduced mitochondrial protein (Sen et al., 2015; Schatton et al., 2017). Sod2 scavenges mitochondrial free radicals (Weisiger and Fridovich 1973). Sod2 null mutant flies only live 24 h and have increased oxidative damage and decreased ATP (Duttaroy et al., 2003; Paul et al., 2007; Godenschwege et al., 2009). Pink1 is a component of the mitophagy pathway used to cull damaged mitochondria (Pickles et al., 2018). Upon damage, Pink1 is stable in the mitochondrial outer membrane and recruits Parkin, an E3 ubiquitin ligase, ultimately leading to degradation of the organelle through mitophagy (Narendra et al., 2010; Ziviani et al., 2010). Pink1 mutant flies have shortened lifespans and mitochondrial damage (Clark et al., 2006; Park et al., 2006; Yang et al., 2006). Disease-causing mutations in CLUH have not yet been identified, possibly because loss of CluH in mouse is nonviable (Schatton et al., 2017). Mutations in the human orthologs SOD2 and PINK1 are known to cause mitochondrial damage and disease in people (Nomiyama et al., 2003; Valente et al., 2004; Mollsten et al., 2007). In our current study we examined mitochondrial-associated protein abundance in clu, Sod2, and Pink1 null mutant flies using tandem mass tag (TMT) quantitative mass spectrometry analysis. Our TMT-mass spectrometry analysis showed that specific subsets of mitochondrial proteins are either less or more abundant depending on the mutation. We analyzed protein differences using Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), Gene ontology (GO) and Protein Annotation through Evolutionary Relationship (PANTHER) analyses (Chen et al., 2013; Szklarczyk et al., 2015; Kuleshov et al., 2016). This revealed that loss of Sod2 had little effect on protein reduction, whereas loss of Clu and Pink1 decreased proteins involved in mitochondrial translation. However, Clu loss specifically decreased protein abundance of mitochondrial respiratory complex proteins. For more abundant proteins, Clu, Sod2, and Pink1 loss increased proteins involved in stress response, such as metabolism and protein folding. However, Clu loss also specifically increased the abundance of proteins involved in vesicle transport and cytoskeletal organization. In addition, using qPCR we found that several Clu low abundance candidates had increased transcript levels in all three genotypes. Finally, since Clu is a ribonucleoprotein, we used Clu immunoprecipitation and RT-PCR to identify five less abundant mitochondrial respiratory complex proteins that bind Clu, which could explain why these proteins have low abundance with Clu loss. Thus, using TMT mass spectrometry, we demonstrated that there appears to be non-specific effects on the mitochondrial proteome due to general mitochondrial dysfunction, and Clu-specific effects on mitochondrial respiratory proteins (less abundant) and proteins involved in vesicle transport and cytoskeletal organization (more abundant). These results help elucidate how single gene mutations responsible for mitochondrial disease cause overlapping and specific symptoms. The following fly stocks were used for experiments: w 1118 (Wildtype), clueless d08713 /Cyo Act GFP (Cox and Spradling 2009). Sod2 Δ2 /Cyo Act GFP and Pink1 B9 /FM7i were obtained from the Bloomington Drosophila Stock Center. Flies were reared on standard cornmeal fly media at 22 or 25°C. 50 mg of frozen adult flies were homogenized, using a 1 ml Wheaton glass homogenizer and a loose pestle (VWR, Randor, PA), in 1 ml of complete Mitochondrial Isolation Buffer [MIB: 250 mM Sucrose, 10 mM tris-Cl (pH 7.4), 5 mM EDTA, 15 mM MgCl2, 1X protease inhibitor cocktail (PIC) and 1 mM DTT]. Nuclei and unbroken cells were removed by centrifuging twice at 1,700 g for 10 min. Crude supernatant was spun again at 10,000 g for 15 min. Supernatant (cytosolic fraction) was removed carefully without disturbing the pellet (mitochondria). The mitochondrial pellet was washed twice with 1 ml MIB without PIC and DTT. The pellet was immediately frozen in dry ice and stored at −80°C until further processing. For mitochondrial protein extraction, frozen mitochondria were suspended in RIPA buffer (Millipore Sigma Cat #R0278) containing 1% sodium dodecyl sulfate (SDS) and kept on ice for 5 min followed by boiling the sample for 5 min. Extract was then spun at 16,000 g for 10 min and supernatant was removed to fresh tubes. Protein concentrations were measured using BCA reagent (Pierce BCA protein assay Kit, Thermo Fisher Scientific, Waltham, MA, United States), aliquoted and flash frozen on dry ice and stored at −80°C for further use. 50 µg of protein samples were sent for quantitative (TMT) mass spectrometry analysis. Protein samples were reduced and alkylated using DTT [15 mg/ml in 100 mM Triethylammonium bicarbonate (TEAB)] and Iodoacetamide (36 mg/ml in 100 mM TEAB) respectively. Samples were then precipitated with TCA/acetone. Protein pellets were re-constituted in 20% acetonitrile (ACN)/120 mM TEAB and digested with Trypsin/LysC (Promega). Peptides in each sample, 50 µg in 100 µL 110 mM TEAB, were labeled with a TMT 10plex label, in 41 µL acetonitrile, according to the Thermo Fisher protocol. Labeled peptides were combined to a total of 600 µg and split into three aliquots. Peptides were then cleaned (from detergent, small molecules, lipids, and TMT labels access) using Pierce detergent removal columns (Thermo Fisher Scientific, Waltham, MA, United States) and were fractionated by basic reverse phase (bRP) into 24 fractions each containing an average 8,33 µg of protein/fraction. Peptides were analyzed by liquid chromatography/tandem mass spectrometry (LCMS/MS) using a nano-LC-Orbitrap-Lumos2 in FTFT interfaced with a nano-LC 1200 system (Thermo Fisher Scientific, Waltham, MA, United States) using reverse-phase chromatography (2%–90% acetonitrile/0.1% FA gradient over 90 min at 300 nL/min) on 75 mm × 150 mm ProntoSIL-120-5-C18 H column (3 µm, 120 Å (BISCHOFF) (Bischoff MZ-ANALYSENTECHNIK GmbH D-55129 Mainz/Germany). Eluted peptides were sprayed directly into a Lumos mass spectrometer through a 1 µm emitter tip (New Objective, Inc. New Littleton, MA) at 2.4 kV. Survey scans (Full MS) were acquired within 350–1,600 Da m/z on an Orbi-trap using the Data dependent Top 15 method with dynamic exclusion of 15 s. Precursor ions were individually isolated with 0.6 Da, then fragmented (MS/MS) using HCD activation collision energy 38. Precursor and fragment ions were analyzed at a resolution of 120,000 AGC, target 1xe6, max IT 50 ms and 50,000, AGC target 1xe5, max IT110 ms, respectively, for three cycles. Tandem MS2 spectra were processed by Proteome Discoverer (v2.2 Thermo Fisher Scientific). MS/MS spectra were analyzed with Mascot v.2.6.2 (Matrix Science, London, United Kingdom) against 2017RefSeq_83_Drosophila melanogaster and a small DB containing enzymes, BSA, trypsin missed cleavage 2, files RC (recalibration with the same database), precursor mass tolerance 3 ppm, fragment mass tolerance 0.01 Da and Carbamidomethyl on C, TMT 6plex on N-terminus as fixed, and methionine oxidation, Deamidation NQ TMT 6plex on K as variable modifications. Peptides identified in Mascot searches were re-scored with Percolator within the Proteome Discoverer to select identified peptides with a confidence threshold 0.01% False Discovery Rate b (Mascot search with Percolator re-scoring) and to calculate the protein and peptide ratios. Only Peptide Rank 1 was considered. The database search identified ∼6150 proteins at high, medium, and low confidence with at least one peptide identified at 1% FDR Rank1. Ratios were calculated for the average of the two replicates. ANOVA analysis of the individual protein was used as a statistical method to calculate p values. Data from each mutant were compared with wild type control. For a detailed analysis of each genotype, raw data were curated into separate excel files along with the abundance ratios and p-values. The abundance ratio and p-value were converted into their respective logarithmic scales. Next, we used GraphPad Prism to plot the values in a volcano plot with a statistically significant threshold (p ≤ 0.05 or −log10[p-value] ≥ 1.3] as well as Fold Change cut-off lines on the graphs. Western blotting was performed as previously described (Sen et al., 2013; Sen et al., 2015). In short, protein samples from adult flies were extracted in a 1X SDS sample buffer. samples were run on 4%–15% TGX gels (Cat # 4561086, Bio-rad Laboratories, Hercules, CA) and transferred on to a nitrocellulose membrane (Thermo Fisher Scientific, Waltham, MA, United States) using a Trans-blot Semi-dry apparatus (Cat # 1073940, Bio-rad Laboratories, Hercules, CA). After blocking, blots were probed against appropriate primary and secondary antibodies. anti-Clu (1:15,000) (Cox and Spradling 2009), anti-TOM20 (1:2000, Santa Cruz Biotechnology, Dallas, TX), anti-ATP5A (1:100,000, Abcam, Cambridge, United Kingdom). The following antibodies were kindly provided by Dr. Edward Owusu-Ansah from Columbia University Medical Center, NY: anti-ND-ASHI (1:3,000), anti-ND-SGDH (1:3,000), anti-ND-17 (1:3,000), anti-ND-17.2 (1:3,000) and anti-UQCR-C2 (1:3,000) (Murari et al., 2020). Total RNAs were isolated from wild type and clu mutant adult flies using a Direct-zol™ RNA MiniPrep Plus Kit (Zymo Research, Irvine, CA) as per manufacturer’s recommended protocol. One microgram of total RNA was reverse transcribed using a High-Capacity cDNA Reverse Transcription Kit (Cat # 4368814, Thermo Fisher Scientific, Waltham, MA, United States) in a 20 µL reaction. cDNA was later diluted with water to 80 µL. For gene expression analysis, quantitative PCR was performed using TaqMan Gene Expression Master Mix (Thermo Fisher Scientific, Waltham, MA, United States) in a 10 µL reaction with 2 µL diluted cDNA and one of the following TaqMan probes: Dm01806850_g1 (Tom20), Dm02136274_g1 (ND-42), Dm01820354_g1 (ND-19), Dm01804649_g1 (Cyp4ac2), Dm02145551_g1 (Mil), Dm01822473_s1 (Hsp23), Dm01816546_s1 (Pepck), Dm01835343_g1 (ND-30), Dm01794109_g1 (COX4), Dm01830822_g1 (Levy) and Dm02151827_g1 (RPL32) (endogenous control) (Thermo Fisher Scientific, Waltham, MA). Fold changes were measured based on ddCt values compared to the endogenous transcript RpL32. ddCt values were converted to 2^[ddCt] to better represent the exponential nature of PCR. The average of four 2^[ddCt] values for each sample was plotted in bar graphs in GraphPad Prism. The S.E.M. and p values (unpaired t-test) were calculated using GraphPad PRISM. S2R+ were grown in 10 cm dishes and cross linked using a Stratalinker (Stratagene, San Diego, CA). After washing twice in cold 1X PBS cells were lysed in IP buffer [20 mM HEPES, pH 7.4; 50 mM KCl, 0.02% Triton X-100, 1% NP-40 (sub), 1 mM EDTA, 0.5 mM EGTA, 5% glycerol] supplemented with 1 mM DTT. Extract was incubated with oligo-dT magnetic beads to isolate mRNAs. mRNA was eluted and the 2nd step of the immunoprecipitation was performed using anti-Clu antibody or IgG-guinea pig as a control. Total RNA as well as Clu-bound mRNAs were isolated using RNA isolation kit (Zymo Research, Irvine, CA). RNA was stored at −80°C until further use. RT-PCR was performed using NEB one-step RT-PCR kit (New England Biolabs, Cat #E5315S) and gene specific primers (Supplementary Figure S10) for the following genes: ND-19, ND-ASHI, ND-SGDH, ND-42, ND-23, UQCR-14, UQCR-Q, CYP4AC2, COX4, COX5B, LEVY, HSP22, mRpS16, ATPsynCF6, and TOM20. Mitochondria were isolated using standard differential centrifugation. In short, 50 mg of adult flies were homogenized using a 1 ml glass homogenizer with mitochondrial isolation buffer [MIB: 250 mM Sucrose, 10 mM tris-Cl (pH 7.4), 5 mM EDTA, 15 mM MgCl2] supplemented with protease inhibitor cocktail (Millipore Sigma, St. Louis, MO, United States) and 0.5% BSA. Extract was centrifuged twice at 1,000 g for 5 min to obtain a clear lysate. Finally, the lysate was centrifuged at 6,400 g for 15 min to obtain a pellet enriched in mitochondria. Proteins from mitochondria were extracted using mitochondrial extraction buffer (Invitrogen, Thermo Fisher Scientific, Waltham, MA, United States) and then clearing the extract by centrifuging at 20,000 g for 30 min. Protein concentrations were determined using BCA reagents (Pierce BCA protein assay Kit, Thermo Fisher Scientific, Waltham, MA, United States). About 7.5 µg of proteins were loaded into 3%–12% bis-tris gel (Thermo Fisher Scientific, Waltham, MA, United States). The inner chamber of the apparatus was filled with a 1x cathode buffer (Invitrogen, Thermo Fisher Scientific, Waltham, MA, United States) and the outside chamber was filled with 1x Native PAGE running buffer. The gel was run at 150 V for 2 h in a cold room. For CN-PAGE, the cathode buffer was replaced with 1X running buffer after 30 min of run. The gel was fixed using the suppliers’ protocol and stained with colloidal blue (Invitrogen, Thermo Fisher Scientific, Waltham, MA, United States) or silver stained using the supplier’s protocol (SilverQuest Silver Stain Kit, Invitrogen, Thermo Fisher Scientific, Waltham, MA, United States). For in-gel activity assays for mitochondrial respiratory chain complexes, CN-PAGE gels were transferred to cold water. Complex IV + I activities: CN-PAGE gels were incubated in complex IV assay buffer [50 mM sodium phosphate buffer, pH 7.4; 0.5 mg/ml diaminobenzidine tetrahydrochloride (ThermoFisher Cat # 112090250), 1 mg/ml Cytochrome c from equine heart (Millipore Sigma Cat# C2506)] at room temperature for 25–30 min. After the brown bands appeared at a desired intensity, the assay solution was replaced with water and the gel was rinsed for 1 min before adding Complex I assay buffer [2 mM Tris-Cl, pH 7.5; 0.1 mg/ml NADH (Millipore Sigma Cat # 10107735001), 2.5 mg/ml Nitrotetrazolium Blue chloride (Millipore Sigma Cat# N6876)]. Gels were incubated at room temperature for additional 15–20 min to get prominent purple bands. The reaction was stopped by adding 1/10th volume of acetic acid, then the gel was rinsed with water and imaged. To determine MRC complex levels, BN-PAGE gels were run twice with unique biological and technical replicates. For MRC complex activity assays, for CI activity, complexes were isolated two times and the assay was performed five times. For CIV activity, complexes were isolated twice and the assays was performed two times. Statistically significance for CI activity was done using GraphPad PRISM with an unpaired t-test. The graphs for CI and CIV were made using GraphPad PRISM. To control for the effect of general mitochondrial dysfunction on mitochondrial protein levels, and to learn more about changes to mitochondrial proteomes in different mutants, we isolated mitochondria from clu, Sod2 and Pink1 mutant adults and performed tandem mass tag (TMT) mass spectrometry using isobaric labeling (Figure 1A). From the mitochondrial extract, we identified 4,970 proteins from clu, 4,927 from Sod2, and 4,973 proteins from Pink1 samples (Figures 1B–D; Supplementary Tables S1, S2). Mitomax is a web resource containing 2,126 mitochondrial proteins which were curated using proximity-based labeling or other mitochondrial isolation-based approaches (Yin et al., 2013; Lotz et al., 2014; Chen et al., 2015). We compared the proteins identified from our TMT mass spectrometry analysis with the Mitomax database to identify the common proteins (Figures 1B–D; Supplementary Table S3). As with the total number of proteins identified with TMT mass spectrometry, the number of identified mitochondrial proteins common between each mutant and Mitomax were similar (Figures 1B–D). To identify which proteins were statistically less or more abundant in each mutant background, we plotted protein abundance and found that while Sod2 and Pink1 mutants had a similar number of proteins more (red) and less (green) abundant, clu had double the number of less abundant mitochondrial proteins (Figures 1E–G). Clu, Sod2 and Pink1 affect mitochondrial processes in different ways. Thus, we wanted to understand the differences between how each mutant affects mitochondrial protein abundance. First, we compared the overlap of mitochondrial protein identity for less and more abundant proteins between all three mutants (Figures 2A,B). For less abundant proteins, a greater proportion are unique in clu mutants [58% (92 out of 158)] compared to Sod2 (38%, 22 out of 57) and Pink1 (22%, 18 out of 80) mutants (Figure 2A). Only 7% (21 out of 295) are common among all three (Figure 2A). For the more abundant mitochondrial proteins, 28%, 15% and 16% are unique between clu, Sod2, and Pink1 mutants, respectively (Figure 2B). Noticeably, the more abundant proteins common between all three mutants constitute the highest number (104) (Figure 2B). Since loss of Clu had the greatest effect on the mitochondrial proteome, we labeled the less and more abundant mitochondrial proteins for clu superimposed on the Volcano plots for proteins identified from Sod2 or Pink1 extract (Figures 2C–F, green and red dots, respectively). The less abundant proteins in clu mutants are randomly distributed on Volcano plots for Sod2 and Pink1, indicating there is not a strong correlation in which proteins are less abundant between the genotypes (Figures 2C,D, green dots). In contrast, more abundant proteins identified in clu mutant mitochondrial extract are mostly found in the upper right quadrant of Sod2 and Pink1 Volcano plots, indicating they are more similar (Figures 2E,F, red dots). There are many web-based algorithms available to analyze protein datasets to identify enriched pathways (Huang da et al., 2009; Chen et al., 2013; Mi et al., 2019). By using several methods, one has an increased chance of recognizing false enrichments. To better understand the differences and similarities in mitochondrial proteomes in clu, Sod2 and Pink1 mutants, we first analyzed which protein classes were affected using PANTHER (Mi et al., 2019; Mi et al., 2021). PANTHER is a bioinformatic classification system that combines gene function, ontology and pathways to create functionally related subfamilies [Protein Class (PC)]. PANTHER analysis showed that the largest protein class for less abundant proteins in clu and Sod2 mutants was metabolite interconversion enzyme (Figures 2G,H, red). In clu mutants, the second largest protein class was translational protein, which was the largest class in Pink1 mutants (Figures 2G,I, dark blue). It is important to note that this analysis identified the largest number of genes and protein class hits in clu mutants (119 and 103, respectively) whereas many fewer were identified in Sod2 (22 and 27, respectively) and Pink1 (52 and 42, respectively). Thus, the pie slices for Sod2 and Pink1 represented fewer proteins compared to clu (Supplementary Table S4). PANTHER analysis for more abundant proteins in all three mutants was different. First, the abundance of each protein class was more similar between all three compared to between the less abundant proteins (Figures 2J–L vs. Figures 2G–I). The five top hit classes were metabolite interconversion enzyme, protein modifying enzyme, cytoskeletal protein and membrane traffic protein (Figures 2J–L). Second, clu mutants again had more gene and protein class hits (170 and 132, respectively), but Sod2 and Pink1 had more hits compared to less abundant proteins. Overall, this suggests that more abundant proteins are functionally more similar between the three mutants compared to less abundant proteins. The protein class hits identified using PANTHER analysis are quite broad categories. A second way we analyzed changes to protein abundance was to use FlyEnrichr (Chen et al., 2013; Kuleshov et al., 2016). FlyEnrichr has several output options for identifying enriched pathways. We chose “biological process” which gave a more granular view of GO terms compared to the protein classes of PANTHER. FlyEnrichr analyses were performed under default conditions with the term GO Biological Process 2018 where the significance of the term was determined using combined scores (c-score = ln(adj p-value) ∗ z-score) in each dataset and adjusted p-value < 0.05 (Chen et al., 2013). Examining the less abundant proteins, 50%–70% of the top ten GO term hits for all three mutants were for mitochondrial function, including mitochondrial translation, mitochondrial gene expression, and electron transport (Table S5). As clu loss had the greatest number of reduced proteins, the combined score for the top ten hits was higher compared to Pink1 and Sod2. This combined score reflects three different significance values, thus, the higher the score, the more likely the resulting classification is real and not a false positive. While FlyEnrichr showed Sod2 loss also affected protein abundance for various classes of mitochondrial function, since the scores were somewhat low for each category and there were so many fewer proteins with low abundance, classification confidence was not as robust. 50% of the top ten GO terms for the proteins that are reduced in clu mutants were related to electron transport and oxidative phosphorylation, which was unique to Clu. There were very few mitochondrial associated categories shared for the top ten GO terms between all three mutants (one for clu, two for Pink1 and none for Sod2) (Supplementary Table S5). GO terms for more abundant proteins were quite varied (Supplementary Table S5). We also performed GO analysis using FlyEnrichr to assess the 21 shared downregulated proteins and the 104 shared upregulated proteins. We found that shared downregulated proteins are mostly involved in mitochondrial DNA replication and DNA metabolic process, whereas the common upregulated proteins are involved in microtubule depolymerization, organelle organization, and secretary pathway regulation (Figures 2A,B; Supplementary Table S5). To further analyze which mitochondrial proteins are less and more abundant in clu, Sod2 and Pink1 mutants, we performed STRING analysis which groups proteins by function (Figure 3; Supplementary Figures S1–S6, Supplementary Table S6) (Szklarczyk et al., 2015). For each protein, closer proximity, with more connections and shared color, correlates with increased associated, known or predicted function (Szklarczyk et al., 2015). In clu mutants, the less abundant proteins fell into two major categories (Figure 3A; Supplementary Figure S1). One category was composed of proteins predominantly involved in mitochondrial respiration and electron transport chain and the second category contained proteins involved in mitochondrial translation, e.g., mitochondrial ribosomal proteins (mRPs) (Figure 3A, dashed circles, Supplementary Figure S1, Supplementary Table S6). In contrast, STRING analysis indicated that mitochondrial proteins that are down in Sod2 mutants did not form any functional cluster (Figure 3B; Supplementary Figure S2, Supplementary Table S6). However, there was a clear functional node of less abundant proteins in Pink1 mutants that was shared with clu mutants: proteins involved in mitochondrial translation (Figure 3C, dashed circle, Supplementary Figure S3, Supplementary Table S6). Thus, mitochondrial proteins related to respiration are specifically less abundant with loss of clu and mRPs are less abundant in clu and Pink1 mutants. STRING analysis for more abundant mitochondrial proteins indicated overlapping functional nodes between the three mutant genotypes. More abundant mitochondrial proteins in clu mutants generated two major categories (Figure 3D, dashed circles, Supplementary Figure S4, Supplementary Table S6). One major cluster was common between clu, Sod2, and Pink1 mutants and was composed of proteins involved in stress response, e.g., protein folding, proteolysis, gluconeogenesis and amino acid catabolism (Figures 3D–F; Supplementary Figures S4–S6, Supplementary Table S6). clu mutants had the greatest number of increased proteins in amino acid catabolism, as was previously demonstrated for loss of CluH (Schatton et al., 2017). clu mutants also had a unique STRING cluster involved in vesicle transport and cytoskeletal organization (Figure 3D; Supplementary Figure S4, Supplementary Table S6). Thus, it appears that the stress of general mitochondrial dysfunction increases proteins involved in stress response, with clu absence also increasing a specific class of proteins involved in vesicle transport and the cytoskeleton. Finally, we performed Gene Set Enrichment Analysis (GSEA) (Subramanian et al., 2005) to analyze the pathways represented by the less and more abundant mitochondrial proteins in the three mutants (Supplementary Figures S7–S9). GSEA analysis was performed using the web-based functional enrichment tool WebGestalt (WEB-based Gene SeT AnaLysis Toolkit) (Wang et al., 2017). The GSEA pathway analysis based on KEGG (Kyoto Encyclopedia of Genes and Genomes) and Reactome supported the observation from the STRING analysis (Supplementary Figures S3,S7–S9). For both analyses, genes encoding proteins belonging to the classes oxidative phosphorylation and mitochondrial translation were downregulated in clu and Pink1 mutants. Metabolic pathways were also compromised in clu mutants. As per KEGG analysis, oxidative phosphorylation pathway proteins were reduced in Sod2 mutants. The relative statistical significance for the KEGG analysis is more clearly indicated in Volcano plots for all three mutants (Supplementary Figure S8). Proteins that were upregulated in clu mutants involve amino acid metabolism as well as pathways for membrane trafficking and stress response. In Pink1 mutants, proteins belonging to the TCA cycle and pyruvate metabolism were upregulated. Reactome Pathway analysis did not find any significant changes for Sod2 mutants (Supplementary Figure S9). In all of the above cases, FDR ≤0.05 was the cut-off limit. Our STRING analysis indicated that mitochondrial proteins related to respiration were specifically less abundant with clu loss (Figure 3A). The largest category in this clu-specific STRING cluster was mitochondrial respiratory chain (MRC) components (Figure 4A, green). Analyzing a breakdown of MRC proteins that were less abundant for Complex I (CI), Complex III (CIII), Complex IV (CIV) and Complex V (CV) in each mutant background indicated that Sod2 loss had little effect on MRC proteins, clu loss had the strongest effect, and Pink1 loss was intermediate (Figure 4B, green). We further compared the relative changes of specific MRC proteins using a heat map (Figure 4C). For each Complex, we compared the MRC proteins that are less abundant in clu mutants (Figure 4B, green) to their abundance in Sod2 and Pink1. For all the proteins that are less abundant in clu mutants, Sod2 mutants showed little change whereas Pink1 mutants had an intermediate amount of difference for the MRC candidates (Figure 4C). To validate our findings, we performed Western blots for representative proteins from CI, CIII, CIV and CV (Figure 4D). The western blot analysis confirmed that the levels of various subunits were greatly reduced in clu mutants with no effect in Sod2 and with Pink1 affecting some proteins. We used Tom20 as a loading control because our mass spectrometry data indicated no change in the level of Tom20 between the three genotypes and wild type (Supplementary Table S2). As the majority of MRC proteins was greatly reduced in clu mutants, we wanted to verify the integrity and activity of the respiratory complexes. To analyze the level of all MRCs, we ran mitochondrial extracts on Blue Native PAGE (BN-PAGE) and Clear Native PAGE (CN-PAGE) (Figure 4E). The band intensity was reduced for most complexes in clu mutants. Complexes isolated from Sod2 mutants had similar band intensity compared to wild type, whereas complexes from Pink1 mutants were only somewhat decreased in agreement with our heatmap analysis and western blots. Next, we assessed the activity of CI, CIV and the super complex (SC) using in-gel activity assays (Figures 4F–H). Activities of CI and CIV were highly reduced in clu mutants as compared to wild type and Sod2 (Figures 4F–H). CI and CIV activity were also significantly reduced for Pink1 mutants. Thus, respiratory complex integrity and activity was reduced for clu and Pink1, whereas Sod2 was mostly unaffected. Changes in protein levels can be due to several reasons. For example, protein degradation can be up or downregulated, mRNAs can become stabilized or destabilized or transcription can be up or downregulated. Since clu loss in particular affected MRC abundance and activity, and clu loss specifically decreased proteins involved in mitochondrial respiration (Figure 3A), we focused on MRC proteins to determine whether the reduced amount present in TMT mass spec analysis was due to decreased transcript level (Figure 5A). To do this, we performed quantitative PCR analysis to check the relative abundance of several candidate transcripts (Figure 5B). We chose six proteins less abundant in clu (ND-19, ND-30, ND-42, CYP4AC2, COX4, and LEVY) and one that showed no change (TOM20) between the mutants and wild type (Figure 5B; Supplementary Table S2). As expected, TOM20 transcript levels were mostly consistent between the three genotypes. Surprisingly, for steady state levels of transcript, loss of any of the three mutants significantly increased transcript levels. This was regardless of how abundant the protein was (Figure 4C). The only exception among the chosen candidates was CYP4AC2, a protein that may be involved in insect hormone biosynthesis, which was decreased in clu and Sod2 mutants, but not Pink1. Conversely, we were interested in steady state transcript levels for a small number of high abundance candidates and chose candidates identified in clu mutants (Figure 5C). We analyzed HSP23, PEPCK, and MIL (Figure 5D). As with less abundant proteins, all three transcripts were increased in all three mutants. These data suggest that the steady state levels of transcript are not related to lower protein abundance as measured by TMT mass spec and that general mitochondrial dysfunction causes increased transcript levels for our candidates identified in both low and high abundant clu proteins. Since Clu/CLUH binds nucleus-encoded mRNA, we analyzed if mitochondrial proteins that are less abundant in clu mutants associate with Clu, focusing on MRC transcripts. To do this we performed immunoprecipitation using anti-Clu or control IgG Guinea Pig antibodies followed by targeted RT-PCR against representative candidates across the respiratory chain components. We also used control RNA as a template for a positive control. We tested whether RT-PCR recognized products for the following mRNAs: ND-19, ND-ASHI, ND-SGDH, ND-42, ND-23, UQCR-14, UQCR-Q, CYP4AC2, COX4, COX5B, LEVY, HSP22, mRpS16, ATPsynCF6, and TOM20 (Figure 5E). After comparing with products from IgG-Guinea pig control IP, we found a subset of the low abundance candidates positively identified in the pellet following Clu immunoprecipitation and RT-PCR: ND-19, ND-ASHI, ND-SGDH, COX4 and UQCR-Q (Figure 5E, asterisks). Six low abundance candidates were not identified with RT-PCR: ND-42, UQCR-14, Levy, COX5B, ATPsynCF6, and mRpS16. Furthermore, the most significantly highly abundant candidate, Hsp22, was also not identified. These experiments clearly suggest that Clu binds selected mRNAs encoding mitochondrial respiratory chain proteins. Mitochondria were once viewed as static organelles whose main function was to provide ATP. Research from the past decades has established that these organelles are highly dynamic and change shape, numbers and activity to suit the needs of different tissues and cell types (Chan 2006; Youle and van der Bliek 2012). In addition, mitochondrial defects including generalized oxidative damage, mtDNA mutations and single nDNA mutations contribute to many diseases (Zeviani et al., 2003). As our understanding has increased, so has the number of proteins associated with mitochondrial function (Area-Gomez and Schon 2014). mtDNA encodes only 13 proteins, thus mitochondria rely on nucleus-encoded genes for all functions, including mitoribosome assembly and function, fission and fusion, movement along the cytoskeleton and many metabolic processes. Mitochondria have four compartments: the outer mitochondrial membrane, the intermembrane space, the inner mitochondrial membrane and the matrix. Thus, the mitochondrial proteome can be defined and compartmentalized in several ways. In addition, the outer mitochondrial membrane associates with large classes of proteins, including those involved in transport and other peripherally associated functions. Researchers obtain mitochondrial proteomic data from whole tissue/cell extract and from isolated mitochondrial extract which contributes to the variability of proteins. In this study, we used crude mitochondrial extract from Drosophila adult mutants 1–4 days old. This ensured we would capture not just proteins present in the four mitochondrial compartments, but also mitochondrially associated cytoskeletal elements and other peripheral proteins. A caveat is that potentially spurious proteins would also be present as evident from our TMT mass spectrometry analysis. There are several databases that are valuable resources for analyzing mitochondrial proteomes in different organisms (Sardiello et al., 2003; Lott et al., 2013; Chen et al., 2015; Smith and Robinson 2019; Rath et al., 2021). These databases are periodically updated using more advanced experimental and computational inputs. For example, the mammalian mitochondrial database, MitoCarta, has been updated with mitochondrial compartment and pathway specific information from mitochondria isolated from fourteen tissue samples (Rath et al., 2021). In Drosophila, MitoMax, a newly developed mitochondrial protein database, provides a comprehensive view of the Drosophila mitochondrial proteome incorporating other databases like MitoDrome, MitoMiner, MitoCarta as well as from mitochondrial isolation-based proteomic analysis. These databases highlight the complexity and dynamic nature of the mitochondrial proteome. Clu, Sod2, and Pink1 are critical for mitochondria, but function in very different ways. Loss of all three proteins reduces ATP and increases mitochondrial oxidative damage. Drosophila clu, Sod2 and Pink1 mutants have reduced lifespan, although Pink1 mutants live substantially longer than the other two (Duttaroy et al., 2003; Clark et al., 2006; Park et al., 2006; Sen et al., 2013). Clu/Cluh are essential ribonucleoproteins associated with ribosomal proteins that may regulate mRNAs for co-translational import into mitochondria (Sen and Cox 2016; Vardi-Oknin and Arava 2019; Hemono et al., 2022). Clu forms large, dynamic cytoplasmic particles that are sensitive to stress (Cox and Spradling 2009; Sheard et al., 2020; Sheard and Cox 2021). SOD2 is an important free radical scavenger located in the mitochondrial matrix. SOD2 converts damaging superoxide into hydrogen peroxide (Weisiger and Fridovich 1973). Finally, Pink1 is involved in mitophagy (Pickles et al., 2018). Pink1 also binds mRNAs encoding MRCs mRNAs at the mitochondrial outer membrane and thus may function in co-translational import (Gehrke et al., 2015). Thus, while all three proteins are crucial for mitochondrial function and ultimately cause similar damage, they perform very different roles in the cell. Proteomic analysis is available for mouse CluH KO and Drosophila Pink1 mutants (Vincow et al., 2013; Schatton et al., 2017). Proteins isolated from whole liver extract from E18.5 CluH knockout mice were compared to that of E18.5 wild type mice using LC/MS, then compared with a control P1 mouse liver fully labeled with heavy [13C6] lysine as a SILAC control (Schatton et al., 2017). Using this technique, Schatton et al. identified 525 proteins that mapped to mitochondria, 40% of which were reduced in the CluH knockout mouse liver extracts with ≥1.5 fold change. They showed that CluH KO mouse livers have reduced protein levels for mitochondrial proteins involved in metabolic pathways and oxidative phosphorylation. This analysis did not find any significant increase in mitochondrial proteins or transcripts levels. We also found clu mutants have significantly fewer mitochondrial proteins involved in mitochondrial respiration and translation. In fact, clu mutants had the largest number of less and more abundant mitochondrial proteins. We also found that all MRC proteins are less abundant. As with CluH knockout mice, we also saw reduced respiratory complex activity. Using STRING analysis, we identified distinct classes of mitochondrial proteins that were more abundant, including proteins involved in stress such as proteins involved in metabolism, protein folding and amino acid catabolism. This stress class was shared by Sod2 and Pink1 mutants. Finally, clu mutants had more abundant cytoskeletal proteins and vesicle transport proteins, although all three mutants showed some association with increased cytoskeletal elements by GO term Biological Process analysis. Cells mutant for clu, CluH, the Arabidopsis ortholog FRIENDLY MITOCHONDRIA, and the Dictyostelium ortholog CluA, have highly mislocalized and clumped mitochondria which could be one explanation (Zhu et al., 1997; Fields et al., 1998; Cox and Spradling 2009; El Zawily et al., 2014; Gao et al., 2014). However, Pink1 and Sod2 mutants also have mislocalized mitochondria, but do not increase the levels of these protein classes (Sen et al., 2015; Sheard et al., 2020). Clu has been shown to have a role in muscle integrity, thus perhaps it has a greater effect on cytoskeletal elements (Wang et al., 2016). To determine whether Pink1 is involved in mitophagy, Vincow et al. (2013) used SILAC labeling to identify whether proteins have longer half-lives in Pink1 mutant heads, potentially due to impeded mitophagy. To do this, they fed mutant or control flies heavy D3-leucine five to 10 days then performed mass spectrometry (Vincow et al., 2013). From this analysis, Vincow et al. (2019) showed that only MRC proteins had longer half-lives, compared to non-MRC proteins, and that of the 45 identified MRC proteins, this effect was primarily due to those that are in the membrane. We found that loss of Pink1 resulted in fewer proteins involved in mitochondrial translation, such as mitochondrial ribosomal proteins, which was similar to clu mutants. In addition, Pink1 mutant flies appeared to have more abundant proteins related to stress, namely, heat shock proteins and proteins involved in metabolism and amino acid catabolism. Vincow et al. (2019) also examined the half-life of mitochondrial proteins in a transheterozygote Sod2 mutant and found no difference in protein half-life compared to control. We used a Sod2 null mutant which only lives 1 day (Duttaroy et al., 2003). For less abundant mitochondrial proteins, Sod2 showed the least correlation with specific pathways or GO terms. However, these mutants had more abundant proteins involved in stress and metabolism. The less abundant proteins shared between all three mutants were overwhelmingly represented by GO terms involved in mitochondrial processes (8/10). Of the top ten GO terms, five were clearly mitochondrial and three were likely mitochondrial. An additional term was axonal transport of mitochondria (GO:0019896). One GO term appears to potentially be a false positive since it is tightly associated with humoral immune response, although mitochondrial diseases are associated with disruptions to immune response (Kapnick et al., 2018). In general, the common less abundant proteins support that the resulting mitochondrial damage from loss of each gene causes disruption to mitochondrial biosynthetic processes, such as mtDNA replication and maintenance and biomolecule synthesis. Perhaps more surprising were the two shared classes of proteins that were more abundant. It is possible that these two classes are false positives, however, there are potential reasons why they would be more abundant. Of the top ten shared GO terms, 4/10 were microtubule organization and 4/10 were synaptic function. Control of microtubule polymerization would likely affect mitochondrial movement and positioning in the cell. General mitochondrial damage caused by a variety of insults is well known to cause mitochondrial clustering in various cell types (Al-Mehdi et al., 2012; Agarwal and Ganesh 2020; Sheard and Cox 2021). Whether this mechanism protects the cell from increasing oxidative damage or is a result of damage is not known. The second more abundant class appeared to be related to synaptic function. Since our sample was whole body, it would include all central and peripheral nervous system tissues. Since loss of mitochondrial function likely plays a role in neurodegenerative disease, increased mitochondria-associated proteins involved in synaptic function could be upregulated in response to decreased mitochondrial function in order to support synapses (Verstreken et al., 2005; Cai and Tammineni 2017). There exist many mitochondrial diseases that arise from single nDNA gene mutations (Zeviani et al., 2003; Area-Gomez and Schon 2014). To better understand disease etiology, researchers try to understand the molecular mechanisms of each protein in order to shed light on how lack of the process could cause mitochondrial dysfunction and disease. However, studies often do not examine the overall effect particular mutations have on the mitochondrial proteome. Given that mitochondria are an important metabolic nexus in the cell, it can be difficult to parse whether resulting tissue decline is due directly to loss of a single protein or is a secondary effect from general mitochondrial damage. This work suggests that both things occur. clu, Sod2, and Pink1 mutant flies appear to have more abundant proteins related to stress supporting a general response to mitochondrial damage that is shared by all three mutations. Since clu (and possibly Pink1) may function in co-translational import, the less abundant proteins could be directly related to loss of co-translational import. As our proteomic analysis looks only at the endpoint of protein abundance, it does not address dynamic changes to transcription or translation. Future studies comparing global transcript changes and mRNA stability could shed light on the dynamism of the mitochondrial proteome with loss of these three genes.

PMC9644188

Bicko Steve Juma,Asunta Mukami,Cecilia Mweu,Mathew Piero Ngugi,Wilton Mbinda

Targeted mutagenesis of the CYP79D1 gene via CRISPR/Cas9-mediated genome editing results in lower levels of cyanide in cassava

26-10-2022

cassava,CRISPR/Cas9,cyanide,MeCYP79D1,targeted mutagenesis

Cassava is the world’s most essential food root crop, generating calories to millions of Sub-Saharan African subsistence farmers. Cassava leaves and roots contain toxic quantities of the cyanogenic glycoside linamarin. Consumption of residual cyanogens results in cyanide poisoning due to conversion of the cyanogens to cyanide in the body. There is a need for acyanogenic cassava cultivars in order for it to become a consistently safe and acceptable food, and commercial crop. In recent years, the CRISPR/Cas system, has proven to be the most effective and successful genome editing tool for gene function studies and crop improvement. In this study, we performed targeted mutagenesis of the MeCYP79D1 gene in exon 3, using CRISPR/Cas9, via Agrobacterium-mediated transformation. The vector design resulted in knockout in cotyledon-stage somatic embryos regenerated under hygromycin selection. Eight plants were recovered and genotyped. DNA sequencing analysis revealed that the tested putative transgenic plants carried mutations within the MeCYP79D1 locus, with deletions and substitutions being reported upstream and downstream of the PAM sequence, respectively. The levels of linamarin and evolved cyanide present in the leaves of mecyp79d1 lines were reduced up to seven-fold. Nevertheless, the cassava linamarin and cyanide were not completely eliminated by the MeCYP79D1 knockout. Our results indicate that CRISPR/Cas9-mediated mutagenesis is as an alternative approach for development of cassava plants with lowered cyanide content.

Targeted mutagenesis of the CYP79D1 gene via CRISPR/Cas9-mediated genome editing results in lower levels of cyanide in cassava Cassava is the world’s most essential food root crop, generating calories to millions of Sub-Saharan African subsistence farmers. Cassava leaves and roots contain toxic quantities of the cyanogenic glycoside linamarin. Consumption of residual cyanogens results in cyanide poisoning due to conversion of the cyanogens to cyanide in the body. There is a need for acyanogenic cassava cultivars in order for it to become a consistently safe and acceptable food, and commercial crop. In recent years, the CRISPR/Cas system, has proven to be the most effective and successful genome editing tool for gene function studies and crop improvement. In this study, we performed targeted mutagenesis of the MeCYP79D1 gene in exon 3, using CRISPR/Cas9, via Agrobacterium-mediated transformation. The vector design resulted in knockout in cotyledon-stage somatic embryos regenerated under hygromycin selection. Eight plants were recovered and genotyped. DNA sequencing analysis revealed that the tested putative transgenic plants carried mutations within the MeCYP79D1 locus, with deletions and substitutions being reported upstream and downstream of the PAM sequence, respectively. The levels of linamarin and evolved cyanide present in the leaves of mecyp79d1 lines were reduced up to seven-fold. Nevertheless, the cassava linamarin and cyanide were not completely eliminated by the MeCYP79D1 knockout. Our results indicate that CRISPR/Cas9-mediated mutagenesis is as an alternative approach for development of cassava plants with lowered cyanide content. Cassava (Manihot esculenta, Crantz), a root crop, is predominantly grown in tropical and sub-tropical countries for its carbohydrate-rich roots and is the fourth leading calorie-producing crop worldwide (Bull et al., 2018). Cassava can grow well in minimal rainfall conditions and less fertile soil types; their roots can survive in the soil for 1–2 years without decaying (Tomlinson et al., 2018). These agronomic attributes make cassava a popular food crop, especially for approximately 5 million subsistence farmers in Africa, which is prone to drought and hunger, thereby making it a valuable food security crop (Jarvis et al., 2012). In particular, cassava leaves are consumed in west African countries and are an excellent source of proteins, vitamins, fibers, and minerals (Saurabh et al., 2014). Moreover, both cassava leaves and tubers can be used as animal feed as well as in starch, ethanol, and textile manufacturing industries (Alla et al., 2013). With the exception of seeds, all cassava tissues contain potentially toxic levels of cyanogenic glycosides, i.e., 95% linamarin and 5% lotaustralin. Cyanogenic glycosides are used by the plant as herbivore repellants (Siritunga and Sayre, 2004) and have recently been found to play a role in the transport of nitrogen from leaves to roots in its reduced form (Islamiyat et al., 2018). In the presence of enzymes, cyanogenic glycosides subsequently undergo hydrolysis to yield hydrogen cyanide (HCN) (Li et al., 2020). Although edible storage roots possess a higher cyanide (CN) level (10–500 mg CN equivalents/kg dry weight) than the maximum recommended levels for foods (10 mg CN equivalents/kg dry weight), this level is approximately 20-fold lower than that in leaves (200–1,300 mg CN equivalents/kg dry weight), which is reportedly the highest CN content (Echeverry-Solarte et al., 2013). Consumption of poorly processed cassava, particularly by a nutritionally compromised population (low protein intake), results into cyanide-related health disorders. The severity of these disorders depends on the level and frequency of cyanogen exposure and the pre-existing health condition of the consumer. Chronic, low-level cyanide exposure has been associated with the development of tropical ataxic neuropathy and hyperthyroidism, whereas acute cyanogen poisoning has been associated with outbreaks of konzo, a rapid and permanent paralysis, which in most cases causes death (Kashala-Abotnes et al., 2019). A contributing factor for cyanide-induced disease is cysteine levels in the diet since sulfur-containing amino acids are required for converting cyanides into less toxic thiocyanates (Blagbrough et al., 2010). Numerous approaches have been employed to lower the levels of cyanogenic glycosides in cassava. The most frequent traditional procedures used are pounding or tissue-maceration, boiling, sun-drying, baking and steaming, and fermentation (Ani, et al., 2019). Tissue rupture liberates linamarin from the vacuole, allowing linamarase to de-glycosylate it. Boiling is not an efficient method because of the high temperatures involved and only has a 50% cyanogen elimination rate (Taleon et al., 2019). Owing to a processing temperature of ≥100°C and the durability of linamarin in neutral or weak acid conditions, the cyanide elimination rate by baking and steaming remains negligible. In comparison to the other traditional methods, sun-drying is rather effective and is associated with a relatively low cyanide retention rate because a temperature of <55°C is employed, which is the optimal temperature for linamarase activity and cyanogen breakdown (Airaodion et al., 2019). These methods are time-consuming and do not guarantee the complete removal of cyanogenic glycosides, thereby generating harmful food products. Cassava breeding strategies for reducing the production of cyanogenic glycoside have been explored using conventional technologies. These strategies are targeted at combining pest and disease resistance with favorable agronomic features and minimal cyanogenic potential (Zhang et al., 2017). Because most subsistence farmers choose highly cyanogenic cassava cultivars, these strategies have proved ineffective. The reasons for this preference include taste preference, herbivory reduction, and theft protection (Naveena et al., 2021). Furthermore, cassava crops with a low cyanogenic potential do not produce a high yield of tubers owing to a reduced level of nitrogen transfer from the leaves to the roots, which is a precursor for the production of asparagine, an amino acid present in the roots (Cressey and Reeve, 2019). Due to an extended life cycle, high heterozygosity of allopolyploid plants, poor bloom and seed set, and inbreeding depression as well as the time-consuming and labor-intensive nature of the techniques employed to reduce cyanogenic glycoside levels, conventional cassava breeding does not seem feasible (Ceballos et al., 2004). This has necessitated alternative technologies that would precisely alter the genes responsible for synthesizing cyanogenic glycosides (Behera and Ray, 2017). Owing to the ability to precisely alter the genomes of living organisms, genome editing (GE) has transformed biological research. GE techniques include zinc finger nucleases (ZFNs), transcriptional activator-like effector nucleases (TALENs), and clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated nuclease (Cas) system (Kamburova et al., 2017). ZFNs are DNA cleavage proteins with the ability to cleave DNA sequences at any location in the molecule. Although ZFNs and TALENs have been widely employed since 2002 and 2011, respectively, as GE techniques for humans, animals, and plant cells, their effectiveness is limited (Samanta et al., 2016). ZFNs frequently generate unintended off-target mutations owing to their limited specificity. Construction of vectors for ZFNs and TALENs is a time-consuming and effort-intensive process (Juma et al., 2021). Consequently, since 2013, the focus has shifted to the use of CRISPR/Cas9, and more recently, to various novel CRISPR/Cas variants (Liu et al., 2020). CRISPR/Cas has higher efficiency and success rate, as well as lower cost, than any other GE technique. Several GE techniques, including CRISPR/Cas9, have recently been established in cassava, with applications ranging from yield enhancement to drought tolerance (Odipio et al., 2017), disease resistance (Gomez et al., 2019; Veley et al., 2021), and herbicide tolerance (Hummel et al., 2018). Production of cyanogen in cassava continually occurs in intact plants through a mechanism involving valine and/or isoleucine as a precursor (Hamza et al., 2020). The process is catalyzed by highly similar proteins, namely CYP79D1/D2 (85% identical). They catalyze the conversion of precursors to their respective oximes (Tawanda et al., 2017). Their production varies naturally among cultivars, indicating that cyanogen levels can be modulated without altering other desirable plant properties. Previous work demonstrated that CYP79D1 is a good target for reduction the levels of cyanogenic glycosides levels in cassava leaves and roots (Siritunga and Sayre, 2003; Jørgensen et al., 2005; Piero, 2013). Here, we focused on the MeCYP79D1 gene, which encodes the valine monooxygenase I enzyme involved in biosynthesis of cyanogen in cassava. Therefore, we considered the CYP79D1 gene a suitable target for generating acyanogenic cassava lines using the CRISPR/Cas9 system. Thus, we aimed to considerably reduce the potentially toxic cyanogenic glycoside levels in cassava plants by knocking out the CYP79D1 gene. The cassava plants (TMS 60444) were obtained from International Livestock Research Institute’s in vitro germplasm in Nairobi, Kenya, and maintained on a cassava micropropagation medium: Murashige and Skoog (MS) basal salt with vitamins (Murashige and Skoog, 1962) supplemented with 2% sucrose and 3 g/L gelrite at pH 5.8. The reference cassava genome was searched using BLAST for nucleotide sequences that were identical to the Manihot esculenta valine monooxygenase I gene in Phytozome 13 (version 8.1) (https://phytozome-next.jgi.doe.gov/; Bredeson et al., 2016). A single copy of the candidate gene, MeCYP79D1, was identified. The CRISPOR algorithm (http://crispor.tefor.net/), an online tool, was used to select a gRNA target within the CYP79D1 gene ( Supplementary Material 1A ) (Concordet and Haeussler, 2018). The chosen gRNA target was located in the exon 3 of the genomic locus LG13 ( Supplementary material 1A ). An off-target analysis was performed for the chosen gRNA target sequence, alongside other considered CYP79D1 targets, using the Cas-OFFinder online software (http://www.rgenome.net/cas-offinder/) ( Supplementary Material 2 ). A pair of complementary DNA oligonucleotides was synthesized (Sigma Aldrich, USA). The oligonucleotides were annealed and the resulting duplex was then cloned into the empty plant CRISPR‐Cas9 vector (Sigma Aldrich) digested by BsaI. The empty plant CRISPR‐Cas9 vector construct included the SpCas9 coding sequence from S. pyogenes as well as a gRNA scaffold with a BsaI restriction site to facilitate the insertion of a single gRNA. Hygromycin-resistance gene (hptII) was also included upstream of the gRNA to aid the selection of transgenic cells during the regeneration cycle. Cas9 expression was driven by the 35S Cauliflower mosaic virus promoter, while gRNA expression was driven by the U6-26 promoter ( Supplementary Material 1B ). PCR and Sanger sequencing were used to confirm the presence of the integrated gRNA and its stability. The binary plasmid, pCRISPR/Cas9-MeCYP79D1 ( Supplementary Material 1B ), carrying the gRNA was transformed into the Agrobacterium tumefaciens strain GV3101 using freeze–thaw method. Agrobacterium-mediated transformation was utilized to deliver CRISPR/Cas9 gene editing tools into immature leaf lobes of the cassava cultivar, TMS 60444. One week old emerging leaf lobes from in vitro grown cassava plants were harvested and subsequently injured using sterile scalpel blades. Approximately 25 µL of Agrobacterium tumefaciens (strain GV3101) bacterial suspension from an infection medium was administered to explant tissues and incubated for 5 min in the dark. The leaf explants were infected with bacterial suspension with an empty vector as a negative control. Using a sterile filter paper, excess infection medium was drained and the explant tissues were placed with adaxial side touching the solid co-cultivation medium (MS basal salts supplemented with 2% (w/v) sucrose, B5 vitamins, 100 mg/L casein hydrolysate, 0.5 mg/L CuSO4, 10 mg/L 2,4-dichlorophenoxyacetic acid (2,4-D) supplemented with 200 µM acetosyringone, 3 g/L gelrite at pH 5.8). The plates containing the explants in co-cultivation medium were wrapped with parafilm to prevent contamination and incubated in the dark for 3 days at 28°C for the incorporation of the Agrobacterium into the leaf explants and provide optimal condition for callus formation. After co-cultivation with A. tumefaciens strain GV3101 for 3 days, explants were transferred to a solid resting medium (MS basal salts supplemented with 2% (w/v) sucrose, B5 vitamins, 100 mg/L casein hydrolysate, 0.5 mg/L CuSO4, 10 mg/L 2,4-D and 3 g/L gelrite, supplemented with 250 mg/L timentin) for 2 days to inhibit any further growth of Agrobacterium, incubated in the dark at 28°C. Copper sulfate was added to increase the growth and visibility of embryo. As a negative control, a different batch of explants co-cultured with A. tumefaciens harboring an empty vector were placed in the resting media. Live calli formed were transferred from the resting medium to a selection medium (MS basal salts supplemented with 2% (w/v) sucrose, B5 vitamins, 100 mg/L casein hydrolysate, 0.5 mg/L CuSO4, and 10 mg/L 2,4-D supplemented with hygromycin (10 mg/L), and timentin (15 mg/L) to select for putatively transformed callus, incubated in the dark at 28°C (Syombua et al., 2019). After 2 weeks of culture, the proliferating callus was dissected into smaller pieces to ensure solid contact with the media before being transferred onto a fresh second selection medium supplemented with 20 mg/L hygromycin and 15 mg/L timentin. The callus was incubated in the selection medium for 4 weeks. When the calli were 8 weeks old, they were moved onto the solid MS medium enhanced with B5 vitamins, 2% sucrose, 1 mg/L 1-naphthaleneacetic acid (NAA), 20 mg/L hygromycin, and 15 mg/L timentin, and left in the dark at 28°C; they were cultured for a maximum of 4 months and sub-cultured every 2 weeks. The cotyledon-stage embryos were selected and sub-cultured in a somatic embryo maturation medium, containing MS medium supplemented with B5 vitamins, 2% sucrose, 1 mg/L 6-benzylaminopurine (BAP), 0.01 mg/L NAA, 0.5 mg/L gibberellic acid (GA3), 20 mg/L hygromycin, 15 mg/L timentin, 3 g/L gelrite and a pH of 5.8, and placed in the dark at 28°C. The number of cotyledonary embryos generated from each callus was counted after 4 weeks of culturing. For phenolic compound adsorption, green cotyledonary embryos with distinct shoot and root apices were placed in glass bottles containing 50 mL of hormone-free desiccation solution. This medium contained 0.8% activated charcoal, 2% sucrose, MS salts, and B5 vitamins, all of which were solidified with 3 g/L gelrite and incubated at 28°C under 16-h/8-h light/dark cycle. After 7–14 days, the number of germinated embryos was counted and transferred to a cassava micropropagation medium containing MS salt with vitamins supplemented with 3% sucrose, 5 mg/L hygromycin, 10 mg/L timentin, and solidified with 3 g/L gelrite (pH 5.8); the embryos in this medium were subsequently incubated at 28°C under 16-h/8-h light/dark cycle. Putative gene-edited cassava plants with distinct shoots and roots were carefully removed from the medium to minimize root damage and transferred to small pots filled with peat moss and covered with a plastic bag; these pots were placed in a glasshouse for 2 weeks at room temperature to regulate humidity and temperature. Using a hand sprayer, a mist of water (50 mL) was added. After 2 weeks, the plastic bags were gradually removed to allow the plantlets to acclimatize to the glasshouse environment. The surviving plants were then transplanted to larger pots filled with a mixture of peat moss and forest soil (50/50 v/v), and the plants were cultivated on an open bench in a greenhouse maintained at 28°C under natural light and artificial illumination with an approximate light/dark cycle of 16 h/8 h. Subsequently, the plants were placed into potted soil, and the watering interval (150 mL) was reduced to once a week. Leaf tissues were collected from 3 months old acclimatized in vitro regenerated plants. Approximately 0.5 g of tissue was placed in a 2-mL Eppendorf tube containing ceramic beads and vortexed to fine powder. Genomic DNA was extracted using the cetyltrimethylammonium bromide (CTAB) method (Doyle and Doyle, 1990) and treated with RNase A to remove RNA contamination. The putative transgenic plants were subjected to PCR analyses to confirm the integration of T-DNA using primers specific to the Cas9 gene and target gene ( Supplementary Material 3 ). The Cas9 gene was amplified using gene-specific primers complementary to a 900-bp amplicon to confirm the presence of the Cas9 gene. Each PCR reaction was performed in a 20 µL (total volume) of reaction mixture. The Cas9 gene was amplified under the following conditions: 95°C for 5-min initialization; 35 cycles of denaturation at 95°C for 10 s, annealing at 53°C for 30 s, extension at 72°C for 1 min; and a final extension of 72°C for 5 min. Amplification with gene-specific primers (CYP79D1; 350 bp in length) confirmed the gene of interest. The following conditions were used to perform the PCR reaction: initialization at 95°C for 5 min, denaturation at 95°C for 10 s, annealing at 53.83°C for 30 s, extension at 72°C for 1 min, and a final extension at 72°C for 5 min. A negative control containing 2 µL of non-transformed plant DNA and a positive control containing 2 µL of plasmid DNA were run alongside the DNA extracted from putatively transformed plants. PCR products were resolved on a 1.5% gel and visualized under UV light. Amplicons from target gene-specific primers were washed using Exo SAP-IT (Thermo Fischer Scientific, USA) and subjected to targeted Sanger sequencing using CYP79D1 F and CYP79D1 R primers to characterize CRISPR/Cas9-induced mutations. Raw sequences were trimmed, with overlapping paired-end reads were merged into a single sequence and aligned with the wild-type reference sequence of the MeCYP79D1 gene using Bioedit alignment software v7.2 with default settings to identify any insertions or deletions (indels). Genome editing efficiency was calculated by dividing the total number of transgenic lines by the number of mutant lines. Total RNA was extracted from the leaf tissues (30 mg) of both in vitro putative transgenic plants and non-transgenic control cassava plants using RNeasy Mini Kit (Qiagen, GmbH, Hilden, Germany) according to the manufacturer’s instruction, and On-Column DNase digestion was performed for 15 min at room temperature using 1 unit DNase (Invitrogen, Carlsbad, CA). RNA concentration was measured using Nanodrop ND-2000 spectrophotometer. DNase was inactivated according to the manufacturer’s instructions to avoid the digestion of newly synthesized cDNA. First strand cDNA synthesis was performed using 1 µg of total RNA and reverse transcriptase from the LunaScript™ RT SuperMix Kit according to the manufacturer’s protocol. The synthesized cDNA was amplified by PCR using 1× PCR buffer, 1.5 mM MgCl2, 0.1 mM dNTP, 2.5 units of Taq polymerase, 0.4 μM of each primer specific for Cas9 gene primer sequences ( Supplementary Material 3 ). To check the quality of the synthesized cDNA, Actin-7 (ACT) amplification was performed as an internal control using ACT specific primers ( Supplementary Material 3 ). The RT-PCR products were subsequently run on an agarose gel [1% (w/v)] at 100 V for 45 min and visualized under UV light. High performance liquid chromatography (HPLC) was used to measure linamarin content in plantlets grown in vitro. Approximately 5 g of 4 months old cassava leaves were harvested and immediately homogenized in a blender for 14 s at low speed, followed by homogenization for 1 min (2×) at high speed with 25 mL of 0.25 M chilled sulfuric acid. To remove insoluble materials, the homogenates were filtered using a filter cloth. The homogenizer jar was washed with 40 mL of the 0.25 M chilled sulfuric acid before filtering the homogenates again in the aforementioned manner. Negative control was a chilled 0.25 M sulfuric acid solution without any plant tissues. The filtrates were centrifuged for 10 min at 10,000 rpm at 4°C. The clear supernatant fluid was collected and stored at −20°C. To facilitate absolute quantification of linamarin, a standard stock was prepared from solid linamarin (A.G. Scientific, Biochemical Manufacturer, USA; purity ≥ 98) resuspended in water to obtain a stock solution of 100 µg/mL and stored at −20°C. While performing the assay, the standard was serially diluted at concentrations of 10–100 µg/mL (10, 20, 40, 60, 80, and 100) and subjected to HPLC analysis. Three technical replicates of the standard stock were prepared. Cassava extract samples were analyzed using a HPLC system, with the mobile phase consisting of methanol and water (25:75, v/v). A C18 HPLC column was used for the analysis, with a flow rate of 1 mL/min and an injection volume of 10 µL (column temperature 40°C). Detection was done at a wavelength of 214 nm. The gas bubbles from the mobile phase were removed before use. HPLC data acquisition and analysis were performed using an Excel spreadsheet. The fresh weight of linamarin in the plant extracts was measured by plotting a calibration curve of the peak area values obtained from the serially diluted linamarin standard solution and by using the following formula: y=mx + c; here, y was the concentration of crude linamarin, and x was the peak area reading obtained from HPLC. The total cyanide concentration in the leaves of hygromycin-resistant transgenic TMS 60444 cassava lines and wild-type cassava carrying the empty vector was measured using a picrate assay kit (Bradbury et al., 1999). Young cassava leaves were harvested from 4 months old acclimatized cassava, chopped using scissors, and ground immediately. The buffer discs were placed in a flat-bottomed bottle, and 100 mg of the ground leaves were placed on top of it. Clean water (1.0 mL) was added using a plastic pipette and the solution was mixed gently. A yellow indicator paper was immediately added without allowing it to touch the liquid, and the bottle was immediately closed with the screw-capped lids. This process was performed in triplicate (biological replicates) for each sample. For the positive control, the buffer/enzyme paper disc was placed in a bottle along with the standard pink paper disc, with the subsequent addition of 1 mL of clean water. A yellow indicator paper was then placed and the bottle was closed tightly using the screw-capped lid. For the negative control, the standard pink paper was placed in a bottle with 1 mL of water added to it, and the bottle was closed tightly using the screw-capped lid. All bottles were subsequently incubated at room temperature for 24 h. The bottles were opened, and the indicator papers matched against the color shades on the color chart provided in the cassava cyanogen kit. The total cyanide content of the leaves was estimated in parts per million (ppm), equivalent to HCN (mg)/cassava fresh weight (kg), based on the readings given in the color chart, with the negative control being zero and the positive control producing a color equivalent to an estimated cyanide concentration given in ppm. The plastic backing of the indicator paper was carefully removed and the paper was placed in a test tube followed by the addition of distilled water, accurately measured to 5.0 mL. The test tubes were incubated at room temperature for about 30 min, with intermittent gentle stirring. The absorbance of the solutions was measured at 510 nm, and the value of the negative control was subtracted subsequently. The total cyanide content in ppm was calculated using the equation: total cyanide content (ppm) = 396 × absorbance. Wild-type and transgenic TMS 60444 cassava plantlets were grown in the greenhouse under normal conditions. The agro-morphological traits were characterized by measuring plant height, leaf length, leaf width, number of leaves per plant, and stalk length after being incubated for 4 months in the green house (Nadjiam et al., 2016). A statistical analysis of the phenotypic data and expression levels was performed by employing two-tailed Student’s t-test in the GraphPad Prism software (*p<0.05). To generate transgenic plants with targeted mutations in MeCYP79D1 gene, a gene-specific CRISPR/Cas9 vector construct was designed and used for transformation. A ranked list of prospective off-target sites was produced using the Cas-OFFinder online tool (http://www.rgenome.net/cas-offinder/), which was utilized to evaluate for potential off-targets ( Supplementary Material 2 ). This sorted list of probable off-target sites aids in the selection and assessment of intended target sites, assisting in the development of CRISPR/Cas systems with low off-target effects, as well as the identification and measurement of CRISPR/Cas induced off-target cleavage in cells. The gRNA for CRISPR/Cas9 editing was synthesized using the target sequence highlighted in yellow ( Supplementary Material 2 ). Agrobacterium-mediated transformation and somatic embryogenesis were performed as previously described by Odipio et al. (2017) and Syombua et al. (2021). After repeatedly subculturing the tissue and performing antibiotic selection on a selective medium ( Figures 1A–C ), multiple hygromycin-resistant cotyledon-stage embryos were generated from original leaf explants ( Figures 1D, E ). Culturing of antibiotic-resistant continued on a hygromycin-containing medium, consequently producing plantlets on a selective medium ( Figures 1F, G ). In total, 5.91% independent lines of the regenerating somatic embryos were recovered after selection on the hygromycin-containing-medium ( Supplementary Material 4 ); 1.78% of the leaf explants germinated to produce plantlets, thereby generating eight independent transgenic plant lines which subsequently propagated in the greenhouse ( Supplementary Material 5 ). To validate exogenous T-DNA insertion in the transgenic plantlets, DNA extracted from eight independent transgenic events were analyzed by PCR to detect the presence of T-DNA and to amplify the targeted MeCYP79D1 gene using gene-specific primers for the Cas9 and target gene ( Supplementary Material 3 ). All the putative transgenic plants tested positive for the presence of Cas9, which were referred to as TMS1, TMS2, TMS3, TMS4, TMS5, TMS6, TMS7, and TMS8, respectively; however, no amplification was observed for the wild-type ( Supplementary Material 6A ). A 350-bp fragment corresponding to the target region of the MeCYP79D1 gene in cassava was amplified in all putative transgenic cassava plantlets as well as in the wild-type cassava plantlet ( Supplementary Material 6B ). The nature of mutation acquired in putative transgenic cassava plantlets was evaluated by performing targeted Sanger sequencing of PCR amplicons. Mutations in the MeCYP79D1 gene were observed in all transgenic plants ( Supplementary Material 7 ). Both nucleotide deletions and substitutions were observed ( Figure 2 ). According to the mutations detected, the mutagenesis efficiency of pCRISPR/Cas9–MeCYP79D1 construct was 100% (8/8). Deletions occurred more frequently than substitutions ( Figure 2 ). Combined analysis of the results indicated that deletions were the most frequent type of mutation (75%), with four base-pair deletions, which occurred three nucleotides upstream of the PAM sequence, and found in the following T0 plants: TMS1, TMS2, TMS3, TMS4, TMS5, and TMS6. These deletions occurred within the target site of the gene of interest. Meanwhile, substitution was observed in the TMS7 and TMS8 samples, accounting for 25%, with one nucleotide substitution (substitution of nucleotide “G” with “A”) reported. The substitutions were observed four nucleotides downstream of the PAM site, which was located outside the target site ( Figure 2 ). The sequence analysis of the wild-type TMS 60444 amplicons showed no INDELs as well as substitutions in the target region of the gene of interest ( Figure 2 ). Thus, the results suggested that pCRISPR/Cas9–MeCYP79D1-gRNA effectively and precisely guided Cas9-mediated genomic DNA cleavage. RT-PCR analysis detected Cas9 gene expression in all eight transgenic lines of the cultivar TMS 60444 but not in the wild-type line ( Supplementary Material 8 ), showing Cas9 gene expression in the transgenic cassava plants. PCR amplification products of Cas9 measuring 900 bp in length were identified in the transformants, which was consistent with the expected size of amplicons. However, no Cas9 PCR amplicons were observed in the wild-type control ( Supplementary Material 8A ). Actin7 primers successfully amplified the target sequence in the case of cDNA derived from wild-type plants ( Supplementary Material 8B ). Linamarin levels in the leaves of gene-edited TMS 60444 cassava plantlets were measured using HPLC, with age-matched wild-type cassava plantlets being positive controls. The HPLC readings of the linamarin standard provided the peak areas, which were used to generate the linear response graph curve. Linamarin concentration in the crude extract of non-transgenic (WT) plantlets ranged from 2.405 to 3.143 g/kg fresh weight, whereas that in transgenic (mecyp79d1) plantlets ranged from 0.496 to 0.731 g/kg fresh weight ( Figure 3 ). The results revealed high linamarin content in non-transgenic (WT) cassava plantlets with a mean fresh weight of 2.72 ± 0.17 g/kg in comparison with 0.65 ± 0.03 g/kg in transgenic (mecyp79d1) cassava plantlets ( Supplementary Material 9 ). Besides linamarin quantification, cyanide levels were also measured in the leaves of wild-type and mutant TMS 60444 acclimatized plantlets using picrate assay (Bradbury et al., 1999) ( Figure 4 ). Because the cyanide content can vary considerably between the leaves of the same plant and among different plants of the same cultivar (Siritunga and Sayre, 2004), three different leaf samples were analyzed per mutant plant to account for experimental variability. The mean cyanide level in non-transgenic (WT) plants was 201.0 ± 0.99 mg/kg fresh weight, which was considerably higher than that in transgenic (mecyp79d1) plants at 17.44 ± 2.482 mg/kg fresh weight ( Supplementary Material 10 ). Therefore, MeCYP79D1 gene knockout in cassava led to a drastic reduction in cyanide content relative to their wild-type counterparts ( Figure 4 ). Mutant and wild-type TMS 60444 cassava plants were analyzed for plant height, leaf length, leaf breadth, petiole length, and the number of leaves per plant to confirm if mutations in the MeCYP79D1 gene alter agronomic parameters ( Figure 5 ). No significant difference was noted between transgenic and non-transgenic cassava plants for any of the evaluated agronomic traits ( Figure 6 ; Supplementary Material 11 ); however, the transgenic plant lines suffered from insect infestation and their leaves were destroyed between 4 and 6 months of storage in the greenhouse ( Figures 5A, C ). After spraying the transgenic plants with insecticides, they were seen to grow well but had thinner stems than the non-transgenic lines ( Figures 5B, D ). Functional genomics as well as molecular design and breeding studies of cassava fundamentally revolve around the scope to modify its genome to create mutant plant lines. Recently, CRISPR/Cas9 gene editing technology has substantially evolved, allowing scientists to edit and knock out plant target genes as well as create mutants in a convenient, quick, and efficient manner (Chen et al., 2019). Despite the emergence of CRISPR/Cas9 genome editing technology, to date, only few reports are available on its use for trait improvement and biotic stress resistance in tropical cassava (Juma et al., 2021). Cassava genome editing has the potential to create new avenues for resolving biotic and abiotic restrictions in cassava production and post-harvest utilization (Odipio et al., 2017). We therefore exploited the CRISPR/Cas9 system for targeting the cassava CYP79D1 gene which encodes a key enzyme in the cyanogenic glycoside biosynthesisfor key enzyme in the cyanogenic glycoside biosynthesis (Siritunga and Sayre, 2003). Silencing the CYP79D1 by RNAi resulted in lowered cyanogenic glycosides in cassava due to impaired biosynthesis of cyanogenic glycosides (Liu et al., 2017). To effectively knockout the CYP79D1 gene in cassava using the CRISPR/Cas9 system, a single guide RNA was designed for the CYP79D1 genomic region. Existing genetic transformation systems (Odipio et al., 2017; Syombua et al., 2019) were employed to integrate the CRISPR/Cas9 tools into the embryogenic cells. Through A. tumefaciens-mediated cassava transformation and antibiotic selection as the primary methods used for delivering CRISPR/Cas9 components, eight independent positive transgenic plantlets were obtained ( Figure 1H ). This is because transfection with Agrobacterium typically results in simple gene insertion events of transfer DNA (T-DNA) sequence in a binary plasmid, with a low frequency of transgene silencing (Wang et al., 2016). The A. tumefaciens strain GV3101 was utilized in the study to deliver the pCRISPR/Cas9–MeCYP79D1 construct into leaf explants because it has previously been shown to have a high transformation rate of up to 65% (Chetty et al., 2013). The study describes a rapid, simple, effective, and stable Agrobacterium-mediated strategy for transforming TMS 60444 plant cultivars with A. tumefaciens strain GV3101 carrying the pCRISPR/Cas9– MeCYP79D1plasmid containing the hptII selectable marker to facilitate selection of transgenic cassava plants using hygromycin antibiotic. In the present study, cassava regeneration frequency and transformation efficiency were low, in the range of 5.91-6.82% and 1.33-2%, respectively ( Supplementary Materials 4 and 5 ), respectively. Syombua et al. (2021) also reported a similar transformation frequency of 0.5% for TMS 60444 cultivar. The low efficiency observed could be explained by the selective stress of calli caused by hygromycin and timentin antibiotics. Accordingly, hygromycin and timentin had also been used to select transformed tissues in the present study, and both these antibiotics are exceedingly toxic to cassava tissues. Furthermore, the low regeneration frequencies and efficiencies can also be a result of recalcitrant embryo either in the globular stage or during the transition from torpedo to cotyledonary stage (Ochatt and Revilla, 2016). Molecular analysis was performed on the independent transgenic cassava lines. The findings of PCR and RT-PCR analyses of the putative transgenic lines indicated that the T-DNA derived from pCRISPR/Cas9–MeCYP79D1 construct was stably integrated into the cassava genome. An RT-PCR assay performed on the eight transgenic lines revealed Cas9 gene expression in all of them. The results revealed that the transformation approach used in this study has high replication potential with regard to genetic transformation of recalcitrant cassava genotypes. This gene editing process for knocking out the CYP79D1 gene in cassava using CRISPR/Cas9 is consistent with previously reported knockout process followed for other cassava plant genes, including the ones encoding phytoene desaturase (Odipio et al., 2017), the viral AC2 protein (Mehta et al., 2019), protein targeting to starch 1 (PTST-1) and granule-bound starch synthase (GBSS) (Bull et al., 2018), 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) (Hummel et al., 2018), multiple TFL 1-like floral repressor (Odipio et al., 2018), eukaryotic translation initiation factor 4E (elF4E) isoforms (Gomez et al., 2018), methylesterase 3 (MeSIII) (Li et al., 2020), and MeSWEET10a (Veley et al., 2021). The nature of mutations within the CYP79D1 gene among the regenerated T0 plants was identified by Sanger sequencing performed on each individual plant. The sequencing results showed the presence of either indels or substitutions in the CYP79D1 gene in of all of the eight regenerated T0 plant (100%). The indels detected in the TMS1-6 T0 plants were identical 4 bp-long deletions located 3 bp upstream from the PAM site.The deletions reported here were almost certainly caused by nonhomologous end-joining (NHEJ) DNA repair following cleavage by Cas9, as previously reported in cassava subjected to CRISPR/Cas9-mediated alteration of the PTST-1 and GBSS genes (Bull et al., 2018), MeSIII gene (Li et al., 2020), and genes encoding elF4E (Gomez et al., 2018). The presence of a single nucleotide mutation downstream of the targeted region might suggest that the homology-directed repair (HDR) pathway was activated to repair the double-stranded breaks (DSBs) occurring in the CYP79D1 gene. This rare occurrence seems to indicate that CRISPR/Cas9 can direct mutation outside the target region (Odipio et al., 2017). Linamarin levels in transgenic plants were significantly lower than those in their wild-type counterparts (p<0.05), which were below 10 mg/kg, the set limit for cassava to be classified as safe for consumption. The wild-type cassava showed a higher linamarin level than that established by the Food and Agriculture Organization/World Health Organization as the accepted levell (FAO/WHO, 1999). Complete elimination of the cyanogenic glycoside linamarin, in the genome-edited plants was not achieved because only one gene, namely CYP79D1, encoded for the enzyme necessary for catalysis in the first step (dedicated stage) of linamarin production in cassava (Gunasekera et al., 2018). Cassava is a paleotetraploid plant with two genes encoding the CYP79D enzyme. The first step in the biosynthesis of cyanogenic glycosides in cassava is mediated by two enzymes, valine monooxygenase I and valine monooxygenase II, which are encoded by the genes CYP79D1 and CYP79D2, respectively (Bredeson et al., 2016). These two genes are 85% identical. The results were consistent with those of previous studies which used RNAi technology to produce alterations in the functioning of the CYP79D1 gene in cassava plants, resulting in approximately three-fold lower cyanide concentrations than the cyanide content found in wild-type cassava (Siritunga and Sayre, 2003; Jørgensen et al., 2005; Piero, 2013). CRISPR/Cas9-mediated CYP79D1 gene modification in P. pastoris also resulted in similar aberrations, as reported by Jiang et al. (2021). According to the findings of the present study, CRISPR/Cas9 genome editing technology is more efficient than the RNAi technology, when it comes to reverse genetics as it allows one to completely inactivate the gene by e.g. introducing frame-shift mutations. The CRISPR/Cas9 approach allows for a more precise and faster method of generating gene knockouts in cassava, as compared to traditional breeding methods, without a major impact on agronomic traits in the case of the CYP79D1 gene. Furthermore, determination of cyanide content using picrate test yielded comparable results to the determination of linamarin concentration. The transgenic plants had a cyanide level which was nearly seven-times lower than that in their wild-type counterparts, which corroborates with the results previously reported by Taylor et al. (2012) and Piero (2013). It was previously reported that CYP79D1/D2 gene knockdown performed using the RNAi technology lowers the cyanide content in cassava leaves and roots by three-fold (Siritunga and Sayre, 2003; Jørgensen et al., 2005; Piero, 2013). The CRISPR/Cas GE technology is therefore superior owing to its efficiency, simplicity, and preciseness. Analysis of agronomic traits of the transgenic cassava plantlets acclimatized in the greenhouse revealed no substantial difference from those of wild-type plants ( Figure 5 ); these were similar to the findings reported by Veley et al. (2021). Nevertheless, some differences were still observed between the transgenic and non-transgenic plants, which could have been caused by either environmental shock or herbivore predation. Herbivores, whiteflies, and aphids attacked the leaves and roots of transgenic plants, while the non-transgenic plants remained unaffected ( Figure 5A ) (Brandt et al., 2020). This could be related to an alteration in the linamarin biosynthesis pathway, which facilitates the defense mechanism of plants by generating hydrogen cyanide when cyanogenic glycosides come in contact with their corresponding enzymes during a herbivore attack. This is similar to the findings of a study conducted by Siritunga and Sayre (2003), who reported that cyanogenic glycosides act as insect repellants in cassava. Furthermore, transgenic plants developed thinner stems than non-transgenic plants likely owing to changes in the biosynthesis of cyanogenic glycosides ( Figure 5B ) (Tawanda et al., 2017). Linamarin is used in the biosynthesis of aspartate, an amino acid, which is subsequently transported to the roots of an intact plant as a translocable form of reduced nitrogen. This promotes the development of big roots and stems (Echeverry-Solarte et al., 2013; Tawanda et al., 2017). Thus, mecyp79d1-mutant plants were successfully created in our gene editing study, which also provided deeper insights into the regulatory mechanism of cyanogen synthesis in cassava plants. In the present study, we investigated the potential roles of the CYP79D1 gene in biosynthesis of cyanogenic glycosides in cassava using CRISPR/Cas9 genome editing system. The mecyp79d1 cassava plants exhibited significant reduction in the levels of cyanogenic glycosides as compared to the wild-type, while showing increased susceptibility to insect pests and grew with thinner stems as compared to the wild-type counterparts. Our study contributes to a better understanding of the underlying role of the CYP79D1 gene in the biosynthesis of cyanogenic glycosides and downstream importance of the secondary metabolites. In addition, it provides the evidence for feasibility of using CRISPR/Cas9 to precisely edit the CYP79D1 gene and to determine its function. Thus, our study can pave a way for the editing of other CYP79 genes in cassava, thereby advancing plant biology and helping in solving food security related issues. The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding author. BJ performed all the experiments, analyzed the data and wrote the manuscript, AM assisted in some of the investigations and data analysis, CM, MN and WM supervised the work, contributed with experimental design and coordination, and reviewed the manuscript, WM conceptualized the idea. All authors contributed to the article and approved the submitted version. This research was supported by The World Academy of Sciences (Grant No. 18-175 RG/810/AF/AC G - FR3240303653) and the International Centre for Genetic Engineering and Biotechnology (Contract No. CRP/KEN20-03). We are grateful to Kenyatta University providing the laboratory space at the Plant Transformation Laboratory to perform this work. The authors acknowledge The World Academy of Sciences (TWAS) and the International Centre for Genetic Engineering and Biotechnology (ICGEB) for providing research grants. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

PMC9644473

Cong Gao,Shuai Lu,Rong Zhou,Junjie Ding,Jialiang Fan,Binying Han,Moxian Chen,Baohua Wang,Yunying Cao

Phylogenetic analysis and stress response of the plant U2 small nuclear ribonucleoprotein B″ gene family

08-11-2022

Splicing factor,U2B″,Bioinformatics,Subcellular localisation

Background Alternative splicing (AS) is an important channel for gene expression regulation and protein diversification, in addition to a major reason for the considerable differences in the number of genes and proteins in eukaryotes. In plants, U2 small nuclear ribonucleoprotein B″ (U2B″), a component of splicing complex U2 snRNP, plays an important role in AS. Currently, few studies have investigated plant U2B″, and its mechanism remains unclear. Result Phylogenetic analysis, including gene and protein structures, revealed that U2B″ is highly conserved in plants and typically contains two RNA recognition motifs. Subcellular localisation showed that OsU2B″ is located in the nucleus and cytoplasm, indicating that it has broad functions throughout the cell. Elemental analysis of the promoter region showed that it responded to numerous external stimuli, including hormones, stress, and light. Subsequent qPCR experiments examining response to stress (cold, salt, drought, and heavy metal cadmium) corroborated the findings. The prediction results of protein–protein interactions showed that its function is largely through a single pathway, mainly through interaction with snRNP proteins. Conclusion U2B″ is highly conserved in the plant kingdom, functions in the nucleus and cytoplasm, and participates in a wide range of processes in plant growth and development. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-022-08956-0.

Phylogenetic analysis and stress response of the plant U2 small nuclear ribonucleoprotein B″ gene family Alternative splicing (AS) is an important channel for gene expression regulation and protein diversification, in addition to a major reason for the considerable differences in the number of genes and proteins in eukaryotes. In plants, U2 small nuclear ribonucleoprotein B″ (U2B″), a component of splicing complex U2 snRNP, plays an important role in AS. Currently, few studies have investigated plant U2B″, and its mechanism remains unclear. Phylogenetic analysis, including gene and protein structures, revealed that U2B″ is highly conserved in plants and typically contains two RNA recognition motifs. Subcellular localisation showed that OsU2B″ is located in the nucleus and cytoplasm, indicating that it has broad functions throughout the cell. Elemental analysis of the promoter region showed that it responded to numerous external stimuli, including hormones, stress, and light. Subsequent qPCR experiments examining response to stress (cold, salt, drought, and heavy metal cadmium) corroborated the findings. The prediction results of protein–protein interactions showed that its function is largely through a single pathway, mainly through interaction with snRNP proteins. U2B″ is highly conserved in the plant kingdom, functions in the nucleus and cytoplasm, and participates in a wide range of processes in plant growth and development. The online version contains supplementary material available at 10.1186/s12864-022-08956-0. In contrast to the model of the 1 gene-1 polypeptide in prokaryotes, eukaryotic genes contain exons and introns. They generate mature mRNAs via splicing, which is an important post-transcriptional regulatory link during pre-mRNA splicing [1]. In the splicing process, the U1 snRNP identifies and binds to the 5'-terminal splicing site of the intron [2]. Subsequently, U2 cofactor identifies the 3' terminal splicing site to make the U2 snRNP combine with the branching point and form a pre-spliceosome (complex A). Afterwards, U4/U6 and U5 snRNPs bind to complex A and form complex B. Subsequently, the U4/U6 snRNP dissociates, and the U2 snRNP combines with the U6 snRNP to form a catalytic centre [3, 4]. Shortly afterwards, U1 and U4 snRNPs are removed from the complex, and U6 snRNPs bind to the 5' terminal splicing site to form a complex C-spliceosome [5]. Finally, a splicing reaction occurs. Different transcripts are produced by alternative splicing (AS) and are eventually translated into different proteins [6]. In eukaryotes, AS is an important channel of protein diversification and gene expression regulation and includes five modes. In addition, polyadenylation sites and variable selection of promoters can increase mRNA diversity [7]. Splicing is regulated by trans-acting factors, splicing regulatory elements (SREs) and RNA secondary structures [8]. SREs can be classified into silencers and enhancers. Silencers include intron splicing silencers (ISSs) and exon splicing silencers (ESSs), and enhancers include intron splicing enhancers (ISEs) and exon splicing enhancers (ESEs) [9]. Most activating proteins that bind to ISEs and ESEs are splicing regulatory (SR) proteins, including one or two RNA recognition motifs (RRMs) and one SR domain containing abundant arginine and serine. RRM recognised by two amino acid sequences (RNP1 and RNP2) is the most commonly used RNA-binding domain in eukaryotes [10]. A typical view of RNA–RRM interactions is that single-stranded RNA binds to a β-folded surface. Good electrostatic interactions, hydrogen bonding, and stacking between RNA bases and aromatic residues located in the RNP motif are considered major factors involved in RNA binding [11]. Splicing is also regulated by the trans-acting factor-spliceosome, which includes five small nuclear RNAs and more than 100 core proteins [12]. Spliceosomes can be divided into two categories: major and minor spliceosomes [13]. Major spliceosomes, including U1, U2, U4, U5, and U6 snRNPs [14], remove introns whose 5' termini have a “GU” splice site and whose 3' termini have an “AG” splicing site. Minor spliceosomes [15], including U5, U11, U12, U4, and U6 [16], are splice introns whose 5' termini have an “AU” splice site and whose 3' termini have an “AC” splicing site. The spliceosome is a multi-subunit RNA–protein complex. Currently, the identified splicing-related proteins are divided into five types: major snRNP proteins, splicing factors, splicing regulation factors, novel spliceosome proteins, and possible splicing-related proteins. Major snRNP proteins are divided into seven protein families: Sm core proteins, U1 snRNP-specific proteins, 17S U2 snRNP-specific proteins, U5 snRNP-specific proteins, U4/U6 snRNP-specific proteins, Tri-snRNP-specific proteins, and 18S U11/U2 snRNP-specific proteins. The major types of spliceosome-U2-dependent proteins contain several guanosine-rich U-snRNPs (U1, U2, U5, and U4/U6) and an array of non-snRNP proteins. U4 and U6 typically form complexes. Each snRNP type [17], including a conserved U-snRNA, binds to the ring containing seven Sm core proteins (B/B″, D3, D2, D1, E, F, and G) and a specific protein of each snRNP. Sm proteins are highly conserved RNA-binding proteins classified into seven Sm core proteins and like-Sm (LSm) proteins. The heptamer ring formed by the seven core Sm proteins is the basic structure that binds most snRNPs. Only U6 snRNP binds to LSm2-8 to form heptamers [18]. Recent research has shown that the structure of human 17S U2 snRNP is consistent with the electron microscope structure of previously isolated U2 snRNP [19, 20] and its entire structure in the human spliceosome [21–24]. Human 17S U2 snRNP presents a bipartite three-dimensional (3D) structure, as observed through high-resolution cryo-electron microscopy. The 17S U2 snRNP is located in the nucleus and is the active form of the U2 snRNP. It binds to the branching point of the pre-mRNA during the splicing assembly. The U2 snRNP plays an important role in selecting the mRNA precursor branching site adenosine, a nucleophile, in the first step of splicing [25]. The mature 17S U2 snRNP includes 12S U2 snRNP formed by U2 snRNA, Sm protein, U2A', and U2B″ [26], and two splicing factors (SF3a and SF3b) [27]. The first step in 17S U2 snRNP formation involves the Sm protein complex and U2A'-U2B″ dimer binding to the Sm site and stem-loop IV of U2 snRNA, respectively, forming 12S U2 (Fig. S1). In addition, the binding of U2B″ to the corresponding sites requires the participation of U2A', and the specific binding of U2B″ may be attributed to this common binding. In vitro, U2B″ is able to bind human stem-loop IV, Drosophila U2 snRNA stem-loop IV, and stem-loop II of human U1 snRNA. Yeast U2 snRNA nucleotides that form base pairs with branching sites were initially isolated in a fulcrum-interacting stem-loop [28]; however, it is not clear whether human U2 snRNA folds in a similar manner. The second step is to combine SF3b with 12S U2 to form 15S U2 snRNP and then combine with SF3a to form functional 17S U2 snRNP [29]. As a spliceosomal protein, U2B″, which is an important tool for studying the evolution of RNA-binding patterns and RNA-binding specificity, belongs to the U1A/U2B″/SNF family. Although a high degree of sequence and structural conservation has been found in the family [30], proteins from modern members of this family have unique RNA-binding properties [31]. Three proteins recognise their RNA targets using RNA recognition motifs (RRMs); however, their N-terminal RRMs differ from most RRMs in their high affinity and specificity for RNA. Notably, these homologous proteins contain two RRMs, whereas yeast contains only one [32]. These phenomena were probably caused by evolution. The RRMs of the three proteins showed a high degree of conservation, similar to the two RNA stem loops that they recognised. When comparing modern RRMs, it has become apparent that each has unique RNA-binding properties, while U1A binds exclusively to U1 stem-loop II and U2B″ binds exclusively to U2 stem-loop IV in humans [33]. Although they are components of snRNPs, how U1A and U2B″ participate in splicing remains unclear; in fact, snRNP recombination had no effect on splicing in the absence of U1A in vitro [34]. Other experiments used fly homologous SNF mutations to exclude the protein from either U1 or U2 snRNP, resulting in relatively mild phenotypic consequences [35]. In contrast, mutants that simultaneously knock out U1A and U2B″ in C. elegans are embryonically lethal, similar to knocking out SNF in Drosophila [36]. It is possible that these proteins have other functions. In worms, U1A and U2B″, which are functionally redundant, interact with various snRNPs. The knockout of these two genes is necessary for a lethal phenotype. However, the absence of one does not lead to a lethal phenotype, and the other replaces the missing phenotype and binds to its corresponding snRNP. U2B″ homologous genes have been identified in vertebrates, yeast, and plants. Although the genes play an important role in the formation of the spliceosome in yeast, their mechanisms of action remain unclear. Analysis of the C. elegans genome has revealed two members of the U1A/U2B″ family. Notably, the two genes are present in a single operon. None of them has unique sequence features [32]. The phenomenon is difficult to explain from a molecular perspective, especially based on protein structure. Therefore, analysing the function of proteins from an evolutionary perspective could provide a novel avenue for enhancing our understanding protein function. Especially in plants, little is known about the specific functions of U2B″. Consequently, there is a growing interest in the elucidation of plant U2B″ proteins and how they mediate U2 snRNP, which could also involve numerous U2B″-interacting proteins. The present study provides a phylogenetic description of plant U2B″ and provides a clue for future functional studies in plant AS regulation. In this work, we studied the U2B″ gene family across different plants and analysed their phylogenetics, gene and protein structures, spatiotemporal gene expression profiles under different stimulation conditions, and subcellular localisation. The results of the present study will provide basic information on the phylogeny, structure, and expression of the gene family and lay a foundation for further functional identification of plants. The design process for the entire study is illustrated in Fig. S2. To identify U2B″ genes in different plant species, we conducted a BLAST search using the Arabidopsis U2B″ protein (AT2G30260) against the Phytozome database. After filtering the sequences, 117 putative U2B″ sequences from 80 different plant species were obtained. The sequences were divided into five subfamilies (Fig. S3), including 45 dicotyledons and two monocotyledons in blue, 17 monocotyledons in pink, one fern in white, four bryophytes in green, and nine algae in yellow. Regarding the distribution of the number of genes in the 80 species, 50 species contained one gene, 24 species contained two genes, five species contained three genes (Gossypium hirsutum, Malus domestica, Medicago truncatula, Populus trichocarpa, Salix purpurea), and one species (Triticum aestivum) contained four genes (Table S1). Gene copy numbers and species were mapped to a phylogenetic tree (Fig. S3). There were 45 dicotyledons, including 23 species with a single copy of the gene, 17 species with a double copy, and five species with three copies, and two monocotyledons, including one species with a single copy and one species with a double copy in the blue subfamily. In addition, the pink subfamily contained only monocotyledons, with a total of 17 species, including 12 species with a single copy, four species with a double copy, and one species with four copies. The white subfamily contained only one fern with a single copy. The green subfamily includes three bryophytes and three monocotyledons. Monocotyledons were all single copies, whereas bryophytes included two species with single copies and one species with a double copy. The yellow subfamily consisted of nine algae and contained eight species with single copies and one species with a double copy. The 117 sequences from 80 plant species could provide us with a more comprehensive view of the evolutionary and developmental relationships among U2B″gene family. The clear topological structure and high bootstrap values (red) supporting each branch indicate the validity of this phylogenetic reconstruction of the U2B″ gene family. In addition, the yellow subfamily constitutes the basic part of the phylogenetic tree, which is far from other subfamilies, indicating that it is significantly associated with U2B″ in other plants. The green subfamily represents bryophytes, and the white subfamily of ferns is closely related to the yellow subfamily, which represents algae. However, the pink subfamily, representing monocotyledons, is closely related to the blue subfamily, representing dicotyledons, and is located far from the three subfamilies mentioned above. Multiple sequences from the same species were closely clustered in the same subfamily. Notably, each subfamily contained not only the plants it represented but also plants represented by other subfamilies. For example, the green subfamily contained not only bryophytes but also monocotyledons (Ananas comosus, Spirodela polyrhiza, Zostera marina). The blue subfamily, which represents dicotyledons, also contained two monocotyledons (Ananas comosus, Musa acuminata). The phenomena may be attributed to the degrees of plant development and evolution. Excluding the example above, the subfamilies strictly corresponded to the plants they represented. To understand the general function of plant U2B″, it is necessary to study its genomic structure and its conserved gene motifs. For visualisation, the gene structure and corresponding motifs were linked in a phylogenetic tree (Fig. 1). Surprisingly, more than 80 sequences showed a 5 exon-2 UTR structure (Fig. 1, right panel). The proportion was close to 70%, indicating that they are highly conserved in the plant genomic structure across plant genomes, which suggests strong functional conservation of U2B″. Most of the remaining sequences had 3–6 exons, with a few exceptions. For example, Chlamydomonas contains seven exons. It should be noted that there were a few sequences with a single intron, or even no introns. For example, one of the two sequences of Arabidopsis thaliana had two introns, and the other had only one intron, whereas one of the two sequences of Capsella rubella had two introns and the other had no introns. In addition, the gene structures of the yellow, green, and white subfamilies were smaller, the pink subfamily was larger, and the blue subfamily was different in size, and there was no obvious trend. Although gene structure variation was minimal among U2B″, we determined whether motif composition in their cDNA sequences reflected any differences. Further investigation of conserved DNA motifs showed that 89% of the 104 sequences had similar sequence characteristics and contained 8–10 of the top 10 identified motifs (Fig. S4A) and were located at similar sequence positions (Fig. 1, middle panel). The remaining 13 sequences, whose motifs were less than eight in number, were evenly distributed in all subfamilies except the green subfamily. Therefore, the gene structure may have a subtle relationship with conserved motifs. Conserved domains and motifs were analysed for phylogenetic tree construction (Fig. 2). Different subfamilies of proteins were highly conserved (Fig. S5). A total of 106 peptides were predicted to contain an N-terminal domain named RRM1 and C-terminal domain named RRM2 (Fig. 2, right panel). In addition, there were eight peptides with only one RRM2, and one peptide with only one RRM1. This may lead to a decline in RNA recognition capacity. It is worth noting that two peptides (Prunus persica, Salix purpurea) did not have RRMs, possibly because they had more than one copy of the gene. Although some domains are lost during development and evolution, other peptides can perform similar functions. Since the differences in protein domains were very minor, we attempted to explore differences in the motifs (Fig. 2, middle panel) of the protein sequences, with obvious differences identified. The numbers of motifs in the yellow, green, and white subfamilies were lower than those in the pink and blue subfamilies. The differences could be related to the different degrees of development and evolution among different subfamilies. Since the protein domains were highly conserved, we estimated amino acid conservation. First, amino acid multiple sequence alignment was performed (Fig. S5). As expected, there was a high degree of conservation. The yellow, green, and white subfamilies differed from the other two subfamilies. The number of red amino acids was obviously greater, and the degree of red was greater, indicating that the yellow, green, and white subfamilies were closer together, whereas the pink and blue subfamilies were closer to one another, consistent with the results of the phylogenetic analysis. However, the two RRM domains of U2B″ also showed differences. The amino acid sequences of the RRM1 domain were highly conserved, whereas those of RRM2 were not as highly conserved. This may be related to the functions of the proteins, even though they all recognise the RNA. In addition, to demonstrate this point in more detail, we selected some plants (A. thaliana, Zea mays, Dunaliella salina, Sphagnum fallax) and plotted a two-dimensional map of their U2B″ proteins, which also revealed a high degree of conservation (Fig. 3A). Generally, the RRM1 domain is located between the 12th and 83rd amino acids, while the amino acid range of the RRM2 domain varies slightly between the 154th and 230th amino acids. Both RRM domains belong to the cl17169 superfamily (SF) and all proteins have disordered structures between the two domains. Furthermore, two had a coiled-coil structure. A 3D model of plant U2B″ was constructed based on this template (Fig. 3B). The results of the conservative estimation of the 3D model were consistent with those of the amino acid multiple sequence alignment. RRM1 showed high quality, whereas RRM2 showed low quality. To further study the potential expression profiles of plant U2B'', the 1500-bp sequence upstream of the genes was analysed using PlantCARE. From 117 sequences, 12,885 elements were identified, including 1162 blank elements and 1339 unnamed elements, accounting for 9% and 10% of the total, respectively. Subsequently, the remaining elements were screened, and 1731 elements, accounting for 13% of the total, were removed. Finally, 8653 elements, accounting for 68% of the initial total, were included in the analysis. First, we classified the elements into four categories according to their function (Fig. S6A, Table S2), hormone response (879 elements), light response (541 elements), regulation of basic transcription (5932 elements), and stress response (1186 elements). An additional element was not clearly defined (115 AAGAA-motif). Hormone-responsive elements included 252 ABA-responsive elements (ABREs), 177 as-1 motifs, 177 CGTCA motifs, 96 estrogen response elements (EREs), and 177 TGACG motifs. Notably, three of five motifs had the same number, although whether they corresponded to each other remains to be further studied. The light-responsive elements comprised 162 BOX4, 288 G-box, and 91 TCT motifs. The most abundant regulatory elements of basic transcription included three common elements:2848 TATA boxes, 2768 CAAT boxes, and 316 AT-TATA boxes. The remaining stress-responsive elements were divided into four categories:143, 523, 316, and 204. We then combined the distribution map of elements (Fig. 4, right panel) on the sequence with the phylogenetic tree (Fig. 4, left panel). Excluding the white subfamily, the distribution of various elements in different subfamilies was relatively uniform. In general, the blue and pink subfamilies were distributed more than the other two subfamilies (Table S3). Owing to the considerable differences among family members, the number of promoters in the blue and pink subfamilies is also discrete. This may be due to differences in the degrees of evolution and development of different subfamilies. Notably, the numbers of elements in some sequences (Oropetium thomaeum, Ostreococcus lucimarinus, Micromonas sp. RCC299) were significantly lower than those in other sequences, indicating that they can only respond to a small number of stimuli. By contrast, they would have a single means of regulating gene expression. Subsequently, we performed an enrichment analysis of the stimuli of the sequence response (Fig. 5 and Fig. S6B). Among the 117 sequences, 112 responded to hormones, 113 to light, and 115 to stress, accounting for 96%, 97%, and 98%, respectively. Among them, 109 sequences (Fig. S6A) responded to the three stimuli simultaneously, exhibiting very high correlation. Notably, each group had two sequences that could respond to two of the three stimuli. Surprisingly, three sequences from three different plants (Amaranthus hypochondriacus, Arabidopsis lyrata, Populus trichocarpa) could only respond to stress. A specific analysis was then performed (Fig. 5). Surprisingly, only four sequences contained all elements at the same time. However, there were 49 sequences with 9–11 elements and 15 sequences with 5–8 elements simultaneously. Overall, the findings suggest that the U2B″ can respond to diverse stimuli and has a complex response network. Knowledge of the subcellular localisation of proteins is also essential for the understanding of their functions. To further understand the function of U2B″, we performed subcellular localisation analysis to identify where U2B″ functions inside the cell (Fig. 6). First, we used web tools to predict the subcellular localisation of U2B″ in several species. The results were intriguing. In lower plants, U2B″ tends to be located in the cytoplasm, whereas in higher plants, U2B″ tends to be located in the nucleus (Fig. 6A). This means that over the course of evolution, the functions of U2B″ may have diverged based on its distinct positions. Considering that it is a component of the spliceosome and that splicing is essentially completed in the nucleus, we speculated that it may be located in the nucleus. To test this, we selected rice, which is one of the world’s most important crops. NLS-mCherry (a nuclear localisation marker) was selected as the control, to observe whether U2B″ was present in the nucleus. As expected, both were located in the nucleus. U2B″ and mCherry exhibited strong fluorescence, and a yellow signal was observed when the signals were merged (Fig. 6B). Notably, mCherry also had a strong localisation signal in the nucleolus, but U2B″ did not. This indicates that U2B″ plays a role in the nucleoplasm or nuclear membrane. Sequence analysis showed that the subsequences "KRKK" and "KKRR" in the junction structure between the two RRMs mainly promoted U2B″ to locate in the nucleus (Fig. 6C). The two RRMs are both disordered structures, suggesting that the structure could influence the positioning behaviour of U2B″. In addition, U2B″ has cytoplasmic localisation signals. Analysis of the transmembrane domain showed that U2B″ was within the cell, indicating that it had no localisation signal on the cell membrane (Fig. 6D). qPCR was performed to further study the levels of expression of U2B″ in plants. Based on the results of promoter analysis, we selected four stress treatments: cold, drought, salt, and heavy metal cadmium (in the text referred to as Cd or Cd2+) (Fig. 7, Table S4). In general, excluding Cd stress, the levels of expression in the aboveground parts were higher than those in the belowground parts. The results suggest that U2B″ has contrasting regulatory mechanisms in the roots and shoots. Notably, there were not only differences in the levels of expression in the two parts but also contrasting expression patterns. U2B″ was not sensitive to Cd2+ and drought in roots, but showed significant differences in the shoot. U2B″ expression began to increase significantly after 3 h of drought treatment, which was sustained for 6 h. At 6 h, the relative expression was twice that of the control. However, it began to decline after 12 h of drought stress treatment. In contrast, the level of expression was significantly downregulated after 3 h of Cd treatment, and the relative expression was only 59% that of the control. However, it began to increase after 6 h and reached a level of significant difference, which was 77% of that of the control. After 12 h of Cd treatment, it began to decrease and finally reached the same level as 3 h, which was 60% of the control. When rice was treated with cold and salt, both roots and shoots exhibited sensitivity. Although they all exhibited sensitivity to stress, their response trends were distinct. Root and shoot exhibited similar trends under the salt treatment; both decreased first (40% and 81% of the control, respectively), increased (85% and 111% of the control, respectively), and then declined (56% and 95% of the control, respectively). However, the difference was that U2B″ was more responsive in the root. The difference between the salt treatment and the control was at most 60% in the root, whereas in the shoot, it was only 19%. In addition, U2B″ showed contrasting response patterns in the roots and shoots when subjected to cold stress. The levels of expressions were the lowest in the roots after 6 h of chilling treatment, and the highest in shoots after 3 h of chilling treatment. Four representative monocotyledons, four dicotyledons, four algae, and three bryophytes were selected for this study. By comparing their gene structures, we observed that AS was not common in the U2B″ families (Fig. S7). AS occurred three times in the selected monocotyledons, twice in dicotyledons, and once in bryophytes; however, it did not occur in algae. In addition, more than two occurrences of AS were non-existent in the plants. Among the 15 selected plants, five plants had AS, including twice in Arabidopsis, while the other four had single occurrences. In Z. mays transcripts, only one of the five exons was preserved after AS. In the other four splicing isoforms, there was no significant difference between the gene structure and representative transcript forms, with some sequence differences observed in the UTRs. Overall, U2B″ may not undergo large splicing changes and may act uniformly on substrates with conserved RRM motifs across isoforms. U2B″ needs to be translated into a protein to perform its function. We selected three common plants (Arabidopis thaliana, Oryza sativa, Z. mays) and constructed an interaction network (Fig. 8, Table S5) based on data obtained from STRING. We obtained 9, 10, and 10 interacting proteins in A. thaliana, O. sativa, and Z. mays. The numbers of interacting proteins showed clear consistency, suggesting that the U2B″ protein has a fairly complex interaction network in different plants. Regarding the genes encoding the corresponding proteins, there was an interesting phenomenon in which genes in Z. mays are evenly distributed on chromosomes, whereas those in A. thaliana and O. sativa were concentrated on a few chromosomes. Furthermore, among the nine proteins interacting with AT2G30260, there were nine snRNP proteins, one RNA-binding protein, and one putative splicing factor. Excluding a protein in Z. mays that has an unknown function, there were eight and seven snRNP proteins in O. sativa and Z. mays, respectively. In addition, both O. Sativa and Z. mays had an Sm-like protein, and it should be noted that the interaction protein in Z. mays also contains a zinc ion binding protein. In terms of the types of interacting proteins, the U2B″ protein showed a high degree of consistency in different plants. Notably, each plant has a unique interaction protein that differs from that of other plants. This may be caused by different degrees of evolution and development. Structural analysis of the spliceosome has long been considered one of the most promising research fields in structural biology. Several human diseases can be attributed to incorrect splicing of genes or regulation of spliceosomes. In addition, 35% of genetic disorders in humans are caused by the variable splicing of single genes caused by gene mutations [37]. Some diseases are caused by mutations in splice proteins that affect the splicing of many transcripts. Some cancers are also involved in incorrect regulation of splicing factors [38, 39]. In a previous study, a small percentage of medulloblastoma samples had the same non-coding mutations. Following a preliminary study, the authors were surprised to find that this mutation (A > G) affected more than a small number of people, as nearly 100% of the adult patients with a certain disease subtype had the mutation. In addition, another mutation (A > C) in the same genome position is mainly present in liver cancer and chronic lymphoid leukaemia. Surprisingly, the mutation is located in the RNA-binding site of an important splicing RNA (U1 snRNA). Although at present, research on splicing mainly focuses on humans, there are still many reports related to plants. Some studies have found that the deletion or mutation of the mu1a gene of Magnaporthe (M. oryzae U1A, MU1A) may lead to abnormal splicing of the precursor mRNA, affecting the normal expression of some proteins, normal growth and metabolism processes, and ultimately, the pathogenicity of Magnaporthe. In the defence response to fungal infection, AS may be as important as traditional transcriptional control in C. sublineola-inoculated sorghum seedlings, as reported in a study based on next-generation sequencing technologies [40]. To screen for pathogenic effectors that regulate plant AS, a fluorescence reporting system was established to screen nine splicing regulation (SREs) effectors from 87 effectors of Phytophthora infestans [41]. Further studies have shown that SRE3 in combination with U1-70 K physically manipulates the plant AS mechanism, thereby regulating AS-mediated plant immunity. The biological clock of Arabidopsis not only controls gene transcription but also influences its post-transcriptional regulation by influencing AS [42]. Six differential AS events occurred in the defence response of germs in mock-inoculated and S. sclerotiorum-inoculated susceptible and tolerant B. napus plants, as determined by an analysis of 18 RNA-seq libraries [43]. However, the potential mechanism via which splice proteins affect splicing regulation remains unclear. The work described in this study provides an introductory layout for such works in future. In the present study, we identified 117 U2B″ genes in 80 plant species. In the phylogenetic tree, plants with higher degrees of evolution and development were closely clustered together, and plants with lower degrees of evolution and development were similarly closely clustered together. Surprisingly, U2B″ in different plants showed consistently high conservation in multiple analyses. Nearly 70% of the sequences showed a 5 exon-2 UTR structure, and 90% of the sequences were predicted to contain an N-terminal domain named RRM1 and a C-terminal domain annotated as RRM2. Generally, if a gene is highly conserved in many species, it is believed that these similarities across species indicate that the gene performs some basic functions necessary for many life forms, and therefore retains these sequences during evolution. These sequences are generally necessary for life activities, and mutations in these sequences often lead to death. Therefore, few mutations have been preserved during evolution, so that the genes are highly conserved. The same is true for U2B″, which may perform one or more functions essential for life. We prefer U2B″ to perform a single or a few functions essential for life. U2B'' knockout did not lead to a lethal phenotype in worms [32]. U1A replaces snRNP and binds to its corresponding snRNP. However, we found that the N- and C-terminal domains of the protein were different in the multisequence alignment. Although both domains can recognise RNA, the N-terminus is more conserved than the C-terminus. We speculate that RRM1 performs the most basic functions required by all species, whereas RRM2 performs distinct functions in evolution and development. The U2 snRNP is a subcomplex in early spliceosomal assembly. U2B″ plays an important role in the snRNP complex. However, very few studies have elucidated its biological function in plants. Promoter analysis revealed that according to the complex response elements, U2B″ could respond to various stimuli. First, many stress-responsive elements were identified, including AREs, MYBs, and STREs. As a necessary cis-regulatory element for anaerobic induction, AREs play an important role in plants. MYB-MYC is a plant-specific element widely involved in plant growth, development, and response to abiotic stress. STRE is reportedly involved in a variety of stress responses in Neurospora, including heat, osmotic, and oxidative stress, and the molecular mechanism mediated by STRE may not be conserved [44]. In addition to many stress response components, there are a large number of hormone-responsive elements upstream of U2B″, including ABRE, activating sequence-1, CGTCA motif, ERE, and the TGACG motif. ABREs are response elements of ABA [45] that play a variety of important roles in plants. In higher plants, AS-1 of the cauliflower mosaic virus 35S promoter mediates SA- and IAA-inducible transcriptional activation [46]. As MeJA-responsive elements, the CGTCA and TGACG motifs are found in a large number of plants. The estrogen response element (ERE) is a conserved DNA sequence in the promoters of the estrogen target genes. It can bind to estrogen receptors, is transcriptionally regulated, and is usually located in the promoters of its target genes [47]. These results indicate that U2B″ is involved extensively in plant growth, development, and stress responses. We also confirmed this using qPCR. However, the interaction protein of U2B″ is relatively simple. These include the major snRNPs common to the three plants and other minor proteins specific to each plant. This may be caused by different degrees of evolution and development. Overall, the above studies show that U2B″ can respond to a variety of external stimuli but can only interact with a limited number of proteins. In the present study, 117 U2B″ genes were identified in 80 plant types. Comprehensive bioinformatic analysis and partial experiments of the gene family were performed. Considering that U2B″ is highly conserved in different plants and is located in the nucleus, it can respond to a variety of external stimuli, indicating that it is extensively involved in plant growth and development. However, the mechanism by which U2B″ regulates plant growth and development requires further study. The A. thaliana U2B″ protein (AT2G30260) was used as the original sequence for carrying out a protein BLAST search with an e-value cutoff of 1e-10 [48] against 80 plant genome sequences from Phytozome v12.1. Consequently, 117 putative U2B″/U2B″-like sequences were identified for subsequent research. Protein sequences of the plant U2B″ identified above were obtained for phylogenetic tree construction to infer clustering patterns and evolutionary relationships. The transcript with the longest coding sequence was selected for loci with multiple splicing subtypes. Subsequently, multiple sequence alignment of proteins were performed using Muscle V3.8 with the default settings [49]. A phylogenetic tree was constructed using MrBayes v3.2.2 (Jones model) [50]. The structural information of genes and proteins was obtained from the Phytozome v12.1 and Pfam protein family databases. The top 10 detected motifs were obtained using default settings on the MEME server from the cDNA and protein sequences of plant U2B″ [51]. The 1.5-kb promoter sequences of plant U2B″ were selected from the Phytozome database and used for the prediction of cis-elements using PlantCARE [52]. Arabidopsis U2B″ protein was input to STRING and PlantSPEAD [53] to find the most interacting partners. The CDS of OsU2B″ (LOC_Os03g18720.1) was amplified using gene-specific primers (5'-CCTGTTGTTTGGTGTTACTTAAGCTTATGTTGTCCGGCGACATACC-3' and 5'- TCCTCGCCCTTGCTCACCATGGATCCTCACTTCTTTGCGTAGGATATAGCC-3') and inserted into the GFP (pGreen II-UBI-GFP) vector to generate a fusion construct according to Lu et al. [1]. NLS − mCherry (SV40 large − T antigen NLS, a signal peptide guiding GFP into the nucleus) was selected as a marker for nuclear localisation [54]. Nipponbare rice seedlings grown at approximately 30 °C for one week were selected, and their stems were collected to extract protoplasts. Equal volumes of the constructed plasmids and extracted protoplasts were mixed with 40% PEG4000 solution at a ratio of 1:5 (v/v), cultured overnight at 30 °C, and observed under a laser confocal microscope (Leica TCS SP8) more than three times. The subcellular localisation of U2B″ in some species was predicted using the online PSORT database. NucPred was used to predict the nuclear localisation of protein sequences [55]. DeepTMHMM was used to predict the protein transmembrane domains. Nipponbare seeds were first soaked in carbendazim for one day and then in water for another day. The seeds were then transferred to the climate chamber and subjected to stress when they grew two true leaves (22 d). Materials were grown at 22 °C/20 °C under a 16 h/ 8 h light/dark photoperiod and a light intensity of 120 μmol m −2 s −1 in an incubator (QY-14; Nanjing Quanyou Electronic Technology Co., Ltd., China). Hydroponics was used in all four stress treatments, including cold treatment at 8 °C and drought treatment, with 20% PEG6000, salt treatment with 100 mmol L−1 sodium chloride, and heavy metal cadmium treatment with 100 μmol L−1 cadmium sulfate. Total RNA was isolated using TRIzol reagent (Invitrogen) and converted to complementary DNA (cDNA) using PrimeScript™ RT Master Mix (TAKARA) according to the manufacturer’s protocol. RT-qPCR was performed using a 7500 Real-time PCR Detection System (Bio-Rad) in conjunction with SYBR Fast qPCR Mix (TAKARA) and was repeated at least three times. Forward and reverse primers of OsU2B″ were used to produce a single amplification (5'- GCAACCGAAGATGGTTCTACTG -3' and 5'- GCTTTGGGCAGCAGCATTAG -3'). Gene expression was presented as a heat map using TBtools v1.048 [56]. OsACTIN (LOC_Os05g36290) was used as a reference gene. Homology modelling was carried out using the A. thaliana U2B″ protein sequence (UniProtKB AC: O22922) based on the template 4pkd.1. B using SWISS-MODEL [57, 58]. Amino acid multiple sequence alignment and conservation scores based on frequency-based differences were obtained using the default values from NCBI. Additional file 1: Fig. S1. Formation of mature 17S U2 snRNP. The upper one is12S core particle, the middle one is 15S pre-mature particle, and the lower oneis 17S functional maturity particle. Continuous thin black lines represent U2 snRNA. Fig. S2. The design process of the whole article. Fig. S3. Circle phylogenetic tree representation of the available plant U2B″ gene family. Phylogenetic analysisof plant U2B″ gene family was carried out by using software MrBayes v3.2.2. The posterior probability values are labeled at each major branch. Blue for dicotyledons, pink for monocotyledons,white for ferns, green for bryophytes, and yellow for algae. Fig. S4. Motifs of genomic structure and protein structure analysis. (A) Consensus sequence of top ten identified DNA motifs are listed in ascending order. (B) Consensus sequence of top ten identified amino-acid motifs are listed in ascending order. Fig. S5. The multiple sequence alignment of RRM domains for the conservative analysis. The sequences are arranged from top to bottom in phylogenetic tree. Fig. S6. Promoter classification and enrichment analysis. (A) Statistics of motifs function and number. The x-axis represents the number of elements. (B)Overall enrichment statistics of motifs in response to stress, hormones and light.Fig. S7. AS profile analysis. Summary of annotated alternatively spliced transcript isoforms for identified U2B″ genes. Pink represents monocotyledons, blue represents dicotyledons, green represents bryophytes, and yellow represents algae.Additional file 2: Table S1. Sequence summary of plant U2B″ gene phylogenetic analysis.Additional file 3: Table S2. Specific data of enrichment analysis.Additional file 4: Table S3. Statistical analysis of promoter distribution.Additional file 5: Table S4. Expression of U2B″ in rice under different stresses. Additional file 6: Table S5. Sequence summary of plant U2B″ protein-protein interaction network.

PMC9644492

Xianshuo Cheng,Tao Shen,Ping Liu,Shaojun Fang,Zhibin Yang,Yunfeng Li,Jian Dong

mir-145-5p is a suppressor of colorectal cancer at early stage, while promotes colorectal cancer metastasis at late stage through regulating AKT signaling evoked EMT-mediated anoikis

08-11-2022

miRNA 145-5p,Anoikis,EMT,Colorectal cancer

Background: miR-145-5P is generally considered as a tumor suppressor at early stage of colorectal cancer, but up-regulation occurs in the progressive and later stages which is associated with metastasis, indicating miR-145-5p may play dual role in colorectal cancer (CRC). To explore the detailed mechanism of miR-145-5p in carcinogenic is of importance. Methods: The expression pattern of miR-145-5p in CRC patients was downloaded from TCGA database, and the probable mechanism involved in the carcinogenic effect of miR-145-5p was predicted by bioinformatics analysis. Then, interference of miR-145-5p on SW480 and SW620 cells was conducted, and the influences on tumor cell viability, invasion ability, epithelial-mesenchymal transition (EMT), anoikis, and relative protein expression were examined respectively. Results: A total of 522 CRC patients’ data indicated that miR-145-5p expression was significantly higher in metastatic CRC than that in non-metastatic CRC, and higher expression of miR-145-5p was correlate with worse prognosis. Overexpression of miR-145-5P-5p enhanced the proliferation and invasion ability of SW620, but inhibited them in SW480. EMT was induced in SW620 after miR-145-5p overexpression and mesenchymal–epithelial transition (MET) was induced in SW480, resulted in the decreased apoptotic rate in SW620 and elevated apoptotic rate in SW480 respectively. Western blot results showed that AKT signaling pathway was involved in the miR-145-5p evoked EMT-mediated anoikis process in SW620 and SW480 cells. Conclusion: miR-145-5p is a tumor suppressor at early stage of CRC, and an oncogene at advanced stage of CRC. AKT signaling evoked EMT-mediated anoikis might be the pathway by which miR-145-5P regulates CRC cell invasion and metastasis. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-022-10182-6.

mir-145-5p is a suppressor of colorectal cancer at early stage, while promotes colorectal cancer metastasis at late stage through regulating AKT signaling evoked EMT-mediated anoikis miR-145-5P is generally considered as a tumor suppressor at early stage of colorectal cancer, but up-regulation occurs in the progressive and later stages which is associated with metastasis, indicating miR-145-5p may play dual role in colorectal cancer (CRC). To explore the detailed mechanism of miR-145-5p in carcinogenic is of importance. The expression pattern of miR-145-5p in CRC patients was downloaded from TCGA database, and the probable mechanism involved in the carcinogenic effect of miR-145-5p was predicted by bioinformatics analysis. Then, interference of miR-145-5p on SW480 and SW620 cells was conducted, and the influences on tumor cell viability, invasion ability, epithelial-mesenchymal transition (EMT), anoikis, and relative protein expression were examined respectively. A total of 522 CRC patients’ data indicated that miR-145-5p expression was significantly higher in metastatic CRC than that in non-metastatic CRC, and higher expression of miR-145-5p was correlate with worse prognosis. Overexpression of miR-145-5P-5p enhanced the proliferation and invasion ability of SW620, but inhibited them in SW480. EMT was induced in SW620 after miR-145-5p overexpression and mesenchymal–epithelial transition (MET) was induced in SW480, resulted in the decreased apoptotic rate in SW620 and elevated apoptotic rate in SW480 respectively. Western blot results showed that AKT signaling pathway was involved in the miR-145-5p evoked EMT-mediated anoikis process in SW620 and SW480 cells. miR-145-5p is a tumor suppressor at early stage of CRC, and an oncogene at advanced stage of CRC. AKT signaling evoked EMT-mediated anoikis might be the pathway by which miR-145-5P regulates CRC cell invasion and metastasis. The online version contains supplementary material available at 10.1186/s12885-022-10182-6. Colorectal cancer (CRC) is a clonal disease which has a favorable prognosis at localized stage and develops progressively to advanced stages with worse prognosis [1]. To date, CRC is the second leading cause of cancer-related deaths worldwide, and invasion and metastasis are significantly associated with the poor prognosis of CRC [2]. Studies have found that miR-145-5p expression was decreased as early as the pre-adenomatous polyp stage [3], and the downregulation of miR-145-5p was significantly associated with poor overall survival (OS) in CRC [4], suggesting that miR-145-5p may affect pathogenesis of CRC. miR-145-5p is encoded by miR-145-5P gene which is located on Chromosome 5: 149,430,646–149,430,733 forward strand [5]. This miRNA is mainly considered as a tumor suppressor miRNA in diverse types of cancers, including bladder cancer, breast cancer, cervical cancer, cholangiocarcinoma, renal cancer, and gastrointestinal cancers. In CRC, functional studies have demonstrated that miR-145-5p could suppress CRC migration and invasion, by down-regulating the expression of TWIST1 [6], TUSC3[7], MAPK1 [8], and SIP1 [9], and inhibiting the PAK4-dependent pathway [10]. However, several studies suggested that miR-145 might function as an oncogene. Yuan W et al. reported that miR-145 expression was positively correlated with lymph node metastasis in CRC [11]. Another study showed that miR-145-5p expression was significantly higher in stage III/IV than in stage II CRC patients [12]. Available study indicated that miR-145-5p may play dual roles in CRC, in which it is down-regulated at the early stage as a tumor suppressor, and up-regulation occurs in the progressive and later stages as an oncogene. However, the detailed mechanism of miR-145-5p in carcinogenic has not been fully explained. Herein, we first revealed the expression pattern of miR-145-5p in CRC patients from TCGA database, and explored the probable mechanism involved in the carcinogenic effect of miR-145-5p by bioinformatics analysis. Then, SW480 cell line which derived from the primary tumor, and SW620 cell line derived from a lymph node metastasis of the same patient at the time of recurrence one year later [13] were selected to examine the role and mechanisms of miR-145-5p in different stages of CRC. Bioinformatics analysis indicated that miR-145-5p expression in metastatic CRC was significantly higher than that in non-metastatic CRC, and higher expression of miR-145-5p was correlate with worse prognosis. miR-145-5p interference results demonstrated that miR-145-5p may promote tumor invasion through AKT signaling driven epithelial-mesenchymal transition-mediated anoikis resistance in SW620, inversely in SW480. Gene expression and corresponding clinical data of CRC, together with adjacent normal mucosa tissues was downloaded from the TCGA platform (https://tcga-data.nci.nih.gov/tcga/). The difference of expression pattern of miR-145-5p between the paired cancer tissues and adjacent normal mucosa tissues was compared first. Then, the relationships of miR-145-5p expression with the clinicopathological characteristics, and the effect of miR-145-5p expression on overall survival of patients were analyzed. Finally, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) functional enrichment analysis was conducted to explore the potential biological pathway involved in CRC pathogenesis regulated by miR-145-5p expression. Human CRC cell lines SW620 and SW480 (American Type Culture Collection, ATCC) were maintained in RPMI 1640 (Invitrogen), supplemented with 10% fetal bovine serum (FBS, Invitrogen) and antibiotics (100 U/mL penicillin and 100 µg/mL streptomycin) in a 5% CO2 incubator at 37℃. miRNA -control, miR-145-5p mimics, miR-145-5p inhibitors -control and miR-145-5p inhibitors were synthesized by Tiangen Biotech (Beijing) Co., Ltd (the sequences were listed in supplemental Table 1). Plasmid was purchased from Tiangen Biotech (Beijing) Co., Ltd. Transient transfections was conducted using Lipofectamine RNAiTM (#C0535, Beyotime). Transfection efficiency was determined by reverse transcription-quantitative PCR (RT-qPCR). Total RNA was extracted using TRIzol® reagent (Invitrogen; Thermo Fisher Scientific, Inc.), and cDNA was synthesized using Prime Script™ RT Master Mixture (Takara Biotechnology Co., Ltd.). A SYBR Prime Script miRNA RT-PCR kit (Takara Biotechnology Co., Ltd.) was used for the detection and quantitation of miR-145-5p expression. The primers were synthesized by Tiangen Biotech (Beijing) Co., Ltd, and the sequences were as follows: miR-145-5p forward, 5’-CGGTCCAGTTTTCCCAGGA-3’, and reverse, 5’-AGTGCAGGGTCCGAGGTATT-3’. miR-145-5p-loop: 5’-GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACAGGGAT-3’. To evaluate the influence of RNA transfection on cell viability, cells without interference (WT group), cells transfected with miRNA-control (NC group), and cells transfected with miR-145-5p mimics (OV group) or miR-145-5p inhibitors (IN group) were seeded on 96-well plate at a concentration of 3000 cells/well and cultured as described. Then, cell viability was quantified using a Cell Counting Kit-8 (CCK‐8, Beyotime, China) at 0, 24, 48 and 72 h. The invasion ability of the transfected cells was detected in a Transwell model (8 mm; Millipore). The upper chamber was coated with Matrigel matrix gel (BD Biosciences). Cells (5 × 104 cells/well) resuspended in 200 µl of FBS-free medium were inoculated into the upper chamber, and 600 µl of complete medium containing 10% FBS was added to the lower chamber. After 24 h of incubation, uninvaded cells were removed from the upper surface of the membrane using a cotton swab, and the invaded cells were fixed and stained with 1% crystal violet solution. Finally, light microscopy was performed to photograph and count six random fields of view in each group. At 48 h after RNA transfection, cells from different groups were seeded onto coverslips in 6-well dishes. After completely adhered to the coverslips, cells were fixed with 4% paraformaldehyde for 30 min, permeabilized with 1% (v/v) Triton X-100 for 10 min at room temperature (RT), and then blocked with 1% goat serum for 1 h at RT. Subsequently, the coverslips were incubated with primary antibodies (anti E-cadherin, Abcam; anti vimentin, Abcam) at 4℃ overnight. Next day, the coverslips were washed three times with PBS and incubated with Cy3- conjugated secondary antibodies (Beyotime) for 1 h at RT, then all the coverslips were counterstained with 4’-6-diamidino-2-phenylindole (DAPI, Sigma-Aldrich). Finally, photomicrographs were captured with a Nikon Total Internal Reflection Fluorescence microscope. The semi-quantitative analysis of immunofluorescence staining was conducted using Image Pro Plus software. Cell apoptosis was detected using an Annexin V–FITC Apoptosis Detection Kit (Beyotime) by Flow cytometry according to the manufacturer’s instruction. Briefly, cells from different groups were seeded in Poly-Hema-coated (Sigma-Aldrich) dishes at a concentration of 3 × 105 cells/dish. After 48 h of culture, the cells were harvested and tested in triplicate. At 48 h after RNA transfection, Cells were lysed using a protein extraction reagent containing protease inhibitor (Beyotime). Total protein concentrations were determined using a BCA protein assay reagent (Beyotime). Then, western blot was implemented as previously described [14]. The primary antibodies used in this study were as follows: mouse anti-human E-cadherin (Abcam), mouse anti-human vimentin (Abcam), rabbit anti-human p-AKT (Ser473) (Abacm), and rabbit anti-human AKT (C67E7) (Cell Signaling Technology) and GAPDH (Beyotime). The semi-quantitative analysis of the western blots was conducted using Image J software. SPSS 19.0 (SPSS Inc., Chicago, IL) software was used for all the statistical analyses. Results were expressed as mean ± standard deviation (SD). Student’s t test and a one-way analysis of variance (ANOVA) were used for comparison of cell proliferation and invasion between different groups. All experiments concerning cell culture were performed independently at least three times. Potential associations between miR-145-5p expression and the clinicopathological characteristics were evaluated using the chi-square test. Patient overall survival was estimated by the Kaplan-Meier method. P < 0.05 was considered statistically significant. The RNA-Seq data and corresponding clinical information of 522 CRC tissues, including 11 paired adjacent normal mucosa tissues was collected for bioinformatics analysis. The general information of the collected samples was shown in Table 1, the expression of miR-145-5p was negatively correlated with age (χ2 = 8.955, P = 0.003). The comparison results of CRC tissues with paired adjacent normal mucosa tissues suggested miR-145-5p expression was downregulated in CRC (9/11 downregulated, P = 0.018 Fig. 1 A). However, the expression of miR-145-5p was significantly higher in CRC with metastasis (including III stage and IV stage) than without metastasis (P < 0.05 Fig. 1B), which was in accordance with the results of N stage (χ2 = 7.054, P = 0.008), M stage (χ2 = 7.297, P = 0.007) and pathological stage (χ2 = 6.983, P = 0.008) shown in Table 1. Furthermore, Kaplan-Meier survival analysis indicated that CRC patients with high expression of miR-145-5p had significantly lower overall survival rates (log rank = 4.932, P = 0.026 Fig. 1 C). In order to clarify the possible mechanism of miR-145-5p in promoting or restricting CRC progression, KEGG and GO analysis was conducted on the collected data. A total of 298 genes were found to express differently between miR-145-5p high expression group and miR-145-5p low expression group, and all the 298 genes were significantly downregulated in miR-145-5p high expression group (Supplementary Table 2). KEGG and GO functional enrichment analysis showed that these differently expressed genes were enriched in 30 pathways, mostly were cell adhesions related pathways, such as KEGG pathways of ECM-receptor interaction, Cell adhesion molecules and Focal adhesions (Fig. 2 A, 2B) and GO terms of cell-cell adhesion via plasma-membrane adhesion molecules, cell junction assembly and extracellular matrix organization (Fig. 2 C, 2D). Additionally, PI3K/AKT signaling was also enriched (Fig. 2B). After interference, the transfection efficiency was validated by RT-qPCR at 48 h. As shown in Fig. 3 A, 3B, transfection with miR-145-5p mimics could significantly increase the expression of miR-145-5p in both SW620 and SW480. Furthermore, the CCK-8 results demonstrated that upregulation of miR-145-5p could promote the proliferation of SW620 cells but inhibit it of SW480 cells in a time dependent manner (Fig. 3 C, 3D). Similarly, the overexpression of miR-145-5p could also enhance the invasion ability of SW620 but decrease it of SW480 cells, as indicated by the results of transwell assay (Fig. 3E F). Immunofluorescence staining was conducted to verify the influence of miR-145-5p on the epithelial–mesenchymal transition (EMT) process of SW620 and SW480 cells, which is a crucial step for the disruption of cell-cell adhesion and cell-ECM interaction. After RNA interference, we found that E-cadherin expression was downregulated (Fig. 4 A) significantly (P < 0.001) and vimentin expression was upregulated (Fig. 4B) significantly (P < 0.001) in miR-145-5p mimic transfected SW620 cells at 48 h (Fig. 4 C). In SW480 cells, E-cadherin expression was upregulated (Fig. 4D) significantly (P < 0.001) and vimentin expression was downregulated (Fig. 4E) significantly (P < 0.001) after miR-145-5p mimic transfection, in contrast to that of SW620 cells(Fig. 4 F). After miR-145-5p RNA transfection, cells were suspensively cultured. As shown in Fig. 5, overexpression of miR-145-5p resulted in the significantly decreased apoptotic rate of SW620 cells and significantly elevated apoptotic rate of SW480 cells (Fig. 5 A, 5B). Conversely, after miR-145-5p inhibitor transfection, apoptotic rate of SW620 cells significantly elevated and that of SW480 cells significantly decreased (Fig. 5 C, 5D). Since PI3K/AKT pathway was enriched in the bioinformatics analysis, expression of p-AKT and AKT was detected by western blot after miR-145-5p interference, the expression of E-cadherin and Vimentin were also examined simultaneously. In SW620 cells, p-AKT expression was upregulated after miR-145-5p overexpression compared with wild type and normal control. Meanwhile, E-cadherin expression was inhibited and vimentin expression was increased (Fig. 6 A, 6B). The opposite results were observed in SW480 cells after miR-145-5p overexpression, as AKT signaling was suppressed, the expression of E-cadherin was increased and vimentin expression was inhibited respectively (Fig. 6 C, 6D). miR-145-5p inhibitor transfection had opposite effect on SW620 and SW480 cells compared with miR-145-5p mimic transfection. Briefly, p-AKT and vimentin expression was downregulated and E-cadherin expression was upregulated in miR-145-5p inhibitor transfected SW620 cells (Fig. 6E F), while p-AKT and vimentin expression was upregulated and E-cadherin expression was downregulated in miR-145-5p inhibitor transfected SW480 cells (Fig. 6G H). The results suggested that EMT-mediated anoikis process may be correlated with AKT signaling pathway, regulated by miR-145-5p expression in SW620 and SW480 cells. miRNA is a class of 20–22 nucleotide non-coding RNA molecules that negatively regulate gene expression by inhibiting the translation and stability of target mRNAs [15]. Numerous studies proved that the alteration of miRNA expression is involved in the development of CRC. Among them, miR-145-5p was generally considered to be a tumor suppressor in CRC. By targeting KLF4 [16, 17] and SOX2 [18], miR-145-5p could inhibit the invasion and metastasis of CRC. However, recent study suggested that miR-145-5p exerts oncogenic effect in CRC, as miR-145-5p was upregulated in CRC patients with lymph node or liver metastasis compared to those without metastasis [11]. Hence, it is important to reveal the function of miR-145-5p at different stage of CRC. In the present study, the expression profiles and clinicopathological characteristics of 522 CRC patients from TCGA database were analyzed firstly. In accordance with previous study, the expression of miR-145-5p in cancer tissues is significantly lower than that in normal mucosa. Besides, higher miR-145-5p expression was observed in stage III/IV CRC tissues than in stage I/II, and worse overall survival rate was accompanied with higher miR-145-5p expression. These finding indicated that down-regulation of miR-145-5p might participate in the initiation of CRC, while re-expression of miR-145-5p might contribute to the deterioration of CRC. KEGG and GO analyze are valid bioinformatics methods in exploring the potential biological pathway concerned with target gene [19]. The differently expressed genes were mostly enriched in cell adhesions related pathways based on the collected data, which means miR-145-5p might regulate CRC metastasis. EMT is the initial step of cancer invasion, dissemination, and metastasis, during which epithelial cells loss adhesion between tumor cells and the extracellular matrix (ECM) to acquire mesenchymal characteristics and invasive abilities [14, 20]. Accumulating evidence has now indicated that EMT is one of the critical mechanisms that confers cancer cells resistance to anoikis [21–25], then creates a favorable opportunity for tumor proliferation, invasion, and metastasis [26]. It has been demonstrated that PI3K/AKT pathway is a major downstream pathway of miR-145-5p, which is involved in the regulation of invasion and metastasis in different tumor types, mainly in squamous cell carcinoma, bladder cancer and NSCLC[27–29]. AKT activation is a key step for PI3K/AKT signaling pathway to the downstream. Existing evidence has suggested that the activation of AKT is involved in the biological functions of miR-145-5p, such as inhibiting the proliferation of hepatocellular carcinoma [30], inhibiting cardiac fibrosis [31], and promoting the sustained contraction of vascular smooth muscle cells [32]. In CRC, Yin et al. found that miR-145-5p could inhibit AKT activation by targeting N-RAS and IRS1 and suppress VEGF expression in SW116 and HCT116 cells [33]. In the present study, PI3K/AKT signaling pathway was also enriched in the downloaded TCGA data. Further cellular transfection experiments showed that upregulation of miR-145-5p significantly decreased the proliferative and invasive abilities of SW480 accompanied with reduce of p-AKT expression, indicating that miR-145-5p acts as a tumor suppressor in the early stage of CRC. On the contrary, upregulation of miR-145-5p in SW620 significantly increased its proliferative and invasive abilities, and the increase of p-AKT demonstrated that miR-145-5p may act as an oncogene in promoting metastasis. The dual functions of miR-145-5p in different stage of CRC were verified by transfecting SW620 and SW480 cells with miR-145-5p inhibitor in this study. The flow cytometry and western blot results suggested that AKT signaling evoked EMT-mediated anoikis process was significantly activated in SW620, but suppressed in SW480 cells, when miR-145 expression was inhibited. Interestingly, a previous study reported that miR-145-5p may act as a tumor suppressor in SW620 when using a higher mimic concentration of 50 nM [9]. This might be the main reason that a higher expression of miR-145-5p is positively correlated with metastasis and poor prognosis, although miR-145-5p expression in the cancer tissues is significantly lower than normal mucosa. The present study had some limitation. Although the results of this study proved that miR-145-5p expressed inversely at different stages of CRC, and AKT signaling evoked EMT-mediated anoikis was regulated by miR-145-5p. The target gene of miR-145-5p through which AKT signaling was regulated was not revealed. Besides, in vivo study was not conducted in this study, which may lead to the less reliability of the conclusion of the current work. Further research based on animal model of CRC was needed to clarify the mechanism of miR-145-5p in regulating CRC progress. The present study consulted to available TCGA data of 522 CRC patients, and found miR-145-5p was down-regulated in CRC, while upswung in III/IV stage with metastasis. Interference of miR-145-5p in SW480 and SW620 cells demonstrated that miR-145-5p played a paradoxical role in the development of CRC, and AKT signaling evoked EMT-mediated anoikis might be the pathway by which miR-145-5p regulates CRC cell invasion and metastasis (Fig. 7). These findings indicated that miR-145-5p was a promising prognostic and predictive markers for CRC in clinical practice. Below is the link to the electronic supplementary material. Supplementary Material 1 Supplementary Material 2

PMC9644515

Soudeh Ghafouri-Fard,Tayyebeh Khoshbakht,Bashdar Mahmud Hussen,Sara Tharwat Abdullah,Mohammad Taheri,Mohammad Samadian

A review on the role of mir-16-5p in the carcinogenesis

08-11-2022

miR-16-5p,Cancer,Biomarker,Expression,Malignancies

miR-16-5p is microRNA with important roles in the development of diverse malignancies including neuroblastoma, osteosarcoma, hepatocellular carcinoma, cervical cancer, breast cancer, brain tumors, gastrointestinal cancers, lung cancer and bladder cancer. This miRNA has 22 nucleotides. hsa-miR-16-5p is produced by MIR16-1 gene. First evidence for its participation in the carcinogenesis has been obtained by studies reporting deletion and/or down-regulation of these miRNAs in chronic lymphocytic leukemia. Subsequent studies have shown down-regulation of miR-16-5p in a variety of cancer cell lines and clinical samples. Besides, tumor suppressor role of miR-16-5p has been verified in animal models of different types of cancers. Studies in these models have shown that over-expression of this miRNA or modulation of expression of lncRNAs that sponge this miRNA can block carcinogenic processes. In the current review, we summarize function of miR-16-5p in the development and progression of different cancers.

A review on the role of mir-16-5p in the carcinogenesis miR-16-5p is microRNA with important roles in the development of diverse malignancies including neuroblastoma, osteosarcoma, hepatocellular carcinoma, cervical cancer, breast cancer, brain tumors, gastrointestinal cancers, lung cancer and bladder cancer. This miRNA has 22 nucleotides. hsa-miR-16-5p is produced by MIR16-1 gene. First evidence for its participation in the carcinogenesis has been obtained by studies reporting deletion and/or down-regulation of these miRNAs in chronic lymphocytic leukemia. Subsequent studies have shown down-regulation of miR-16-5p in a variety of cancer cell lines and clinical samples. Besides, tumor suppressor role of miR-16-5p has been verified in animal models of different types of cancers. Studies in these models have shown that over-expression of this miRNA or modulation of expression of lncRNAs that sponge this miRNA can block carcinogenic processes. In the current review, we summarize function of miR-16-5p in the development and progression of different cancers. MicroRNAs (miRNAs) are small-sized transcripts that regulate expression of genes at post-transcriptional level through specific targeting of mRNAs. With sizes about 21–25 nucleotides, miRNAs are originated from coding and non-coding transcription units in introns, exons or intergenic areas [1]. They are produced in a multi-step process involving both nuclear and cytoplasmic proteins. They are involved in the carcinogenic process, since they can regulate expression of several oncogenes and tumor suppressor genes as well as activities of cancer-associated pathways [2]. Expression pattern and function of several miRNAs have been assessed in different cancer types. Since these small-sized transcripts are stable in the circulation or other biofluids, they represent potential biomarkers for diagnostic and follow-up purposes [3]. Dysregulation of miRNAs has been correlated with evolution of cancers, hence they are regarded as molecular tools for non-invasive assessment of cancer occurrence and its prognosis [4]. miR-16-5p is an example of this class of transcripts with important roles in the development of diverse malignancies including neuroblastoma, osteosarcoma, hepatocellular carcinoma, cervical cancer, breast cancer, brain tumors, gastrointestinal cancers, lung cancer and bladder cancer. This miRNA has 22 nucleotides and is present in Homo sapiens. Homo sapiens hsa-miR-16-5p is produced by MIR16-1 gene. miR-16-1 is allocated at 13q14.3 along with miR-15a. This miRNA cluster is the target of 13q deletions in chronic lymphocytic leukemia (CLL). miRNAs encoded by this locus have tumor suppressor functions. First evidence for its participation in the carcinogenesis has been obtained by studies reporting deletion and/or down-regulation of these miRNAs in (CLL) [5]. The tumor suppressor functions of miR-15a/16 − 1 are exerted through targeting the BCL2 oncogene. Through a high-throughput study in a leukemic cell line model, Colin et al. have found enrichment in AU-rich elements in the elements of the miR-15a/16 − 1 signature [6]. Subsequently, different studies have assessed role of miR-16-5p in the carcinogenesis using in vitro and in vivo techniques. Moreover, expression pattern of miR-16-5p has been evaluated in clinical samples gathered from patients with diverse malignancies. In the current review, we summarize function of miR-16-5p in the development and progression of different cancers using the above-mentioned lines of evidence. The reason for selection of this miRNA in this review article is the important role of this miRNA in the suppression of carcinogenesis, its down-regulation in a variety of solid and hematological malignancies and its potential as an anti-cancer target. The following strategy was used for selection of papers: publication in full-text English language in a peer-reviewed journal and detailed description of conducted methods. In addition, papers should include in vitro functional studies or expression assays in clinical samples. Cell line studies have indicated important roles of miR-16-5p in the carcinogenesis. Moreover, these studies have shown the inhibitory effects of this miRNA on transcription of several genes, particularly a number of known oncogenes. An in vitro study in neuroblastoma has shown interaction between miR-15a, miR‐15b and miR‐16 and MYCN transcript. Based on the results of luciferase reporter assay these miRNAs bind with 3’UTR of MYCN transcript leading to suppression of its expression. Forced up-regulation of these miRNAs has decreased proliferative potential, migratory ability, and invasion of neuroblastoma cells [7]. Another study in neuroblastoma has shown that the oncogenic circular RNA circ-CUX1 enhances tumorigenesis of neuroblastoma and their glycolysis through targeting miR-16-5p. Moreover, miR-16-5p tumor suppressor impact has been partially decreased by transfection of circ-CUX1 overexpressing vectors. DMRT2 has been found to be targeted by miR-16-5p in neuroblastoma cells [8]. miR-16-5p has been to be down-regulated in osteosarcoma cell lines compared with control cells, parallel with up-regulation of Smad3. Up-regulation of miR-16-5p has suppressed proliferation, migratory potential and invasive features of osteosarcoma cells and increased the cytotoxic effects of cisplatin on these cells. Moreover, miR-16-5p over-expression has led to reduction of Smad3 expression. Notably, cells harboring Smad3 mutation have not responded to miR-16-5p over-expression, indicating that miR-16-5p suppresses invasive properties of osteosarcoma cells through suppressing expression of Smad3 [9]. miR-16-5p effect in suppression of tetraspanin 15 gene has also been involved in the inhibition of osteosarcoma cells proliferation, migration and invasion [10]. Figure 1 shows tumor suppressor role of miR-16-5p in different types of cancer. The long non-coding RNA (lncRNA) AGAP2-AS1 which targets miR-16-5p has been shown to be up-regulated in hepatocellular carcinoma cell lines. This lncRNA could promote proliferation, migratory aptitude, invasiveness and epithelial-mesenchymal transition (EMT) of these cells through acting as a sponge for miR-16-5p. ANXA11 has been found as a target of miR-16-5p in hepatocellular carcinoma cells, mediating the impacts of miR-16-5p and AGAP2-AS1 in these cells and enhancing activity of AKT signaling. Notably, hypoxia has been shown to increase levels of AGAP2-AS1 in these cells [11]. Another study has confirmed down-regulation of miR-16-5p in hepatocellular cancer cells. Dual-Luciferase reporter gene assay has validated the regulatory role of miR-16-5p on expression of Insulin like growth factor1 receptor (IGF1R). IGF1R down-regulation has decreased the suppressive role of miR-16- 5p on proliferation ability and metastatic potential of hepatocellular cancer cells [12]. Moreover, down-regulation of miR-16-5p by lncRNA TTN-AS1 has been shown to promote resistance to sorafenib through enhancement of expression of cyclin E1 [13]. Finally, another study in hepatocellular carcinoma has shown that SNHG22 increases tumorigenic ability of cancer cells and their angiogenesis though induction of DNA methylation in miR-16-5p [14]. In cervical cancer cells, miR-16‐5p affects radiosensitivity through regulation of expression of coactivator‐associated arginine methyltransferase 1 [15]. Moreover, it can influence metabolic reprogramming and chemoresistance through regulation of Pyruvate Dehydrogenase Kinase 4 (PDK4) expression [16]. In breast cancer cell, down-regulation of miR-16-5p has been associated with high migratory and proliferative potential of cells, induction of cell cycle progression and reduction of cell apoptosis. miR-16-5p could restrain activity of the Nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) pathway and reduce expression of AKT3 gene, thus inhibiting development of breast cancer [17]. miR-16-5p could also suppress proliferation of breast cancer cells through down-regulating expression of ANLN [18]. The inhibitory effect of miR-16 -5p in breast cancer cells proliferation and invasiveness can be mediated through regulation of Vascular Endothelial Growth Factor A (VEGFA) expression [19]. Finally, ATXN8OS has been shown to enhance tamoxifen resistance through sponging miR-16-5p [20]. Moreover, miR-16‐5p has been shown to be commonly down‐regulated in astrocytic gliomas. This miRNA could regulate proliferation and apoptosis of these cells as well as effect of cytotoxic agents on these cells [21]. Another study in glioma cells has shown that TIIA could inhibit viability of cells, their migratory potential and invasiveness, and decrease levels of Cyclin D1, Matrix metallopeptidase 9 (MMP-9) and Vimentin via regulation of miR-16-5p/Talin-1 axis [22]. Summary of studies that evaluated expression of miR-16-5p or its partners in cell lines is presented in Table 1. The tumor suppressor role of miR-16-5p has been verified in animal models of different types of cancers. Studies in these models have shown that over-expression of this miRNA or modulation of expression of lncRNAs that sponge this miRNA can block carcinogenic processes. For instance, transplantation of miR-15a‐, miR‐15b‐ and miR‐16‐expressing neuroblastoma cells into extremely immunodeficient mice has suppressed formation of tumors as well as expression of MYCN, suggesting that these miRNAs have a tumor suppressor role in neuroblastoma through targeting MYCN [7]. Another study in xenograft model of neuroblastoma has shown that knock down of the miR-16-5p-targeting circ-CUX1 leads to reduction of tumor growth [8]. In animal models of hepatocellular carcinoma, up-regulation of AGAP2-AS1 has enhanced tumor growth via down-regulating miR-16-5p [11]. Moreover, down-regulation of TTN-AS1 decreases tumor size and resistance to sorafenib through enhancement of expression of miR-16-5p [13]. In cervical cancer models, silencing of miR-16-5p target, PDK4 has enhanced efficacy of chemotherapy [16]. Moreover, silencing of DLX6-AS1 which targets miR-16-5p decreases tumor size [25]. Other studies in breast cancer, chordoma/chondrosarcoma, gastric cancer, lung cancer, colorectal cancer, bladder cancer and cholangiocarcinoma have confirmed a tumor suppressor role for miR-16-5p (Table 2). Down-regulation of miR-16-5p has been verified in clinical samples obtained from patients with different malignancies. Moreover, AGAP2-AS1 that decreases miR-16-5p levels has been shown to be up-regulated in hepatocellular carcinoma tissues, particularly in metastatic and recurrent ones. In addition, expression levels of AGAP2-AS1 and miR-16-5p have been correlated with clinical parameters and poor prognosis of patients with this type of cancer [11]. In neuroblastoma, up-regulation in circ-CUX1 that sponges miR-16-5p has been correlated with advanced TNM stage, low differentiation grade and lymph node metastasis [8]. In breast cancer patients, miR-16-5p has been shown to have low expression. Notably, patients with low expression of miR-16-5p have been found to have a lower survival rate compared with those having high expression of miR-16-5p [17]. In the majority of CLL cases, miR-15a and miR-16-1 have been shown to be lost or down-regulated [6]. Moreover, assessment of GO database has led to identification of enrichment of MCL1 Apoptosis Regulator, BCL2 Family Member (MCL1), B-cell lymphoma 2 (BCL2), ETS Proto-Oncogene 1 (ETS1), or Jun Proto-Oncogene, AP-1 Transcription Factor Subunit (JUN) in miR-16 signature. Notably, these genes are involved in the regulation of apoptosis and cell cycle [6]. Several studies have reported down-regulation of this miRNA in nearly all examined malignant tissues except for ovarian cancer tissues. Similarly, lncRNAs or circRNAs that decrease expression of miR-16-5p have been found to be up-regulated in cancer samples compared with non-cancerous controls (Table 3). miR-16-5p is an example of miRNAs with tumor suppressor role in almost all assessed tissues. This speculation is based on the observed down-regulation of this miRNA in nearly all examined malignant tissues except for ovarian cancer tissues. Moreover, a number of studies have reported up-regulation of lncRNAs that target this miRNA or specific targets of this miRNA. This miRNA has been found to be sponged by some lncRNAs and circRNAs, namely LINC00662, LINC00649, LINC00473, LINC00210, PVT1, XIST, AGAP2-AS1, DLX6-AS1, TTN-AS1, circ-CUX1 and hsa_circ_0005721. These observations indicate the complexity of the network through which miR-16-5p exerts its tumor suppressor effects. Moreover, abnormal up-regulation of the mentioned lncRNAs and circRNAs is regarded as a possible mechanism for down-regulation of miR-16-5p along with genomic variations in the genetic locus of this miRNA. Phosphoinositide 3-kinase (PI3K)/AKT, Phosphatase and tensin homolog (PTEN)/AKT, NF-κB, Hippo and E1-pRb-E2F1 pathways are among signaling pathways being affected by dysregulation of miR-16-5p. Thus, down-regulation of miR-16-5p can lead to over-activity of cancer-related signals enhancing cell survival. Down-regulation of miR-16-5p or up-regulation of lncRNAs/circRNAs that sponge this miRNA has been shown to be associated with malignant features of different cancers such as neuroblastoma, osteosarcoma, renal cell carcinoma and colorectal cancer, indicating a role for miR-16-5p as a prognostic marker in human cancers. In fact, down-regulation of this miRNA has been detected in samples with low level of differentiation and high propensity to local and distant metastases. Thus, patient with low levels of expression of this miRNA has exhibited poor clinical outcomes. Since this miRNA can be detected in the peripheral blood, it represents a novel non-invasive strategy for early detection of cancer. However, since it is down-regulated in several types of cancers, the type of cancer cannot be detected through this route. Moreover, evaluation of levels of miR-16-5p in cancer patients can be used for follow-up after removal of primary tumor. The mechanism behind down-regulation of miR-16-5p in malignant tissues is not investigated thoroughly, although deletion in the genomic region coding this miRNA is a putative mechanism. Moreover, up-regulation of lncRNAs/circRNAs that sponge this miRNA is a well-established mechanism for its down-regulation in different cancers. Induction of DNA methylation in miR-16-5p is another mechanism of down-regulation of this miRNA in cancers [14]. Future studies are needed to find possible epigenetic alterations that affect transcription of precursor of miR-16-5p. Different studies have shown the effects of miR-16-5p in regulation of chemosensitivity, radiosensitivity as well as response to the targeted therapy by sorafenib. From a clinical point of view, up-regulation of miR-16-5p is a potentially effective modality for suppression of tumor growth and defeating chemotherapy resistance. However, introduction of miR-16-5p mimic into cancerous cells needs a specific strategy to shield the miRNA mimics from self-hydrolysis or degradation by RNases. Without these considerations, the short half-life of naked RNA mimics reduces the potential effects of miRNAs [51]. Moreover, issues regarding the toxicity or nonspecific cell-targeting nature of miRNA carriers should be solved. These issues have attenuated the pace of entering miRNA mimics into the clinical setting. Cumulatively, miR-16-5p is a putative tumor suppressor miRNA that can be used as a therapeutic modality in different cancers. However, the biosafety and bioavailability issues should be solved before introduction of this modality in clinical settings.

PMC9645528

David Zapletal,Eliska Taborska,Josef Pasulka,Radek Malik,Karel Kubicek,Martina Zanova,Christian Much,Marek Sebesta,Valeria Buccheri,Filip Horvat,Irena Jenickova,Michaela Prochazkova,Jan Prochazka,Matyas Pinkas,Jiri Novacek,Diego F. Joseph,Radislav Sedlacek,Carrie Bernecky,Dónal O’Carroll,Richard Stefl,Petr Svoboda

Structural and functional basis of mammalian microRNA biogenesis by Dicer

03-11-2022

Dicer,helicase,DExD,miRNA,cryo-EM,mirtron,dsRNA,RNAi,dsRBD,TARBP2,PKR

Summary MicroRNA (miRNA) and RNA interference (RNAi) pathways rely on small RNAs produced by Dicer endonucleases. Mammalian Dicer primarily supports the essential gene-regulating miRNA pathway, but how it is specifically adapted to miRNA biogenesis is unknown. We show that the adaptation entails a unique structural role of Dicer’s DExD/H helicase domain. Although mice tolerate loss of its putative ATPase function, the complete absence of the domain is lethal because it assures high-fidelity miRNA biogenesis. Structures of murine Dicer•–miRNA precursor complexes revealed that the DExD/H domain has a helicase-unrelated structural function. It locks Dicer in a closed state, which facilitates miRNA precursor selection. Transition to a cleavage-competent open state is stimulated by Dicer-binding protein TARBP2. Absence of the DExD/H domain or its mutations unlocks the closed state, reduces substrate selectivity, and activates RNAi. Thus, the DExD/H domain structurally contributes to mammalian miRNA biogenesis and underlies mechanistical partitioning of miRNA and RNAi pathways.

Structural and functional basis of mammalian microRNA biogenesis by Dicer MicroRNA (miRNA) and RNA interference (RNAi) pathways rely on small RNAs produced by Dicer endonucleases. Mammalian Dicer primarily supports the essential gene-regulating miRNA pathway, but how it is specifically adapted to miRNA biogenesis is unknown. We show that the adaptation entails a unique structural role of Dicer’s DExD/H helicase domain. Although mice tolerate loss of its putative ATPase function, the complete absence of the domain is lethal because it assures high-fidelity miRNA biogenesis. Structures of murine Dicer•–miRNA precursor complexes revealed that the DExD/H domain has a helicase-unrelated structural function. It locks Dicer in a closed state, which facilitates miRNA precursor selection. Transition to a cleavage-competent open state is stimulated by Dicer-binding protein TARBP2. Absence of the DExD/H domain or its mutations unlocks the closed state, reduces substrate selectivity, and activates RNAi. Thus, the DExD/H domain structurally contributes to mammalian miRNA biogenesis and underlies mechanistical partitioning of miRNA and RNAi pathways. Dicer endoribonucleases generate small RNAs for microRNA (miRNA) and RNA interference (RNAi) pathways (Paturi and Deshmukh, 2021). Both are fundamentally important eukaryotic mechanisms providing sequence-specific control of gene expression and protection against viruses and transposable elements (TEs). Although biogenesis of gene-regulating miRNAs require a single cleavage of genome-encoded small stem-loop precursors (pre-miRNA) (Bartel, 2018), RNAi entails processive cleavage of long double-stranded RNA (dsRNA) into small interfering RNAs (siRNAs) with gene-regulating or defensive roles against viruses or TEs (Ketting, 2011). Vertebrate genomes carry a single highly conserved Dicer (Dicer-1) gene (Jia et al., 2017), which encodes a ∼220 kDa multidomain protein that appears dedicated to the miRNA pathway. Cryoelectron microscopy (cryo-EM) of human Dicer revealed a protein architecture that resembles the letter “L,” with a complex helicase domain at the base, tandem RNase III domains in the core, and Piwi/Argonaute/Zwille (PAZ)-platform domains at the cap (Lau et al., 2009, 2012; Taylor et al., 2013; Liu et al., 2018). During miRNA biogenesis, the PAZ domain, which has a strong affinity for substrates with blunt-ends or short 3′ protruding overhangs (Lingel et al., 2003; Song et al., 2003; Yan et al., 2003), binds the base of a pre-miRNA stem loop and the two RNase III domains function as catalytic “half sites,” each cleaving one strand of the double-stranded substrate (Zhang et al., 2004). This yields a small RNA duplex whose length is determined by the distance of RNase III cleavage sites from the PAZ domain (MacRae et al., 2006). The helicase domain, which has a clamp-like architecture of the RIG-I family of RNA helicases (Fairman-Williams et al., 2010), is located near RNase III domains and is composed of three globular subdomains: an N-terminal DExD/H subdomain (HEL1), which is separated by an insertion subdomain (HEL2i) from a helicase superfamily C-terminal subdomain (HEL2) (Figure 1A). The helicase also contacts the substrate (Lau et al., 2009, 2012; Taylor et al., 2013; Liu et al., 2018), but its exact function in the mammalian Dicer is enigmatic. In animals and plants, the helicase domain and its ATPase activity appear linked to evolution of specialized Dicer variants and divergence of small RNA pathways. The miRNA-producing Dicer-1 in Drosophila does not hydrolyze ATP and its helicase domain is degenerated (Tsutsumi et al., 2011). In contrast, animal Dicers supporting RNAi, such as DCR-1 from C. elegans and DCR-2 from Drosophila have an intact helicase domain and hydrolyze ATP (Ketting et al., 2001; Liu et al., 2003; Cenik et al., 2011; Welker et al., 2011). ATP hydrolysis enables threading dsRNA substrates through Dicer’s helicase (Sinha et al., 2018; Wei et al., 2021). In mammals, the highly conserved DExD/H domain in the miRNA-producing Dicer has invariantly preserved residues that would be necessary for ATPase activity (Jia et al., 2017; Cordin et al., 2006), but it does not exhibit the activity (Provost et al., 2002; Zhang et al., 2002) and inhibits RNAi instead (Ma et al., 2008; Kennedy et al., 2015). This paradox has not been resolved and the role of Dicer’s helicase domain within mammalian small RNA pathways remains unclear. Mice offer an outstanding model to study partitioning of miRNA and RNAi pathways as both pathways have essential roles relying on distinct Dicer isoforms expressed from a single gene. The miRNA pathway employs the full-length Dicer and is essential for gene control in embryo development and cell differentiation (reviewed in Park et al., 2010). RNAi is essential for oocytes and is supported by an oocyte-specific DicerO isoform, which lacks the DExD/H subdomain and generates miRNAs and siRNAs (Murchison et al., 2007; Tang et al., 2007; Flemr et al., 2013; Stein et al., 2015). Here, we provide evidence explaining how the DExD/H domain functions in an ATP-independent manner, segregates miRNA from the RNAi pathway in vivo, and makes Dicer an essential gatekeeper in miRNA biogenesis. To understand the importance of Dicer’s helicase function in vivo, we produced mice carrying point mutations in the conserved HEL1 motifs, Walker A (69GNT) and Walker B (175DQCH), and mice lacking HEL1 entirely (DicerΔHEL1 mutant; Figures 1A and S1). The DicerΔHEL1 allele essentially encodes an HA-tagged DicerO protein. A control allele, designated DicerSOM, was produced previously (Taborska et al., 2019) to express HA-tagged full-length Dicer but lacking introns 2–6 like the DicerΔHEL1 allele (Figures 1A and S1H). The DicerΔHEL1 allele expressed the expected truncated Dicer variant (Figures 1B and S1K), and its functionality was confirmed in embryonic stem cells (ESCs), where it generated ∼10× more siRNAs from long dsRNA than normal Dicer (Figure 1C). The catalytically inactive DicerGNT/GNT and DicerDQCH/DQCH mutant mice were born in the expected Mendelian ratios (Figure 1D), appeared normal and were fertile. Small RNA analysis of DicerGNT/GNT E15.5 embryos revealed minimal changes in the miRNome (Figure 1E). In contrast, mating of DicerΔHEL1/+ animals did not yield weaned DicerΔHEL1/ΔHEL1 progeny (Figure 1D). DicerΔHEL1/ΔHEL1 mutants showed embryonic growth retardation (Figure 1F) and died perinatally (Figure S2A), whereas DicerSOM/SOM animals have normal viability (Taborska et al., 2019). Recovered DicerΔHEL1/ΔHEL1 newborns were cyanotic, had breathing difficulties, and a body weight ∼60% of heterozygous and wild-type siblings. DicerΔHEL1/ΔHEL1 mice had anatomical aberrations including heart defects (Figure 1G) and underdeveloped lungs with reduced branching (Figures 1G and S2C). A contributing factor to the lethal phenotype could be reduced number of red blood cells and hemoglobin amount per red blood cell (Figure S2D). Detrimental effects of DicerΔHEL1 protein could be either associated with toxicity of endogenous RNAi or with aberrant miRNA homeostasis. Small RNA analysis of DicerΔHEL1/ΔHEL1 E15.5 embryos showed strong miRNome dysregulation (Figures 1H and S2E). At the same time, analysis of 21- to 23-nt-long RNAs in DicerΔHEL1/ΔHEL1 E15.5 and ESCs did not find genomic loci giving rise to abundant pools of siRNAs from long dsRNA (Figure S2F). siRNAs from an inverted repeat in Optn 3′ region were increased but their abundance in DicerΔHEL1/ΔHEL1 embryos was negligible (Figure 1I). Thus, miRNAs were the most affected abundant Dicer-derived small RNAs in DicerΔHEL1/ΔHEL1 mutants. At E15.5, homozygous loss of HEL1 altered the expression of ∼1/4 embryonic miRNAs (386 of 1,199 miRNAs with abundance >1 read per million [RPM]; Figure 1H; Table S1) with approximately equal numbers of upregulated and downregulated miRNAs. Relative miRNA expression changes correlated well between DicerΔHEL1/ΔHEL1 embryos and DicerΔHEL1/ΔHEL1 ESCs (correlation coefficient 0.811, Figure S2G), suggesting that the miRNome remodeling considerably reflects direct effects of DicerΔHEL1 on miRNA biogenesis. Strikingly, a half of the 50 most upregulated miRNAs in DicerΔHEL1/ΔHEL1 E15.5 embryos were mirtrons (Figure 1H; Table S1), non-canonical miRNAs whose precursors are spliced out specific small introns (Berezikov et al., 2007; Ladewig et al., 2012). Upregulated mirtron precursors featured relatively long stems and/or loops (Figures 2A–2C), miR-3102 comprising such a long stem that it carries two consecutive miRNAs (Chiang et al., 2010). The increase in mirtron expression was not transcriptional as mirtron-encoding host genes were not upregulated in DicerΔHEL1/ΔHEL1 ESCs (Table S2). Consistent with RNA sequencing (RNA-seq) data, DicerΔHEL1 cleaved the 5′ radiolabeled pre-miR-7068 (the most upregulated mirtron) in vitro more efficiently than normal Dicer (Figure 2D). Notably, both Dicers cleaved pre-miR-7068 in vitro also in non-canonical ways, producing a fragment corresponding to a partial precursor cleavage at the 5′ end of a 3p miRNA (Figure 2D). Taken together, DicerΔHEL1 is more tolerant of extended pre-miRNA stems and loops of mirtrons than the full-length enzyme, suggesting that HEL1 physiologically restricts biogenesis of small RNA from such substrates. Another impact of DicerΔHEL1 on miRNome concerned passenger strands (miRNA∗), the miRNA strands less likely to be loaded onto AGO effector protein. There was a striking preferential upregulation of miRNA∗ from the downstream strand of the stem loop precursor (denoted 3p) and downregulation of miRNA∗ from the upstream strand (5p) in both, DicerΔHEL1/ΔHEL1 E15.5 embryos and DicerΔHEL1/ΔHEL1 ESCs (Figures 2E and S2H). A slight opposing effect was observed for many leading (much more abundant) miRNA counterparts, but in some cases, the opposing effect was stronger, and exceptionally strong in case of miR-15a (Figures 2F and S2I). Since passenger strands typically have much lower abundance than main strands, their high relative increase would be expected to cause a minor, if experimentally detectable, reduction of corresponding 5p leading miRNAs. To sum up, the loss of HEL1 affects the thermodynamic sensing of the 5′ end of 3p miRNA and facilitates its selection for AGO loading. Strand selection has been associated with Dicer’s binding partner TARBP2 (Noland et al., 2011). Since TARBP2 binds the HEL2i subdomain adjacent to HEL1 (Liu et al., 2018; Wilson et al., 2015), we examined whether the loss of HEL1 impairs binding of TARBP2 to DicerΔHEL1. Co-immunoprecipitation of TARBP2 with Dicer showed that TARBP2 remains associated with DicerΔHEL1 (Figure S3A), suggesting that miRNome remodeling in DicerΔHEL1/ΔHEL1 E15.5 embryos is not caused by the loss of interaction between Dicer and TARBP2. Importantly, analysis of miRNome in Tarbp2−/− E15.5 embryos (Pullagura et al., 2018) identified 84 differentially regulated miRNAs (>1 RPM, DESeq p value < 0.05, Table S1), majority of which followed a similar trend also in DicerΔHEL1/ΔHEL1 E15.5 embryos (Figure 3A). Therefore, we hypothesize that HEL1 and TARBP2 exert similar but non-redundant thermodynamic sensing, which controls selection of the 5′ end of a 3p miRNA. RNA-seq data revealed two features of miRNA biogenesis present in subsets of differentially expressed miRNAs:partial precursor cleavage and fidelity of mature miRNA biogenesis. These features can be demonstrated on miR-15a and miR-145a, two miRNAs exhibiting increased 3p miRNA∗ levels in DicerΔHEL1/ΔHEL1 and Tarbp2−/− E15.5 embryos (Figure 3A) and displaying a strong opposing effect on 5p miRNA and its 3p miRNA∗ levels (Figures 3B and S3B). In case of miR-15a, RNA-seq data revealed an asymmetric pre-miR-15a cleavage, where Dicer would cleave at the 5′ end of a 3p miRNA, whereas the concurrent cleavage at the 3′ end of a 5p miRNA would not occur. A partially cleaved pre-miR-15a fragment is produced by the full-length Dicer, whereas the relative amount of the pre-miR-15a fragment is higher in DicerΔHEL1/ΔHEL1 samples (Figure S3C). In depth analysis of RNA-seq data identified tens of miRNAs having a miR-15a-like frequency of fragments cleaved just at the 5′ end of a 3p miRNA (Table S3). The partial cleavage by Dicer is also observed for pre-miR-15a (Figure 3C) but not for miR-145a in vitro (Figure S3D). Since the partial cleavage was also made by the full-length Dicer (Figures 3C and S3C), it appears to be a miRNA-specific feature pronounced by DicerΔHEL1 because of its higher activity and altered thermodynamic sensing. Whether the intrinsic partial cleavage by DicerΔHEL1 facilitates 3p miRNA∗ strand selection similarly to defective RNase IIIb mutations (Anglesio et al., 2013) requires further investigation. In case of miR-145a, the strand switch correlated with an apparent cleavage position shift at the 5′ end of miR-145a-3p resulting in high abundance of a two-nucleotide shorter miR-145a-3p isomiR in DicerΔHEL1/ΔHEL1 mutants (Figure 3D). The loss of two G:C base pairs and presence of a 5′ A nucleotide should favor the shorter miR-145-3p strand selection (Medley et al., 2021). This observation prompted a systematic analysis of the 5′-terminal nucleotide fidelity because the cleavage position defining 5′-terminal nucleotides in 3p miRNAs affects nucleotides 2–7, known as the “seed sequence” guiding target recognition and binding (Brennecke et al., 2005; Lewis et al., 2003). A change in the seed sequence would be biologically significant even if miRNA abundance would not change (Mencía et al., 2009). Shifts in the 5′ end of 3p miRNAs had a similar pattern in DicerΔHEL1/ΔHEL1 E15.5 embryos and ESCs (Figures 3E and S3E), suggesting that most of them are a direct consequence of the loss of HEL1. However, RNA-seq data do not allow to distinguish a truly altered cleavage point of DicerΔHEL1 from altered strand selection among isomiRs. In any case, a 5′ end terminal nucleotide shift was found in at least 20% of abundant 3p miRNAs in DicerΔHEL1/ΔHEL1 E15.5 embryos and ESCs. In contrast, terminal nucleotide fidelity in DicerGNT/GNT E15.5. mutant was essentially unaffected (Figure S3E). Notably, terminal nucleotide fidelity was found to be also affected in Tarbp2−/− embryos (Pullagura et al., 2018), and approximately, a half of the cleavage alterations in DicerΔHEL1/ΔHEL1 samples were observed in Tarbp2−/− embryos (Figure S3E). It is likely that the loss of TARBP2 affects miRNA biogenesis through thermodynamic sensing/strand selection of variably cleaved pre-miRNAs, but we cannot exclude that absence of TARBP2 also affects cleavage fidelity. To obtain further insights into the role of HEL1, we determined the 3.8-Å-resolution cryo-EM structure of mouse full-length Dicer in the apo form and the 4.2-Å-resolution structure of the complex of the full-length mouse Dicer with a 59-nt pre-miR-15a (Figures 4A–4C, S4, and S5; Table S4); this miRNA was selected for its unique behavior in DicerΔHEL1/ΔHEL1 mutants (Figures 2 and 3B). Akin to human Dicer, the overall structure of mouse Dicer shows an identical “L shape” architecture (Liu et al., 2018), adopting a “closed” state (Figure 4B). Cryo-EM data also suggest that the helicase domain is flexible around HEL1 to some extent, which is consistent with previous observations (Taylor et al., 2013; Liu et al., 2018; Figure S4). The highest resolution of the full-length metazoan Dicer determined so far allowed us to dissect the molecular details of the closed state. We identified the residues at the interface between DExD/H and RNase IIIb that lock the closed state of Dicer (Figure 4D). Interestingly, these aliphatic and aromatic amino acids residues are conserved across vertebrates but not in invertebrates (Figure 4E). Similarly as reported for human Dicer-RNA structure (Liu et al., 2018), the full-length Dicer•pre-miR-15a structure captured Dicer only in the pre-cleavage state in our cryo-EM data (Figures 4C and S5A–S5H). In the pre-cleavage state, the PAZ domain anchors the 3′ end of the pre-miR-15a, but not the 5′ end, in contrast to the human enzyme binding pre-let-7 (Liu et al., 2018; Tian et al., 2014), likely reflecting the absence of the human-specific α helix in the PAZ domain (Figure S5I). The β sheet face of the dsRNA-binding domain (dsRBD) binds the central double-helical region of the pre-miR-15a (Figures 4C and S5J), in contrast to other members of the dsRBD family, which typically interact with RNA via their α-helical face (Stefl et al., 2005). The terminal loop of pre-miR-15a binds to the outer rim of the helicase subdomains HEL2i and HEL2 (Figure 4C). Overall, these interactions are characteristic of the Dicer closed state and position pre-miRNA away from the RNase III catalytic sites. The closed state may allow Dicer to recognize specific structural features of miRNA precursors, whereas it would impair processing of mirtrons and dsRNAs because they cannot be optimally recognized due to steric hindrance in the pre-cleavage state (Figure S5K). Importantly, the RNA-bound full-length Dicer closed state is virtually identical to that of the apo form and is stabilized by the same residues at the DExD/H and RNase IIIb interface (Figure 4D). We thus examined their functional significance in Dicer variants where the five key residues (LLLHH, Figure 4E) in HEL1 were changed to LKKKK or to VTLQC (residues in aligned Drosophila DCR-2 sequence). In addition, we replaced the entire HEL1 with the HEL1 from Drosophila DCR-2 (D.m. HEL1 variant). On the RNase IIIb side, we substituted residues Y1688 and V1775 together with F1760 to alanines. All variants were expressed in Pkr−/− NIH 3T3 cells, and their effect on RNAi was tested using long dsRNA expression targeting a luciferase reporter described previously (Demeter et al., 2019). The LKKKK, VTLQC, and D.m. HEL1 variants as well as the V1755A/F1650A RNase IIIb variant stimulated RNAi indistinguishably from ΔHEL1 (Figure 4F), suggesting that these substitutions unlock the closed state equally well as the loss of the entire HEL1 subdomain. Notably, the V1755A/F1650A variant highlights the significance of the interface between DExD/H and RNase IIIb because it has high RNAi activity in the presence of the intact HEL1 domain. These data imply that the equilibrium between the closed and open states is sensitive to structural alterations at the interface between DExD/H and RNase. To understand the structural mechanism by which DicerO can support both RNAi and miRNA pathways, we used cryo-EM to analyze this murine Dicer isoform in its apo form and in complex with a miRNA precursor. Although the structure of DicerO in the apo form could not be determined due to its inherent flexibility, we were able to determine the 6.2-Å-resolution cryo-EM structure of DicerO in complex with pre-miR-15a (Figures 5A, 5B, and S6A–S6G; Table S4). Importantly, our DicerO-RNA structure captured DicerO exclusively in a cleavage-competent state (Figure S6). The overall structure of the DicerO•pre-miR-15a complex shows that the helicase and DUF283 domains had faint densities in the cryo-EM data and could not be built into the model (Figure S6). A control experiment showed that DicerO on the grid was intact suggesting that the weak and missing protein density is due to their inherent flexibility in the cleavage state (Figure S6). This is in contrast to the structural observations for DCR-1 from Drosophila (Jourevleva et al., 2022) where the helicase and DUF283 domains do not exhibit such flexibility. In the cleavage-competent state, the PAZ-Platform cassette anchors the 3′ and 5′ ends of the pre-miR-15a and the RNA is accommodated in the positively charged groove formed by the RNase IIIa/b domains (Figure 5B). The dsRBD of Dicer interacts with the RNA using its α-helical face and contacts the minor and major grooves of the pre-miRNA, whereas the β1-β2 loop binds to the terminal loop of the pre-miRNA (Figures 5B and S6I). The dsRBD clamps the RNA in the catalytic sites of Dicer and the alignment of the RNA with the catalytic sites suggests that mouse DicerO cleaves pre-miR-15a between bases G22 and G23 and between bases G37 and C38, producing a 22-nt miRNA duplex (Figure S6J). This matches pre-miR-15a cleavage sites annotated in the miRbase (Kozomara et al., 2019). Interestingly, the terminal loop of pre-miR-15a interacts with the dsRBD and the RNase IIIb domains but exhibits imperfect alignment with the RNase IIIb catalytic site, which is consistent with the asymmetric cleavage of pre-miR-15a at the 5′ end of 3p miRNA described above (Figures 3C and S3C). Modeling of different substrates in the cleavage state of DicerO revealed accommodation of miR-7068 and long dsRNA without steric hindrance (Figure 5D). This is consistent with higher affinity of DicerO for longer perfectly complementary dsRNAs (Figure 5D), higher affinity of full-length Dicer to pre-miRNAs compared with longer perfectly complementary dsRNAs (Figure S6K), and stimulation of RNAi in human cells by expressing the human equivalent of DicerO (Figure 5E). We conclude that DicerO without HEL1 exists in an open state that allows direct loading of precursors for both RNAi and miRNA pathways. It has been reported that Dicer’s accessory proteins, such as TARBP2 and ADAR1, associate with the DExD/H helicase domain and stimulate cleavage of pre-miRNAs by Dicer (Liu et al., 2018; Wilson et al., 2015; Chendrimada et al., 2005; Ota et al., 2013). We hypothesized that TARBP2 may stimulate the transition from the pre-cleavage to cleavage state and increase the probability to capture the latter state by cryo-EM. To this end, we reconstituted the ternary complex between Dicer, pre-miR-15a, and TARBP2 by direct mixing and omitting size exclusion chromatography to preclude selection of a given state and to preserve the conformational diversity of the sample. Cryo-EM analysis of the ternary complex revealed the co-existence of two states that resembled the pre-cleavage and cleavage states based on the 2D class averages and 3D classification (Figures S7A–S7K). The pre-cleavage state dominates the single-particle population, and we determined its 3.81-Å-resolution cryo-EM structure (Figures 6A, 6B, and S7A–S7G). The Dicer•pre-miR-15a•TABRP2 structure in the pre-cleavage state showed an identical closed structure as that of the Dicer•pre-miR-15a complex (Figure 4C). As for TARBP2 in the ternary complex, our cryo-EM reconstruction clearly resolves the interaction between the dsRBD3 of TARBP2 with the HEL2i helicase subdomain (Figure 6B). The densities of the dsRBD1 and dsRBD2 of TARBP2 are weaker, which suggests these two dsRBDs do not bind pre-miR-15a specifically but rather bind in multiple registers, with a slight preference for specific shape in the central regions where we observed weak densities (Stefl et al., 2010; Wang et al., 2011). Nonetheless, we could unambiguously fit the dsRBDs to these densities (Figure 6C). TARBP2 dsRBD1 and 2 (dsRBD12) bind pre-miR-15a in mutual asymmetric arrangement in the context of the Dicer•pre-miR-15a•TABRP2 ternary complex, in contrast to isolated siRNA, which has been shown to be recognized symmetrically by TARBP2 dsRDB12. In the pre-cleavage state, the dsRBD of Dicer binds RNA using its β-sheet face which is a non-canonical arrangement (Stefl et al., 2005). A minor population of about 15% of particles in our cryo-EM data resembled the cleavage state (Figure S7) that we observed for DicerO (Figures 5 and S6). Using these particles, we determined the 5.91-Å-resolution cryo-EM structure of the Dicer•pre-miR-15a•TABRP2 complex (Figures 6D and S7H–S7K). Overall, the structure of the Dicer•pre-miR-15a•TABRP2 complex in the cleavage state (Figure 6D) is strikingly similar to the structure of DicerO•pre-miR-15a complex (Figure 5B); the dsRBD of Dicer interacts via its α-helical face and clamps pre-miRNA in the positively charged groove formed by the RNase IIIa/b domains (Figure 6D). Notably, the structure of this ternary complex lacks not only the helicase and DUF283 domains but no density was also found for TARBP2. Furthermore, the binding register of TARBP2 dsRBD2 of the pre-cleavage state is incompatible with the RNA-binding register of Dicer dsRBD (Figure S7L). This implies that TARBP2 binding may promote unlocking the closed state of Dicer, an effect similar to the removal of HEL1. Subsequent accommodation of pre-miRNA in the dicing state may dismantle TARBP2-RNA interactions. At the same time, TARBP2 could hold the pre-miRNA in place when Dicer oscillates between its common closed and rare open states. We conclude that mammalian Dicer likely functions by a two-step mechanism (Figure 7): (1) binding of enzyme to substrate forms an inactive closed complex that facilitates substrate selection and (2) upon binding of the substrate and TARBP2, Dicer switches into an active open state that allows repositioning of the substrate into the catalytic site of Dicer. Understanding the molecular principles governing co-existence and partitioning of miRNA and RNAi pathways is important since both pathways are of great biological, medical, and biotechnological importance. Different mechanistic and functional partitioning of miRNA and RNAi pathways exists in Metazoa: C. elegans utilizes a single Dicer for both pathways, whereas two dedicated Dicer paralogs evolved in Drosophila, the ATP-dependent DCR-2 for RNAi, and the miRNA pathway-supporting Dicer-1 with degenerated DExD/H helicase domain. The mammalian Dicer supports the miRNA pathway, although it is ATP-independent (Provost et al., 2002; Zhang et al., 2002) and does not efficiently process dsRNA into siRNA in vivo (Demeter et al., 2019; Nejepinska et al., 2012). Our results reveal how mammalian Dicer is specifically adapted to produce small RNAs, how it is committed to the miRNA pathway, and how it suppresses endogenous RNAi. We propose a model (Figure 7) where the DExD/H (HEL1) evolved into an ATP-independent critical structural element of mammalian Dicer’s architecture. An interaction between the DExD/H and RNase IIIb domains locks the rest of the helicase domain (HEL2 and HEL2i) in a stable closed state in which Dicer recognizes miRNA precursors by anchoring three elements: the RNA ends, the central region, and the terminal loop. Substrate loading into this pre-cleavage state was observed also for the human Dicer bound to let-7a (Liu et al., 2018). The pre-cleavage state may serve as a kinetic trap for diffusion-driven screening of optimal substrates and suppress biogenesis of small RNAs from substrates such as long dsRNA or mirtrons, which deviate from conventional miRNA precursors. This cleavage-incompetent arrangement of the Dicer-substrate complex appears a specific feature of mammalian miRNA biogenesis because such a structural arrangement is not observed in Drosophila or in plant Dicer-substrate structures (Liu et al., 2018; Wei et al., 2021; Jourevleva et al., 2022; Wang et al., 2021). Notably, high confidence mammalian miRNAs (Fromm et al., 2022) have a pre-miRNA length distribution, which is distinct from Drosophila or C. elegans (Figure 7C). This may reflect an impact of Dicer’s architecture on evolution of its substrates where the highly conserved Dicer’s rigid architecture would offer a stable structural “mold” for adaptive evolution of vertebrate miRNAs precursors. This could be a significant factor behind extraordinary expansion of vertebrate miRNAs (Campo-Paysaa et al., 2011), which stochastically evolved into Dicer substrates from random RNA structures (Meunier et al., 2013). Analysis of miRNAs in DicerΔHEL1/ΔHEL1 mice and ESCs suggests that the DExD/H domain also has an important function in thermodynamic sensing and strand selection after the substrate cleavage. Multiple factors ensure guide strand selection, including Dicer itself, TARBP2, and AGO proteins (Noland et al., 2011) and properties of the RNA duplex itself (Noland and Doudna, 2013). Our results imply that DExD/H contributes to sensing RNA duplex thermodynamic asymmetry in a similar but non-redundant fashion as TARBP2 and that this may be the major function of DExD/H post-cleavage. The high conservation of the mammalian DExD/H domain thus may originate from the need to preserve structural integrity of the domain to perform its non-canonical role in miRNA biogenesis while its ATPase activity became irrelevant. Substitutions of the conserved amino acid residues in the DExD/H domain, which mediate interaction with the RNase IIIb domain, increase Dicer’s RNAi activity in cultured cells to the level achieved with the ΔHEL1 mutant (Figure 4F). It suggests that these mutations destabilize the closed state of Dicer and shift the equilibrium toward the open state. Notably, the K70N (GNT) mutation had measurable effects on miRNome as the abundance of miR-7068 mirtron, the most upregulated miRNA in DicerΔHEL1/ΔHEL1 E15.5 embryos, increased 2-fold in DicerGNT/GNT E15.5 embryos (Figures 1E and 1H). These data support the notion that Dicer function is sensitive to the structural integrity of the domain. Structural analyses of Dicer-RNA complexes from plants and animals showed that there are subtle variations in the RNA ends recognition by the PAZ-platform domain, miRNA length measurement, and strand-biased cleavage (Liu et al., 2018; Sinha et al., 2018; Wei et al., 2021; Jourevleva et al., 2022; Wang et al., 2021). However, there are fundamental differences in how Dicer employs its helicase domain in different model species. DCR-1 from Drosophila also exists in an equilibrium between closed and open conformations but utilizes a conformational selection mechanism in which a rare, open conformation recognizes authentic pre-miRNA (Jourevleva et al., 2022). This contrasts with mammalian miRNA biogenesis in which the closed state of Dicer forms a stable pre-cleavage complex incorporating pre-miRNA architecture. Furthermore, the helicase domain in DCR-1 in Drosophila exhibits relatively low flexibility at different stages of miRNA processing when compared with the open state of mammalian Dicer where the unlocked helicase domain is highly flexible. Cryo-EM structures of DCR-2 from Drosophila and DCL1 from Arabidopsis showed that the dsRNA substrates are threaded through the helicase domain in ATP-dependent fashion and that the helicase domain clamps dsRNA (Sinha et al., 2018; Wei et al., 2021). In contrast, our cryo-EM data show that murine Dicer supports miRNA biogenesis by the aforementioned two-step mechanism: (1) Dicer locked in the closed state recognizes a miRNA precursor and forms the pre-cleavage state and (2) Dicer switches into the open state that allows loading of the substrate into the catalytic site of Dicer. TARBP2 is able to shift the equilibrium from the closed toward the open state of Dicer as suggested by capturing the cleavage state in the presence of TARBP2 but not in its absence. Alternatively, TARBP2 can support the formation of the cleavage state by facilitating the accommodation of pre-miRNA into a rare occurring open state. The DExD/H helicase appears to be involved only in the first step to recognize the substrate. In the second step the helicase dissociates from the core of Dicer to enable formation of the cleavage state. The first step of this mechanism appears to be a common feature of mammalian Dicers (Liu et al., 2018), whereas the second step is consistent with cryo-EM data for DCL-3-siRNA (Wang et al., 2021) and with suggestion that TARBP2 may facilitate conformational changes in human Dicer upon RNA binding (Taylor et al., 2013). Our model also explains how absence of DExD/H activates efficient siRNA biogenesis (Figure 7A). However, the lethal phenotype of DicerΔHEL1/ΔHEL1 mice demonstrates that DicerO cannot substitute the full-length Dicer in vivo because DicerO does not support miRNA biogenesis equally well. This implies caution and careful assessment of miRNome remodeling should a truncated Dicer variant be considered a therapeutic agent, such as the proposed gene therapy for Dicer deficiency in macular degeneration based on an N-terminally truncated Dicer variant termed OptiDicer (Wright et al., 2020). This may also explain why biologically important endogenous RNAi may have evolved in mouse oocytes where miRNAs are biologically irrelevant (Ma et al., 2010; Suh et al., 2010) and why this mechanism of activation of RNAi pathway did not occur more frequently during mammalian evolution. Analysis of small RNAs in ΔHEL1 mutant mice and ESCs revealed increased abundance of mirtrons, biased strand selection, and altered terminal nucleotide fidelity. However, pre-cleavage, cleavage, and post-cleavage effects could only be partially distinguished from the RNA-seq data. RNA-seq data suggest that the DExD/H domain also functions in thermodynamic sensing and strand selection. This notion is supported by similar effects observed for a set of miRNAs in ΔHEL1 and Tarbp2 mutants. There are two possible scenarios to be examined: (1) DExD/H and TARBP2 have similar but independent functions in restricting thermodynamic sensing of the 5′ end of a set of 3p miRNAs. The loss of either DExD/H or TARBP2 is then sufficient to shift the balance toward 3p strand loading. (2) DExD/H and TARBP2 are functionally coupled in restricting the thermodynamic sensing; hence, the loss of either DExD/H or TARBP2 disrupts this functional coupling and yields increased 3p strand loading. However, this function cannot be resolved using existing RNA-seq and structural data. Furthermore, additional structures are needed. First, higher resolution of Dicer in the cleavage state is needed to reveal additional important structural details. Second, the post-cleavage structures of Dicer will shed light on the release of cleavage products and strand selection associated with the RISC-loading complex, where the DExD/H domain also appears to play a role. Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact Petr Svoboda (svobodap@img.cas.cz). Animals and plasmids are available upon request from the lead contact. Animal experiments concerning DicerGNT and DicerDQCH model were carried out in accordance with the Italian law under a license from the Italian Ministry of Health. Animal experiments concerning DicerΔHEL1 and DicerSOM models were carried out in accordance with the Czech law and were approved by the Institutional Animal Use and Care Committee (approval no. 34-2014). Production of DicerΔHEL1 model was analogous to production of DicerSOM described previously (Taborska et al., 2019). We first produced ESCs with the DicerΔHEL1 allele and then used those for producing chimeric mice and establishing DicerΔHEL1 line upon germline transmission of the DicerΔHEL1 allele. DicerΔHEL1 allele in ESCs (Nagy et al., 1993) was generated using CRISPR-Cas9 (Ran et al., 2013) mediated modification of the endogenous Dicer locus. Pairs of sgRNAs were designed to cleave Dicer genomic sequence in intron 2 (sequence of DNA targets: mDcr_i2a 5′-GTACCCAAATGGATAGAA-3′, mDcr_i2b 5′-GTTGGGATGGAGGTTGTT-3′) and intron 6 (sequence of DNA targets: mDcr_i6a 5′-ACTACGCTAGGTGTAAACAG-3′, mDcr_i6b 5′-TGCAGTCCCCGGACGTTAAAT-3′). A template for homologous recombination was designed to contain an HA-tag at the N-terminus of Dicer coding sequence fused to exon 7 of Dicer and ∼ 1.5 kb overhangs on both ends (Figure S1A). Final genomic sequence of DicerΔHEL1 mice is provided in Document S1. To produce DicerΔHEL1 mouse strain, we first produced mouse chimeras by ESC microinjection into eight-cell – stage embryos (Poueymirou et al., 2007); host embryos were isolated from C57Bl/6NCrl mice (Figure S1). We used two ESC lines with C57Bl/6NCrl background (commonly used JM8A3.N1 and homemade RS7) and one in 129 strain (R1). For the first three rounds of chimera production, we used homozygous and heterozygous mutant ESCs and obtained mice with varying degree mosaicism, but we failed to obtain transmission of the mutant allele into the next generation. During the fourth round, a heterozygous ESC clone D11 derived from R1 ESC line yielded a male with > 80% chimeric fur. Breeding of this male with ICR females finally lead to germline transmission of DicerΔHEL1 allele into the next generation and establishment of the DicerΔHEL1 mouse line. Sequences of the engineered Dicer locus in the mouse genome are provided in the File S1. Phenotype analysis was performed with N3 animals, small RNA seq was done with N7 and N8 embryos (all breedings to ICR background). The DicerGNT allele was generated by replacing wild-type exon 3 with a mutant exon in which Lys60 was mutated to encode asparagine. The Dicer locus was targeted with a vector containing homology arms and a loxP-flanked neomycin cassette 5′ of exon 3 that contained the Lys60Asn mutation. Southern blotting of genomic SacI-digested DNA from individual ESC-derived clones with a 3′ probe was used to identify homologous recombinants, where the DicerGNT-Neo allele displaying a 5.9-kb DNA fragment could be distinguished from the wild-type allele of 7.1-kb fragment size. Cre-mediated recombination resulted in the excision of the loxP-flanked neomycin cassette and the generation of the DicerGNT allele. Mice analyzed in this study were on a C57Bl/6 genetic background. The DicerFH-DQCH allele was generated by retargeting the DicerNeo allele, which contains a Flag-HA-HA sequence 5′ of exon 2 and a loxP-flanked neomycin cassette within intron 2 (Comazzetto et al., 2014). This was achieved with a vector comprised of homology arms, an FRT-flanked hygromycin cassette and exon 5 in which the Glu166 codon was mutated to encode glutamine. Southern blotting of genomic SacI-digested DNA from individual ESC-derived clones with a 3′ probe was used to identify homologous recombinants with the DicerFH-DQCH-Neo-Hyg allele displaying a 7.8-kb DNA fragment. Flp-mediated recombination removed the FRT-flanked hygromycin cassette and generated the DicerFH-DQCH-Neo allele that was identified with the 3′ probe as a 5.9-kb SacI DNA fragment. Cre-mediated recombination led to the excision of the loxP-flanked neomycin cassette and the generation of the DicerFH-DQCH allele. The targeting for both alleles was performed in A9 ES cells. Targeted ES cells were injected into C57BL/6 eight-cell-stage embryos. Targeted mice were crossed to deleter Cre mice (Schwenk et al., 1995) or FLP- expressing transgenic mice (Farley et al., 2000) to remove antibiotic resistance cassettes. The mice analyzed in this study were on a C57Bl/6 genetic background. Mouse ESCs were cultured in 2i-LIF media: KnockOut-DMEM (ThermoFisher) supplemented with 15% fetal calf serum (Sigma), 1x L-Glutamine (Sigma), 1x non-essential amino acids (ThermoFisher), 50 μM β-Mercaptoethanol (ThermoFisher), 1000 U/mL LIF (Isokine), 1 μM PD0325901, 3 μM CHIR99021 (Selleck Chemicals), penicillin (100 U/mL), and streptomycin (100 μg/mL). All plastic was coated with 1% gelatin (Sigma) in PBS. NIH 3T3 fibroblasts were cultured in DMEM (Sigma) supplemented with 10% fetal calf serum, penicillin (100 U/mL), and streptomycin (100 μg/mL). Tail biopsies were processed by PCR genotyping kit (Top-Bio) according to the manufacturer’s protocol. 1 μl aliquots were used for genotyping PCR using 0.5 U/reaction of DNA polymerase (highQu). Genotyping primers are provided in the key resources table. Mice were mated overnight, and the presence of a vaginal plug indicated embryonic day (E) 0.5. The embryos were washed in PBS and fixed in 4% PFA. Pregnant mice were injected with 60 μl of 10mM EdU 1.5 hour before embryo harvest at E10.5 and E14.5. The incorporation of EdU was visualized by Click-it EdU Imaging Kit (Invitrogen) in E10.5 whole mount samples and on 7μm paraffin sections from E14.5 embryos. Apoptosis was visualized in whole mount E10.5 embryos by TUNEL method using In Situ Cell Death Detection Kit, TMR red (Sigma-Aldrich). E18.5 embryos were fixed for 1 week in 4% PFA and stained with Lugol’s Iodine solution for 2 weeks. Stock solution (10g KI and 5g I2 in 100ml H2O) was diluted to 25% working solution in water. Stained specimens were embedded in 2.5% low gelling temperature agarose. Scan was performed on SkyScan 1272 high-resolution microCT (Bruker, Belgium), with resolution set to 4 μm. 20 ul of blood from each E18.5 embryo was collected in tube containing anticoagulant EDTA and diluted with 175uL of V-53D Diluent (Mindray, 105-000146-00). The samples were measured in mode Complete blood count with Differentials (CBC + DIFF) on analyzer Mindray 5300 Vet. One-way Anova with Tukey posttest was used for statistical analysis. Effects of different Dicer isoforms on RNAi-mediated repression in Pkr–/– U-2 OS or 3T3 cells were monitored as described previously (Demeter et al., 2019). Briefly, cells were co-transfected with a plasmid expressing a Dicer variant (or LacZ as a negative control), dsRNA (Lin28IR, RlucIR, or MosIR), a targeted Renilla luciferase reporter with complementary sequences to dsRNA from Lin28IR and RlucIR, and a non-targeted firefly luciferase. For transfection, cells were plated on 24-well plates, grown to 80% density and transfected using Lipofectamine 3000 (Thermo Fisher) according to the manufacturer’s protocol. The total amount of transfected DNA was kept constant (1 μg/well). Specific repression of the targeted Renilla luciferase was estimated as Renilla luciferase activity normalized to the non-targeted firefly luciferase activity, and non-specific effect of MosIR (expressing a non-targeting dsRNA). The value 1.0 corresponds to absence of RNAi, the value of LacZ negative control reflects repression mediated by endogenously-expressed Dicer. Mouse tissues, U-2 OS cells transfected with Dicer variants or ES cells were homogenized mechanically in RIPA lysis buffer supplemented with 2x protease inhibitor cocktail set (Millipore) and loaded with SDS dye. Protein concentration was measured by Bradford assay (Bio-Rad) and 80 μg of total protein was used per lane. Proteins were separated on 5.5% polyacrylamide (PAA) gel and transferred on PVDF membrane (Millipore) using semi-dry blotting for 50 min, 35 V. The membrane was blocked in 5% skim milk in TBS-T, Dicer was detected using anti-HA 3F10 monoclonal primary antibody (High Affinity rat IgG1, Roche #11867431001; dilution 1:500), anti-HA rabbit primary antibody (Cell Signaling, #3724, dilution 1:1,000) or anti-Flag (M2 mouse monoclonal antibody, Sigma #F3165, dilution 1:10,000) and incubated overnight at 4°C. Secondary anti-Rat antibody (Goat anti-Rat IgG, HRP conjugate, ThermoFisher #31470, dilution 1:50,000), HRP-conjugated anti-Mouse Igg binding protein (Santa-Cruz #sc-525409, dilution 1:50,000) or anti-Rabbit-HRP antibody (Santa-Cruz #sc-2357, dilution 1:50,000) was incubated 1 h at room temperature. For TUBA4A and TARBP2 detection, samples were run on 10% PAA gel and incubated overnight at 4 °C with anti-Tubulin (Sigma, #T6074, dilution 1:10,000) or anti-TARBP2 (ThermoFisher #LF-MA0209, dilution 1:1,000) mouse primary antibodies. HRP-conjugated anti-mouse IgG binding protein (Santa-Cruz, #sc-525409, dilution 1:50,000) was used for detection. Signal was developed on films (X-ray film Blue, Cole-Parmer #21700-03) using SuperSignal West Femto Chemiluminescent Substrate (Thermo Scientific). NIH 3T3 cells transfected with plasmids expressing HA-tagged DicerΔHEL1 or DicerSOM variants were lysed in IP Lysis Buffer (10 mM phosphate buffer, pH 7.2, 120 mM NaCl, 1 mM EDTA, 0.5% v/v NP-40, 10% v/v glycerol). Insoluble material was pelleted by centrifugation. Cleared supernatants were diluted 4-times with IP Dilution Buffer (10 mM phosphate buffer, pH 7.2, 100 mM NaCl, 1 mM EDTA, 0.1% v/v NP-40) and incubated with anti-HA magnetic beads (anti-HA mAb, clone #2-2.2.14, ThermoFisher #88836) for 2 h on a rotator. Beads were washed 4-times with IP Dilution Buffer, finally re-suspended in 60 μl water and processed for western blotting. All buffers were supplemented with 1x Protease Inhibitor Cocktail Set (Millipore) and the whole procedure was performed at 4 °C. Cells were plated on 6-well plates and grown to 80 % density. Cells were transfected with 2 μg/well of pCAG-EGFP-MosIR plasmid and cultured for 48 hours. Cells were washed with PBS, homogenized in Qiazol lysis reagent (Qiagen) and total RNA was isolated by Qiazol-chloroform extraction and ethanol precipitation method (Toni et al., 2018). RNA quality was verified by Agilent 2100 Bioanalyzer. Small RNA libraries were constructed using NEBNext Multiplex Small RNA Library Prep Set for Illumina (New England Biolabs) according to the manufacturer’s protocol. Small RNA libraries were size selected on 6% PAAGE gel, a band of 140 - 150 bp was cut from the gel and RNA was extracted using Monarch® Genomic DNA Purification Kit. Quality of the libraries was assessed by Agilent 2100 bioanalyzer. Libraries were sequenced on the Illumina HiSeq2000 platform at the Genomics Core Facility at EMBL. E15.5 embryos were removed from the uterus and washed in PBS. The yolk sac was taken for genotyping and embryos were transferred into RNAlater (Thermo Fisher Scientific). Embryos were homogenized in Qiazol lysis reagent (Qiagen) and total RNA was isolated by Qiazol-chloroform extraction and ethanol precipitation method (Toni et al., 2018). Small RNA libraries were constructed using Nextflex Small RNA-seq kit v3 for Illumina (Perkin Elmer) according to the manufacturer’s protocol; 3′ adapter ligation was performed overnight at 20 °C, 15 cycles were used for PCR amplification and NextFlex beads were used for size selection. Final libraries were sequenced by 75-nucleotide single-end reading using the Illumina NextSeq500/550 platform at the core genomics facility of IMG. RNA-seq data (Table S5) were deposited in the Gene Expression Omnibus database under GEO: GSE196310. Small RNA-seq reads were trimmed in two rounds using fastx-toolkit version 0.0.14 (http://hannonlab.cshl.edu/fastx_toolkit) and cutadapt version 1.8.3 (Martin, 2011). First, 4 random bases were trimmed from left side: fastx_trimmer -f 5 -i {INP}.fastq -o {TMP}.fastq Next, NEXTflex adapters were trimmed. Additionally, the N-nucleotides on ends of reads were trimmed and reads containing more than 10% of the N-nucleotides were discarded: cutadapt --format="fastq" --front=”GTTCAGAGTTCTACAGTCCGACGATCNNNN” --adapter=”NNNNTGGAATTCTCGGGTGCCAAGG” --error-rate=0.075 --times=2 --overlap=14 --minimum-length=12 --max-n=0.1 --output=”$ {TRIMMED}.fastq" --trim-n --match-read-wildcards $ {TMP}.fastq Trimmed reads were mapped to the mouse (mm10) genome using STAR aligner (Dobin et al., 2013) with following parameters: STAR --readFilesIn $ {TRIMMED}.fastq.gz --runThreadN 4 --genomeDir $ {GENOME_INDEX} --genomeLoad LoadAndRemove --readFilesCommand unpigz -c --readStrand Unstranded --limitBAMsortRAM 20000000000 --outFileNamePrefix $ {FILENAME} --outReadsUnmapped Fastx --outSAMtype BAM SortedByCoordinate --outFilterMultimapNmax 99999 --outFilterMismatchNoverLmax 0.1 --outFilterMatchNminOverLread 0.66 --alignSJoverhangMin 999 --alignSJDBoverhangMin 999 Mapped reads were counted using program featureCounts (Liao et al., 2014). Only reads with lengths 19-25nt were selected from the small RNA-seq data: featureCounts -a $ {ANNOTATION_FILE} -F $ {FILE} -minOverlap 15 -fracOverlap 0.00 -s 1 -M -O -fraction -T 8 $ {FILE}.bam The GENCODE gene set (Frankish et al., 2019) was used for the annotation of long RNA-seq data. The miRBase 22.1 (Kozomara et al., 2019). set of miRNAs was used for the annotation of small RNA-seq data for main figures, mirGeneDB annotation of high-confidence miRNAs (Fromm et al., 2022) was used to make sure that results were not biased by annotated low-confidence miRNAs from the miRBase. Statistical significance and fold changes in gene expression were computed in R using the DESeq2 package (Love et al., 2014). Genes were considered to be significantly up- or down-regulated if their corresponding p-adjusted values were smaller than 0.05. First, the relative position of each mature miRNA (“5p” and “3p” for the miRNA-5p and miRNA-3p, respectively) provided by miRBase 22.1. annotation (Kozomara et al., 2019) was manually curated and completed. Second, the miRNA type of each mature miRNA (“miRNA” and ”miRNA∗” for the guide strand and passenger strand miRNA, respectively ) provided by miRBase annotation was completed in this way: 1) The mature miRNAs were assigned into the pair by their hairpin names. The DESeq2 baseMean values of E15.5 and GNT experiments were added to each mature miRNA. 2) The pairs of mature miRNAs with complete miRNA type annotation (both, “miRNA” and “miRNA∗” types were present) were preserved. 3) If there is only one mature miRNA annotated in the hairpin, it is assigned as “single_miRNA”. 4) If the baseMean values of both miRNAs in the pair are lower than 0.25, it is assigned as “lowExp”. 5) For the remaining pairs of mature miRNAs, if the baseMean value of one miRNA is at least double to the second one, it is assigned as “miRNA” / “miRNA∗” or “miRNA∗” / “miRNA”, respectively. Otherwise it is assigned as “notClear”. 6) Finally, the newly determined miRNA types are compared to each other. If the mature miRNAs were determined as “miRNA” in one experiment and as “miRNA∗” in the other, it is assigned as “cellSpecific”. In all the other cases, it there is any discrepancy among the determined miRNA type, it is assigned as “notCLear”. The annotation of the mirtrons was taken from Ladewig et al. 2012. mirGeneDB annotation of high-confidence miRNAs (Fromm et al., 2022) was used to make sure that results were not biased by annotated low-confidence miRNAs from the miRBase The DESeq2 baseMean and fold changes were plotted and visualized by home-made R scripts. The MA plots related to the dominant or passenger strand miRNAs contain only the corresponding miRNAs, all the miRNAs otherwise. Small RNA read clusters (Figure S2F) were identified following the algorithm used in previous studies (Flemr et al., 2013; Demeter et al., 2019). Briefly: 1) Reads were weighted to fractional counts of 1/n where n represents the number of loci to which read maps 2) Reads were then collapsed into a unified set of regions and their fractional counts were summed 3) Clusters with less than 3 reads per million (RPM) were discarded 4) Clusters within 50 bp distance of each other were joined Only clusters appearing in all replicates of the same genotype (intersect) were considered in the final set. Union of coordinates of overlapping clusters were used to merge the clusters between the samples. Clusters were then annotated, and if a cluster overlapped more than one functional category, the following classification hierarchy was used: miRNA > transposable elements > mRNA (protein coding genes) > misc. RNA (other RNA annotated in ENSEMBL or RepeatMasker; Smit et al., 2013–2015) > other (all remaining annotated or not annotated regions). Only miRNAs with DESeq2 baseMean values >= 100 were selected. The cleavage points’ coordinates (CP) were extracted from their miRBase 22.1 annotation (Kozomara et al., 2019). The reads of the lengths 19-25nt were selected from each replicate library. The starting and ending position of all reads were summed up in the CP and its vicinity (+/-15nt) and assigned as 3′-CP of miRNA-5p and 5′-CP of miRNA-3p, respectively. Then, the canonical miRBase CPs were re-defined based on our wild-type data: 1) Position with maximal counts (median among replicates) is assigned as the new CP. 2) If the new CP is more than 7nt outside the canonical one, keep the canonical one. 3) If there are multiple CPs with the same max counts, keep the canonical one. 4) If there are no data / no reads, keep the canonical one. The counts were extracted for each miRNA at the position of the newly defined CP with 5nt flanks on each side. The read counts were re-calculated into read densities. The final matrix was achieved as a subtraction between a mutant and its corresponding wild-type control. Top 50 miRNAs from Dicer mutants were selected based on the absolute value of the difference at the position of CP. Selected miRNAs were ordered by the change of ESC fidelity at the position of CP. All sequence reads were selected that overlapped the corresponding pre-miRNA locus in the sense direction. All coordinates (starting/ending position of the miRNA-5p/-3p) were extracted from the miRBase 22.1 annotation (Kozomara et al., 2019). The categories shown in the Figures 5D and S5D were defined by pre-miRNA boundaries and the two annotated Dicer cleavage points (deviation of the boundaries +/-2nt allowed). Each read was unambiguously assigned into the appropriate category. The percentage from the total number of overlapping reads was calculated. Dual luciferase activity was measured according to Hampf and Gossen (Hampf and Gossen, 2006) with some modifications. Briefly, cells were washed with PBS and lyzed in PPTB lysis buffer (0.2% v/v Triton X-100 in 100 mM potassium phosphate buffer, pH 7.8). A 3-5 μl aliquots were used for measurement in 96-well plates using Modulus Microplate Multimode Reader (Turner Biosystems). First, firefly luciferase activity was measured by adding 50 μl substrate (20 mM Tricine, 1.07 mM (MgCO3)4·Mg(OH)2, 2.67 mM MgSO4, 0.1 mM EDTA, 33.3 mM DTT, 0.27 mM Coenzyme A, 0.53 mM ATP, 0.47 mM D-Luciferin, pH 7.8) and signal was integrated for 10 sec after a 2 sec delay. Signal was quenched by adding 50 μl Renilla substrate (25 mM Na4PPi, 10 mM Na-Acetate, 15 mM EDTA, 500 mM Na2SO4, 500 mM NaCl, 1.3 mM NaN3, 4 μM Coelenterazine, pH to 5.0) and Renilla luciferase activity was measured for 10 sec after a 2 sec delay. Hairpin-expressing plasmids and luciferase reporters are described and deposited in Addgene. RlucIR plasmid expressing a hairpin structure targeted to Renilla luciferase coding region was prepared similarly to MosIR using common cloning techniques. pCIneo plasmid carrying human DICER1 (GenBank: NM_1777438) was prepared by standard molecular cloning procedures. The C-terminal 2× FLAG tag and deletion (dHEL1, dHEL2 and dDExD) variants were prepared using Q5 Site-Directed Mutagenesis Kit (NEB) according to the manufacturer’s instructions. pFastBac plasmids carrying recombinant mouse full-length Dicer and short variant (DicerO) were prepared as follows. The N-terminal fragment containing TwinStrep and HA tags together with TEV protease cleavage site was PCR amplified and inserted into BamHI-SalI restriction sites in pFastBACT1 plasmid (Invitrogen). Subsequently, the C-terminal fragment containing 2xFLAG and 8xHis tags together with TEV protease cleavage site was PCR amplified and inserted into NotI-HindIII restriction sites. Mouse Dicer and DicerO omitting start and stop codons were PCR-amplified from pEF1-MH.Bl-mDcrSOM (Addgene) and pEF1-MH.Bl-mDcrOO (Addgene) plasmids, respectively, and inserted in-frame into SalI-NotI sites of the modified pFastBACT1 plasmid using common cloning techniques. C-terminal 2× FLAG tag and deletion variants were prepared using Q5 Site-Directed Mutagenesis Kit (NEB) according to the manufacturer’s instructions (PCR primers: Twin-HA-TEV_Fwd, Twin-HA-TEV_Rev, 3C-FLAG-His_Fwd, 3C-FLAG-His_Rev, mDicer_SalI_Fwd, mDicerO_SalI_Fwd, mDicer-NotI_Rev). The catalytically inactive variants of Dicer/DicerO were prepared by mutating the key residues E1560 and E1807 of the RNAse III domains into alanine residues (Zhang et al., 2004) using Q5 Site-Directed Mutagenesis Kit (NEB) kit according to the manufacturer’s instructions (PCR primers: mDicer E1560A Forward, mDicer E1560A Reverse, mDicer E1807A Forward, mDicer E1807A reverse). List of all used oligonucleotides can be found in key resources table. All constructs were verified by sequencing. Dicer variants with mutations in HEL1 domain (VTLQC, LKKKK, Y1688A, and V1755A/F1760A) and with swapped HEL1 domain to the one from D. melanogaster Dcr-2 were prepared using Gibson Assembly Cloning kit (NEB) according to the manufacturer’s instructions. The coding sequence and the necessary regulatory sequences of mouse Dicer variants or TARBP2 were transposed into bacmid using E. coli strain DH10bac. The viral particles were obtained by transfection of the bacmids into the Sf9 cells using FuGENE Transfection Reagent (Eastport) and further amplification in Sf9 cells. Dicer variants were expressed in 200 ml of Hi5 cells (infected at 1.2×106 cells/ml) with the corresponding P1 virus at multiplicity of infection >1. The cells were harvested 48 hours post infection, washed by 1x PBS, and stored at -80°C. Subsequent operations were carried out at + 4°C. Pellets were resuspended in ice-cold lysis buffer containing 50 mM Tris (pH 8.0), 300 mM NaCl, 0.4% Triton X-100, 10% (v/v) glycerol, 10 mM imidazole, 1 mM DTT, 2 mM MgCl2, benzonase (250U), and protease inhibitors (0.66 μg/ml pepstatin, 5 μg/ml benzamidine, 4.75 μg/ml leupeptin, 2 μg/ml aprotinin) (Applichem). The resuspended cells were gently shaken for 10 min at 4°C. To aid the lysis, cells were briefly sonicated. The lysate was cleared by centrifugation at 21,000xg for 1 hr at 4°C. The supernatant was passed through a column containing 2.5 ml NiNTA-agarose (QIAGEN). The affinity matrix was washed 5-times with 15 ml of washing buffer (50 mM Tris (pH 8.0), 500 mM NaCl, 1 mM DTT, 2 mM MgCl2, and 10 mM imidazole). The protein was eluted three times with 3.5 ml of elution buffer (50 mM Tris (pH 8.0), 500 mM NaCl, 1 mM DTT, 2 mM MgCl2, and 300 mM imidazole). The fractions containing protein were pooled and concentrated to 1 ml using 100 kDa cut-off Vivaspin Turbo15 (Sartorius). The proteins were further purified on a size exclusion column (Superose 6 Increase 10/300 GL, GE Healthcare) equilibrated with a buffer containing 50 mM Tris (pH 8.0), 150 mM NaCl, 1 mM DTT, 2 mM MgCl2. Fractions containing protein were pooled, concentrated, snap-frozen in liquid nitrogen, and stored at -80°C until further use. Purification of the wild-type Dicer for structural studies included treatment by buffer containing 6 mM EDTA, prior to gel filtration. To preclude the RNA cleavage, the gel filtration buffer (and all buffers in subsequent procedures) contained 2 mM CaCl2 instead of 2 mM MgCl2. TARBP2 was expressed in Sf9 cells (infected at 1.2×106 cells/ml) with the corresponding P1 virus at multiplicity of infection >1. The cells were harvested 48 hours post infection, washed by 1x PBS, and stored at -80°C. TARBP2 was purified as described for Dicer, except for size exclusion chromatography in which Superdex 75 Increase 10/300 GL (GE Healthcare) was used. In vitro synthesized RNA oligonucleotides were diluted to 250 nM with nuclease-free water and mixed with T4 Polynucleotide Kinase buffer. The RNA was refolded by heating the mixture at 95°C for 3 min and snap-cooled on ice for 5 min. After addition of RNase inhibitors (NEB), T4 polynucleotide kinase (NEB), and [γ-32P]-ATP (HARTMANN ANALYTIC), the reaction was incubated at 37°C for 10 minutes. The 5′-radiolabelled RNA was purified on G-25 columns (GE Healthcare) and diluted to a final concentration of 50 nM. The radiolabelled RNA Decade Marker (ThermoFisher Scientific) was prepared according to the manual. The RNA and the marker were aliquoted and stored at -20°C. Time-course experiments were performed in 10 μl, containing 5 nM labelled RNA substrate, and 100 nM DicerSOM and DicerΔHEL, respectively, in 30 mM Tris (pH 7.0), 30 mM NaCl, 1 mM DTT, and 2 mM MgCl2 at 37°C. Increasing concentrations (12.5, 25, and 50) of DicerSOM and DicerΔHEL1, respectively, were mixed with 5 nM labelled RNA substrate in 30 mM Tris (pH 7.0), 30 mM NaCl, 1 mM DTT, and 2 mM MgCl2. After 60 min incubation at 37°C, the reactions were stopped with equal volume of 95% formamide, boiled for 5 min, and analyzed on a 20% polyacrylamide gel containing 8 M urea. After electrophoresis, the gels were exposed for 6-18 hours onto a phosphor imaging screen (Fujifilm). The signal was detected using FLA 9000 phosphorimager (Fujifilm) and analyzed in Multi Gauge v3.2 software. To refold pre-miR-15a RNA, it was heated for 3 min at 95°C and snap-cooled on ice for 5 min. The complex was formed by mixing 1.5 nmol of pre-miR-15a and 0.5 nmol of catalytically inactive Dicer or DicerO variant in 50 μl of 50 mM Tris (pH 8.0), 100 mM NaCl, 1 mM DTT, and 2 mM MgCl2. After 30 min incubation on ice, the mixture was applied onto Superose 6 Increase 5/150 GL (Cytiva) column attached to an ÄKTA Purifier (Cytiva). Fractions containing the complex were collected and concentrated to 0.2 mg/ml. The complex of the wild-type Dicer with pre-miR-15a and TARBP2 was prepared by direct mixing of 150 pmol of pre-miR-15a, 50 pmol of Dicer and 55 pmol of TARBP2 in 50 μl of 50 mM Tris (pH 8.0), 100 mM NaCl, 1 mM DTT, and 2 mM CaCl2. The mixture was incubated on ice for 30 min and applied on CryoEM grid. The purity and homogeneity of the protein was assessed by SDS-PAGE, while RNA was verified by denaturing gel electrophoresis (20% polyacrylamide gel containing 8 M urea) and visualized using SYBR Gold dye (ThermoFisher Scientific). The purified Dicer or Dicer–pre-miR-15a complex were diluted to a concentration of about 1 μM in a buffer containing 50 mM Tris (pH 8.0), 100 mM NaCl, 1 mM DTT, and 2 mM MgCl2. The Lacey carbon M300 grid (SPI supplies) was glow-discharged (15 sec, hydrogen-oxygen) immediately before preparing the cryo-EM specimen. In a Vitrobot Mark IV (ThermoFisher Scientific), 3.5 μl of the protein–RNA complex was applied on the grid from the plasma treated side. The grid was blotted for 5.0 sec, blot force -3, in 100% humidity at 4°C, and plunged in liquid ethane cooled by liquid nitrogen. For Dicer, UltraAuFoil M300 (R1.2/1.3) grid (Quantifoil) was glow-discharged (60 sec, argon-oxygen) and 3.5 μl of the protein was applied from the plasma treated side. The grid was blotted for 3.0 sec, blot force 0 in 100% humidity at 4°C. The data were collected using Titan Krios (ThermoFirsher Scientific) transmission electron microscope using SerialEM software (Mastronarde, 2005). The details about data acquisition, processing, structural refinement and validation are shown in Table S4. The movies were first processed by MotionCor2 (Zheng et al., 2017) for generation of motion corrected, dose-weighted micrograph stacks. The CTF parameters were estimated using GCTF (Zhang, 2016). The micrographs were further manually curated to select for astigmatism lower than 800 Å and CTF fit parameter lower than 4.5 Å. For each dataset, a set of 30-50 randomly selected micrographs was used for manual particle picking using e2boxer.py tool from the EMAN2 (Tang et al., 2007) package. The manually picked particles were used for model generation using crYOLO (Wagner et al., 2019). The particles obtained from full dataset picking were imported into cryoSPARC (Punjani et al., 2017). Further analysis comprised the following steps, 2D classification, ab-initio modelling and 3D Refinement. The initial volume maps were used as a reference for re-analysis of the data using 3D Classification in Relion 3.1 (Scheres, 2012) and/or training of TOPAZ (Bepler et al., 2019) tool to improve the quality of particle picking procedure. The final 3D Refinement was performed in cryoSPARC. The detailed statistics are available in Table S4. Initial PDB coordinates of the Dicer structure were taken from AlphaFold database (Jumper et al., 2021). Regions of low confidence prediction (pLDDT < 50) were excluded from the structure and the remaining blocks of the coordinates were fitted into the density map using UCSF Chimera’s tool ‘Fit in Map (Pettersen et al., 2004)’. The PDB coordinates and the density map were then imported into program Coot (Emsley et al., 2010) and the tool ‘Real Space Refine Zone’ was used to achieve optimal fit of the PDB coordinates within the map. Low resolution regions and regions where the map was lacking density were excluded from the structure. The dsRBD of Dicer was docked into map with rigid body approach and fit was optimized using Phenix ‘rigid_body’ strategy (Liebschner et al., 2019). The coordinates were validated using Coot’s tools ‘Ramachandran Plot’, ‘Rotamer Analysis’, and ‘Density Analysis’. The same procedure was applied to Dicer–pre-miR-15a complex. The initial coordinates of pre-miR-15a were obtained from a modeling server RNAComposer (Antczak et al., 2016; Popenda et al., 2012). The model was fitted and refined into the density map using ProSMART Self Restraints implemented in Coot software. The model of Dicer–pre-miR-15a was fitted and refined into Dicer–pre-miR-15a–TARBP2 pre-cleavage complex density map. The TARBP2 dsRBDs were fitted into the map according to the predicted structure obtained from AlphaFold. TARBP2 dsRBD1 and dsRBD2 were fitted into the non-sharpened map. The coordinates of the Dicer structure and the Dicer–RNA complexes in the pre-cleavage states were subjected to further structural refinement in the Dicer core region using Phenix software and ISOLDE (Croll, 2018). For the cleavage states of Dicer and DicerO, initial PDB coordinates of the Dicer/DicerO structure were predicted by AlphaFold software. After excluding low confidence prediction regions (pLDDT < 50), the structured were fitted into density maps obtained from CryoSparc as described above. Protein domains that were not resolved within the density map (residues 1–500) were excluded from the models. Modelled pre-miR-15a was manually fitted into the density map. MolProbity and PDB Validation tool was used to obtain the overall refinement and structural statistics. Molecular graphics images were produced using the UCSF Chimera (Pettersen et al., 2004) and ChimeraX (Pettersen et al., 2021) package from the Resource for Biocomputing, Visualization, and Informatics at the University of California, San Francisco (supported by NIH P41 RR-01081) and/or Coot (Emsley et al., 2010). In general, all of the experiments were performed with at least duplicate independent biological samples. The number of replicates was influenced by limited availability of the biological material. Differential expression analysis of miRNAs and mRNAs relied on statistics integrated into the DESeq2 tool. One-way Anova with Tukey posttest was used for statistical analysis of blood data. Two sided t-test was used for analysis of RNAi effects in transfection assays. Sample sizes or number of replicates are provided in the text and in figures. No statistical method was used to predetermine sample sizes. For the quantification of the EMSA assays, the analyses were carried out using the Multi Gauge v3.2 software (Fujifilm). GraphPad Prism was used to plot the obtained values (Specific binding with Hill slope) and perform the statistical analysis. The bound fraction was determined as the disappearance of the signal corresponding to the unbound substrate (Lane 0). Each data point represents an average of at least two independent experiments. Error bars represent standard deviation (SD).

PMC9645627

Deepika Gaur,Navinder Kumar,Abhirupa Ghosh,Prashant Singh,Pradeep Kumar,Jyoti Guleria,Satinderdeep Kaur,Nikhil Malik,Sudipto Saha,Thomas Nystrom,Deepak Sharma

Ydj1 interaction at nucleotide-binding-domain of yeast Ssa1 impacts Hsp90 collaboration and client maturation

09-11-2022

Hsp90 constitutes one of the major chaperone machinery in the cell. The Hsp70 assists Hsp90 in its client maturation though the underlying basis of the Hsp70 role remains to be explored. In the present study, using S. cerevisiae strain expressing Ssa1 as sole Ssa Hsp70, we identified novel mutations in the nucleotide-binding domain of yeast Ssa1 Hsp70 (Ssa1-T175N and Ssa1-D158N) that adversely affect the maturation of Hsp90 clients v-Src and Ste11. The identified Ssa1 amino acids critical for Hsp90 function were also found to be conserved across species such as in E.coli DnaK and the constitutive Hsp70 isoform (HspA8) in humans. These mutations are distal to the C-terminus of Hsp70, that primarily mediates Hsp90 interaction through the bridge protein Sti1, and proximal to Ydj1 (Hsp40 co-chaperone of Hsp70 family) binding region. Intriguingly, we found that the bridge protein Sti1 is critical for cellular viability in cells expressing Ssa1-T175N (A1-T175N) or Ssa1-D158N (A1-D158N) as sole Ssa Hsp70. The growth defect was specific for sti1Δ, as deletion of none of the other Hsp90 co-chaperones showed lethality in A1-T175N or A1-D158N. Mass-spectrometry based whole proteome analysis of A1-T175N cells lacking Sti1 showed an altered abundance of various kinases and transcription factors suggesting compromised Hsp90 activity. Further proteomic analysis showed that pathways involved in signaling, signal transduction, and protein phosphorylation are markedly downregulated in the A1-T175N upon repressing Sti1 expression using doxycycline regulatable promoter. In contrast to Ssa1, the homologous mutations in Ssa4 (Ssa4-T175N/D158N), the stress inducible Hsp70 isoform, supported cell growth even in the absence of Sti1. Overall, our data suggest that Ydj1 competes with Hsp90 for binding to Hsp70, and thus regulates Hsp90 interaction with the nucleotide-binding domain of Hsp70. The study thus provides new insight into the Hsp70-mediated regulation of Hsp90 and broadens our understanding of the intricate complexities of the Hsp70-Hsp90 network.

Ydj1 interaction at nucleotide-binding-domain of yeast Ssa1 impacts Hsp90 collaboration and client maturation Hsp90 constitutes one of the major chaperone machinery in the cell. The Hsp70 assists Hsp90 in its client maturation though the underlying basis of the Hsp70 role remains to be explored. In the present study, using S. cerevisiae strain expressing Ssa1 as sole Ssa Hsp70, we identified novel mutations in the nucleotide-binding domain of yeast Ssa1 Hsp70 (Ssa1-T175N and Ssa1-D158N) that adversely affect the maturation of Hsp90 clients v-Src and Ste11. The identified Ssa1 amino acids critical for Hsp90 function were also found to be conserved across species such as in E.coli DnaK and the constitutive Hsp70 isoform (HspA8) in humans. These mutations are distal to the C-terminus of Hsp70, that primarily mediates Hsp90 interaction through the bridge protein Sti1, and proximal to Ydj1 (Hsp40 co-chaperone of Hsp70 family) binding region. Intriguingly, we found that the bridge protein Sti1 is critical for cellular viability in cells expressing Ssa1-T175N (A1-T175N) or Ssa1-D158N (A1-D158N) as sole Ssa Hsp70. The growth defect was specific for sti1Δ, as deletion of none of the other Hsp90 co-chaperones showed lethality in A1-T175N or A1-D158N. Mass-spectrometry based whole proteome analysis of A1-T175N cells lacking Sti1 showed an altered abundance of various kinases and transcription factors suggesting compromised Hsp90 activity. Further proteomic analysis showed that pathways involved in signaling, signal transduction, and protein phosphorylation are markedly downregulated in the A1-T175N upon repressing Sti1 expression using doxycycline regulatable promoter. In contrast to Ssa1, the homologous mutations in Ssa4 (Ssa4-T175N/D158N), the stress inducible Hsp70 isoform, supported cell growth even in the absence of Sti1. Overall, our data suggest that Ydj1 competes with Hsp90 for binding to Hsp70, and thus regulates Hsp90 interaction with the nucleotide-binding domain of Hsp70. The study thus provides new insight into the Hsp70-mediated regulation of Hsp90 and broadens our understanding of the intricate complexities of the Hsp70-Hsp90 network. The Hsp90 family of chaperones are highly conserved across species, and essential for cellular viability in eukaryotes. About 10% of the yeast proteome is dependent on Hsp90 for its maturation and includes the family of kinases, growth hormone receptors and transcription factors [1]. These clients are involved in a number of cellular processes such as cell cycle control, cellular survival and signalling pathways [2–4]. For many of these substrates, Hsp90 is required for maturation, and for others it is involved in their transport or assembly into multiprotein complex. Hsp90 functions in coordination with another major cellular chaperone Hsp70 which first interacts with client proteins, and remodels them for further maturation by Hsp90s [5]. The dynamics of Hsp70-Hsp90 interactions and its functional significance are under intense investigation. Each protomer of the homodimeric Hsp90 consists of a highly conserved nucleotide-binding domain (NBD), a middle domain (MD) and a C-terminal domain (CTD). In the absence of ATP, Hsp90 adopts an open V-shape conformation having dimerized CTD with NBDs of each protomer away from each other [6]. The ATP binding at NBD leads to conformational changes that cause the N-terminal domain to transiently dimerize and associate with the middle domain. Also, a segment of NBD folds over the nucleotide-binding pocket and interacts with ATP. The long charged linker consisting of 56 amino acids present between the nucleotide-binding domain and the middle domain of Hsp90 contributes to flexibility towards these conformational changes in the protein [7]. The substrate primarily interacts with MD of Hsp90 [8]. The binding of Hsp90 to its co-chaperone such as Aha1 or Hch1 stimulates ATP hydrolysis which facilitates the release of substrates and thus the initiation of a new reaction cycle [9,10]. Hsp90 is assisted by different co-chaperones that either modulates its ATPase activity or facilitate substrate transfer as well as conformational changes. In S. cerevisiae, the heterocomplex Hsp70-Sti-Hsp90 is required for substrate transfer from Hsp70 to Hsp90 where Sti1 acts as the bridge protein and interacts simultaneously with MEEVD motif present at the C-terminus of Hsp90 and C-terminal EEVD motif of ADP bound Hsp70 [11,12]. Additionally, Sti1 is also known to act as an inhibitor of Hsp90 basal ATPase activity [13]. As a substrate is transferred to Hsp90, Sti1 dissociates from Hsp90 and is replaced by another TPR domain-containing protein, such as Cpr6, Cpr7 or Cns1, as Hsp90 traverses through different stages. Cns1 is essential for cellular viability, and deletion of Cpr7 results in temperature sensitivity [14]. Another TPR protein Tah1 in complex with Pih1 interacts with Hsp90 and regulates its ATPase activity [15]. Hsp70 interacts with partially unfolded substrates to prevent their aggregation, and promote folding back to the native state. Many of these substrates are further transferred to Hsp90. The additional requirement of Hsp90 action for some Hsp70 substrates is still not clear. Unlike eukaryotes, prokaryotes lack homologs of the bridging protein Sti1 that bridges Hsp70 with Hsp90. Instead, a direct interaction between the E.coli Hsp70 (DnaK) and Hsp90 (HtpG) has been observed [16]. Similar interaction has also been seen for yeast and mammalian Hsp70 and Hsp90 [17,18]. One study showed that direct interaction between E.coli Hsp90-Hsp70 results in conformational changes in both proteins [19]. Further, these direct interactions lead to synergistic stimulation of ATP hydrolysis activity and are required for client reactivation in vitro [20]. It has been shown also that cells carrying mutations in the middle domain of the yeast Hsp90 that disrupt its interactions with NBD of Hsp70 are unable to grow at 37°C [17]. Further, in the absence of Sti1, these Hsp90 mutants are unable to support growth even at the optimal temperature of 30°C [17]. However, the molecular basis of this importance of Hsp70-Hsp90 interactions remains unclear. Similar to Hsp90, the Hsp70 family of proteins are highly conserved across different species; from prokaryotes to mammals. Hsp70 chaperones are present in different cellular compartments including the cytosol, endoplasmic reticulum, lysosomes, and mitochondria, and perform a variety of functions including those linked to protein trafficking, cellular signaling, protein folding and degradation [21]. Hsp70 consists of three domains; the N-terminal nucleotide-binding domain, the substrate-binding domain and a C-terminal domain that acts as a lid over the substrate-binding domain. Similar to Hsp90, the Hsp70 reaction cycle is regulated by its interaction with various co-chaperones. The co-chaperones not only regulate Hsp70 activity but also provide functional specificity to different Hsp70 isoforms. Hsp40 assists in substrate transfer as well as stimulates Hsp70 ATPase activity [22,23]. Nucleotide exchange factors, in turn, exchange ADP with ATP which facilitates substrate release from Hsp70 to initiate a new reaction cycle [24,25]. In the present study, we attempted to elucidate the requirement of different structural regions of Hsp70 in Hsp90 chaperoning activity. To examine Hsp70 role, we employed S. cerevisiae strain expressing Ssa1, and lacking remaining three isoforms, as sole source of Ssa Hsp70. Using random mutagenesis, we isolated various Hsp70 mutants defective in their role in the maturation of the Hsp90 client protein v-Src. We show that the Hsp70 mutants were defective in their direct interaction with Hsp90, and have enhanced interaction with Ydj1. The data suggest that Hsp90 direct interaction at nucleotide-binding domain of Hsp70 is regulated by Hsp40 binding at the same region of Hsp70. The Ydj1-mediated regulation of Hsp70-Hsp90 interaction plays a crucial role in client protein maturation and thus cell survival under stress conditions. Hsp70 plays a critical role in the maturation of Hsp90 clients such as v-Src, Ste11 and many growth hormone receptors [26–28]. S.cerevisiae encodes for four highly homologous members of cytosolic Ssa Hsp70 members. To examine the Hsp70 region critical for Hsp90 function, we used the yeast strain A1 that lacks all four chromosomally encoded Ssa1-4 Hsp70s and expresses Ssa1 from a plasmid-encoded gene, as the sole Ssa Hsp70 source. The Rous sarcoma viral tyrosine kinase, v-Src was used as an Hsp90 client. The heterologous overexpression of matured v-Src leads to growth arrest in S.cerevisiae due to uncontrolled phosphorylation of tyrosine residues present in most of the cellular proteins [29]. Similar to a previous report, overexpression of v-Src in the A1 strain from a galactose-inducible promoter led to poor growth onto solid SGal media (S1A Fig) [26]. To identify Ssa1 residues required for Hsp90 activity, we randomly mutagenized Ssa1 to isolate mutants that suppress v-Src-mediated toxicity. The plasmid-encoding Ssa1 was mutagenized and the mutant library was transformed into a strain harbouring galactose inducible v-Src and expressing wt Ssa1 from the tet repressible promoter. The transformants were plated onto doxycycline plate to repress wt Ssa1 expression and colonies that showed optimal growth onto solid SGal media were selected for further analysis (S1A Fig). The plasmids were extracted from well-grown colonies, and reconfirmed for their ability to suppress v-Src-mediated toxicity. Six Ssa1 alleles displayed a reproducible reduction of v-Src toxicity (Fig 1A and Table 1). We further chose to focus on particular Ssa1 alleles on the basis that i) the selected mutant showed relatively strong suppression of v-Src toxicity, and ii) the mutation was located at a site distal to that known to interact with the bridging protein Sti1. The Hsp70 mutations distal to the Sti1 interaction site was chosen not to disrupt the Hsp70-Hsp90 interaction mediated by the bridge protein. Among the 6 isolated mutants, A1-Δ604–642 showed insertion of a stop codon at the 604th residue leading to deletion of 39 residues at the C-terminal end of Ssa1. This mutant lacks the EEVD motif present at the C-terminus of Hsp70 required for interaction with Sti1. Among the remaining mutants, A1-T175N and A1-D158N showed reproducibly better suppression of v-Src toxicity and thus used for further studies (Figs 1A and S2). Both T175 and D158 residues were found to be conserved among different Ssa Hsp70 isoforms (Ssa1-4 and human Hsc70 and Hsp70 (S1B Fig). The ability of A1-T175N and A1-D158N to reduce the v-Src-mediated growth defect was further confirmed by the growth of the strains expressing either of the two mutant proteins as the sole source of Ssa Hsp70 and overexpressing v-Src. As shown in Fig 1B (S1 Dataset), the A1 strain overexpressing v-Src grew poorly whereas those expressing the T175N (A1-T175N) or D158N (A1-D158N) mutant Ssa1s grew markedly better. As seen, the presence of Ssa1-T175N had a more pronounced effect than Ssa1-D158N in reducing v-Src toxicity. We further examined whether the effect of Ssa1-T175N and Ssa1-D158N on v-Src toxicity was dominant or recessive. The plasmid encoding wt Ssa1 or the mutant alleles were transformed into a wt strain harboring plasmid encoding galactose inducible FLAG-v-Src and all four chromosomally-encoded Ssa Hsp70s. The transformants were grown in liquid SD media, and then serially diluted onto solid media with dextrose or galactose as the carbon source. As seen in S3 Fig, as compared to wt cells lacking v-Src, significant growth defect was observed in cells upon v-Src expression. The expression of either wt Ssa1 or its mutant alleles could not rescue cells from v-Src-mediated toxicity. These results suggest that the effect of Ssa1-T175N and Ssa1-D158N on v-Src mediated growth defect is recessive. The experimental 3D structure of Ssa1 is not yet known, we have used a modelled structure available in AlphaFold database (https://alphafold.ebi.ac.uk/entry/P10591). Fig 1D shows the Ssa1 structure with the location of the D158N and T175N mutations. Interestingly, both the mutations lie in the nucleotide-binding domain, and not the substrate-binding domain of Ssa1 (Fig 1C). The above results show that v-Src-mediated toxicity is reduced in strains expressing Ssa1-T175N or Ssa1-D158N. To examine whether the reduced toxicity is due to poorer maturation of v-Src, we overexpressed v-Src in strains expressing wt or mutant Ssa1, and measured the kinase abundance and activity using anti-FLAG and anti-phosphotyrosine antibodies, respectively. As shown in Fig 2A (S2 Dataset), v-Src levels were found to be similar in A1, A1-T175 or A1-D158, suggesting that the v-Src abundance was not affected. To elucidate this further, we examined the fraction of v-Src present in the soluble versus the pelleted fraction of the cellular lysate. As seen in Fig 2A (S2 Dataset), though about similar amount of v-Src was present in soluble and pellet fraction in A1 strain, most of the kinase was found as insoluble fraction A1-T175N (~90%) and A1-D158N (~70%) cells suggesting that most of the kinase present in strains with mutant Ssa1 accumulate as inactive aggregates. We further monitored the v-Src kinase activity and found that as compared to A1, the activity was compromised in A1-D158N, and markedly reduced in A1-T175N (Fig 2A) (S2 Dataset). Previous studies show that inactive v-Src is degraded relatively faster, and thus reduced maturation of v-Src results in lower steady-state levels of the protein [30]. However, our results show that though the kinase activity varies in A1, A1-T175N and A1-D158N, but overall v-Src level in similar in different strains. We further measured the abundance of Hsp70 and Hsp90 which are known to affect v-Src maturation (Fig 2B) (S2 Dataset). As seen, both Hsp70s and Hsp90s are present at similar levels in A1 versus the A1-T175N and A1-D158N strains, suggesting that v-Src aggregation in these strains is unrelated to the changes in expression level of these chaperones. The diminished maturation of v-Src in the A1-T175N and A1-D158N strains could be due to an effect of the mutation on the Hsp70 structure compromising its ability to promote substrate folding. We thus monitored the secondary structure of Ssa1, Ssa1-T175N and Ssa1-D158N using far-ultraviolet circular dichroism spectroscopy (S5 Fig) (S15 Dataset). The secondary CD is widely used to estimate secondary structural contents such as α-helices and β-sheets in proteins. As seen, the secondary structure of the Ssa1 mutants was found to be similar to that of the wt Ssa1, suggesting that the mutations did not cause any significant alteration to the protein structure. As heat shock proteins affect v-Src maturation, we examined the abundance of major heat shock proteins such as Ydj1, Hsp104 and Sse1 in cells expressing either of the Ssa1 mutants as the sole Ssa Hsp70 source (S4 Fig). As seen above, the Ssa Hsp70 mutants were found to be expressed at a level similar to wt Ssa1. Similarly, we did not observe any difference in the expression level of other chaperones in A1-T175N or A1-D158N strains when compared to A1. We next examine whether the effect of the Ssa1 mutations (A1-T175N and A1-D158N) on Hsp90 client protein is specific for v-Src or more general, affecting other known Hsp90 client proteins. Ste11, a mitogen-activated protein kinase kinase kinase is a well-known Hsp90 client protein [31]. The reduced maturation of Ste11 results in reduced activation of the transcription factor Ste12. As Ste12 binds to pheromone response elements, a defect in Ste11 maturation adversely affects transcriptional activation of a pheromone-responsive reporter gene. Thus the reporter lacZ under control of a promoter containing three repeats of the pheromone response element (PRE-lacZ) is widely used to monitor Ste11 maturation [32]. Ste11 contains three N-terminal regulatory domains. A constitutively active Ste11ΔN that lacks these regulatory domains causes inhibition of cell growth due to a combined activation and suppression of mating pathway signalling, and a high osmolarity response signalling, respectively [33]. To examine the effect of Ste11ΔN on the growth of cells expressing different Ssa1 allele, the plasmid expressing the constitutively active kinase under control of galactose-inducible promoter was transformed into yeast strains, and a pool of 10–11 transformants was serially diluted onto solid media containing dextrose or inducer galactose as carbon sources. As shown in S6A Fig, cells expressing Ste11ΔN displayed a growth defect in cells carrying wt Ssa1 (S6A Fig). In contrast, strains expressing either Ssa1-T175N or Ssa1-D158N did not show any significant growth defect, and grew quite similar to the A1 strain lacking the kinase expression (S6A Fig), suggesting reduced maturation of Ste11ΔN in strains expressing the mutant Ssa1s. To examine whether reduced Ste11ΔN-mediated toxicity is related to the kinase expression, we examined the steady state level of Ste11ΔN in A1-T175N and A1-D158N. As evident in S6B Fig, the expression level of His6-Ste11ΔN in A1-T175N and A1-D158N is similar to that in A1. Hsp90 is required for the Ste11 pathway activity [31]. We further examined Ste11ΔN maturation by monitoring its effect on the activity of the reporter enzyme β-galactosidase expressed from the promoter containing PRE sequence. As shown in S6C Fig (S16 Dataset), the strain expressing wt Ssa1 showed significant β-galactosidase activity. As compared to A1, A1-T175 and A-D158 displayed a more than 8 fold reduction in the activity of β-galactosidase suggesting reduced maturation of Ste11ΔN or other client protein in the pathway, in strains expressing the mutant Ssa1 Hsp70s. We further compared the Ste11 pathway activity in the A1-T175N and A1-D158N strains with that in A1. The A1-T175N, A1-D158N and A1 strains were transformed with a plasmid encoding PRE-lacZ, and β-galactosidase activity was monitored as described in Materials and Methods. The A1 strain expressing wt Ssa1 showed a significant increase in β-galactosidase activity in response to α-factor (S6D Fig) (S17 Dataset). As seen, the activity was reduced by more than 40 fold in the strain expressing Ssa1-T175N instead of Ssa1. A similar reduction in pheromone-dependent signalling was observed in A1-D158N, suggesting a poor maturation of Ste11 (or other cellular factor in the pathway) in strains expressing A1-T175N or A1-D158N mutant as the sole Ssa Hsp70 [34]. As v-Src is an Hsp90 client, its reduced maturation could be due to an effect on its interaction with Hsp90 in cells expressing mutant Ssa Hsp70 isoforms. We thus monitored the interaction of the kinase client with Hsp90 or Hsp70 by immunoprecipitating FLAG-v-Src using anti FLAG antibody-bound beads. The beads were washed, and bound proteins were probed with antibodies against Hsp70s or Hsp90s. Due to low levels of v-Src in the cell supernatant, we could not capture v-Src-Hsp90 interactions in strains expressing mutant Ssa Hsp70s. However, we could successfully compare client protein interactions with Ssa Hsp70s. As seen in Fig 3 (S3 Dataset), wt Ssa1 interacted with v-Src and such v-Src-Hsp70 interaction increased with the Ssa1-T175N or Ssa1-D158N protein, suggesting that v-Src affinity with the mutant Ssa Hsp70s is relatively higher than with the wt Hsp70. Above studies show that v-Src interaction with mutant Ssa Hsp70 is stronger than with wt Ssa1. As the co-chaperone Ydj1 plays a crucial role in v-Src transfer to Ssa Hsp70 [35], we explored whether Ydj1-Hsp70 interaction varied with the mutant Ssa Hsp70s. Purified His6-Ydj1 was bound over the Co2+-NTA beads, and incubated with cellular lysate from strain expressing wt or mutant Ssa1s. The proteins bound to Ydj1-immobilized beads were eluted and probed on immunoblots with an anti-Hsp70 antibody. As shown in Fig 4A, relatively more of Ssa1-T175N and Ssa1-D158N was eluted as compared to wt Ssa1, suggesting that the mutant Ssa Hsp70s interacted more strongly with Ydj1. The eluted fractions from Ydj1 bound beads were further probed with an anti-Hsp90 antibody. A similar amount of Hsp90 was eluted from strains expressing wt or mutant Ssa Hsp70 isoforms (Fig 4A). We further monitored Ydj1 interaction with wt Ssa1 and its mutant versions using Bio-layer Interferometry (BLI) as described in Materials and Methods. BLI, an optical label-free method based upon changes in the interference pattern of light, is extensively used to monitor biomolecular interactions in real-time. The purified Ydj1 was immobilized on a biosensor surface, and Ssa Hsp70 was used at varying concentrations as an analyte in a solution containing 5mM ATP. As shown in Fig 4B (S4 Dataset), incubation of a Ydj1-immobilized biosensor tip with a solution containing Ssa1, Ssa1-T175N or Ssa-1D158N led to an increased BLI response. Furthermore, at similar concentrations of Hsp70s, the binding response was much stronger for Ssa1-T175N or Ssa1-D158N than wt-Ssa1, suggesting that Ydj1 affinity is higher for Ssa1-T175N and Ssa1-D158N as compared with wt-Ssa1, which is in agreement with the above pulldown assay showing relatively stronger binding of Ydj1 with Ssa1-T175N or Ssa1-D158N. Further Ssa1-T175N displayed a stronger binding than Ssa1-D158N to Ydj1. The data presented show that the T175 and D158 residues influence Ydj1 interaction to Ssa Hsp70. Both residues lie in the NBD of Ssa Hsp70. As Hsp40 proteins primarily interact with their J-domain to Hsp70s, we modelled the complex of the J-domain of Ydj1 (J-Ydj1) with NBD of Ssa Hsp70 (NBD-Ssa1) to identify whether the T175 and D158 residues lie at, or near to, the binding site of Ydj1. We extracted the NBD region from the 3D structure of Ssa1 Hsp70 from S.cerevisiae (shown in Fig 5A). S7 Fig shows the alignment between NBD of S. cerevisiae Ssa1 and E. coli DnaK. The NBD of Ssa1 was docked to the J-domain of Ydj1 (pdb ID 5VSO) using the HDOCK protein-protein docking webserver [36]. As expected from previous studies, we found interaction between D36 in the HPD motif of J-Ydj1 with R169 of NBD-Ssa1 (Figs 5B and S8B). Additional interactions observed between the J-Ydj1 and NBD-Ssa1 from protein-protein-docked complex are mentioned in Table 2. The docked complex of Ssa1 NBD and the J-domain of Ydj1 were superimposed onto the available E.coli DnaK-J domain of the DnaJ complex (pdb id 5NRO) with RMSD of 0.475 Å (S8A Fig). As shown in S8B Fig, the modelled structure of the NBD(Ssa1)-J(Yjd1) complex shows that the T175 and D158 residue of DnaK lie near to the Ydj1 binding site. We further examined Hsp70-Hsp90 interaction in cells expressing wt or mutant Ssa1. The cells expressing His6-tagged wt or mutant Ssa1 Hsp70s were grown until an O.D.600nm ~1.0. The cells were collected, and cellular lysates were incubated with Co2+-NTA beads to capture His6-tagged Ssa Hsp70. The beads were washed, and bound proteins were eluted. The eluted fractions were probed with anti-Hsp90 or anti-His6 antibodies on immunoblots. As seen in Fig 6A (S5 Dataset), although similar levels of Hsp70s were detected, Hsp90 levels were found to be significantly reduced in cells expressing mutant Ssa Hsp70s, as compared to those expressing wt Ssa1. These results suggest that the mutant Ssa Hsp70s, compared to wt Ssa1, bind at relatively lower affinities to Hsp90. Similarly, we examined the interaction between purified His6 tagged Sti1 with Hsp70 and Hsp90. We found no difference between interaction of Sti1 with both Hsp70 and Hsp90 in A1 and A1-T175N expressing yeast strains, suggesting that loss of Hsp70 and Hsp90 interaction is not due to Sti1 (S9 Fig). Next, we examined whether the Ssa1 mutant proteins, defective in their interaction with Hsp90 in vivo, also exhibited similar defect in vitro. To examine this, His6-Ssa1, His6-Ssa1-T175N, His6-Ssa1-D158N, and Hsp82 were purified, and interaction between Ssa Hsp70 and Hsp90 was monitored using BLI. Hsp82 was immobilized onto a CM5 biosensor and immersed into a solution containing wt-Ssa1, Ssa1-T175N or Ssa1-D158N as analyte. The binding was monitored as an increase in the BLI response (Fig 6B) (S6 Dataset). As seen, at the equilibrium phase of association, the BLI response for Hsp82 to wt-Ssa1 interaction was about 2 and 4 fold higher than that of Ssa1-D158N and Ssa1-T175N, respectively. Overall these results suggest that T175N and D158N position in Ssa1 is crucial for Hsp90 interaction. Since the mutant Ssa Hsp70s interacted poorly with Hsp90s, and as Hsp70 is required for Hsp90 function, we wondered if disruption of Hsp70-Hsp90 interaction might have an effect on cell growth under suboptimal conditions requiring increased activity of the chaperones. To examine the effect of Ssa1-D158N and Ssa1-T175N on their ability to support essential functions required for cellular viability, we compared the growth of the A1-D158N and A1-T175N strains with that of A1 at different temperatures. Cells were grown in liquid YPAD media at 30°C, and further serially spotted onto solid growth media. The cellular growth was monitored at 16°C, 30°C and 37°C. As evident from S10A Fig, the A1-D158N strain grew similarly to the wt A1 strain at 30°C however, partial growth defect was observed at both 16°C and 37°C. As compared to A1, the A1-T175 strain showed poor growth at 16°C, and a severe growth defect at 37°C. Further, we compared the growth rate of the A1, A1-T175N and A1-D158N strains at 30°C and 37°C in liquid YPAD media. Similar to as seen above, we found that the A1-T175N strain grew slowly at 30°C compared to the A1 and A1-D158N strains (S10B Fig and S18 Dataset). We did not observe any growth of the A1-T175N strain at 37°C (S10C Fig and S19 Dataset). Overall, these results suggest that cells expressing the Ssa1 mutants grow poorly at suboptimal temperatures and that Ssa1-T175N is unable to support growth at higher temperatures (37°C). The relatively poor growth of A1-T175N and A1-D158N at suboptimal temperatures could be due to lack of activation of protective stress responses in these strains. To monitor stress response, we transformed cells with plasmid encoding HSE-lacZ that expresses β-galactosidase from promoter containing heat shock elements (HSE). Under stress, the heat shock factors (Hsf1) bind to HSE resulting in transcriptional activation of downstream genes. The transformants were first grown in selective liquid SD media at 30°C, and further incubated at 37°C for 4 hours to induce heat shock response. An equal number of cells were then used for β-galactosidase assays as described in Materials and Methods. As shown in S10D Fig (S20 Dataset), as compared to A1, both A1-D158N and A1-T175N showed about a 2 fold higher β-galactosidase activity indicative of an elevated heat shock response in these strains. The growth defect observed for A1-D158N and A1-T175N at suboptimal temperatures, as well as a higher heat shock response, could be be due to an inability of the cellular proteostasis machinery to support folding of protein substrates involved in essential processes. Therefore, we monitored reactivation of in-vivo expressed thermolabile firefly luciferase after its denaturation at higher temperature. Luciferase is a widely used substrate of Hsp70. The cells expressing firefly luciferase were grown at 30°C until mid-exponential phase and subsequently shifted to 48°C for 30 minutes. The refolding of the thermally-denatured luciferase was initiated by recovering cells at 30°C and monitoring, luciferase activity by measuring increase of luminescence after 60 minutes. The luciferase activity post heat shock was compared with that obtained before heat treatment. Since, Hsp104 is essentially for disaggregation, we used an hsp104Δ strain to monitor basal refolding of denatured luciferase. As expected, hsp104Δ cells showed only poor luciferase refolding (S10E Fig) (S21 Dataset). The A1 strain showed about 12 fold better luciferase refolding as compared to hsp104Δ. The luciferase reactivation in A1-T175N and A1-D158N was found to be about 2 and 6 fold respectively, lower as compared to that in A1 suggesting that as compared to wt Ssa1, both mutants were partially defective in luciferase refolding in vivo. Hsp70 interacts with Hsp90 directly through its nucleotide-binding domain and indirectly through the bridge protein Sti1 [11,16,17]. Deletion of Sti1 is known to impair the maturation of Hsp90 clients such as v-Src (and thus reduce v-Src toxicity), likely by affecting Hsp90 functions or the transfer of clients from Hsp70 to Hsp90. Since our data suggest that D158N and T175N residues influence Ssa1 interaction with Hsp90, we wondered whether the observed reduction of v-Src toxicity in the A1-T175N or A1-D158N strain was due to disruption of Hsp70-Hsp90 interaction via Sti1 or the nucleotide-binding domain. To approach these possibilities, we attempted to examine the effect of a Sti1 deletion in the A1-T175N and A1-D158N strains on v-Src toxicity. If the reduced toxicity in strains expressing the mutant Hsp70s is due to disruption of Sti1 mediated Hsp70-Hsp90 interaction, the lack of Sti1 in these strains should not have an additive effect on the cellular growth upon v-Src overexpression. We thus attempted to delete STI1 in A1, A1-T175N and A1-D158N as described in Materials and Methods. Interestingly, while STI1 could be deleted in the A1 strain, a similar knockout was not possible in A1-T175N and A1-D158N. We further attempted to shuffle a plasmid encoding Ssa1 in a strain lacking Sti1 (SY289) with one encoding either of the Ssa mutations (pRS315-SSA1, pRS315-SSA1-T175N or pRS315-SSA1-D158N). The strain SY289 expressing wt Ssa2 from a URA3-containing plasmid was transformed with a LEU2-based plasmid encoding wt or mutant Ssa1 Hsp70s. The transformants were patched onto solid SD media containing fluoroorotic acid (FOA). The cells were further replicated onto SD media lacking leucine. As seen in Fig 7A, whereas cells expressing wt Ssa1 showed normal growth onto solid SD media containing FOA and lacking leucine, no growth was visible onto FOA for sti1Δ cells harboring plasmids encoding either Ssa1-T175N or Ssa1-D158N. These results suggest that the Ssa1 mutant alleles, when present as the sole source of Ssa Hsp70, are unable to support cell growth in the absence of Sti1. To further confirm whether Sti1 is required for cellular growth of A1-T175N and A1-D158N or not, we constructed A1, A1-T175N and A1-D158N strains, expressing Sti1 from a plasmid-borne gene under the control of a doxycycline-repressible promoter, and lacking chromosomally encoded Sti1. The cells were grown onto solid SD growth media with and without doxycycline. As seen in Fig 7B, though all strains grew in the absence of doxycycline, only those with mutant Ssa1 failed to grow in the presence of the repressor. Similar results were observed when cells were grown in the presence and absence of doxycycline-containing liquid SD media (Fig 7C) (S7 Dataset). We found that in the absence of Sti1 expression, the A1-D158N strain grows only partially in liquid growth media, and no increase in growth was observed for the A1-T175N strain. Collectively, these results show that Sti1 is required for optimal growth in cells expressing A1-T175N or A1-D158N as a sole source of Ssa Hsp70. In addition to Sti1, Hsp90 requires the assistance of various other co-chaperones. While co-chaperones such as Sti1 and Aha1 modulates Hsp90 ATPase activity, others such as Cpr7, Cpr6, Hch1, Tah1, Sba1, and Pih1 modulate conformational transition of Hsp90 during various stages of its reaction cycle [9,10,15]. We thus examined whether the observed effect on cell growth in A1-T175N and A1-D158N is specific for Sti1 or more general to any of the other Hsp90 co-chaperones. The gene encoding the desired Hsp90 co-chaperone in A1 was replaced with the gene encoding KanMX4. The knockout strains were transformed with a plasmid encoding for Ssa1-T175N or Ssa1-D158N. 3–4 transformants were then pooled and patched onto solid SD media containing FOA to shuffle out a wt Ssa1 encoding plasmid. The FOA plate was then replica-plated onto solid SD media lacking leucine or uracil. As seen in Fig 7D, wt Ssa1 supported normal growth in the absence of either of the Hsp90 isoform or any of their co-chaperones. The cellular growth in cpr7Δ, sti1Δ and hsp82Δ was relatively slow as compared to other knockout strains. As expected, sti1Δ did not support growth in any of the strains expressing the mutant Ssa Hsp70s. For A1-T175N, cells possessing both Hsp90 isoforms, one of the Hsp90 isoforms, or deletions of Hsp90 co-chaperones, except sti1Δ grew poorly. The absence of either of Hsp90 isoforms or any other Hsp90 co-chaperone other than Sti1 in A1-D158N had no significant effect on cellular growth. Overall, these results show that the growth defect observed in A1-T175N and A1-D158N is specific for sti1Δ and not general for other Hsp90 co-chaperones. Structurally, Sti1 comprises of 3 TPR domains (TPR1, TPR2A and TPR2B), and two aspartate- and proline-rich domains (DP1 and DP2). The TPR domains, TPR1 and TPR2 bridge Hsp70 and Hsp90 by interacting at EEVD and MEEVD residues present at their C-terminus, respectively [12]. Though the exact role of the DP1 and DP2 domains is not clear, they have been shown to promote client activation in vivo. To further elucidate Sti1 functions in the A1-T175N and A1-D158N strains, we examined the ability of various Sti1 derivatives to complement Sti1’s role in supporting cell growth in these mutants. S11A Fig shows schematics of various Sti1 derivatives that were examined to support cell growth in the absence of Sti1 in cells expressing Ssa1-T175N or Ssa1-D158N as sole Ssa Hsp70. The derivatives either lack the TPR region required for interaction with Hsp70 or Hsp90, or the linker region that facilitates conformational changes in Sti1. The genes encoding these derivatives were subcloned under control of the GPD promoter and transformed into A1, A1-T175N or A1-D158N lacking Sti1 and harboring a Ura3-containing plasmid encoding Ssa2 (S11A Fig). A pool of 5–6 transformants was patched onto solid media with and without FOA to examine the growth of cells without wt Ssa2. As shown in S11B Fig, A1 cells expressing wt Sti1 or any of its derivatives showed optimal growth onto solid media containing FOA. When complemented with wt Sti1, A1-T175N and A1-D158N grew similar onto media with and without FOA. When complemented with a Sti1-derivative lacking linker region, the A1-T175N and A1-D158N displayed relatively slow growth on growth media with FOA. Other Sti1 derivatives expressing only DP2 or TPR2B or TPR2A-TPR2B-DP2 could not complement Sti1 function in supporting growth in cells expressing mutant Ssa1 as sole Ssa Hsp70. Similarly, Sti1 derivatives lacking region DP2 or TPR2B-DP2 did not support growth in sti1Δ A1-T175N and A1-D158N strains. These results suggest that full-length Sti1 is important to support optimal cells growth, and that the function of the linker region is not crucial for the growth phenotype observed in cells expressing either of mutant Ssa1s as a sole source of Ssa Hsp70 (S11B Fig). The strain expressing Hsp70 mutant Ssa1-T175N shows increased aggregation of v-Src and an elevated heat shock response, suggesting perturbations in proteostasis. We thus carried out a proteome-wide analysis to examine the fate of cellular proteins in the A1-T175N strain. Fig 8A describes the schematics of the TMT-based mass spectrometry study utilized to explore the proteome. The changes in the abundance of proteins in the soluble fraction of cellular lysates were examined upon repression of Sti1 expression post 10h (S12A and S12B Fig) (S22 Dataset) and compared with that before repression (0h). The mass spectrometry study showed not many changes in solubility of most of the proteins in A1 strain (Fig 8B and S2 Excel sheet). The mass spectrometry identified 3166 proteins in the supernatant fraction of which 2691 were used for further analysis as the rest of the proteins showed significantly lower abundance. Proteins which showed >2 fold difference in abundance (10h versus 0h fraction) were considered to be significantly altered. Based on this criteria, we observed lower abundance of a major fraction (3.7%) of proteins in the soluble fractions upon Sti1 repression. Among them, a majority was found to be kinases and transcription factors indicating altered Hsp90 activity (Fig 8C and S1 Excel sheet). GO term analysis showed that pathways involved in signalling, signal transduction, and protein phosphorylation are markedly downregulated in the Ssa1-T175N background upon repression of Sti1, which is as expected from a compromised Hsp90 activity (Fig 8D). Many of the kinases, such as Ypk1, Slt2, Yck2, and Cdc28 involved in Ser/Thr phosphorylation and cell cycle control were found to be decreased upon Sti1 repression in the A1-T175N mutant strain [37–42]. Repression of Sti1 in A1-T175N also reduced the abundance of various transcription factors, such as Taf2, Taf9, Brf1, and DNA Polymerase subunits such as Pol12 & Pol31 as well as various ribosomal proteins such Rpl24A, Rpl24B, Rpl23A, Rpl38 and Rps1B [43–46]. We also found a reduced abundance of Cog6 (49.1%), Vps45 (49%), Vps1 (48.7%), Sft1 (41.5%) and Erv25 (48.9%) in the soluble fraction which are critical component for vesicular trafficking. The decreased abundance of Conserved Oligomeric Golgi (Cog6) may result in defect in ER to golgi trafficking, vesicular tethering and IPOD formation [47,48]. Vps45 is essential for vacuolar protein sorting and its lower abundance results in a defect in vesicular trafficking to endocytic pathways and reduced iron uptake in vacuoles [49]. Similarly, depletion of Erv25 results in an increase of the unfolded protein response (UPR) [50]. We found serine/threonine kinases (S/T kinase), such as Pkc1 involved in the maintenance of cell wall integrity, to be significantly depleted in the soluble fraction. Similarly, the inactivation of kinases Hrk1 and Mck1 is known to result in loss of ion homeostasis by lower activation of Pma1 on cellular membrane [51,52]. Cdc28, Cak1 and Cdc48 that are known to regulate cell cycle were also found to be down-regulated upon Sti1 repression in A1-T175N strain. Their inactivation is known to result in failure of both meiosis and mitosis and leads to cell cycle arrest. Cdc48 is also involved in retrograde transport of proteins, ERAD and membrane fusion [53,54]. Depletion or mutation in Cdc48 leads to ER stress and also increased cell death due to apoptosis [55]. We further analysed the modulation of Hsp90 interactors and found that about 35% of those interactors were downregulated. Similar analysis with proteins involved in different biological functions showed that various kinases (~13%) and Hsp90 clients (~14%), were also downregulated suggesting that Hsp90 functions are compromised in A1-T175N (Fig 8G and Table 3 and S9 Dataset). Overall the proteome-wide analysis showed that depletion of Sti1 in cells expressing Ssa1-T175N leads to large scale alterations in the proteome which could be due to altered effect on Hsp90 activity upon mutations in Ssa1-T175N. We had also checked the solubility in A1 upon Sti1 repression. We plotted volcano plot. We could only observe 2 proteins which were significantly downregulated upon Sti1 repression (Fig 8B and S2 Excel sheet). A previous study [18] have shown the enrichment of stress response and protein folding/refolding processes upon Hop/STI1P knockout in cell lines. We could not observe such changes in our experimental set up due to differences in suppression of Sti1. Nevertheless, this indicates the specificity of decrease in solubilty is related to Ssa1-T175N mutant only. To further examine the altered solubility of Hsp90 clients, we examined the abundance of one of the Hsp90 substrates, Hog1 (Mitogen-activated protein kinase) in both cellular lysate supernatant and pellet [56]. The protein level was monitored before and after Sti1 repression post 10h. As seen, the decrease of Hog1 levels in the soluble fraction and increase in the pellet fraction after Sti1 repression was relatively more in A1-T175N compared to A1 (Fig 8E and 8F) (S8 Dataset). Overall, these results suggest an important aspect of Hsp70-Hsp90 direct interactions, mediated by the nucleotide-binding domain, in promoting solubility of the proteome including diverse pathways such as transcription, translation, organellar specific protein sorting, cell cycle control, stress responses and protein degradation. The Hsp40 Ydj1 assists Hsp70 function by stimulating ATPase activity as well as substrate transfer [22,57]. The above pull-down and BLI studies show that Ydj1 interaction with Ssa1 mutant proteins is relatively stronger than with wt Ssa1. We thus examined whether this increased affinity of Ydj1 with Ssa1 mutants also affected their protein refolding activity. The unfolded heat-denatured luciferase is an obligate Hsp70 substrate for its refolding. The refolding of luciferase is further enhanced when Hsp90 is co-incubated with Hsp70s in a refolding buffer [58]. The luciferase was denatured upon incubation at 45°C for 7 min, and the refolding was initiated by incubating denatured luciferase with Ssa1, Ssa1-T175N or Ssa1-D158N in the presence of Ydj1 at 25°C for different time intervals. As seen in Fig 9C(S12 Dataset), Ydj1 alone is unable to refold heat-denatured luciferase. As seen by an increase in luminescence, the fraction of refolded luciferase increased when Ssa1, Ssa1-T175N or Ssa1-D158N was added into a refolding buffer containing Ydj1. The refolding increased in a time-dependent manner and nearly saturated at about 30 min. At all incubation times, the increase in luminescence was relatively more pronounced with Ssa1-T175N or Ssa1-D158N than with wt-Ssa1. After 30 min of incubation, luciferase refolding was about 60-fold in the reaction containing Ssa1-T175N:Ydj1 or Ssa1-D158N:Ydj1 while it was only 30-fold with Ssa1:Ydj1. Overall, the data suggest that in the presence of Ydj1, Ssa1-T175N or Ssa1-D158N are more efficient than Ssa1 in refolding luciferase in vitro. We further examined the effect of Ydj1 on Ssa1 and its mutant’s ATPase activity. As seen in Fig 9D (S13 Dataset), in the presence of Ydj1, ATPase activities of Ssa1, Ssa1-T175N and Ssa1-D158N were similar. We further examined how Hsp90 cooperated with Ssa1 mutant proteins in the refolding of denatured luciferase. The Hsp82 isoform of Hsp90 was added to the refolding reaction containing either wt Ssa1 or its mutants, Ydj1, and Sti1 as bridge protein between Hsp70 and Hsp90. As expected, the presence of Hsp90 further enhanced luciferase refolding (Fig 9A and 9B) (S10 and S11 Datasets). After 30 min of incubation in the refolding buffer, luciferase refolding was about 80-fold in the reaction containing Ydj1:Sti1:Hsp82 with wt Ssa1 as compared to 60 and 80-fold with Ssa1-T175N and Ssa1-D158N, respectively. The fold increase in luciferase refolding upon addition of Hsp90 and Sti1 in a reaction containing Ydj1:Ssa1(wt or mutant) is 4, 2 and 2 for wt Ssa1, Ssa1-T175N and Ssa1-D158N respectively as compared to reaction containing Ydj1 and different variants of Hsp70 only, suggesting that Hsp90 mediated increase in the substrate refolding is lower with Ssa1 mutants than with wt Ssa1 (Figs 9A and 9B and S13) (S10, S11, S23 and S24 Datasets). Above data shows that the identified Ssa1 mutant proteins interact relatively strongly with Ydj1, interact poorly with Hsp90, and does not support growth in the absence of Sti1. Our previous study shows that one of the Ssa Hsp70 isoforms, Ssa4 interacts poorly with Ydj1 [26]. In order to examine whether Ssa1-Ydj1 interaction regulates viability of sti1Δ cells, we constructed the homologous mutations in the remaining three Ssa Hsp70 isoforms, including Ssa4 and monitored their ability to support cellular survival in the absence of Sti1. Ssa2/3/4 were mutated at the position homologous to T175 and D158 in Ssa1 with Asn. The wt and designed alleles (Ssa2/3/4-T175N and Ssa2/3/4-D158N) were expressed from the same Ssa2 promoter as used for Ssa1. The wt strain or that lacking Sti1 harboring a URA3-containing plasmid encoding wt Ssa2 as the sole source of Ssa Hsp70 was transformed with LEU2-containing plasmid encoding wt or the designed mutant Ssa Hsp70 isoforms. 3–4 transformants were pooled and patched onto FOA containing growth media for counter-selecting the Ssa2-encoding plasmid. As seen, cells expressing wt Ssa Hsp70 isoforms showed optimal growth onto FOA in the presence and absence of Sti1. Though STI1 cells expressing T175N or D158N mutant allele of Ssa1, Ssa2 or Ssa4 grew well, those carrying Ssa3-T175N/Ssa3-D158N showed significant growth defects (Fig 10A). As expected, cells expressing Ssa1 mutants did not show any growth in the absence of Sti1. Similarly, sti1Δ cells expressing Ssa2-T175N and Ssa3-T175N did not grow, while relatively poor growth was observed with Ssa2-D158N and Ssa3-D158N. Interestingly, similar to cells expressing wt Ssa4, those expressing Ssa4 alleles, Ssa4-T175N and Ssa4-D158N, grew well even in the absence of Sti1 (Fig 10A). We further monitored the interaction of A1, A2, A3 and A4 isoforms of Hsp70 with Ydj1 using biolayer interferometry. As evident in Fig 10B (S14 Dataset), different Ssa Hsp70 isoforms showed varied level of affinities with Ydj1; Ssa1 has the highest, while Ssa2 and Ssa3, both showed similar affinity, followed by Ssa4 which had the least binding affinity to Ydj1. Overall, the results show that Ssa Hsp70 isoforms, though highly homologous, function differently in the Hsp90 chaperoning pathway, and that the functional distinction is, in part, regulated by their interaction with Ydj1. Hsp70 and Hsp90 together form a multi-chaperone machinery. The formation and functioning of this dynamic complex are regulated by various co-chaperones. The two chaperones are bridged by Sti1 that also modulates their ATPase activity, and promotes transition among various conformations formed during the reaction cycle [12]. Similarly, Ydj1 modulates the opening and closing of the Hsp70 substrate-binding pocket for client proteins to adopt a form for subsequent processing by Hsp90. The Ydj1 and Hsp90 have been recently shown to compete for the same region on Hsp70 however, the significance of such dynamics is not clear [59]. In the present study, using yeast that express Ssa1 in the absence of other Ssa Hsp70s, we show that a delicate balance exists between the interaction of Hsp70 with Ydj1 versus Hsp90 and any perturbations from such dynamic equilibrium adversely affects Hsp90 function and thus its role in the maintenance of proteastasis. Hsp70 consists of three functional domains; N-terminal ATPase binding domain, substrate-binding domain and a C-terminal lid. Our mutagenesis study on Ssa1 discovered mutations in the Hsp70 region that inhibited maturation of Hsp90 client proteins v-Src and Ste11. The loss of client maturation suggests Hsp70/Hsp90 network is functionally impaired by the amino acid substitution in the Hsp70 isoform. The two potent Ssa1 alleles unable to support the client maturation had amino acid substitution in the same nucleotide-binding domain. The identified amino acids critical for Hsp90 function were also found to be conserved across species such as in E.coli DnaK and constitutive Hsp70 isoform (HspA8) in humans (Table 1). Interestingly, among the isolated mutants affecting Hsp90 function, no substitution was observed in the substrate-binding domain of Ssa1. At the C-terminal domain, no single amino acid substitution was observed however, in agreement with role of Sti1, a Ssa1 allele lacking the entire 39 amino acid stretch including Sti1 interaction motif (EEVD) was isolated. Overall these findings suggest that in addition to the well-studied role of the C-terminus of Hsp70 (through Sti1), the Hsp90 function is sensitive to changes in N-terminal domain of Hsp70s. The reduced client maturation in cells expressing the Ssa1 mutants could be either due to a defect of Hps70 activity or a downstream effect on Hsp90 action. Several lines of evidence suggest that it’s the loss of Hsp90 action that affects client folding in the presence of these Ssa1 mutants as source of Ssa Hsp70. First, all identified mutations show poor affinity with Hsp90. Second, the luciferase refolding with the Ssa1 mutant is higher as compared to wt Ssa1 suggesting that Ssa1 refolding activity is not altered due to mutations. Third, the fold increase in luciferase refolding by Hsp90 is lower in presence of the mutant Hsp70s than the wt isoform. Thus collectively, these observations suggest that Hsp90 is relatively less efficient with mutant Ssa1 Hsp70s indicating a potential role of the Hsp70 subregion encompassing these mutations in Hsp90 functions. The loss of v-Src maturation leads to its aggregation and subsequent degradation [60]. The reduced phosphorylation of cellular proteins in A1-T175N and A1-D158N expressing v-Src suggests that the kinase activity is adversely affected in these strains. In agreement with lower kinase activity, v-Src was primarily found in the pellet fraction of cellular lysate indicating that the kinase is not natively folded. Interestingly there is no enhanced degradation of the misfolded client in strains expressing Ssa1 mutants which resulted in accumulation of intracellular v-Src aggregates, as observed in cell-pellet fraction. Why misfolded v-Src or also other kinase Ste11ΔN is not degraded remains to be studied however it could be due to altered interaction of the mutant Hsp70s/Hsp90s with its co-chaperones required for their degradation. Indeed, the mutant Hsp70s binds with relatively higher affinities with Ydj1, which in turn could affect their interaction with other co-chaperones such as Sse1 and Fes1, known to facilitate Hsp70 degradation activity [61,62]. Hsp40s not only stimulate Hsp70 activity but also regulates its functional specificity [63]. The pull-down with His6-Ydj1 as bait protein shows that the Ydj1 binds with higher affinity to mutant Ssa1s than with wt Ssa1. Also, as compared to wt Ssa1, the Ssa1-T175N and Ssa1-D158N are 1.5 to 2 fold more active in refolding denatured luciferase indicating that increased affinity of Ydj1 with the mutant Hsp70s further enhances their substrate refolding activity. The luciferase refolding efficiency was further increased upon addition of Hsp90 however, interestingly, the enhancement was found to be higher in reaction containing wt Ssa1 than the mutants suggesting that the mutants cooperate relatively poorly with Hsp90 in substrate refolding. The in vitro results are also in agreement with in vivo findings showing lower client maturation in cells expressing the Hsp70 mutants than wt Ssa1 as a sole source of Ssa Hsp70. As the C-terminal domain of wt and the mutant Ssa1 Hsp70s is identical, the data suggest that the ability of Hsp90 in substrate refolding is not only dependent upon its interaction through Hsp70-C terminus but also on its interaction with the nucleotide-binding domain of Hsp70. The Hsp90 interacts with nucleotide-binding as well as C-terminal domains of Hsp70s. The in vitro BLI analysis confirms that the direct Hsp70/Hsp90 interaction is weaker with Ssa1 mutants. The in vivo pull-down analysis for Ssa1 interacting partners showed a lower abundance of the Hsp70/Hsp90 complex in A1-T175N and A1-D158N strains, even though Sti1 is expressed in the cells. The data indicate that Hsp90 binding to the nucleotide-binding domain of Hsp70 might regulate downstream interaction of Hsp90-Sti1 with C-terminal domain of Hsp70. Since Ssa1 mutants interact with higher affinity to Ydj1, it is likely that the co-chaperone interaction at NBD leads to conformational changes in Hsp70 affecting its interaction with the bridge protein Sti1. This is in agreement with previous findings that the co-chaperone interaction at the nucleotide binding domain of Hsp70 affects conformational changes in the substrate binding and the C-terminal domain of the protein [64]. The increased affinity of Ydj1 with the mutant Ssa Hsp70s would make it less likely to be free for further interaction with the Sti1-Hsp90 resulting in a reduced level of the ternary complex. The relatively lower increase in luciferase refolding upon addition of Sti1-Hsp90 to refolding reaction containing mutant Ssa1 is likely to be related to weak interaction of the mutant Ssa Hsp70 with wt Hsp90. Overall, these results suggest that the nucleotide-binding domain of the Hsp70 regulates its C-terminal mediated interaction with Sti1-Hsp90. The pull-down assay with Ydj1 as a bait protein showed that it binds with similar affinity to Hsp90 in strains expressing Ssa1-T175N or Ssa1-D158N. This interaction of Ydj1 with Hsp90 is more likely to be a direct interaction rather than through Hsp70 as Ydj1 binds with different affinities to mutant Ssa1 Hsp70 though interaction with Hsp90 is similar in strain expressing different Ssa1 mutants. As Ydj1 interaction with Hsp90 remains similar in cells expressing wt or the mutant Hsp70, the Ydj1-Hsp90 direct collaboration is less likely to be the basis of either reduced v-Src maturation or lower Hsp70-Hsp90 complex formation in A1-T175N or A1-D158N strain. The Sti1 and not other Hsp90 co-chaperones, was found to be essential for cellular growth in strain expressing Ssa1-T175N or Ssa1-D158N as sole Ssa Hsp70, suggesting that it’s primarily the client transfer activity from the Hsp70 to Hsp90 that is impaired in these strains. The growth defect was more pronounced at a higher temperature even in the presence of Sti1. Since at higher temperature proteins are more prone to misfold and aggregate, the cellular demand for a functional Hsp70 and Hsp90 chaperones would increase with an increase in temperature. The reduced cellular growth of A1-T175N and A1-D158N could be due to the compromised Hsp70 activity. Alternatively, as the Hsp70 mutations lie in an interacting region of Hsp90 [16,59], it’s possible that in these strains Hsp90 activity is compromised in a manner that is not compensated by the interaction of Hsp70-Hsp90 by Sti1. This suggests non-redundant roles of Hsp70-Hps90 interaction through the region surrounding amino acids T175/D158 versus bridge protein Sti1. The Ydj1 is synthetically lethal with Sti1 as the ydj1Δsti1Δ remains inviable [65]. It is interesting to note that Ydj1 interacts better with Ssa1 mutants (Ssa1-T175N and Ssa1-D158N) and also promote their ability to refold denatured luciferase suggesting that the growth defect upon Sti1 deletion in these mutants is not due to depletion of Ydj1 function. Among the various Sti1 derivatives that were examined, none except that lacking linker region could complement Sti1 function in supporting cellular growth in A1-T175N and A1-D158N suggesting that the linker region is not essential for such Sti1 function. Interestingly, Sti1 derivative lacking only DP2 region also did not support growth suggesting an important role of the domain in client protein maturation. This is in agreement with a previous study suggesting the essential role of DP2 in client activation, possibly by either direct binding to substrates or facilitating their conformational changes required for client transfer to Hsp90 [12]. DP2 alone also could not complement the Sti1 function. Further, as construct lacking DP2 contains all Sti1 region important for Hsp70 and Hsp90 interaction yet not able to complement Sti1 function suggest that the bridging the two chaperones alone is not sufficient for Hsp90 chaperoning activity in A1-T175N and A1-D158N strains. In summary, since Sti1 does not have a client folding activity of its own, and yet is required to support growth in A1-T175N and A1-D158N suggests that the client transfer activity from Hsp70 to Hsp90 gets adversely affected in cells expressing Ssa1-T175N or Ssa1-D158N as sole Ssa Hsp70. The modeled structure of the complex of nucleotide-binding domain of Ssa1 with the J-domain of Ydj1 shows that the identified mutation is in the interacting region of the two proteins. Indeed, our pull-down study with His6-Ydj1 as bait protein as well as BLI study show that the Ssa1 mutants bind with higher affinity to Ydj1. Further, T175 and D158 lie in the Hsp70 region that interacts with Hsp90 [16,59]. The identified T175N mutation is homologous to the mutation found in DnaK that affects its binding with Hsp90. Overall, these findings suggest that the identified Ssa1 mutations are at a region that interacts with both Ydj1 and Hsp90. As the Ssa1 mutants bind with lower affinity to Hsp90, it’s possible that under in vivo conditions, both Hsp90 and Ydj1 compete for the same region in Ssa1, and Ydj1 having relatively higher affinity to the mutant Hsp70s competes out Hsp90. Further decrease in Hsp90 binding to Hsp70 in A1-T175/D158 lacking Sti1 might result in complete disruption of formation of Hsp70-Hsp90 complex resulting in the cellular lethality (Fig 11). This is in agreement with the data showing that mutation homologous to Ssa1-T175N or Ssa1-D158N in Ssa4, can support cellular viability in the absence of Sti1. As Ydj1 interacts with a lower affinity with Ssa4 than Ssa1, it might not be able to compete out Hsp90 for its binding with Ssa4 mutants [26]. The current study show the effect of identified mutation on Hsp90 substrates v-Src, Ste11 and Hog1, and similar effect on other substrates remains to be seen. The reduced growth defect in sti1Δ cells with D158N as compared to T175N mutant of Ssa2/3 could be related to their difference in the binding affinity to Ydj1. Indeed the binding affinity of Ssa1-T175N is relatively higher than Ssa1-D158N with Ydj1. The growth defect of T175N was found to be more pronounced with Ssa3, as cells expressing Ssa3-T175N grew much poorly even in the presence of Sti1. The results further supports previous findings that though highly homologous, the different Ssa Hsp70 chaperones function distinctly in Hsp90 chaperone pathway [26]. Overall these results show that Ydj1 affinity to Ssa Hsp70s plays a critical role in formation of Hsp70-Hsp90 complex and thus Hsp70 role in Hsp90 chaperoning pathway. The role of other Hsp40s that interact to a similar region of Hsp70, in the Hsp90 chaperoning function remains to be determined. Also, the current study is carried out with strains expressing desired Ssa Hsp70 in the absence of other three isoforms, and thus how the presence of different Hsp70 isoforms affect interactions and thus coordination with their co-chaperones in wt strain need to be further explored. Recent studies show the significance of postranslational modifications of chaperones in their cellular functions, also known as chaperone code [66]. Interestingly, T175 in Ssa1 is known to be phosphorylated, and D158 has proximity to sites on Ssa1 that undergo modifications [67]. Thus its possible that alteration of these modifications by substitution of residues at 158 and 175 to asn affects the chaperone function resulting in loss of Hsp90 client maturation as well as growth defect upon deletion of Sti1. Both Hsp70s and Hsp90s play crucial role in the maintenance of a wide range of cellular proteins. Thus cellular health is quite susceptible to changes in either Hsp70 or Hsp90 activity. In prokaryotes, that lack bridge protein Sti1, the sole mode of Hsp70-Hsp90 interaction is through the nucleotide-binding domain of Hsp70. Though the Hsp90 interaction with Hsp70 nucleotide-binding domain remains conserved in eukaryotes, they evolved an additional mode of interaction through bridge protein Sti1. We show that the Hsp90 interaction at the NBD of Hsp70 has a dominant effect on its interaction with the C-terminal domain of Hsp70 through Sti1. The bimodal interaction of Hsp90 with Hsp70 provides an additional regulation in determining the functional specificities within highly homologous Hsp70s beneficial for better fitness of cells under stressful conditions. It is possible that under optimal conditions with lower load of misfolded proteins, the requirement of Hsp90s is low. Under these conditions, more of Ydj1 is involved in Hsp70 activation required for many diverse cellular functions. Under stress conditions, the Ydj1 might get sequestered with a much larger fraction of misfolded proteins, resulting in higher interaction of Hsp90 with NBD domain of Hsp70 and thus increased abundance of Hsp70-Hsp90 complex required for maintenance of protein homeostasis. The strains and plasmids used in this study are described in Tables 4 and 5. Strain SY135 (A1) has pRS315-SSA1 as sole source of Ssa Hsp70 isoform. The gene encoding Sti1, Hsp82, Hsc82, Cpr6, Cpr7, Sba1, Aha1, Hch1, Tah1 and Pih1 was knocked out using standard homologous-based recombination approach. For protein purification, the gene encoding Ssa1 was PCR amplified, digested with BamHI and XhoI, and subcloned into plasmid pRS416-PGPD-His6 digested with same enzymes to generate pRS416-PGPD-His6SSA1. The plasmid encode from 5’ to 3’ direction His6-tag, TEV protease site and gene encoding Ssa1. Similarly, Ssa1-T175N and Ssa1-D158N were subcloned to generate plasmids pRS416-PGPD- His6SSA1-T175N and pRS416-PGPD- His6SSA1-D158N respectively. PCR amplified His6-STI1 was cloned in pCM189 vector at BamH1 and Xma1 Sites. STI1 under native promoter and terminator was cloned in pRS327 vector using Pst1 and HindIII restriction sites. All constructs were further confirmed by DNA sequencing. pRS315-SSA1 was mutagenized using Forsburg hydroxylamine mutagenesis−based method [68]. The mutagenesis buffer is composed of 0.35 g hydroxylamine hydrochloride, 450 μL of 5 M NaOH, 4.55 mL of ice-cold sterile MQ water, pH 6.7. 10 μg of pRS315-SSA1 was incubated in 500 μl of mutagenesis buffer for 48 hours. The mutant library thus obtained was purified using GeneJET PCR purification kit (ThermoFisher, catalog number 0702) and used for further transformation in S. cerevisiae. Media composition is similar to as described before [69]. For induction by galactose driven promoter, cells were grown in liquid SRaff growth media until 1 O.D.600nm. The cells were collected by centrifugation, washed with sterile water and further subcultured at 0.5 O.D.600nm for 6 hours in SGal media. The cell cultures were grown at 30°C unless otherwise mentioned. Cells grown in liquid media were harvested by centrifugation, and lysed using glass beads. The lysis was carried out using bead ruptor (OMINI BEAD RUPTOR 24) at 3000 rpm for 30 seconds. The lysate was further fractionated into supernatant and pellet. 10–20 μg of total protein was separated onto 10% SDS-PAGE and transferred onto PVDF membranes. The primary antibodies used in the study are as follows: anti-FLAG antibody (F3165 Sigma), anti-Phosphotyrosine (05–321 Millipore), anti-Hsc82 (ab30920 Abcam), anti-Ydj1 (SAB5200007 Sigma), anti-His6 antibodies (Pierce, USA-MA1-21315), anti-Hsp70 (ADI-SPA-822-F Enzo Lifesciences), and anti-Pgk1 (catalog number 459250 Invitrogen). Secondary antibodies used in this study include anti-mouse IgG HRP linked (7076, Cell signalling technologies) and anti-rabbit IgG HRP (7074, Cell signalling technologies). Hsp82 and Sti1 were purified as described earlier [26]. Ydj1 was purified similar to method mentioned before [70]. Ssa1, Ssa1-T175N and Ssa1-D158N were purified as described earlier [69]. Strain harboring plasmid pRS416-PGPD-His6SSA1, pRS416-PGPD-His6SSA1-T175N or pRS416-PGPD-His6SSA1-D158N was grown in liquid YPAD media for 24–48 hours at 30°C. Cells were centrifuged and resuspended into buffer containing 20 mM HEPES, 150 mM NaCl, 20 mM KCl and 20 mM MgCl2, pH 7.4. Cells were lysed using glass beads followed by sonication for 30 minutes. Nickel based metal affinity resin was used for the purification of Hsp70s. The His6 tag was removed by incubation of purified protein with His6-TEV. The reaction mixture was incubated with cobalt based metal affinity resin, and TEV protease was removed in the bound fraction. Protein purity was confirmed onto 10% SDS-PAGE. Immunoprecipitation was performed as described earlier [26]. Briefly, cells were resuspended in 20 mM Tris (pH 7.5) buffer containing 150 mM NaCl, 0.5 mM EDTA, 0.1% Triton-X100, 1 mM PMSF, and lysed using glass beads (OMINI BEAD RUPTOR 24). The clarified lysate was incubated with anti-FLAG antibody conjugated agarose resin (Sigma A2220) for 16h at 4°C. The beads were washed with buffer containing 150 mM NaCl, 0.1% Triton X-100 in 20 mM Tris (pH 7.5). Immunoprecipated proteins were separated onto 10% SDS-PAGE, transferred onto PVDF membrane and probed with appropriate antibodies The pull-down assay using purified proteins was performed as described earlier [71]. Purified His6-Ydj1 was bound to cobalt based affinity resin for 1 hour, followed by incubation with clarified yeast lysate for 1h. Bound fraction was eluted with buffer containing 20mM EDTA. The His6-Ssa1, His6-Ssa1-T175N and His6-Ssa1-D158N expressed from plasmid borne gene were used for in vivo pull-down studies. Briefly, clarified yeast lysate was incubated with cobalt based metal affinity resin for 1h. The beads were further washed with buffer containing 20 mM HEPES, 150 mM NaCl, 20 mM KCl and 20 mM MgCl2, 60 mM Imidazole and 0.1% Tween-20, pH 7.4. Bound proteins were eluted using buffer containing 20 mM HEPES, 500 mM NaCl, 20 mM KCl and 20 mM MgCl2 and 300 mM Imidazole. Quantitative Real-Time PCR was used to study mRNA transcripts of v-SRC. 18S-rRNA was monitored as housekeeping gene. The cells were harvested and total RNA was purified using HiPurA Yeast RNA Purification Kit (HiMedia, MB611). About 100ng of RNA was used to prepare cDNA using iScript Select cDNA Synthesis Kit (Bio-rad, 1708896). 50 ng of cDNA was used as template for quantitative Real-Time PCR (qRT-PCR) using Biorad SYBR kit (1725270) on Biorad Real-time PCR system. Luciferase refolding was performed as described earlier [26]. Firefly luciferase (80 nM) was denatured by incubating it at 45°C for 7 minutes. The refolding of denatured luciferase (40 nM) was carried out in the presence of 0.6 μM Ydj1 and 2 μM of Hsp70 (Ssa1, Ssa1-T175N or Ssa1-D158N). Refolding was performed at 25°C and initiated upon addition of 1 mM ATP. Effect of Hsp90 on luciferase refolding was investigated by incubating denatured luciferase (40 mM) in the presence of 0.3 μM Ydj1, 0.5 μM of Hsp70 (Ssa1, Ssa1-T175N or Ssa1-D158N), 2.4 μM Sti1 and 0.9 μM Hsp82. Cells harboring pMR135 were grown in liquid SD media until O.D.600nm reaches ~1. Cells were harvested and resuspended in fresh liquid SD media to 0.3 O.D.600nm. 100 μg/ml of cycloheximide was added to cells suspension to stop protein synthesis. To denature luciferase, cells were incubated at 48°C for 30 minutes. Luciferase was further refolded by shifting cell to 30°C. The refolding was monitored by adding 50μl of D-luciferin (23 μg/sample) to 200μl of cell aliquot [72]. Cells were co-transformed with pRS412-PGAL1-Ste11ΔN and PRE-lacZ or only with PRE-lacZ [32]. Transformants with pRS412-PGAL1- His6 Ste11ΔN and PRE-lacZ were grown in liquid SD media until 1–1.2 O.D.600nm and further subcultured at 0.5 O.D.600nm in SGal growth media to induce His6-Ste11ΔN expression for 6 hours. Transformants with PRE-lacZ alone were grown in liquid SD media until 1–1.2 O.D.600nm and further subcultured at 1 O.D.600nm in SD media containing 5 μM of α-factor for 6 hours. To monitor β-galactosidase activity, 1 O.D.600nm cells were permeabilized using 3 freeze thaw cycle in liquid N2 and further incubated with 200 μl of ortho-Nitrophenyl-β-galactoside (ONPG (4 mg/ml)) at 30°C for 15 minutes followed by addition of 200 μL of 1M Na2CO3. The culture was centrifuged at 13000 rpm for 10 minutes and supernatant was used for measuring absorbance at 420nm. Cells were transformed with HSE-lacZ [73]. Transformants were grown until 1–1.2 O.D.600nm at 30°C. Cells were further normalized at 1 O.D.600nm and incubated at 37°C for 4 hours. The β-galactosidase activity was monitored as described above for PRE-lacZ assay. Far UV CD spectra were recorded on a JASCO-J-815 spectropolarimeter. The CD spectra were recorded over 250–195 nm in a cuvette of 1-mm pathlength with 10 nm/min of scan rate at 25°C. The CD signal was converted into Mean Residue ellipticity (MRE) as described before [74,75]. Bio-layer interferometry studies were carried out in Octet K2 instrument (Fortebio) at 30°C. Hsp90 was immobilized on Amine Reactive 2nd Generation (AR2G) biosensors by amine coupling chemistry using 1:1 ratio of 0.1 M N-Hydroxysuccinimide (NHS) and 0.4 M 1-ethyl-3-(3-dimethylaminopropyl)-carbodiimide (EDC). Biosensor treated as above except with buffer lacking Hsp90 was used as reference control. The biosensors were then dipped into 1M ethanolamine solution for 600 seconds. Ssa1 and its mutants were prepared in 1X assay buffer (25 mM Hepes (pH 7.4), 150 mM NaCl, 20mM MgCl2, 20mM KCl) containing 0.1% Tween-20 (v/v) and 0.05% Triton-X-100 (v/v) along with 1 mM ATP. Association was monitored for 200 seconds by dipping the Hsp90 bound biosensors into various concentrations of Ssa1 or its mutants. The dissociation was monitored by dipping the biosensors in 1X assay buffer at shaking speed of 1000 rpm for 200 seconds. The non-specific signal obtained from reference biosensor was subtracted from corresponding signal obtained upon Hsp90 immobilized biosensors. Binding curves was further analyzed using software ‘Data Analysis 9.0’ available from ForteBio. BLI for Ydj1 and Hsp70 was performed as described earlier [26]. The 3D structure of Hsp70 from Saccharomyces cerevisiae was taken from AlphaFold Protein Structure Database (https://alphafold.ebi.ac.uk/entry/P10591). Hsp70 NBD region was extracted from the 3D structure and docked with Hsp40 J-domain (pdb id 5VSO) from Saccharomyces cerevisiae on HDOCK server. The docked complex was visualised in Discovery Studio to identify the interacting residues. Tet-OFF promoter regulatable Sti1 plasmid harboring DD502 and NK402 strains were grown into dextrose synthetic medium without uracil amino acid until saturation. 0.025 O.D.600nm cells were inoculated in secondary culture and grown until log phase. Cells were pelleted, washed with 1X PBS and 40 O.D.600nm cells were collected for yeast cell lysate fractionation (0h time point). 0.06 O.D.600nm cells were inoculated in 10 μg/ml of Dox containing repression medium and grown for 10 hr. After 10 hr cells were pelleted, washed and 40 O.D.600nm cells were collected for yeast cell lysate fractionation (10h time point). 40 O.D.600nm cells were lysed in 1 ml of lysis buffer (25 mM HEPES, 150 mM NaCl, 1 mM EDTA, 1 mM EGTA, 10% Glycerol) using zirconium beads in Fast Prep Zymo bead beator at 4000 rpm of 30 on/ 30 off for 4 cycles at 4°C. Cell debris were removed by spinning at 4000 rpm for 1 min. Clarified lysates were spun for 13000 rpm for 30 min and fractionated into supernatant and pellet fractions. 30 μg of total proteins in supernatant fractions were used for TMT based QMS analysis. Samples were reduced with 100 mM DL-dithiothreitol (DTT) at 56°C for 30 min and processed using the modified filter-aided sample preparation (FASP) method [76]. In short, reduced samples were transferred to 30 kDa MWCO Pall Nanosep centrifugation filters (Pall Corporation) and washed twice with 8 M urea. Additional washes with digestion buffer (0.5% sodium deoxycholate in 50 mM TEAB) was performed before and after alkylation with 10 mM methyl methanethiosulfonate for 20 min at room temperature. Protein digestions were performed using Trypsin (Pierce MS grade) in digestion buffer, first with 0.3 μg Trypsin at 37°C overnight followed by new addition of 0.3 μg trypsin and incubation at 37°C for three hours. Produced tryptic peptides were collected by centrifugation and labelled using TMT 10-plex isobaric mass tagging reagents (Thermo Scientific) according to the manufacturer instructions. Labelled samples were combined and sodium deoxycholate was removed by acidification with 10% TFA. Peptides were desalted using Pierce Peptide Desalting Spin Columns (Thermo Scientific) following the manufacturer’s instructions. The combined desalted TMT-labeled sample was fractionated by basic reversed-phase chromatography (bRP-LC) using a Dionex Ultimate 3000 UPLC system (Thermo Fischer Scientific). Peptide separations were performed using a reversed-phase XBridge BEH C18 column (3.5 μm, 3.0x150 mm, Waters Corporation) and a gradient from 3% to 100% acetonitrile in 10 mM ammonium formate buffer at pH 10.00 over 22 min. The fractions concatenated into 15 or 22 fractions that were evaporated and reconstituted in 20 μl of 3% acetonitrile, 0.2% formic acid for nLC-MS/MS analysis. The fractions were analyzed on an orbitrap Lumos or Fusion Tribrid mass spectrometer interfaced with Easy-nLC1200 liquid chromatography system (Thermo Fisher Scientific). Peptides were trapped on an Acclaim Pepmap 100 C18 trap column (100 μm x 2 cm, particle size 5 μm, Thermo Fischer Scientific) and separated on an in-house packed analytical column (75 μm x 35 cm, particle size 3 μm, Reprosil-Pur C18, Dr. Maisch) using a gradient from 3% to 100% acetonitrile in 0.2% formic acid over 85 min at a flow of 300 nL/min. MS scans were performed at 120 000 resolution. MS/MS analysis was performed in a data-dependent, with top speed cycle of 3 s for the most intense doubly or multiply charged precursor ions. Precursor ions were isolated in the quadrupole with a 0.7 m/z isolation window, with dynamic exclusion set to 10 ppm and duration of 45 seconds. Isolated precursor ions were subjected to collision induced dissociation (CID) at 35 collision energy. Produced MS2 fragment ions were detected in the ion trap followed by multinotch (simultaneous) isolation of the top 10 most abundant fragment ions for further fragmentation (MS3) by higher-energy collision dissociation (HCD) at 65% and detection in the Orbitrap at 50000 resolutions, m/z range 100–500. Identification and relative quantification was performed using Proteome Discoverer version 2.4 (Thermo Fisher Scientific). The Swissprot Saccharomyces cerevisiae proteome database (December 2019) was used for the database search, using the Mascot search engine v. 2.5.1 (Matrix Science, London, UK) with MS peptide tolerance of 5 ppm and fragment ion tolerance of 0.6 Da. Tryptic peptides were accepted with 0 missed cleavage; methionine oxidation was set as a variable modification, cysteine methylthiolation, TMT-6 on lysine and peptide N-termini were set as fixed modifications. Percolator was used for PSM validation with the strict FDR threshold of 1%. TMT reporter ions were identified in the MS3 HCD spectra with 3 mmu mass tolerance, and the TMT reporter intensity values for each sample were normalized on the total peptide amount. Only the unique identified peptides were taken into account for the relative quantification. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD034983 [77]. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.

PMC9646470

Remco T. P. van Cruchten,Daniël van As,Jeffrey C. Glennon,Baziel G. M. van Engelen,Peter A. C. ‘t Hoen,,Alexander Manta

Clinical improvement of DM1 patients reflected by reversal of disease-induced gene expression in blood

10-11-2022

Myotonic dystrophy type 1,Biomarker,RNA-seq,Peripheral blood,Therapeutic Response,Lifestyle intervention

Background Myotonic dystrophy type 1 (DM1) is an incurable multisystem disease caused by a CTG-repeat expansion in the DM1 protein kinase (DMPK) gene. The OPTIMISTIC clinical trial demonstrated positive and heterogenous effects of cognitive behavioral therapy (CBT) on the capacity for activity and social participations in DM1 patients. Through a process of reverse engineering, this study aims to identify druggable molecular biomarkers associated with the clinical improvement in the OPTIMISTIC cohort. Methods Based on full blood samples collected during OPTIMISTIC, we performed paired mRNA sequencing for 27 patients before and after the CBT intervention. Linear mixed effect models were used to identify biomarkers associated with the disease-causing CTG expansion and the mean clinical improvement across all clinical outcome measures. Results We identified 608 genes for which their expression was significantly associated with the CTG-repeat expansion, as well as 1176 genes significantly associated with the average clinical response towards the intervention. Remarkably, all 97 genes associated with both returned to more normal levels in patients who benefited the most from CBT. This main finding has been replicated based on an external dataset of mRNA data of DM1 patients and controls, singling these genes out as candidate biomarkers for therapy response. Among these candidate genes were DNAJB12, HDAC5, and TRIM8, each belonging to a protein family that is being studied in the context of neurological disorders or muscular dystrophies. Across the different gene sets, gene pathway enrichment analysis revealed disease-relevant impaired signaling in, among others, insulin-, metabolism-, and immune-related pathways. Furthermore, evidence for shared dysregulations with another neuromuscular disease, Duchenne muscular dystrophy, was found, suggesting a partial overlap in blood-based gene dysregulation. Conclusions DM1-relevant disease signatures can be identified on a molecular level in peripheral blood, opening new avenues for drug discovery and therapy efficacy assessments. Supplementary Information The online version contains supplementary material available at 10.1186/s12916-022-02591-y.

Clinical improvement of DM1 patients reflected by reversal of disease-induced gene expression in blood Myotonic dystrophy type 1 (DM1) is an incurable multisystem disease caused by a CTG-repeat expansion in the DM1 protein kinase (DMPK) gene. The OPTIMISTIC clinical trial demonstrated positive and heterogenous effects of cognitive behavioral therapy (CBT) on the capacity for activity and social participations in DM1 patients. Through a process of reverse engineering, this study aims to identify druggable molecular biomarkers associated with the clinical improvement in the OPTIMISTIC cohort. Based on full blood samples collected during OPTIMISTIC, we performed paired mRNA sequencing for 27 patients before and after the CBT intervention. Linear mixed effect models were used to identify biomarkers associated with the disease-causing CTG expansion and the mean clinical improvement across all clinical outcome measures. We identified 608 genes for which their expression was significantly associated with the CTG-repeat expansion, as well as 1176 genes significantly associated with the average clinical response towards the intervention. Remarkably, all 97 genes associated with both returned to more normal levels in patients who benefited the most from CBT. This main finding has been replicated based on an external dataset of mRNA data of DM1 patients and controls, singling these genes out as candidate biomarkers for therapy response. Among these candidate genes were DNAJB12, HDAC5, and TRIM8, each belonging to a protein family that is being studied in the context of neurological disorders or muscular dystrophies. Across the different gene sets, gene pathway enrichment analysis revealed disease-relevant impaired signaling in, among others, insulin-, metabolism-, and immune-related pathways. Furthermore, evidence for shared dysregulations with another neuromuscular disease, Duchenne muscular dystrophy, was found, suggesting a partial overlap in blood-based gene dysregulation. DM1-relevant disease signatures can be identified on a molecular level in peripheral blood, opening new avenues for drug discovery and therapy efficacy assessments. The online version contains supplementary material available at 10.1186/s12916-022-02591-y. Myotonic dystrophy type 1 (DM1) is a neuromuscular disease with a worldwide average prevalence of around 1 in 8000 people and a high unmet clinical need [1]. DM1 is considered the most frequently occurring adult-onset form of muscular dystrophy. This degenerative multisystem disease is characterized by a wide range of symptoms including myotonia, muscle weakness and dystrophy, fatigue, apathy, cataracts, obesity, and insulin resistance. Next to a severe decrease of life quality, DM1 patients suffer from a reduced life expectancy mostly due to problems with cardiac and respiratory function. Currently, no curative therapy exists. DM1 is caused by the expansion of a CTG trinucleotide microsatellite repeat in the 3′ UTR of the DM1 protein kinase (DMPK) gene [2–4]. Unaffected individuals carry up to approximately 37 CTG triplets in DMPK, while in DM1 patients this ranges from 50 to even a few thousand repetitions. Depending on the inherited repeat length, DM1 can become manifest at birth or early in life but more frequently becomes apparent in adulthood [1]. In general, the disease manifestation is earlier and more severe with longer repeat expansions. Interruption of the CTG repeat by variants such as CCG or CGG is associated with milder symptoms [5]. The expanded CTG repeat is thought to cause disease mainly via an mRNA gain-of-function mechanism, in which aberrant hairpin structures formed by long CUG repeats are central [6–9]. Directly or indirectly, these hairpin structures dysregulate the function of RNA binding proteins from the muscleblind-like (MBNL) and CUGBP Elav-like (CELF) families, leading to widespread disturbed RNA processing and consequently altered functions of various proteins [10–12]. Although proven to be the disease-causing mutation, clinical symptoms of DM1 are only moderately associated with the CTG repeat or the dysregulation of specific proteins which suggests an involvement of other mechanisms in symptom expression [13–16]. While there are many promising therapeutic oligonucleotides, small molecule drugs, and gene therapies in the (pre) clinical pipeline for some of the signs and symptoms of DM1, none is expected to reach widespread clinical application soon. Physical training and increasing activity are currently being applied to relieve DM1 symptoms with marked improvements in relatively mildly affected DM1 patients [17, 18], which has furthermore shown to induce biochemical responses in DM1 mouse models [19, 20]. The to-date largest clinical trial in DM1 was OPTIMISTIC: Observational Prolonged Trial In Myotonic dystrophy type 1 to Improve Quality of Life-Standards, a Target Identification Collaboration [18]. The OPTIMISTIC clinical trial included over 250 well-characterized DM1 patients from four centers in Europe, where the effects of cognitive behavioral therapy (CBT) and optional graded exercise therapy were closely monitored over 16 months via more than twenty outcome measures. Notably, the CBT intervention was tailored towards the specific needs of the patient in a shared decision-making process between the patient and the psychotherapist, allowing for a personalized intervention. The trial has shown significant, yet heterogenous improvements for various signs and symptoms, as well as the capacity for social activity and participation in DM1 [21]. Here, we set out to find molecular profiles associated with the disease-causing CTG repeat and therapy response based on full blood mRNA sequencing before and after the CBT intervention of 27 patients from the OPTIMISTIC cohort. Given the accessibility of peripheral blood, it has increasingly been used for the successful identification of disease biomarkers for a variety of neurological and psychiatric disorders such as Duchenne muscular dystrophy (DMD), Huntington’s disease, major depressive disorder, and DM1 [22–25]. Furthermore, the multisystem nature of DM1 is known to be reflected by various laboratory abnormalities of blood samples, supporting the relevance of peripheral blood for the identification of disease-relevant information [26]. We analyzed gene expression levels as a function of CTG repeat size (as a proxy for disease load/severity) and of the therapy response. Next, we combined these findings and compared the results to various previously published datasets. We were able to identify 608 genes significantly associated with the CTG repeat and further illustrate that 97 of these genes returned towards more normal expression levels in clinical CBT responders. Patients of the OPTIMISTIC intervention arm were treated with a personalized form of CBT. The customization of the intervention was based on a selection of different treatment modules: regulating sleep-wake patterns, compensating for the reduced patient initiative, formulating helpful beliefs about fatigue and myotonic dystrophy type 1, optimizing social interactions, and coping with pain [18]. The individual module selection was made based on a shared decision-making process between experienced and specifically trained CBT therapists and patients. Samples and metadata used for this study were all gathered during the OPTIMISTIC clinical trial [18]. At the different time points in the trial, blood was drawn and a wide range of clinical outcome measures were recorded. Figure 1 has been generated to illustrate the heterogeneity in changes across all grouped outcome measures, with annotation of all individual outcome measures in the legend. In order to maximize the generalizability of the study findings, the goal of the patient sampling was to obtain a balanced subset of the OPTIMISTIC intervention group (n=128). Additionally, by capturing the whole range of therapy responses in a continuous uniform distribution, as assessed by the primary clinical trial outcome DM1-Activ-c [27], strong linear associations between non-responders and responders could be studied. To promote future research, the sampling was furthermore done on the most completely characterized patients. To achieve this, several filter steps have been applied before the random sampling. Patients were selected for which the DM1-Activ-c questionnaire results were available at each time point (n=104), with less than 20% missing values for other outcome measures (n=81) and without a variant CTG repeat (n=74). Homogeneity of baseline disease severity was accounted for by selecting patients that were within one interquartile range (IQR) of the mean for the baseline variables DM1-Activ-c, 6MWT, and CTG-repeat length (n=45). One patient was excluded because of polypharmacy (n=44). One patient was excluded because of a drop of 57 points of the DM1-Activ-c score between the baseline and 5-month assessments with a subsequent increase of 55 points between the 5- and 10-month assessments (n=43). The distribution of these 43 patients over the clinical sites A, B, C, and D was A12, B11, C16, and D4. Therefore, all patients from site D were selected. For the remaining sites, a stratified random sampling approach was implemented, where patients were randomly sampled from the different sites and a maximum of two patients with the same change in DM1-Activ-c were selected. This process of random selection was repeated until a reasonable site and delta-DM1-Activ-c distribution was achieved, defined as more than 7 patients for sites A, B, and C, resulting in the final selection of 30 patients. Due to the unavailability of samples and unsuccessful RNA sequencing, three patients were later excluded (n=27). The final selection featured a site distribution of 5 times center A, 8 B, 10 C, 4 D, and 22 unique changes DM1-Activ-c scores, with no change in the DM1-Activ-c score being present more than twice. Blood drawn during the OPTIMISTIC trial was collected in Tempus tubes and centrally stored at the New Castle MRC Centre for Rare & Neuromuscular Diseases biobank with strict SOPs and temperature control (−80°C). RNA was locally isolated in Nijmegen using the Tempus Spin RNA Isolation Kit (Applied Biosystems/Thermo Fisher Scientific) according to the manufacturer’s instructions. The concentration and RNA Integrity Number (RIN) were checked using Fragment Analyzer (Thermo Fisher Scientific). The mean RIN value was 8.9 and all were > 7.5. Hemoglobin mRNA was depleted using the Globinclear kit (Thermo Fisher Scientific). Libraries were prepared using NEBNext Ultra II Directional RNA Library Prep Kit (Illumina) according to the manufacturer’s instructions for a polyA mRNA workflow using UMI-indexed adapters. The size distribution (between 300 and 500 bp) was confirmed using Fragment Analyzer. A total of 150-bp paired end sequencing was performed with a NovaSeq6000 machine (Illumina) at a library concentration of 1.1 nM, generating > 30 M read pairs per sample. All raw sequencing data and associated genotype/phenotype/experimental information is stored in the European Genome-phenome Archive (EGA) under controlled access with Dataset ID EGAS00001005830 [28]. Adapter sequences and low-quality base calls were removed from fastq files using cutadapt 3.4 via TrimGalore 0.6.6 at no other default parameters than the --paired flag [29]. Trimmed fastq files were mapped to the human genome version hg38.95 using STAR 2.7.0 at default parameters and --outSAMtype BAM SortedByCoordinate [30]. After indexing using samtools [31] at default parameters, PCR duplicates were removed from the bam files using umi-tools dedup with the flags --spliced-is-unique, --paired and --output-stats (Additional file 1: Table S1) [32]. Strandedness was verified via RSeQC’s infer_experiment [33]. After indexing the deduplicated bam files, reads were counted for overlap with hg38.95 genes via HTSeq with parameters --format bam --order pos and --stranded=yes [34]. EPIC, quanTIseq, and xCell algorithms were applied to the count tables to verify that the cell type compositions were similar at the two time points [35–37]. GATK HaplotypeCaller and Picard GenotypeConcordance were used to check the correct matching of samples from the same patient [38, 39]. Splice analysis was performed using rMATS v4.1.0 [40] via the same gtf as for STAR/HTSeq with the parameters and flags: -t paired --readLength 150 --variable-read-length --novelSS --libType fr-firststrand --statoff. In order to validate our findings, we obtained several external DM1 gene expression datasets of different tissue types (tibialis muscle [11, 41], heart [15, 42], brain [12, 43], and peripheral blood [25, 44]). Additionally, table S5 from Wang et al. was directly obtained from the publication [11]. The dataset EV10 from Signorelli et al. was obtained for the association between gene expression (logFC) and body or performance test in DMD patients [22]. To compare DM1 samples to controls, a two-sided two-sample Wilcoxon test was performed using row_wilcoxon_twosample in matrixTests R package on normalized, log-transformed gene counts [45]. All statistical analyses were carried out in R [46]. For gene expression analysis, firstly, genes with low read counts before and after CBT were filtered using edgeR filterByExpr with group = before/after CBT and min.count = 50 [47]. Following, normalized logCPM values and weights were calculated from the filtered read counts with Voom in Limma [48]. To achieve an overarching measure for CBT response, first, the changes for each outcome measure were calculated per patient by subtracting the value after 10 months of CBT from that at baseline. Where applicable, outcome measures were multiplied by −1 in order to always associate positive changes with an improved health status. Using R base scale, the changes per outcome measure were then scaled without centering to account for the different scales of the outcome measures. Finally, for each patient, a “Compound Response” score was calculated based on the mean of all scaled outcome measures. Individual contributions towards this compound response score were visualized (Fig. 1). L5ENMO, the mean activity during rest, was a control parameter in OPTIMISTIC and was excluded from this analysis. We first set out to explore differential gene expression before and after the CBT intervention. Gene expression values from Voom (in logCPM) were separately modeled using mixed effect models with before/after CBT (categorical) as fixed effect and patient identity as random effect (1). Gene weights were also carried over from Voom. This analysis has been implemented using lme in the lme4-wrapper lmerTest [49, 50]. lmerTest estimates a p-value for the contribution of fixed effects to the model via Satterthwaite’s degrees of freedom method. Parameters of the fits were extracted with R base summary and p-values were FDR corrected via the Benjamini and Hochberg method with stats p.adjust [51]. In order to study the cohort heterogeneity of gene expression changes, we calculated the logCPM-based difference in gene expression between before and after the intervention for each patient for the 560 genes significantly associated with the CBT predictor of (1) (adjusted p < 0.05). Patients and genes with similar expression changes were clustered using the R package heatmap3 based on the complete linkage method for hierarchical clustering, with gene expression values being centered and scaled per gene [52]. Changes in clinical response (DM1-Activ-c, Six-Minute Walk Test (6MWT), and compound response) were visualized using the corrplot function and added to the heatmap [53]. The changes in DM1-Activ-c and 6MWT were scaled using R base scale without centering to account for the different scales. Next, using the same methodology as for the CBT intervention effect, we set out to explore the associations of the different clinical outcome measures and CTG-repeat length with gene expression. For this purpose, we separately modeled gene expression values with either one of the outcome measures or the CTG-repeat length (at the trial start, (2) as fixed effect and patient identity as random effect. The categorical CBT covariate (before/after) was included for each fit to correct for differences between the two time points. Analogous to the methodology described for the CTG-repeat association analyses, genes associated with overarching clinical response were identified by fitting separate mixed effect models for each gene with the two fixed effects CBT (categorical) and compound response, as well as patient identity as random effect (3). Notably, the compound response variable has only been fitted for gene expression after the CBT intervention (zero at baseline). As such, the compound response predictor reflects the difference between the two time points that can be attributed towards therapy responsiveness, while accounting for non-therapy-specific differences by including the categorical CBT predictor. Potential biomarker candidates were discovered by intersecting the genes significantly associated with the CTG_repeat predictor from model (2) and the Compound_Response predictor from model (3). Pearson correlation coefficients and the associated nominal p-values were calculated between the change in gene expression and the change in clinical score (compound response, delta-DM1-Activ-c) for these potential biomarkers using the corr.test function of the R package “psych” [54]. For the splicing analysis, the PSI values for splice exclusion (SE) events were extracted from the rMATS output and fitted in linear models similar to those for gene expression. Splice events were filtered by excluding exons from the analysis with less than three mapping reads and one junction spanning read in at least 14 samples. The R package ggplot2 was used for representation in volcano and scatter plots [55]. The R package VennDiagram was used to generate the Venn diagram [56]. Gene set enrichment analyses have been independently implemented for the gene sets associated with CBT, CTG-repeat length, compound response scores, and the genes significantly associated with both CTG-repeat length and compound response using gProfiler [57]. For the CBT, CTG-repeat length, and compound response-associated genes, the 500 genes with the lowest nominal p-values were ordered (decreasing) based on their absolute regression coefficients. Subsequent enrichment analyses were implemented using the R client of gProfiler with the parameter orderd_querey = TRUE against a custom background of 10,292 genes expressed in our samples. Multiple testing correction was based on the default setting “g_SCS.” We tested for enrichment (one-sided) pathways within the WikiPathway database. The gene set associated with both CTG-repeat length and compound response was based on an FDR threshold of 10% for the respective regression coefficients, resulting in a gene set of 311 genes. A regular, non-order weighted ORA (over-representation analysis) analysis was run for this gene set with ordered_quere = FALSE. Similar to the other analyses, one-sided (enrichment) pathway discovery was based on the WikiPathway database with the default setting “g_SCS” to correct for multiple testing. For all enrichment analyses, only significant pathways (p-adjusted < 0.05) are reported. The exact scripts and the resulting datasets of the statistical analyses are available via https://github.com/cmbi/DM1_blood_RNAseq For the identification of blood biomarkers that are associated with the clinical response to the CBT intervention, 27 patients from the OPTIMISTIC cohort were selected based on a random stratified sampling procedure. These patients reflected a uniform continuous distribution of therapy responses as assessed by the primary trial outcome, the DM1-Activ-c questionnaire. The sampled set consisted of 14 females and 13 males aged 19–63 years and represented a wide range of CTG-repeat lengths (Additional file 2: Fig. S1). All sampled patients received CBT, and mRNA-sequencing profiles were obtained at baseline and after 10 months of CBT, the primary endpoint of the OPTIMISTIC trial. Large clinical heterogeneity of clinical responses after CBT was observed across all of the different outcome measures. Figure 1 highlights the scaled differences across these outcome measures, color coded into five different groups (cognition and other, DM1-Activ-c, fatigue scores, physical assessments, and quality of life). Because of the large heterogeneity, we defined a compound response score. The compound response score is the mean of all scaled outcome measures (Fig. 1). We first studied the molecular changes that occurred after the CBT intervention by comparing the mRNA expression levels in blood before and after the CBT intervention. We found that 560 genes were significantly up- or downregulated after CBT (277 genes down, 283 up, fold changes ranging between 0.64 and 2.35, Fig. 2A). Hierarchical clustering of patients based on the changes in these 560 genes revealed substantial molecular heterogeneity within this 10-month timeframe. There was no evident concordance between the clustering of samples based on changes in gene expression and the changes in DM1-Activ-c, 6MWT, or compound response score (Fig. 2B). The four genes with the lowest p-values were GGCX, ZNF16, SERBP1, and SLC39A8 (Fig. 2C). Biological pathways significantly associated with these genes were limited to an immunological pathway and an electron transport chain in mitochondria (Table 1). DMPK expression did not significantly change over the course of the study, nor was it associated with the CTG-repeat length (Additional file 2: Fig. S2). Having a readily accessible fingerprint reflecting the molecular dysregulations associated with DM1 is potentially of high value for both clinical and research settings. To this end, we studied the correlations of blood-based gene expression with the disease-causing CTG-repeat length assessed at the start of the trial. The assessments were based on small pool PCR, Acil digestion, and Southern blot, as opposed to (estimations) of CTG-repeat length at birth or disease onset. This was done to minimize potential confounding effects related to the progressive nature of the disease and to assure homogeneity in the measurement methodology. Based on this approach, we identified 608 genes significantly associated with the CTG repeat at an FDR cutoff of 5% (474 positively, 134 negatively, fold changes ranging between 0.76 and 1.23 per 100 CTGs, Fig. 3A). The four genes with the lowest p-values RNF170, IRS2, NDE1, and PRIMPOL showed a clear linear relationship between the CTG-repeat length at baseline and expression values, both before and following the intervention (Fig. 3B). Most enriched pathways were related to immunological processes (IL-3, IL-4, IL-5 signaling; chemokine signaling pathway), yet also pathways related to adipogenesis, hepatocyte signaling, and AGE/RAGE were discovered. Interestingly, the gene MMP9 was among several of the CTG-repeat-associated pathways. To replicate and validate that these findings are disease relevant, we compared the CTG-repeat length effect size with effect sizes reflecting the differences in gene expression between DM1 and controls in various published datasets (Additional file 2: Fig. S3). We found a strong correlation with a study that performed mRNA sequencing on DM1 and control blood samples (Pearson rho: 0.59, Additional file 2: Fig. S3A [25]). However, similar correlations were not observed for studies profiling DM1 and control tissues other than blood (heart [15], brain [12], and muscle [11], Additional file 2: Fig. S3B-S3D). Neither were correlations observed with inferred MBNL activity or muscle strength measures from another study (Additional file 2: Fig. S3E-S3F [11]). Although no correlations were found with the effect size of blood-based physical test assessments in DMD (Additional file 2: Fig. S3G), the CTG-repeat effect size did correlate well with the effect size of blood-based DMD body measures (Pearson rho: 0.41, Additional file 2: Fig. S3H) [22]. In the latter study, comparisons with physical tests and body measures were independently based on the first principal component of a set of different clinical assessments of DMD patients. Thus, while DM1-related molecular dysregulations in blood can be validated in other studies, even from other neuromuscular disorders, they do not necessarily reflect expression dysregulation observed in other tissues. Since DM1 is known as a splicing disease, we also studied the association of the CTG-repeat length with alternative splicing events. Here, four events in three genes (RBM39, FLNA, and CTSZ) reached an FDR threshold of <5% (Additional file 2: Fig. S4). Given the limited and small effects observed, we did not further explore these associations. Next, we studied phenotype-genotype relationships by estimating the associations of gene expression values with individual clinical outcome measures used in the OPTIMISTIC trial. Here, we found virtually no significant associations between gene expression and clinical outcomes. No significant associations after multiple testing correction were found for the DM1-Activ-c questionnaire (Additional file 2: Fig. S5). The four genes with the lowest nominal p-values were SREBF2, ZNF283, SF3B3, and GSKIP. To account for the heterogenic changes in individual outcome measures, we calculated a compound CBT response score that reflects the average scaled therapy response of all outcome measures used in OPTIMISTIC (Fig. 1). Noteworthy, similar ranges of change are observed for the variety of outcome measures due to the applied scaling. As such, each outcome measure contributes similarly to the compound response score. The compound response score can be interpreted as an estimate of an overall difference in capacity between the end and the start of the intervention. We were able to identify 1176 genes significantly (FDR < 0.05) associated with the compound response score (384 positive, 792 negative, Fig. 4A). The four hits with the lowest p-values (PPP1R9B, CSNK1G2, PPP6R1, FBXO48) show a clear linear relationship between changes in gene expression during the trial and the compound response score (Fig. 4B). No enriched pathways were identified for this gene set. Since we were able to identify genes significantly associated with both the CTG-repeat length as well as the average clinical response, we were interested in their intersection. Among the significant hits of both analyses, we found an overlap of 97 genes (Fig. 5A). To further investigate this relationship, we plotted the effect size of the compound response score against the effect size of the CTG-repeat length for each expressed gene (Fig. 5B). For the 97 genes significantly associated with both predictors, a remarkable pattern emerged: genes that were lower expressed in patients with a long CTG repeat showed an increase in expression levels in the patients with a good clinical response and vice versa. This anticorrelation pattern was confirmed by analyzing an earlier dataset comparing gene expression in DM1 and control blood samples [25]. Here, the 97 genes showed a similar association with the DM1 phenotype as has been found with the CTG length in our study, confirming that the gene expression of patients with a good CBT response changed into the direction of the levels observed in healthy controls (Fig. 5C). This remarkable relationship could not be explained by possible confounding between CTG-repeat length and compound response, as the compound response effect size is only slightly affected by first regressing out the CTG-repeat length effect (Additional file 2: Fig. S6). The four genes with the lowest p-values with both the CTG-repeat length as well as the compound response score (either FDR < 0.025) were DNAJB12 (CTG Pearson rho=0.49; CRS Pearson rho=−0.43), HDAC5 (CTG rho=0.65; CRS rho=−0.46), TRIM8 (CTG rho=0.59; CRS rho=−0.59), and ZNF22 (CTG rho=−0.52; CRS rho=0.52). For 81 of the 97 candidate biomarkers, the Pearson correlations were nominally significant for the change in gene expression and the compound response score. Yet, only 2 of the 97 Pearson correlations were nominally significant for the change in gene expression and the change in DM1-Activ-c scores. Enrichment analysis for these 97 genes resulted in mostly immunological pathways (chemokine and IL-3 signaling, microglia pathogen phagocytosis pathway; Table 1). In summary, these results suggest that for a subset of genes significantly associated with the biochemical phenotype caused by the CTG-repeat expansion a reversal of disease-induced gene expression occurred in clinical responders. The association with both molecular dysregulation and clinical response makes this subset of genes highly relevant for the discovery of novel therapeutic targets. The purpose of this study was the identification of DM1-specific therapeutic biomarkers in peripheral blood. The multisystem nature of DM1 is known to be reflected by laboratory abnormalities of peripheral blood, making it together with its accessibility a promising tissue for biomarker studies in this disease [26]. Hence, we used blood samples of 27 DM1 patients from the OPTIMISTIC cohort to study the associations of gene expression with disease severity as well as the response towards the CBT intervention. In an effort to fairly represent the whole OPTIMISTIC cohort and to facilitate the generalizability of the study findings, a stratified random sampling procedure was implemented which resulted in a balanced patient cohort with respect to age, CTG-repeat length, sex, therapy response, and clinical site distributions. Patients with an interrupted CTG repeat were excluded in order to limit molecular heterogeneity induced by slower disease progression rates [5]. Nonetheless, we identified substantial heterogeneity in molecular expression profile changes after the 10-month CBT intervention. Furthermore, we identified gene sets that were significantly associated with the disease-causing CTG repeat as well as with the average response towards the CBT intervention across different clinical outcome measures. Most interestingly, an overlap of 97 genes among these latter two gene sets has been identified, showing a clear trend of more normal expression levels in clinical responders. Based on these different gene sets, several biological pathways associated with DM1 have been discovered, as well as specific genes/gene families with ties to neuro(-muscular) disorders. The OPTIMISTIC study has shown that DM1 patients significantly improve in their capacity for activity and social participation after the CBT intervention [18]. It was furthermore hypothesized that CBT may directly or indirectly improve other biological systems affected by the disease. This hypothesis has been confirmed for muscles of the lower extremity, showing an increase in cross-sectional area as a result of the intervention [58]. Here, we set out to further explore this hypothesis by investigating changes on the molecular level. These changes may be the result of increased physical activity, which has been linked to differences in gene expression in previous studies [59], but may also be a more direct effect of the psychotherapeutic CBT intervention [60]. In line with the results of an earlier study, we have illustrated that the clinical response towards the CBT intervention was rather heterogenous [21]. A novel addition to this finding was the illustration that this heterogeneity extends towards changes in molecular profiles within a 10-month timeframe. Importantly, this heterogeneity could not be explained by changes in the cellular composition of the blood samples between the two time points, as the similarity of cell type composition has been verified. Additionally, this heterogeneity could not be explained by changes of different outcome measures such as the DM1-Activ-c, 6MWT, or compound response. While the CBT intervention likely played a part in this heterogeneity, the magnitude of this contribution could not be assessed due to the lack of a control group. Other factors, such as aging or seasonal effects, may also have contributed to this finding. Across the different gene sets identified in this study, several of the genes with the lowest p-values (SLC39A8, IRS2, FBXO48) and one WikiPathway (transcription factor regulation in adipogenesis) were associated with insulin signaling or more broadly related to metabolism/adipogenesis [61–63]. Dysregulation of insulin signaling has been linked to clinical features of DM1 and is an actively ongoing field of investigation [64]. Aberrant insulin signaling has also been found in other diseases of the nervous system such as depression, with indirect improvements being observed after CBT [65]. Interestingly, the anti-diabetic drug metformin has been shown to improve the mobility of DM1 patients with effect sizes of the 6MWT comparable to those observed in the OPTIMISTIC study [66]. With increasing therapeutic interest in this area, our findings suggest that disease-relevant insulin signaling can be studied on a molecular level in blood samples, highlighting the utility of peripheral blood in this setting. Similarly, across most of the gene sets, we identified several WikiPathways associated with immunological functions (cell-specific immune response, chemokine signaling pathway, IL-3, 4, and 5 signaling). While this may be in part due to a bias introduced by the profiled tissue, the immune system likely plays a role in the DM1 pathophysiology like for many other chronic diseases [67]. As such, blood sample-based immunology studies may be an interesting field of future investigation. The intersection of the genes significantly associated with the disease-causing CTG repeat, as well as the average CBT response across different outcome measures, revealed a subset of 97 genes. These genes are of particular interest for the identification of therapeutic biomarkers, as their disease association has been confirmed in an external dataset and they showed normalization of expression levels in clinical responders. Among the genes with the lowest p-values associated to both CTG-repeat length as well as CBT response were HDAC5, DNAJB12, and TRIM8. In total, the subset of 97 genes consisted of two HDACs (histone deacetylases, HDAC5, HDAC7). HDACs play an important role in transcriptional regulation and compounds that inhibit HDAC enzymes are being studied for their potential effect on a range of human diseases, including neurological disorders [68]. The DNAJB12 protein is a member of the heat shock protein family, with some evidence supporting positive effects of their induction for muscular dystrophy and other muscle wasting conditions [69]. The TRIM family protein TRIM72 has been shown to be an essential component of the cellular membrane repair in muscles, with evidence supporting some positive effects in mouse models of muscular dystrophy [70]. Authors of the same study suggest the potential of other TRIM family members as potential targets in similar disease states, which may support the further investigation of TRIM8 in DM1. Although mostly associated with therapy response, RARA and RXRA were also among the overlapping 97 genes. Stimulating retinoic acid signaling has been linked to muscle regeneration in mouse models via increased proliferation of fibro/adipogenic progenitor cells, highlighting the relevance of this pathway as another potentially DM1-relevant drug target [71]. Taken together, these findings confirm the value of whole blood-based expression profiling for the discovery of therapeutic biomarkers in DM1. Interestingly, the genes significantly associated with the CTG repeat showed a moderate correlation with the genes associated with DMD body measurements of an external study. We hypothesize that these body measures are likely also correlated with age, which in turn reflects disease progression. This suggests that some of our results may therefore not be DM1 specific, but rather reflect non-specific molecular dysregulations shared across different (neuromuscular) disorders. This hypothesis is supported by the significant association between the CTG repeat with MMP9, which is known to be a non-specific biomarker that has for instance been linked to cardiac remodeling after myocardial infarction, inflammation, and DMD [72, 73]. We therefore deem further exploration of shared dysregulations as highly valuable, as this may lead to the discovery of therapeutic targets relevant to a variety of diseases. Although DM1 is known as an alternative splicing disease, only four splice events have been significantly linked to the disease-causing CTG repeat in this study. This may be the result of relatively low DMPK expression in blood [74] and is in line with the absence of strong splice aberrations in blood from DM1 patients compared to controls [25]. DMPK’s low expression in blood cells may also explain the lack of concordance between our disease severity-associated gene expression differences observed in blood with gene expression differences observed in the muscle and brain. So, while whole blood-based transcription profiling can identify disease-relevant molecular dysregulations, these dysregulations do likely not fully reflect the dysregulations observed in other tissue types. Yet, we found a high correlation of the CTG-repeat effect size with the DM1 phenotype effect size of a different blood-based study, as well as with a principal component derived body measure association of a DMD-based study. While the former validates our findings, the latter suggests the possibility of shared, disease-relevant, dysregulations across different neuro(muscular) disorders detectable in peripheral blood. If true, associated pathways might reveal highly interesting targets for drug discovery, as they may have a positive influence on multiple diseases. To find disease-relevant gene expression in blood, we searched for linear associations with the disease-causing CTG-repeat length. While the CTG-repeat length is thought to be the main driver of molecular dysregulation, associations between the progenitor allele length of the CTG repeat with several clinical outcome measures, including DM1-Activ-c and 6MWT, have been found to be only weak-moderate [13]. In line with the previously published challenges to directly relate gene expression to clinical phenotypes, we were not able to find significant, direct associations between clinical outcome measures and gene expression. Still, among the genes with the lowest p-values for the DM1-Activ-c questionnaire was GSKIP, a gene encoding for an inhibitor protein of the known DM1 drug target GSK3-β [75–77]. Given the biological relevance of this finding, we deem it likely that the current study design was underpowered to study the association of gene expression with individual clinical outcome measures, especially when taking clinical and molecular heterogeneity into account. The clinical heterogeneity in therapy response may in part be explained by the personalized nature of the CBT intervention, with therapy foci being tailored towards the needs and wishes of the individual patient. As a consequence, one might expect different outcome measures to be more appropriate for CBT efficacy assessments for different patients. Yet, the identification of molecular signatures associated with therapy response necessitates the use of the same clinical outcome measure. For this reason, and to average out some of the uncertainty inherently associated with the recording of the different outcome measures, we settled on the use of the compound response score. While the scaling assured a more or less equal contribution of each outcome measure to this score, we acknowledge that this combined score is biased by the outcome measures that were used in OPTIMISTIC. Even though we statistically corrected for non-specific molecular changes between the two time points, the lack of RNA-seq profiles from the OPTIMISTIC control arm makes it difficult to state with certainty that the observed molecular changes are due to the therapy itself. However, this does not discount their value as potential therapeutic targets, as they are, regardless of the mediation factor, significantly associated with improved clinical status. Moreover, for this reason, we deemed studying the RNA-seq profiles of the OPTIMISTIC control arm to be less valuable, as these patients did not significantly clinically declined over the 10-month timeframe [18]. Starting from DM1-specific disease determinants, the OPTIMISTIC study has shown that patient-tailored CBT can increase the health status of DM1 patients by improving social participation and activity. It was furthermore hypothesized that the CBT intervention positively challenges the biological system, which has already been confirmed by increased cross-sectional area for muscles of the lower extremities. Making use of the clinical heterogeneity in therapy response, we here additionally confirmed disease-relevant molecular changes in peripheral blood. Not only do our results highlight the utility of peripheral blood to study the multisystem nature of the disease, but also generated the foundation for an upcoming, multi-omics-based drug repurposing study. Additional file 1: Table S1. PCR duplicates.Additional file 2: Figure S1. Patient characteristics. Box-plots illustrating the distribution of age (A) and CTG-repeat length (B) of the 27 patients at baseline, separated by sex. C) Box-plots illustrating the change in DM1-Activ-c score after the intervention versus before, as expressed in Delta DM1-Activ-c scores, separated by sex. Figure S2. DMPK expression. Panel A shows the expression values of DMPK in logCPM before and after the CBT intervention for all 27 patients. Panel B shows the association between the CTG repeat length and the change in DMPK expression, as calculated by expression levels after the intervention minus the expression levels at the start of the trial. In addition, the Pearson correlation of this association is shown. Figure S3. Comparison of gene expression associated to DM1 in other studies with that to the CTG repeat in this study. The mean differences in normalized gene counts were calculated between DM1 and control samples for four studies comparing blood (A (25, 44)), heart (B (15, 42)), brain (C (12, 43)) and tibialis muscle (D, (11, 41)) and plotted against the effect sizes for the CTG-repeat in this study (ReCognitION) for genes that were measured in both studies. In E and F, the correlation of expression to the inferred MBNL activity and muscle strength(11), was compared to our effects for the CTG-repeat. In G and H, gene expression associations in blood of the results of physical tests and several body measurements for Duchenne muscular dystrophy (DMD) patients (22) are compared to CTG-repeat associations from our study. In (22), associations with physical tests and body measurements were based on the first principal component over associated measures, each reflecting respectively 78% and 70% of the associated measurements variances. Depicted on the top left in each graph is the Pearson correlation coefficient for the plotted values with the associated p-values. Figure S4. Splice exclusion and CTG-repeat length. PSI values for splice exclusion events were determined using rMATS [. For each of the PSI values a linear mixed effect model was fitted with the modal CTG repeat length as covariate and patient as random effect. A) Volcano plot of significance (-log 10 of the nominal p-values of the modal CTG effect size) and the CTG effect size for the PSI values. Significant results after FDR correction (p < 0.05) are marked in black. B) For the four PSI values with the lowest nominal p-values from A, the PSI values are plotted against the modal CTG repeat lengths before (blue) and after the CBT intervention (red) including the Pearson correlation coefficients. Figure S5. Gene expression association with DM1-Activ-c. For each gene a mixed effect model was fitted with before/after CBT and DM1-Activ-c scores as fixed effects, while accounting for (random) effects of the individual. The p-values for the fixed effects were estimated via Satterthwaite’s freedom method and FDR corrected. A) Volcano plot of the significance (-10log of the nominal p-value) and effect size of the DM1-Activ-c scores on gene expression. B) For the four genes with the lowest nominal p-values from panel A, the DM1-Activ-c scores are plotted against the gene expression values (logCPM). Blue dots represent baseline values, red dots values after CBT. The regression line indicates the linear association independent of the timepoints. Similarly, the Pearson correlation coefficients shown are independent of the timepoints. Figure S6. Shared explained variance among CTG-repeat and Compound Response predictors. To assess the overlap in gene expression level variance explained by the CTG-repeat length and the Compound Response score, the Compound Response score was fitted on the residuals of the CTG-repeat length as fixed effect, while accounting for random effects of the patient. A) The effect sizes of the Compound Response score as estimated on the CTG-repeat model residuals are plotted against the effect sizes Compound Response scores as presented in this study. The Rho score reflects the Pearson correlation coefficient. B) Analogous to Figure 5B, the Compound Response score effect size as estimated on the CTG-repeat model residuals are plotted against the CTG repeat effect size size as estimated on the CTG-repeat model residuals are plotted against the CTG repeat effect size scaled between -0.25 and 0.25, resulting in the removal of 12 outliers. Colored in purple are the same 97 genes as in Figure 5B.

PMC9646552

35705830

Jing Yu,Hai-Liang Qi,Hong Zhang,Zi-Yu Zhao,Jing-Zhao,Zi-Yuan Nie

Morin Inhibits Dox-Induced Vascular Inflammation By Regulating PTEN/AKT/NF-κB Pathway

15-06-2022

Doxorubicin,Morin,Vascular smooth muscle cells,PTEN,Vascular inflammation

The side effects of doxorubicin (Dox) may influence the long-term survival of patients with malignancies. Therefore, it is necessary to clarify the mechanisms generating these side effects induced by Dox and identify effective therapeutic strategies. Here, we found that interleukin-6 (IL-6), interleukin-1β (IL-1β), and tumor necrosis factor-alpha (TNF-α) levels were significantly increased in vascular tissues of Dox-treated mice and Dox-treated vascular smooth muscle cells (VSMCs). Furthermore, we revealed that Dox downregulated the phosphatase and tension homology deleted on chromosome 10 (PTEN) level while upregulated p-AKT and p65 level in VSMCs in vitro. Overexpression of PTEN in VSMCs partly reversed Dox-induced inflammation. Importantly, we demonstrated that Morin could inhibit Dox-induced inflammation by facilitating an increase of PTEN, thus inhibiting the activation of protein kinase B (AKT)/nuclear factor kappa B (NF-κB)/pathway. Additionally, we showed that Morin could reduce the miR-188-5p level, which was increased in Dox-treated VSMCs. Inhibition of miR-188-5p suppressed Dox-induced vascular inflammation in vitro. In conclusion, Morin reduced the Dox-induced vascular inflammatory by moderating the miR-188-5p/PTEN/AKT/NF-κB pathway, indicating that Morin might be a therapeutic agent for overcoming the Dox-induced vascular inflammation.

Morin Inhibits Dox-Induced Vascular Inflammation By Regulating PTEN/AKT/NF-κB Pathway The side effects of doxorubicin (Dox) may influence the long-term survival of patients with malignancies. Therefore, it is necessary to clarify the mechanisms generating these side effects induced by Dox and identify effective therapeutic strategies. Here, we found that interleukin-6 (IL-6), interleukin-1β (IL-1β), and tumor necrosis factor-alpha (TNF-α) levels were significantly increased in vascular tissues of Dox-treated mice and Dox-treated vascular smooth muscle cells (VSMCs). Furthermore, we revealed that Dox downregulated the phosphatase and tension homology deleted on chromosome 10 (PTEN) level while upregulated p-AKT and p65 level in VSMCs in vitro. Overexpression of PTEN in VSMCs partly reversed Dox-induced inflammation. Importantly, we demonstrated that Morin could inhibit Dox-induced inflammation by facilitating an increase of PTEN, thus inhibiting the activation of protein kinase B (AKT)/nuclear factor kappa B (NF-κB)/pathway. Additionally, we showed that Morin could reduce the miR-188-5p level, which was increased in Dox-treated VSMCs. Inhibition of miR-188-5p suppressed Dox-induced vascular inflammation in vitro. In conclusion, Morin reduced the Dox-induced vascular inflammatory by moderating the miR-188-5p/PTEN/AKT/NF-κB pathway, indicating that Morin might be a therapeutic agent for overcoming the Dox-induced vascular inflammation. Worldwide, cancer by far is the fatal disease with the second highest mortality rate which leads to 606,520 deaths in 2020 in the USA [1]. Despite the development of novel diagnostic technologies, the emergence of targeted drugs, and the recent advancements in radiotherapy technology, chemotherapy remains the cornerstone of cancer treatment [2–5]. Pleasingly, in the last decade, approximately 50% patients with malignancies have been clinically cured and have a long-term survival due to chemotherapy [1]. However, part of survivors who have exposed to the chemotherapeutics may suffer from the long-term side effects, of chemotherapeutics which can adversely affect their quality of life [6–8]. Therefore, it is necessary to clarify the mechanisms generating these side effects and identify effective therapeutic strategies. Doxorubicin (Dox) is an anthracycline antibiotic that has been used widespread in treatment of multifarious malignancies, including solid tumors and hematological neoplasms [9]. The most widely and popular research studies have focused on the Dox-induced cardiotoxicity [10, 11]. Nevertheless, the side effects of Dox are not merely restricted to cardiotoxicity, but also include hepatotoxic, nephrotoxicity, and vascular injury [12, 13]. Vascular inflammation ultimately leads to the development of cardiovascular disorders by dysregulation of proliferation and apoptosis of VSMCs, vascular remodeling, and fibrosis. More importantly, vascular inflammation is also the pathophysiology basis of organ dysfunction [14–17]. Therefore, elucidating the molecular mechanism of vascular inflammation induced by Dox and finding the therapeutic or prevention strategies are urgent requirements. Morin (3,5,7,2',4'-pentahydroxyflavone) is a flavonoid which is separated from fruits and vegetables of the Moraceae family [18]. Extensive studies have confirmed that Morin has multiple pharmacological effects, such as anti-atherosclerosis, antidiabetic, anti-oxidative, anti-inflammatory, and anti-tumor activity by regulating the PTEN/AKT pathway [19–21]. Consistent with several other studies, our previous research has demonstrated that Morin has an anti-leukemia effect on leukemia cells by regulating miR-188-5p/PTEN/AKT pathway [22]. Therefore, we hypothesized that whether Morin could decrease the Dox-induced vascular inflammation by moderating the activation of PTEN/AKT pathway. In the present study, we revealed that Dox could induce the vascular inflammation in vivo and in vitro by regulating miR-188-5p/PTEN/AKT/NF-κB pathway. Crucially, we revealed that Morin inhibited inflammation which induced by Dox by suppressing miR-188-5p and subsequently upregulating PTEN expression. All animal studies were approved by the local Animal Care and Use Committee of Hebei Medical University. Six- to eight-week-old male C57BL/6 mice were housed in a climatically controlled environment, and all efforts were made to minimize suffering. In the Dox-induced vascular inflammation models, male C57BL/6 mice were randomly divided into two groups, namely the control group and the Dox group, and each group contained eight mice. Mice were intraperitoneal injected with 6 mg/kg DOX twice a week for 2 weeks while the control group was treated with an equivalent amount saline. In the Morin-reduced Dox-induced vascular inflammation mouse model, male C57BL/6 mice were randomly divided into three groups, namely the control group, Dox group, and Morin combination with Dox group, and each group contained eight mice. In the Dox group or Morin combination with Dox group, mice were intraperitoneally injected with 6 mg/kg DOX twice a week for 2 weeks while the control group was treated with an equivalent amount saline. In Morin combination with Dox group, the mice were intraperitoneal injected with 25 mg/kg Morin five times per week for 2 weeks. Fourteen days later, all animals were anesthetized, and the abdominal aorta was taken. The vascular tissues were fixed in paraformaldehyde or stored at −80 °C for the future experiments. Immunohistochemistry was carried out as previously described [23]. The antibodies were used as follows: anti-IL-1β (1:100, Proteintech, 16,806–1-AP), anti-IL-6 (1:100, Proteintech, 21,865–1-AP), and anti-TNF-α (1:100, Proteintech, 6029–1-Ig). Images were acquired using a Leica microscope (Leica DM6000B). The fixed smears of vascular tissues or VSMCs were permeabilized by incubation with 0.1% Triton X-100 in PBS for 30 min. After blocking by incubation with 10% normal goat serum (710,027, KPL, USA) for 1 h, the smears were incubated for 2 h at 37℃ with the following antibodies: anti-IL-1β (1:100, Proteintech, 16,806–1-AP), anti-IL-6 (1:100, Proteintech, 21,865–1-AP), and anti-TNF-α (1:100, Proteintech, 6029–1-Ig). The smears were washed with 0.1% Triton X-100 in PBS for three times and stained with secondary antibodies which were fluorescein-labeled (1:50, KPL, 021,516) and 4′,6-diamidino-2-phenylindole (DAPI). Images were captured by confocal microscopy (DM6000 CFS, Leica Microsystems). ELISA was performed in accordance with manufacturer’s instructions. Using a commercial ELISA kit (Proteintech, KE10002, KE10003, KE10007) to test the concentrations of IL-1β, IL-6, and TNF-α which obtained from mouse serum. The absorbance at 450 nm was detected with a Multiskan Ascent (SPECTRAFluor Plus, Tecan). Mice VSMCs were cultured in low-glucose Dulbecco’s modified Eagle’s medium (DMEM, Gibco Life Technologies, Rockville, MD) and 10% fetal bovine serum (GEMINI, USA) in the incubator at 37 °C with 5% CO2. Cells were transfected with Lipofectamine 2000 (Invitrogen) according to the manufacturer’s protocols. The miR-188-5p inhibitor, si-PTEN, and PTEN overexpression plasmid were selected as previously [22]. Total RNA was extracted from the tissues or cultured cells by using QIAzol Lysis Reagent (79,306) according to the manufacturer’s protocol. Then, 2 µg RNA was used to reverse transcription reaction by using M-MLV First-Strand Kit (Life Technologies) for mRNA and the miScripIIRT kit (QIAGEN GmbH) for microRNA. Platinum SYBR Green qPCR SuperMix UDG Kit (Invitrogen) and miScript SYBR Green PCR kit were used qRT-PCR for mRNA and microRNA respectively according to the manufacturer’s instructions. As an internal control, β-actin and U6 were used for the internal control of mRNA and microRNA, respectively. Relative transcripts were calculated using the 2−ΔΔCt method. Proteins from vascular tissue and cultured VSMCs were lysed by protein lysis buffer. Proteins were run on 8–10% SDS-PAGE to separation and then electro-transferred to PVDF membranes (Millipore). The membranes were blocked with 5% milk in TTBS for 2 h at room temperature and incubated overnight at 4 °C using the primary following antibodies: anti-IL-1β (1:1000, Proteintech, 16,806–1-AP), anti-IL-6 (1:1000, Proteintech, 21,865–1-AP), anti-TNF-α (1:1000, Proteintech, 6029–1-Ig), anti-PTEN (1:1000, Abcam, ab32199), anti-pan-AKT (1:1000, Abcam, ab8805), anti-pan-AKT (phospho T308; 1:1000, Abcam, ab38449), and 1:1000 anti-β-actin (1:2000, Santa, sc-47778). In the next day, after washing in the TTBS for three times, membranes were incubated with a 1:5000 dilution of anti-rabbit or anti-mouse antibody (Santa Cruz) for 1 h at room temperature. Protein bands were detected by enhanced chemiluminescence (ECL) Fuazon Fx (Vilber Lourmat). The Student t test was performed to analyze the significant differences between two groups. P < 0.05 was considered the statistically significant. To determine the side effect of Dox in vascular tissues, we first established the Dox-induced inflammation model in vivo and then detected the key inflammatory cytokines, interleukin-6 (IL-6), interleukin-1β (IL-1β), and tumor necrosis factor-alpha (TNF-α) level in vascular tissues and mice serum. As shown in Fig. 1A, B, the immunohistochemical staining and immunofluorescence staining on mice vascular tissues showed that the expression levels of IL-1β, IL-6, and TNF-α were greatly increased in the Dox model group compared with the negative control group. Furthermore, we obtained the protein and mRNA from vascular tissues of mice. Western blot and qRT-PCR results also showed that the mRNA and protein levels of IL-1β, IL-6, and TNF-α were higher than those in negative control group (Fig. 1C, D). Moreover, we detected the level of cytokines in the serum of mice. As shown in Fig. 1E, the levels of IL-1β, IL-6, and TNF-α in Dox-treated group were increased over twofold in the mice serum compared with the negative control group. Together, these results suggest that Dox induces vascular inflammation in vivo. To further explore the effect of Dox-induced inflammation response on the VSMCs, we detected the IL-1β, IL-6, and TNFα expression in Dox-stimulated VSMCs. First, we stimulated VSMCs with 0.5 µM Dox for 24 h or 48 h. qRT-PCR and western blot results showed that stimulation of Dox markedly increased the mRNA and protein levels of IL-1β, IL-6, and TNF-α in VSMCs in a time-dependent manner compared with the negative control (Fig. 2A, B). There was a significant increase (approximately 2–threefold higher) in the levels of IL-1β, IL-6, and TNF-α in the Dox-treated-48 h group compared to the controls. Simultaneously, stimulation of Dox with 0.5 or 1 µM for 48 h led to more than a twofold increase level of IL-1β, IL-6, and TNF-α compared with the negative control. Moreover, immunofluorescence staining showed that the fluorescence intensity of TNF-α (green), IL-1β (red), and IL-6 (red) was obviously enhanced following stimulation of Dox-with 1 µM for 48 h in VSMCs (Fig. 2E). These data demonstrate that Dox could induce inflammation in VSMCs in vitro in a time- and dose-dependent manner. Previous studies have confirmed that the PTEN/AKT/NF-KB pathway plays an essential role in inflammation. Consequently, we then sought to know whether Dox could moderate the activation of PTEN/AKT/NF-KB in vitro. As shown in Fig. 3A, stimulation of Dox in VSMCs dramatically increased phosphorylated AKT (p-AKT) protein level compared with the control group, indicating that Dox could promote phosphorylation of AKT. Meanwhile, qRT-PCR and western blot analysis results showed that the stimulation of Dox to VSMCs upregulated the protein level of p65 while downregulated the mRNA and protein levels of PTEN (Fig. 3B, C). Subsequently, to further identify the role of PTEN in Dox-induced inflammation in VSMCs, we performed rescued experiments. As shown in Fig. 3D, overexpression of PTEN in VSMCs led to a decrease of the expression levels of IL-1β, IL-6, and TNF-α. In contrast, overexpression of PTEN could partly reverse Dox-induced the increase in IL-1β, IL-6, and TNF-α in both protein and mRNA levels (Fig. 3D, E). Collectively, these findings suggest that the activation of the PTEN/AKT pathway plays an important role in Dox-induced inflammation in VSMCs. Morin (3,5,7,20,40-pentahydroxyflavone, C15H10O7) has the structure of one oxygen-containing heterocyclic ring connecting with two aromatic rings. Previous data showed that Morin could function as a potent anti-inflammatory agent by regulating several cell signaling pathways, especially the PTEN/AKT pathway. Therefore, we sought to investigate whether Morin could exert the anti-inflammatory effect on Dox-induced inflammation of VSMCs. To do this, first we treated VSMCs with 1 µM Dox combination with 25 µM Morin at the same time for 48 h. Western blot and qRT-PCR results showed that Morin inhibited the levels of IL-1β, IL-6, and TNF-α which were largely upregulated by Dox stimulation alone (Fig. 4A, B). Specifically, Morin could decrease the mRNA levels of TNF-α by 61%, IL-1β by 41%, and IL-6 by 53% in Dox-treated VSMCs. In addition, we detected the levels of p-AKT, p65, and PTEN by western blot and qRT-PCR. As shown in Fig. 4C, D, the protein levels of p-AKT and p65 were obviously downregulated in Morin combination treatment group compared with Dox stimulation alone. Correspondingly, PTEN expression both at the mRNA and protein levels of VSMCs was increased after treatment with Morin, indicating that Morin could suppress the activation of AKT pathway by increasing PTEN expression. To further investigate the role of PTEN in Morin-reduced VSMC inflammation, we knocked down PTEN expression in VSMCs using si-RNA and then treated with Morin or not. Western blot and qRT-PCR results showed that knockdown of PTEN induced the protein and mRNA levels of IL-1β, IL-6, and TNF-α consistent with the Dox effect, while Morin could partly reverse the induced effect by PTEN depletion in VSMCs (Fig. 4E, F). Based on the above data, Morin could decrease Dox-induced inflammation by regulating the PTEN/AKT/NF-κB pathway in VSMCs. Our previous study has reported that miR-188-5p reduced PTEN expression by directly targeting its 3′-UTR. Morin was implicated in the regulation of the proliferation and apoptosis of chronic myelogenous leukemia (CML) cells by moderating the miR-188-5p/PTEN axis. Therefore, we hypothesized that Morin could enhance the level of PTEN by suppressing the miR-188-5p expression in Dox-induced inflammation of VSMCs. To identify this hypothesis, we stimulated the VSMCs with Dox and performed the qRT-PCR to detect the miR-188-5p expression. As Fig. 5A shows, miR-188-5p level was obviously increased sixfold in the Dox stimulation group compared with the control group. Moreover, VSMCs treated with Morin and Dox combination downregulated miR-188-5p expression which was induced by Dox alone (Fig. 5B). Then, we performed rescued experiments to test the role of miR-188-5p in Dox-induced inflammation of VSMCs. As shown in Fig. 5C, D, knockdown of miR-188-5p in VSMCs treated with Dox could partly alleviate the increase levels of cytokines caused by Dox treated alone (Fig. 5C, D). Altogether, these results indicate that miR-188-5p participates in the Dox-induced inflammation in VSMCs. To further confirm the above findings that Morin could suppress the Dox-induced inflammation in VSMCs, we performed in vivo experiments as follows. Mice were either treated with Dox respectively or co-treated with Dox and Morin together. First, we assessed the expression of inflammatory cytokine in vascular tissues. As shown in Fig. 6A, B, Morin effectively decreased the levels of IL-1β, IL-6, and TNF-α by more than 50% in both protein and mRNA levels, which were increased by Dox treatment. Consistent with the above results, ELISA analysis showed that treatment with Morin could reduce the level of cytokines in serum compared with the Dox alone group (Fig. 6C). Subsequently, we detected whether the activation of the PTEN/AKT pathway was changed in the vascular tissues of Dox-induced inflammation model. As shown in Fig. 6E, the mRNA and protein levels of PTEN were both downregulated in the Dox alone group, while this inhibition effect was reversed in the co-treatment with Morin group. Furthermore, treatment with Morin decreased the expression of p-AKT and p65 in the vascular tissues compared with the Dox group (Fig. 6D). Importantly, qRT-PCR result showed that treatment with Dox induced a fourfold or greater upregulation of the miR-188-5p level, whereas the increased effect could be downregulated when co-treating with Morin (Fig. 6F). The above data strongly suggest that Morin could inhibit Dox-induced inflammation by regulating the miR-188-5p/PTEN/AKT/NF-κB pathway in VSMCs. In the present study, we revealed several key findings as follows: (1) we identified that Dox could promote the inflammation in vascular tissues of the Dox-treated mouse model and in VSMCs. (2) Dox significantly downregulated the PTEN expression level and increased the AKT phosphorylation, thus regulating P65 level. (3) Morin, as an indirect activator of PTEN, increased PTEN level which in turn inhibited AKT pathway activation. (4) Morin plays an anti-inflammatory role in Dox-induced vascular inflammation by regulating miR-188-5p/PTEN/AKT pathway. Previous studies have confirmed that Morin is a potentially anti-infective agent that has a significant anti-inflammatory effect in many infectious diseases. For example, treatment with Morin in LPS-induced mastitis and LPS-induced acute lung injury significant decreased the expression of inflammatory cytokines, including TNF-α, IL-1β, and IL-6. Moreover, the authors found that Morin could inhibit the activation of the NF-κB pathway in LPS-induced mastitis [24, 25]. Additionally, Morin could reduce the TNF-α and IL-6 levels by inhibiting the PI3K/AKT/NF-κB pathway atherosclerosis which considered a chronic inflammatory disease [26]. These findings were consistent with our results. PI3K/AKT/NF-κB pathway is a crucial and quite complex pathway which is involved in physiological functions, including cell survival, proliferation, metabolism, apoptosis, and differentiation [27–29]. PI3K/AKT activation serves an essential role in inflammatory responses [30, 31]. Additionally, activation of the PI3K/AKT pathway is closely related with the NF-κB pathway. On the one hand, AKT phosphorylation could promote NF-κB activation by affecting IκB kinase activity as well as phosphorylation and nuclear translocation of p65. On the other hand, as an important transcription factor, activation of NF-κB could transcriptionally regulate the protein level of PI3K/AKT pathway, and thus contribute to its subsequent activity [32, 33]. In the present study, we revealed that Dox could accentuate the cytokine levels in serum. We speculated that the increase in proinflammatory factors was secreted by macrophages or other inflammatory cells. These proinflammatory factors, such as IL-1β, could activate AKT phosphorylation and NF-κB activation of VSMCs. Stimulated VSMCs secreted more cytokines, rendering the inflammation expanded and making a feedback loop. Our data validated that IL-1β, IL-6, and TNF-α were increased in VSMCs and vascular tissues after treated with the Dox in vitro and in vivo, respectively. Moreover, we found that the phosphorylation of AKT and p65 were upregulated by Dox treatment mean that the activation of AKT/NF-κB pathway. More importantly, we also demonstrated that the PTEN i was inhibited by Dox treatment and thus could help to account for the activation of AKT/NF-κB pathway. Despite the fact that our findings elucidated the mechanism of Dox-induced inflammation of in vascular tissues, this study has several limitations. One of the limitations is that we did not investigate the macrophage behavior in the Dox-treated animal model and cells. However, this might form the basis for future research. PTEN is a crucial negative regulator of the PI3K/AKT pathway that can dephosphorylate phosphatidylinositol 3,4,5-triphosphate (PIP3) to phosphatidylinositol 4,5-biphosphate (PIP2). As an important tumor suppressor, depletion of PTEN promotes susceptibility to tumorigenesis and contributes to tumor cell proliferation, apoptosis, and cell survival and metabolism [34, 35]. Apart from this, PTEN is confirmed to be involved in the inflammatory response. For example, in the IL-10−/− mouse model, overexpression of PTEN suppressed the flagellin-promoted colonic inflammation in epithelial cells by disrupting Mal-TLR5 interaction [36]. In toluene diisocyanate-induced asthma model, upregulation of PTEN could reduce allergen-induced airway inflammation by inhibiting of IL-17 expression [37]. In ischemia–reperfusion injury mouse model, depletion of PTEN by its inhibitor significantly expanded the inflammation and promoted acute kidney injury [38]. In the LPS-induced ameliorates lung damage mouse model, knockdown of PTEN attenuated LPS-induced lung inflammation by regulating the β-catenin pathway [39]. In our present study, we demonstrated that PTEN played a crucial role in Dox-induced vascular inflammation mouse model and Dox-treated VSMCs. Dox treatment decreased PTEN expression in vivo and in vitro, and PTEN upregulation could reduce the release of inflammatory cytokines which was induced by Dox. Our findings indicate that PTEN activation might be a potential therapeutic option in Dox-induced vascular inflammation. The proven mechanisms in loss-function of PTEN include mutation statue, transcription regulation, posttranscriptional regulation, and epigenetic regulation. For example, the high expression of DNA methyltransferase 1 (DNMT1) in breast cancer cell influenced of methylation in the PTEN promoter thus leading to the loss of PTEN [40]. Furthermore, the SALL4-NuRD complex in 293 T cells enhanced the histone hyperacetylation in PTEN promotor, resulting in the depletion of PTEN expression [41]. Dysregulation of NEDD4 in glioblastoma cells regulated PTEN expression by promoting its ubiquitination and degradation [42]. Moreover, associated studies have confirmed that the transcription factors, such as p53, peroxisome proliferator activated receptor gamma (PPARγ), and early growth response 1 (EGR1), positively regulated PTEN expression by binding to its promotor [43–45]. Our previous study observed that PTEN was negatively regulated by miR-188-5p which was overexpressed in CML cells [22]. Moreover, Morin was found to inhibit miR-188-5p expression in CML cells, resulting in the upregulation of PTEN expression. Consistent with the previous research, upregulation of miR-188-5p was observed in the Dox-treated VSMCs and mouse model. Knockdown of miR-188-5p in Dox-treated VSMCs could reduce the inflammation which had the same effect as PTEN overexpression. Of importance, we further confirmed that miR-188-5p was decreased whereas PTEN was increased after Morin treatment in vivo and in vitro. Therefore, we hypothesized that Morin might be participating in the effects of Dox-induced vascular inflammation and anti-leukemia by the common molecular mechanism. Alternatively, our results also suggested that the dysregulation of the miR-188-5p/PTEN pathway might exert an important role in Dox-induced inflammation as well as CML tumorigenesis. However, whether other mechanisms were involved in Morin-reduced inflammation needs further investigation. In conclusion, as a PTEN indirectly activator, Morin reduced the Dox-induced vascular inflammation by moderating the miR-188-5p/PTEN/AKT/NF-κB pathway. Hence, Morin may have a therapeutic value in overcoming the chemotherapy side effects in the future.

PMC9646565

35075615

Seong-Lan Yu,Da-Un Jeong,Yujin Kang,Tae-Hyun Kim,Sung Ki Lee,Ae-Ra Han,Jaeku Kang,Seok-Rae Park

Impairment of Decidualization of Endometrial Stromal Cells by hsa-miR-375 Through NOX4 Targeting

24-01-2022

Endometrial stromal cells,Decidualization,miR-375,Reactive oxygen species,NOX4

Decidualization of the endometrial stromal cells (ESCs) is essential for successful embryo implantation. It involves the transformation of fibroblastic cells into epithelial-like cells that secrete cytokines, growth factors, and proteins necessary for implantation. Previous studies have revealed altered expression of miR-375 in the endometrium of patients with recurrent implantation failure and the ectopic stromal cells of patients with endometriosis. However, the exact molecular mechanisms, particularly the role of microRNAs (miRNAs) in the regulation of decidualization, remain elusive. In this study, we investigated whether decidualization is affected by miR-375 and its potential target(s). The findings demonstrated the downregulation of the expression of miR-375 in the secretory phase compared to its expression in the proliferative phase of the endometrium in normal donors. In contrast, it was upregulated in the secretory phase of the endometrium in infertility patients. Furthermore, during decidualization of ESCs in vitro, overexpression of miR-375 significantly reduced the transcript-level expression of forkhead box protein O1 (FOXO1), prolactin (PRL), and insulin-like growth factor binding protein-1 (IGFBP1), the well-known decidual cell markers. Overexpression of miR-375 also resulted in reduced decidualization-derived intracellular and mitochondrial reactive oxygen species (ROS) levels. Using the luciferase assay, we confirmed that NADPH oxidase 4 (NOX4) is a direct target of miR-375. Collectively, the study showed that the miR-375-mediated NOX4 downregulation reduced ROS production and attenuated the decidualization of ESCs. It provides evidence that miR-375 is a negative regulator of decidualization and could serve as a potential target for combating infertility. Supplementary Information The online version contains supplementary material available at 10.1007/s43032-022-00854-w.

Impairment of Decidualization of Endometrial Stromal Cells by hsa-miR-375 Through NOX4 Targeting Decidualization of the endometrial stromal cells (ESCs) is essential for successful embryo implantation. It involves the transformation of fibroblastic cells into epithelial-like cells that secrete cytokines, growth factors, and proteins necessary for implantation. Previous studies have revealed altered expression of miR-375 in the endometrium of patients with recurrent implantation failure and the ectopic stromal cells of patients with endometriosis. However, the exact molecular mechanisms, particularly the role of microRNAs (miRNAs) in the regulation of decidualization, remain elusive. In this study, we investigated whether decidualization is affected by miR-375 and its potential target(s). The findings demonstrated the downregulation of the expression of miR-375 in the secretory phase compared to its expression in the proliferative phase of the endometrium in normal donors. In contrast, it was upregulated in the secretory phase of the endometrium in infertility patients. Furthermore, during decidualization of ESCs in vitro, overexpression of miR-375 significantly reduced the transcript-level expression of forkhead box protein O1 (FOXO1), prolactin (PRL), and insulin-like growth factor binding protein-1 (IGFBP1), the well-known decidual cell markers. Overexpression of miR-375 also resulted in reduced decidualization-derived intracellular and mitochondrial reactive oxygen species (ROS) levels. Using the luciferase assay, we confirmed that NADPH oxidase 4 (NOX4) is a direct target of miR-375. Collectively, the study showed that the miR-375-mediated NOX4 downregulation reduced ROS production and attenuated the decidualization of ESCs. It provides evidence that miR-375 is a negative regulator of decidualization and could serve as a potential target for combating infertility. The online version contains supplementary material available at 10.1007/s43032-022-00854-w. Decidualization of the endometrium, the process of differentiation of human endometrial stromal cells (ESCs) during the menstrual cycle, occurs during more than 400 menstrual cycles in a woman’s lifetime. Endometrial stromal cells cause decidualization by inducing morphological and functional changes in the endometrium through increased intracellular cAMP levels after ovulation. This process is essential for successful implantation and, therefore, a successful pregnancy [1]. Decidualized human endometrial fibroblasts transform into secretory cells to produce phenotypic markers of decidual cells such as prolactin (PRL) and insulin-like growth factor binding protein-1 (IGFBP-1) [2, 3]. Reportedly, the impairment of this process is associated with a variety of pregnancy-related disorders and is a major cause of implantation failure and infertility [4]. MicroRNAs (miRNAs) are short (approximately 19–25 nucleotides in length) single-stranded non-coding RNA that regulate gene expression by post-transcriptional control of target genes. The activity of miRNAs is generally regulated by perfect or imperfect complementarity within the 3′-untranslated region (UTR) of the target mRNAs [5]. Aberrant miRNA expression has been shown to be associated with several diseases, including cancer, metabolic, and pregnancy-related disorders [6, 7]. Several studies have demonstrated the involvement of miRNAs in the regulation of decidualization. Qian et al. have reported that miR-222 is implicated in endometrial stromal cell differentiation [8], wherein Estella et al. have shown that MiR-96 and miR-135b decrease IGFBP-1 secretion by reducing forkhead box protein O1 (FOXO1) and homeobox A10 (HOXA10) expression in in vitro decidualization [9]. Furthermore, the expression of the members of the miR-200 family has been shown to be upregulated during the in vitro decidualization of ESCs, and aberrant expression of the members of the miR-200 family negatively affects decidualization [10]. MiR-375, located between the CRYBA2 and CCDC108 genes on human chromosome 1, plays a critical role in the biological function of the body [11]. miRNA microarray in the endometrium shows that miR-375 expression is upregulated during the window of implantation (WOI) in the endometrium of patients with recurrent implantation failure (RIF) [12]. In addition, dysregulation of miR-375 has been identified between ectopic and eutopic endometrium in endometriosis [13–16]. Nicotinamide adenine dinucleotide phosphate (NADPH) oxidase (NOX), a membrane-bound enzyme, is an important source of cellular reactive oxygen species (ROS), and the regulation of its activity is essential to maintain healthy ROS levels [17]. In the decidualization of ESCs, NOX mediates cyclic adenosine monophosphate (cAMP)-dependent decidualization [18]. Reportedly, NOX4 is highly expressed in the secretory phase compared to that in the proliferative phase of the endometrium [19], and its silencing inhibits the expression of PRL and IGFBP1 [18]. Although the integral role of NOX4 activation and ROS signaling in initiating the endometrial decidual transformation of human ESCs (HESCs) has been reported [18], the regulatory mechanisms of miR-375 related to endometrial decidualization have not been explored. In this study, we aimed to investigate the correlation between miR-375 expression and decidualization in ESCs and inspect the importance of the miR-375-NOX4 axis in decidualization. The study demonstrates that the NOX4 involved in ROS production could be a direct target of miR-375. Endometrial samples were collected during the proliferative or secretory phase of the menstrual cycle from participants at the Konyang university hospital. Samples representing infertility were collected from participants at the MizMedi hospital. The menstrual stage of the endometrial samples and samples with pathology suggesting endometrial diseases were confirmed by an experienced gynecological pathologist. The volunteer had self-reported regular, normal (21–35 days) menstrual cycles. The proliferative phase samples were collected at 9–11 menstrual cycle days (mcd), and the secretory phase samples were collected at 20–24 mcd in the mid-secretory phase. Moreover, the infertility samples were collected at 20–22 mcd in the mid-secretory phase from patients of normal menstrual cycles (21–42 days) (Table 1). None of the infertility patients were undergoing hormone therapy during sample collection. This study was approved by the Bioethics Committee of Konyang University Hospital (IRB file No. KYUH 2018–11-007) and MizMedi hospital (IRB file No. MMIRB 2018–3). The participants signed informed consent. Immortalized non-neoplastic human endometrial stromal cells (T HESCs) were cultured in the DMEM/F-12 medium without phenol red (Sigma, USA), supplemented with 10% fetal bovine serum (FBS; GIBCO, USA), 1% insulin transferrin selenium (ITS) + Premix (Corning 3454352, USA), and 500 ng/mL puromycin (InvivoGen, USA). Cells were maintained at 37 ℃ in a humidified atmosphere with 5% CO2. For in vitro decidualization, T HESCs were maintained for 6 days in the DMEM/F-12 medium containing 1 μM medroxyprogesterone 17-acetate (MPA; Sigma) and 0.5 mM 8-bromo-cyclic adenosine monophosphate (8-bromo-cAMP; Sigma), 2% FBS, and 1% ITS + Premix. The culture medium was replaced with a fresh medium every 2 days. NOX4 siRNA, the miR-375 mimic, and the miR-375 inhibitor were transfected using Lipofectamine RNAiMAX (Thermo Fisher Scientific, USA) into T HESCs, following the manufacturer’s recommended protocol. Total RNA was isolated from T HESCs or endometrial tissue biopsies using TRIzol® reagent (Ambion, Thermo Fisher Scientific) according to the manufacturer’s instructions. To investigate the mRNA expression, cDNA was synthesized from total RNA using Moloney Murine Leukemia Virus (M-MLV) reverse transcriptase (Promega, USA). Quantitative real-time PCR was performed using iQ SYBR Green Supermix (BioRad) for the IGFBP1, PRL, FOXO1, NOX4, and GAPDH genes using a CFX 96 qPCR (BioRad Laboratories, USA). The primers used for real-time PCR are listed in Supplementary Table 1. The following PCR amplification conditions were used: an initial denaturation step at 95 ℃ for 3 m followed by 40 cycles of denaturation at 95 ℃ for 10 s, annealing for 10 s using suitable primer sets, and extension at 72 ℃ for 10 s. To determine the relative miR-375 expression, cDNA was synthesized using the TaqMan miRNA Reverse transcription kit (Thermo Fisher Scientific, USA), with a reverse transcription miR-375 or RNU6B primer (Thermo Fisher Scientific, USA), according to the manufacturer’s instructions. Then quantitative real-time PCR was performed using TaqMan Master Mix II and TaqMan miRNA assay primer (Thermo Fisher Scientific, USA). The 2−ΔΔct method was used to calculate mRNA or miRNA expression levels using the reference gene. The T HESCs were lysed using lysis buffer (Jubiotech, Korea) containing protease and phosphatase inhibitor (Roche, Swiss). The protein concentrations were measured using the bicinchoninic acid (BCA) assay (Thermo Fisher Scientific). Proteins in cell lysate were resolved by 10% sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE) and transferred to polyvinylidene difluoride (PVDF) membranes (Millipore, Billerica, MA, USA). The blots were then blocked with 5% skim milk (Difco, USA) and probed overnight with primary antibodies at 4 ℃. The primary antibodies used in this study included FOXO1 (1:1000, cell signaling, #2880), NOX4 (1:1000, Abcam, ab109225), and GAPDH (1:5000, cell signaling, #5174). After probing with primary antibodies, the membrane was incubated with horseradish peroxidase-conjugated anti-rabbit secondary antibodies (1:5000, Millipore, AP132P). The expression of the target proteins was determined using an enhanced chemiluminescence kit (Thermo Fisher Scientific). The conditioned media were collected after stimulation for decidualization of T HESCs. The IGFBP1 and PRL levels were measured using the manufacturer’s instructions provided along with the ELISA kit (R&D Systems, USA). The optical density was read using a BioTek microplate reader (BioTek instrument, USA). To investigate whether miR-375 modulates NOX4 expression by binding to its 3´UTR, the NOX4 3´UTR was amplified using a forward primer containing an XhoI restriction site (5´-CCA CTC GAG TGC CAT GAA GCA GGA CTC TA-3´), and a reverse primer containing a NotI restriction site (5´-CCA GCG GCC GCC AGA GTC TTG TGC TGT GTT TTC A-3´). The NOX4 3´UTR region was cloned into the dual-luciferase psiCHECK2 vector (Promega, Madison, WI). Mutagenesis of the binding site of miR-375 was accomplished using the KOD Plus Mutagenesis kit (Toyobo, Osaka, Japan). The sequence of the cloned vectors was verified using DNA sequencing. The cloned vector and the miR-375 mimic were co-transfected into T HESCs using the Lipofectamine 3000 (Thermo Fisher Scientific). Then, the transfected T HESCs were cultured. Afterward, luciferase activity in the transfected cells was measured using the dual-luciferase reporter assay system following the Promega instruction manual (Promega, USA). Luminescent activities of firefly luciferase were used as the internal control for the normalization of transfection efficiency. Luciferase assays were performed in triplicate. To measure the intracellular ROS levels, T HESCs were seeded into the plate and incubated after stimulation with 0.5 mM 8-bromo-cAMP and 1 μM MPA for decidualization. After harvesting the cells, the cells were treated with 25 μM 2´,7´-dichlorodihydrofluorescein diacetate (DCF-DA) for 15 min in the dark at 37 ℃. We estimated the oxidation of the dye by measuring 10,000 events per sample using flow cytometry (FACS, Beckman). To measure the mitochondrial ROS levels, T HESCs were cultured for decidualization, with 0.5 mM 8-bromo-cAMP and 1 μM MPA, and incubated with 5 μM Mitosox™ Red (Thermo Fisher Scientific) for 10 min in the dark at 37 ℃. The cells were visualized using confocal microscopy (Carl Zeiss, Germany), and the fluorescence intensity was determined using ImageJ (http://imagej.nih.gov.ij). The data are presented as the mean ± standard error of the mean (SEM). All experiments were performed in triplicate. The results were analyzed using Student’s t test or Mann–Whitney test. Results were considered statistically significant at P < 0.05 or P < 0.01. The microarray-based miRNA profiling studies have shown that miR-375 is upregulated during the WOI in the endometrium of RIF patients [12]. Therefore, we investigated the change in miR-375 expression between the proliferative and secretory phases of the endometrium to find a link to decidualization. In the normal endometrial tissue biopsy samples, the quantitative RT-PCR revealed downregulation of the expression of miR-375 in the secretory phase samples compared with that in the proliferative phase samples. On the contrary, its expression was upregulated in the secretory phase samples collected from the endometrial biopsies of the patients with infertility (Fig. 1a). Further decidualization of T HESCs in vitro by treating T HESCs with cAMP and MPA demonstrated significant downregulation of the expression of miR-375 in the decidualized T HESCs (Fig. 1b). In addition, the significant upregulation of decidual markers, including FOXO1, PRL, and IGFBP1, confirmed the decidualization status of T HESCs (Fig. 1c–e). These results revealing an inverse relationship between miR-375 expression and decidualization indicate that the suppression of expression of miR-375 is essential for decidualization. To confirm the role of mir-375 on decidualization, the effect of miR-375 overexpression on the mRNA and protein expression of the decidual markers, FOXO1, PRL, and IGFBP-1 was investigated. The findings demonstrated that overexpression of miR-375 significantly reduced the transcript-level expression of FOXO1, PRL, and IGFBP1 in decidual cells than that of the negative control (Fig. 2a–c). miR-375 overexpression also inhibited the expression of FOXO1 protein significantly (Fig. 2d). Further, estimation of the effects of mir-375 overexpression on the secretion of PRL and IGFBP1 in the decidualized cells using ELISA revealed a significant reduction of the expression these two marker proteins in T HESCs transfected with miR-375 mimic (Fig. 2e, f). These results indicate that miR-375 overexpression suppresses the decidualization of ESCs. As the resistance to oxidative stress and the levels of intracellular ROS remarkably increase during decidualization of ESCs [20, 21], we analyzed the intracellular and mitochondrial ROS levels in decidualized T HESCs in vitro. DCFDA oxidation levels were significantly higher in decidualized T HESCs stimulated with 8-br-cAMP and MPA (decidualized) than in those grown in the absence of 8-br-cAMP and MPA (control) (Fig. 3a). The level of mitochondrial ROS was also higher in the decidualized condition than in the control condition (Fig. 3b). Furthermore, the overexpression of miR-375 in T HESCs stimulated with 8-br-cAMP and MPA exhibited reduced intracellular (P < 0.05) and mitochondrial (P < 0.01) ROS production (Fig. 3c, d) than in those grown without 8-br-cAMP and MPA. These results suggest that the target gene of miR-375 could be associated with ROS production. Next, to identify a direct target of miR-375 associated with ROS production, we searched the miRDB database, an online database for miRNA target prediction and functional annotations (http://www.mirdb.org). The search identified 269 genes targeted by miR-375, including the NOX4 gene. Reportedly, NOX4 plays an important role in regulating the levels of intracellular ROS and changes in the expression of decidual markers PRL and IGFBP-1 in HESCs [18]. Therefore, we selected NOX4 for further analyses. Two binding sites were predicted for miR-375 between nt 2226 and 2254 in the 3′UTR of NOX4 mRNA (NM_016931; Fig. 4a). To confirm the direct interaction between miR-375 and NOX4, we performed a dual-luciferase reporter assay with the 3´UTR of NOX4 in T HESCs. The overexpression of miR-375 significantly inhibited the luciferase activity of the mutant at the first binding site, whereas the reduction in luciferase activity of the mutant at the second binding site was non-significant. Moreover, when the reporter at the two binding sites of miR-375 was mutated, no change in luciferase activity was observed. Therefore, we suggest that miR-375 binds onto the second binding site of NOX4 3´UTR (Fig. 4b). Further, the qRT-PCR and western blotting analyses demonstrated that NOX4 mRNA and protein expression were significantly reduced by miR-375 overexpression (Fig. 4c, d). In contrast, inhibition of miR-375 significantly increased the mRNA expression of NOX4 in T HESCs (Fig. 4e). These results indicate the crucial role of mir-375 in regulating NOX4 expression. We have shown that in normal endometrial biopsies, the expression of miR-375 was decreased in the secretory phase than in the proliferative phase of the endometrium (Fig. 1a). Therefore, to unravel the relationship between the expression of NOX4 and decidualization, we analyzed the difference in the expression of NOX4 between the proliferative and secretory phases of the endometrium. NOX4 expression was significantly increased in the secretory phase compared to that in the proliferative phase of the endometrium. Moreover, NOX4 expression was downregulated in the secretory phase of endometrium in the samples collected from patients with infertility (although not significantly compared to normal secretory phase) (Fig. 5a). The expression of NOX4 in the proliferative phase of the endometrium showed an inverse pattern with respect to miR-375 expression (Figs. 1a and 5c). The mRNA and protein expression of NOX4 were significantly increased in T HESCs after in vitro cAMP- and MPA-induced decidualization (Fig. 5b, c). Furthermore, NOX4 knockdown in T HESCs attenuated the expression of decidual markers FOXO1, PRL, and IGFBP-1 (Fig. 5d–f). NOX4 knockdown also reduced intracellular and mitochondrial ROS levels during in vitro decidualization (Fig. 5h, i). Collectively, these results suggested that miR-375-mediated expression changes in NOX4 could be related to the decidualization of ESCs. The uterine ESCs transform into decidual cells during decidualization, which is critical for establishing and maintaining pregnancy, as aberrant decidualization leads to pregnancy loss [22]. Several studies have reported the differential expression of several miRNAs before and after human endometrial stromal decidualization [8, 9, 23]. In addition, it has also been reported that normal ESCs and endometriotic cyst stromal cells are regulated by different mechanisms of miRNA [24], suggesting that miRNAs play important roles in human endometrial stromal decidualization. To the best of our knowledge, we are the first to provide evidence for the crucial role of miR-375 in endometrial decidualization. We demonstrate that the upregulation of miR-375 in HESCs suppresses the decidualization of these cells in vitro and confirm that NOX4 is a direct target of miR-375. Previous studies have reported that miR-375 plays an important role in the formation of pancreatic beta-cells [25]. The progesterone receptor (PGR) has been identified as a direct target of miR-375 in the endometrium of rhesus monkeys [26]. Rekker et al. reported aberrant miR-375 expression in ectopic stromal cells of an endometriosis patient [16]. Similarly, other miRNAs have also been reported to be involved in decidualization. miR-181a regulates the decidualization of ESCs by targeting the Kruppel-like factor 12 (KLF12) [27], while miR-542-mediated IGFBP-1 downregulation is associated with decidualization [28]. miR-194-3p has also been reported to have a relationship with decidualization via the downregulation of PGR [29]. Recently, Qu et al. have reported that miR-542-3p inhibits decidualization through the downregulation of the ILK pathway [30]. In our study, miR-375 was found to be a negative regulator of decidualization in HESCs. During the transformation of ESCs from their normal state into a decidualized state, the decidual stromal cells generate ROS and resist cellular stress signals, to maintain the feto-maternal interface [31]. Cu, Zn-superoxide dismutase (SODs), and mitochondrial Mn-SOD are specific enzymes that scavenge superoxide radicals, the major form of ROS, and are generated after differentiation of the ESCs [32, 33]. Decidualization of ESCs also increases resistance to hydrogen peroxide-induced oxidative stress [20]. Decidual stromal cells differentiated from ESCs transform into protein-secreting cells and induce endoplasmic reticulum (ER) stress by accumulating misfolded proteins to activate secretory pathways in the ER. Consequently, ER stress induces ROS, which is involved in decidualization [34]. In addition, ESCs subjected to decidualization stimuli, such as 8-br-cAMP, MPA, and PGE2, show markedly elevated ROS levels [21]. Recently, resveratrol, well known as an antioxidant and anti-inflammatory agent, has been shown to inhibit decidualization through the downregulation of decidual markers [35]. In addition, oxidative stress increased FOXO1 expression via JNK in follicular granulosa cells in mice [36, 37]. FOXO1 has been reported to be an important factor in the decidualization of HESCs, and FOXO1 knockdown attenuates IGFBP-1 and PRL expression, the well-known decidual markers [38]. The NOX family enzymes are considered the major ROS producers in cells. Inhibition of NOX attenuates cAMP-dependent decidualization, and the silencing of NOX4 (among the NOX family members) inhibits the expression of IGFBP1 and PRL [18]. Degasper et al. have also suggested that NOX4 may play an important role in secretory transformation because it exhibits significantly higher expression in the secretory phase than in the proliferative phase of the endometrium [19]. In this study, we demonstrated that miR-375 downregulates NOX4 expression, and the downregulated NOX4 attenuates ROS production in ESCs. Taken together, we inferred that upregulation of miR-375 and downregulation of NOX4 in the endometrium inhibit decidualization leading to infertility and thereby suggest that miR-375/NOX4 axis plays an important role in pregnancy and could be a potential target for combating infertility. However, further investigations are required to ascertain the physiological and clinical relevance of the miR-375/NOX4 axis in infertility. There are some limitations to this study. The sample of the same menstrual stage was small in size. The sample needs more refined timing across the menstrual cycle. However, more time is needed for refined timing sample collection, and it is very difficult to obtain sufficient endometrium samples for research owing to ethical restrictions. We are currently collecting endometrium samples to unravel the pathways and interactions of the miR-375/NOX4 axis with other critical mechanisms in decidualization to determine their precise relevance to combat infertility. This study, for the first time, offers evidence that miR-375 binds directly to the 3′UTR of NOX4 in ESCs, and the miR-375-mediated NOX4 downregulation reduces ROS production, which consequently attenuates the decidualization of ESCs. These results confirmed that miR-375 acts as a negative regulator of decidualization and could be a potential target for combating infertility. Below is the link to the electronic supplementary material.Supplementary file1 (DOCX 15 KB)

PMC9646666

Fan Zhang,Kang Sun,Wang-Sheng Wang

Identification of a Feed-Forward Loop Between 15(S)-HETE and PGE2 in Human Amnion at Parturition

04-10-2022

15(S)-hydroxyeicosatetraenoic acid,lipoxygenase 15,cyclooxygenase-2,arachidonic acid,inflammation,eicosanoids,metabolomics,lipopolysaccharide,interleukin-1β,serum amyloid A1,AA, arachidonic acid,ALOX, lipoxygenase,COX, cyclooxygenase,dpc, days post-coitus,IL-1β, interleukin-1β,LPS, lipopolysaccharide,mPGES-1, microsomal prostaglandin E synthase-1,PGE2, prostaglandin E2,PG, prostaglandin,PL, preterm labor,PNL, preterm nonlabor,PPARγ, peroxisome proliferator-activated receptor γ,qRT-PCR, quantitative real-time polymerase chain reaction,SAA1, serum amyloid A1,TL, term labor,TNL, term nonlabor

Human parturition is associated with massive arachidonic acid (AA) mobilization in the amnion, indicating that large amounts of AA-derived eicosanoids are required for parturition. Prostaglandin E2 (PGE2) synthesized from the cyclooxygenase (COX) pathway is the best characterized AA-derived eicosanoid in the amnion which plays a pivotal role in parturition. The existence of any other pivotal AA-derived eicosanoids involved in parturition remains elusive. Here, we screened such eicosanoids in human amnion tissue with AA-targeted metabolomics and studied their role and synthesis in parturition by using human amnion fibroblasts and a mouse model. We found that lipoxygenase (ALOX) pathway-derived 15(S)-hydroxyeicosatetraenoic acid (15(S)-HETE) and its synthetic enzymes ALOX15 and ALOX15B were significantly increased in human amnion at parturition. Although 15(S)-HETE is ineffective on its own, it potently potentiated the activation of NF-κB by inflammatory mediators including lipopolysaccharide, interleukin-1β, and serum amyloid A1, resulting in the amplification of COX-2 expression and PGE2 production in amnion fibroblasts. In turn, we determined that PGE2 induced ALOX15/15B expression and 15(S)-HETE production through its EP2 receptor-coupled PKA pathway, thereby forming a feed-forward loop between 15(S)-HETE and PGE2 production in the amnion at parturition. Our studies in pregnant mice showed that 15(S)-HETE injection induced preterm birth with increased COX-2 and PGE2 abundance in the fetal membranes and placenta. Conclusively, 15(S)-HETE is identified as another crucial parturition-pertinent AA-derived eicosanoid in the amnion, which may form a feed-forward loop with PGE2 in parturition. Interruption of this feed-forward loop may be of therapeutic value for the treatment of preterm birth.

Identification of a Feed-Forward Loop Between 15(S)-HETE and PGE2 in Human Amnion at Parturition Human parturition is associated with massive arachidonic acid (AA) mobilization in the amnion, indicating that large amounts of AA-derived eicosanoids are required for parturition. Prostaglandin E2 (PGE2) synthesized from the cyclooxygenase (COX) pathway is the best characterized AA-derived eicosanoid in the amnion which plays a pivotal role in parturition. The existence of any other pivotal AA-derived eicosanoids involved in parturition remains elusive. Here, we screened such eicosanoids in human amnion tissue with AA-targeted metabolomics and studied their role and synthesis in parturition by using human amnion fibroblasts and a mouse model. We found that lipoxygenase (ALOX) pathway-derived 15(S)-hydroxyeicosatetraenoic acid (15(S)-HETE) and its synthetic enzymes ALOX15 and ALOX15B were significantly increased in human amnion at parturition. Although 15(S)-HETE is ineffective on its own, it potently potentiated the activation of NF-κB by inflammatory mediators including lipopolysaccharide, interleukin-1β, and serum amyloid A1, resulting in the amplification of COX-2 expression and PGE2 production in amnion fibroblasts. In turn, we determined that PGE2 induced ALOX15/15B expression and 15(S)-HETE production through its EP2 receptor-coupled PKA pathway, thereby forming a feed-forward loop between 15(S)-HETE and PGE2 production in the amnion at parturition. Our studies in pregnant mice showed that 15(S)-HETE injection induced preterm birth with increased COX-2 and PGE2 abundance in the fetal membranes and placenta. Conclusively, 15(S)-HETE is identified as another crucial parturition-pertinent AA-derived eicosanoid in the amnion, which may form a feed-forward loop with PGE2 in parturition. Interruption of this feed-forward loop may be of therapeutic value for the treatment of preterm birth. Preterm birth remains to be the leading cause of perinatal mortality and morbidity due to lack of reliable predictive and preventive measures, which attributes largely to the insufficient understanding of the initiating mechanism of human parturition (1). Accumulating evidence indicates that human parturition can be initiated by a myriad of signals originated from multiple maternal and fetal tissues (2, 3, 4). Among them, signals originated from the fetal membranes are worthy of particular attention. The importance of fetal membranes-derived signals in labor onset is very well illustrated by the high incidence of preterm birth in chorioamnionitis, a condition of membrane infection which accounts for approximately one-third of preterm birth (5, 6). Among the signals derived from the fetal membranes, prostaglandins (PGs), particularly prostaglandin E2 (PGE2) formed from arachidonic acid (AA) through the cyclooxygenase (COX) pathway (Fig. 1A) in the amnion layer, are one of the best characterized labor initiating signals (7, 8, 9, 10). PGE2 is known to participate in the initiation of parturition by stimulating myometrial contraction, cervical ripening, and fetal membrane activation (11, 12, 13). Inflammatory mediators are known to be the major cause of increased PGE2 synthesis in parturition (14, 15, 16). Depending on the presence or absence of infection, inflammation of the fetal membranes can either be infectious as in chorioamnionitis or sterile as in normal parturition (6, 17, 18). Irrespective of the nature of inflammation, activation of the proinflammatory transcription factor NF-κB with consequently increased expression of COX-2, the rate-limiting enzyme in PG synthesis, is recognized as the primary mechanism accounting for the upregulation of PGE2 production by inflammatory mediators in the amnion at parturition (19, 20, 21). The amnion is particularly rich in AA, which is greatly depleted during parturition for the synthesis of bioactive eicosanoids in the initiation of parturition (22, 23). Although the amnion is known to synthesize the most PGE2 among gestational tissues in parturition (7, 10, 24), a spectrum of other eicosanoids including leukotrienes, HETEs, and epoxyeicosatrienoic acids can also be formed from AA through the lipoxygenase (ALOX) and cytochrome P450 enzyme pathways (Fig. 1A) (25, 26). Yet, very little is known about the role of these AA-derived eicosanoids in parturition. Identification of such eicosanoids may help us gain further insight into the mechanism underlying labor onset in humans, which may offer potential therapeutic targets for preterm birth. In this study, we performed AA-targeted metabolomic study in human amnion with an aim to screen such crucial eicosanoids involved in human parturition. We identified that 15(S)-HETE, formed from the ALOX pathway under the enzymatic action of ALOX15 and ALOX15B (Fig. 1A) (25, 26, 27, 28), might be another important eicosanoid associated with parturition. Studies in human amnion fibroblasts showed that 15(S)-HETE participated in parturition by potentiating proinflammatory mediators-induced activation of the NF-κB/COX-2/PGE2 pathway, and in turn, PGE2 further drove 15(S)-HETE production through induction of ALOX15/15B expression, leading to the formation of a feed-forward loop between 15(S)-HETE and PGE2 syntheses in the amnion at parturition. The parturition-initiating effect of 15(S)-HETE was further illustrated in pregnant mice. This study was performed in accordance with the Declaration of Helsinki. Human fetal membranes and placental villous tissues were obtained from pregnant women with written informed consent under a protocol (No. [2013] N025) approved by the Ethics Committee of Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University. Participating subjects were classified into four groups: elective cesarean section without labor at term (designated as term nonlabor group, TNL); spontaneous labor at term (designated as term labor group, TL); preterm pregnancies terminated by cesarean section without labor for maternal or fetal conditions including placenta previa, vasa previa, and fetal distress (designated as preterm nonlabor group, PNL); and spontaneous preterm labor with no indication of infection (designated as preterm labor group, PL). Upon deliveries, the fetal membranes either within spontaneous or artificial rupture site and the villous tissue from the maternal side of the placenta were collected immediately. The fetal membranes were further separated into the amnion and chorion/decidua layers. The detailed information of recruited women was listed in supplemental Tables S1–S3. The amnion within the artificial rupture site from TNL (n = 8) and the spontaneous rupture site from TL (n = 10) was used to screen AA-derived eicosanoids pertinent to human parturition using AA-targeted metabolomics. The amnion tissue was homogenized in precipitating solvent (1:4 water: acetonitrile) followed by sonication on ice for 30 min. After centrifugation, the supernatant was collected for extraction of AA analogs with the Oasis HLB elution system (Waters, Milford, MA) which was preactivated and equilibrated with methanol and water, respectively. After elution with methanol, the eluted AA analogs were lyophilized and reconstituted in 1% formic acid and 80% acetonitrile solution for analysis with high-performance LC-MS on a UPLC I-Class PLUS System (Waters, Milford, MA) coupled with the 5500 QTRAP system (AB SCIEX, Framingham, MA). Quantitative control samples were used to monitor the stability and repeatability of the system. MultiQuant software was used to extract chromatographic peak area and retention time. The relative quantitative analysis of each AA metabolite was based on the peak area. To confirm the data of AA-targeted metabolomics, the amnion within the artificial rupture site (TNL, n = 14; PNL, n = 10) and spontaneous rupture site (TL, n = 14; PL, n = 10) was collected from deliveries with or without labor at term or preterm. The tissue was snap-frozen in liquid nitrogen and extracted with ethyl acetate after homogenization. After evaporation of ethyl acetate, the extract was reconstituted in the assay buffer for measurement of 15(S)-HETE with an ELISA kit (Cayman Chemical, Ann Arbor, MI) according to the manufacturer’s protocol. To observe whether the abundance of ALOX15 and ALOX15B mRNA and protein changed consistently in the amnion in labor at term and preterm, the amnion within the artificial rupture site (TNL and PNL) and spontaneous rupture site (TL and PL) was collected and snap-frozen in liquid nitrogen for total RNA and protein extraction. The abundance of ALOX15 and ALOX15B protein in the chorion/decidua and placental villous tissues was also compared between TL and TNL groups. For RNA extraction, the snap-frozen tissue was grounded and homogenized with a total RNA isolation Kit (Foregene, Chengdu, China). After examination of RNA quality, reverse transcription was carried out using a Prime-Script RT Master Mix Kit (TaKaRa, Kyoto, Japan). The amount of ALOX15 and ALOX15B mRNA was determined with quantitative real-time polymerase chain reaction (qRT-PCR) using the above reverse-transcribed cDNA and power SYBR® Premix Ex Taq™ (TaKaRa). Housekeeping gene GAPDH was amplified in parallel as an internal control. The relative mRNA abundance was quantitated using the 2-△△Ct method. The primer sequences used for qRT-PCR are illustrated in supplemental Table S4. For protein extraction, the snap-frozen tissue was grounded and homogenized in ice-cold RIPA lysis buffer (Active Motif, Carlsbad, CA) containing inhibitors for protease (Roche, Indianapolis, IN) and phosphatase (Roche). After centrifugation, the supernatant was collected for determination of protein concentration using the Bradford method. The abundance of ALOX15 and AOX15B protein was determined with Western blotting. Briefly, 30 μg protein from each sample was electrophoresed in a sodium dodecyl sulfate-polyacrylamide gel. After transferring to a nitrocellulose membrane blot, the blot was blocked with 5% nonfat milk and incubated with antibodies against ALOX15 (1:500; Thermo Fisher, Waltham, MA; #MA5-25853) and ALOX15B (1:500; Thermo Fisher; #PA5-97456) overnight at 4°C, followed by incubation with appropriate horseradish peroxidase-conjugated secondary antibodies. Peroxidase activity was developed with a chemiluminescence detection system (Millipore, Billerica, MA) and visualized using a G-Box chemiluminescence image capture system (Syngene, Cambridge, UK). Internal loading control was performed by probing the same blot with an antibody against the housekeeping protein GAPDH (1:10,000; Proteintech, Wuhan, China; #60004-1). The ratio of band intensities of ALOX15 and ALOX15B to that of GAPDH was used to indicate ALOX15 and ALOX15B protein abundance. Immunohistochemical staining was carried out on paraffin-embedded amnion tissue sections prepared from TNL (n = 3). After deparaffinization and quenching the endogenous peroxidase activity with 0.3% H2O2, the section was incubated with normal serum to block the nonspecific binding site, followed by incubation with primary antibodies against human ALOX15 (Thermo Fisher; #MA5-25853) and ALOX15B (Thermo Fisher; #PA5-97456) at 1:100 dilution or with nonimmune serum (Proteintech) for negative control overnight at 4°C. After washing, the section was incubated consecutively with a corresponding biotinylated secondary antibody and the avidin-biotin complex conjugated with horseradish peroxidase (Vector Laboratories, Burlingame, CA). The horseradish peroxidase activity was developed as a red color with the substrate 3-amino-9-ethyl carbazole (Vector Laboratories). The slide was counterstained with hematoxylin (blue color) and examined under a regular bright field microscope (Zeiss, Oberkochen, Germany). The entire amnion from the reflected membranes obtained from TNL was used for isolation of amnion fibroblasts and epithelial cells. Briefly, the amnion tissue was digested twice with 0.125% trypsin (Life Technologies Inc, Grand Island, NY) and then washed vigorously with normal saline for isolation of epithelial cells. The epithelial cells in trypsin-digested medium and normal saline wash were collected by centrifugation. The remaining amnion mesenchymal tissue was further digested with 0.1% collagenase (Sigma, St. Louis, MO) to isolate fibroblasts. The isolated amnion epithelial cells and fibroblasts were resuspended and cultured in DMEM containing 10% FBS and antibiotics (Life Technologies Inc). This method of amnion cell isolation yields high purity of epithelial cells (> 99%) and fibroblasts (> 95%), which have been previously characterized by staining of cytokeratin-7, vimentin, and CD45, markers for epithelial, mesenchymal, and immune cells, respectively (29). To compare the abundance of ALOX15 and ALOX15B between amnion fibroblasts and epithelial cells, the cells were cultured for 3 days and then subjected to protein extraction for analysis with Western blotting using antibodies against ALOX15 (1:500) and ALOX15B (1:500) as well as mesenchymal cell marker vimentin (1:10,000; Abcam, Cambridge, MA; #ab11256) and epithelial cell marker E-cadherin (1:1000; Cell Signaling Technology, Danvers, MA; #3195S). To study the effect and regulation of 15(S)-HETE, amnion fibroblasts were cultured for 3 days before reagent treatment in phenol red- and FBS-free DMEM. To study the effect of 15(S)-HETE on COX-2 expression, time course and concentration-dependent studies were conducted. For time course study, amnion fibroblasts were treated with 15(S)-HETE (10 ng/ml; Cayman) for 1, 3, 6, 12, and 24 h. For concentration-dependent study, amnion fibroblasts were treated with 15(S)-HETE for 24 h at concentrations of 1, 10, and 100 ng/ml. To examine the effect of 15(S)-HETE on the induction of COX-2, microsomal prostaglandin E synthase-1 (mPGES-1), the terminal inducible enzyme responsible for the conversion of PGH2 to PGE2, and PGE2 by proinflammatory mediators in amnion fibroblasts, amnion fibroblasts were treated with lipopolysaccharide (LPS) (10 ng/ml; Sigma), interleukin-1β (IL-1β) (1 ng/ml; Sigma), or acute-phase protein serum amyloid A1 (SAA1) (50 ng/ml; PeproTech Inc, Rocky Hill, NJ) in the presence or absence of 15(S)-HETE (10 ng/ml) for 24 h. To examine the effect of 15(S)-HETE on LPS-, IL-1β-, and SAA1-induced phosphorylation of p65, a subunit of NF-κB, amnion fibroblasts were treated with LPS (10 ng/ml), IL-1β (1 ng/ml), or SAA1 (50 ng/ml) in the presence or absence of 15(S)-HETE (10 ng/ml) for 1, 3, and 6 h. To determine the role of NF-κB in the enhancement of LPS-, IL-1β-, and SAA1-induced COX-2 expression by 15(S)-HETE, the cells were treated with LPS (10 ng/ml), IL-1β (1 ng/ml), or SAA1 (50 ng/ml) for 24 h in the presence or absence of 15(S)-HETE (10 ng/ml) with or without siRNA-mediated knockdown of RELA (p65). The method of siRNA transfection is described below. To explore whether PGE2 regulates ALOX15 and ALOX15B expression and 15(S)-HETE production, amnion fibroblasts were treated with PGE2 (0.01–1 μM; Sigma) for 6 h in the presence or absence of EP2 receptor antagonist PF-04418948 (PF; 10 μM; Selleck, Houston, TX) or PKA inhibitor PKI 14–22 amide (PKI; 5 μM; Selleck). The mRNA abundance of ALOX15, ALOX15B, PTGS2 (COX-2), PTGES (mPGES-1), RELA (p65), and GAPDH in amnion fibroblasts was measured with qRT-PCR with primer sequences given in supplemental Table S4. The protein abundance of ALOX15, ALOX15B, COX-2, total p65, phosphorylated p65 at Ser536, and GAPDH in amnion fibroblasts was determined with Western blotting using antibodies against ALOX15 (1:500) and ALOX15B (1:500), COX-2 (1:1000; Cell Signaling; #12282S), total p65 (1:1000; Cell Signaling; #6956S), phosphorylated p65 (Ser536) (1:1000; Cell Signaling; #3033S), and GAPDH (1:10,000). PGE2 and 15(S)-HETE in the culture medium of treated amnion fibroblasts were measured with ELISA kits (Cayman). Detailed methods of qRT-PCR, Western blotting, and ELISA were described above for amnion tissue. Small interference RNA-mediated knockdown of p65 was used to investigate the role of NF-κB in the enhancement of LPS-, IL-1β-, and SAA1-induced COX-2 expression. Cells were transfected immediately after isolation with 50 nM of siRNA (5′-GCCCUAUCCCUUUACGUCATT-3′) (GenePharma, Shanghai, China) against RELA in Opti-MEM (Life Technologies Inc) using an electroporator (Nepa Gene, Chiba, Japan) at 165V for 5 ms. For negative control, randomly scrambled siRNA (5′-UUCUCCGAACGUGUCACGUTT-3′) was used. The cells were then incubated in DMEM containing 10% FBS and antibiotics for three days before treatments with proinflammatory mediators and 15(S)-HETE. The knockdown efficiency of RELA was about 90% (supplemental Fig. S1). Mouse experimentation was conducted following ARRIVE guidelines, which was approved by the Institutional Review Board of Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine. C57BL/6 mice (Charles River, Beijing, China) aging from 10 to 13 weeks were mated overnight. When a vaginal plug was present, it was counted as 0.5-days post-coitus (dpc). Because Alox8 is the mouse homolog of human ALOX15B gene, which catalyzes the formation of 8(S)-HETE rather than 15(S)-HETE, Alox15-encoded ALOX15 is the only enzyme catalyzing the formation of 15(S)-HETE in the mouse [25]. Therefore, only the distribution of ALOX15 was examined in the mouse placenta and fetal membranes (16.5 dpc) with immunohistochemical staining of the paraffin-embedded tissue section with a primary antibody against mouse ALOX15 (1:100; Abcam; #ab244205) following the same protocol as described for human amnion tissue section staining. To study the gestational changes of ALOX15, COX-2, and 15(S)-HETE abundance in the mouse placenta and fetal membranes, the tissues were collected on 14.5, 16.5, and 18.5 dpc. The abundance of ALOX15 and COX-2 in the tissue was determined with Western blotting using antibodies against mouse ALOX15 (1:500; Abcam; #ab244205) and COX-2 (1:1000; Cell Signaling; #12282S) after protein extraction with the RIPA lysis buffer. The abundance of 15(S)-HETE in the tissue was measured with an ELISA kit (Cayman) after extraction with ethyl acetate. To observe whether 15(S)-HETE could induce preterm birth, 15(S)-HETE (50 μg) or an equal volume of solvent was injected subcutaneously every 12 h on 16.5, 17, and 17.5 dpc. Some of the mice (n = 18) were allowed to deliver spontaneously for observation of delivery time, and some (n = 14) were sacrificed on 18 dpc for collection of placentae and fetal membranes to examine the effect of 15(S)-HETE administration on the abundance of COX-2 and PGE2 in the tissue. The abundance of COX-2 was measured with Western blotting with the antibody described above after protein extraction with the RIPA lysis buffer, and PGE2 concentration in the tissue was determined with an ELISA kit (Cayman) after extraction with ethyl acetate. Detailed methods of Western blotting and ELISA were described for human amnion tissue. All data are presented as means ±SEM. The number of repeated experiments in each study was separate experiments reflecting independent patients or animals. After normality testing with the Shapiro-Wilk test, paired or unpaired Student’s t-test or Mann-Whitney U test was used to compare two groups. One-way ANOVA followed by Newman-Keuls multiple-comparisons test was performed when assessing the differences among multiple groups. Pearson correlation analysis was performed to test the correlation between PGE2 and 15(S)-HETE levels. Fisher exact tests were applied to compare the preterm birth rates in the mice study. Statistical significance was defined as P < 0.05. To screen AA-derived eicosanoids which were altered in the amnion at parturition, LC-MS-based metabolomics targeted for AA-derived eicosanoids was performed on human amnion tissue obtained from TL (n = 10) and TNL (n = 8). The demographic and clinical characteristics of recruited women for this study are given in supplemental Table S1. After quality control (supplemental Fig. S2), ten major AA-derived eicosanoids were detected in the amnion tissue (supplemental Table S5). Principal component analysis revealed that the abundance of AA-derived eicosanoids in TL and TNL groups was clustered distinctively (Fig. 1B), suggesting that labor was associated with a distinct profile of alteration in AA-derived eicosanoids in human amnion. Specifically, three eicosanoids (PGE2, PGF2α, and 15-HETE) were significantly increased and two eicosanoids (AA and thromboxane B2) were significantly decreased in TL group when compared to those in TNL group (Fig. 1C, D). Our findings confirmed the depletion of AA, increased PGE2 and PGF2α production, and decreased thromboxane B2 abundance in the amnion in labor at term (22, 30, 31, 32). We also confirmed that the amnion produced much more PGE2 than PGF2α in both labor and nonlabor status (Fig. 1D) (10, 24, 33). Although several previous studies have demonstrated that 15-HETE levels are increased in amniotic fluid and maternal blood in both term and preterm birth (34, 35, 36, 37), we revealed for the first time that 15-HETE abundance was increased in the amnion at parturition. Because the exact role of 15-HETE in parturition has never been specified, we subsequently focused on 15-HETE to further define its role and synthetic regulation in parturition. Two isomers of 15-HETE, 15(S)-HETE, and 15(R)-HETE exist in the body, but the ALOX pathway produces only 15(S)-HETE but not 15(R)-HETE (25, 26, 27, 28, 38). Next, we collected additional human amnion tissue from both term and preterm deliveries to measure the changes of 15(S)-HETE with labor at term and preterm with ELISA. The demographic and clinical characteristics of recruited women for this study are given in supplemental Tables S2 and S3. Results showed that 15(S)-HETE abundance was significantly increased not only in TL group (n = 14) but also in PL group (n = 10) when compared to those in TNL group (n = 14) and PNL group (n = 10), respectively (Fig. 2A, B), suggesting that increased 15(S)-HETE synthesis may be associated with both TL and PL. It is known that 15(S)-HETE is formed from AA through the ALOX pathway under the catalysis of ALOX15 and 15B in humans (Fig. 1A) (27, 28). We next examined whether the abundance of ALOX15 and 15B mRNA and protein was consistently altered in human amnion in TL and PL. Analyses with qRT-PCR and Western blotting showed that the abundance of ALOX15 and 15B mRNA and protein was significantly increased in the amnion in both TL and PL groups when compared to those in TNL and PNL groups, respectively (Fig. 2C–H). Interestingly, ALOX15 and 15B protein were hardly detectable in human placenta. Although ALOX15 and 15B were detectable in the chorion/decidua layer of the fetal membranes, but they displayed no significant changes in TL group (Fig. 2I). These data suggest that increased 15(S)-HETE synthesis occurs mainly in the amnion in human parturition. Immunohistochemical staining showed that ALOX15B distributed almost exclusively in the mesenchymal fibroblasts of human amnion, while ALOX15 appeared to distribute in both mesenchymal fibroblasts and epithelial cells (Fig. 3A, B). Western blotting showed that both ALOX15 and 15B were much more abundant in amnion fibroblasts than in epithelial cells (Fig. 3C, D). In epithelial cells, ALOX15B protein was hardly detectable, while low level of ALOX15 protein was detected (Fig. 3C). Since mesenchymal fibroblasts have also been shown to be the primary site of PGE2 synthesis in human amnion (39), we next used primary human amnion fibroblasts to examine the effect of 15(S)-HETE on PGE2 synthesis. Pearson analysis showed a significantly positive correlation between 15(S)-HETE and PGE2 levels in the amnion in AA-targeted metabolomics study (R = 0.92, P = 7.9e-8) (Fig. 4A), indicating that synthesis of 15(S)-HETE and PGE2 may be associated in the amnion. Unexpectedly, we failed to observe any effect of 15(S)-HETE per se on COX-2 expression in amnion fibroblasts in either time course (10 ng/ml; 1, 3, 6, 12, and 24 h) or concentration-dependent (1, 10, and 100 ng/ml; 24 h) studies (Fig. 4B, C). Surprisingly, we found that 15(S)-HETE (10 ng/ml; 24 h) potently bolstered the induction of PTGS2 mRNA and COX-2 protein expression and PGE2 production by proinflammatory mediators including LPS (10 ng/ml; 24 h) (Fig. 4D–F), IL-1β (1 ng/ml; 24 h) (Fig. 4G–I), and SAA1 (50 ng/ml; 24 h) (Fig. 4J–L) despite the ineffectiveness of 15(S)-HETE on its own (Fig. 4D–L). In addition, we found that 15(S)-HETE (10 ng/ml; 24 h) also enhanced LPS (10 ng/ml; 24 h)-, IL-1β (1 ng/ml; 24 h)-, and SAA1 (50 ng/ml; 24 h)-induced mPGES-1 expression in human amnion fibroblasts (supplemental Fig. S3), suggesting that 15(S)-HETE might potentiate PGE2 production by enhancing the induction of both COX-2 and mPGES-1 expression by proinflammatory mediators in human amnion fibroblasts. NF-κB is a classical proinflammatory transcription factor mediating the expression of a wide array of proinflammatory mediators including COX-2 (19, 20). Thus, we explored the role of NF-κB in the enhancement of proinflammatory mediators-induced COX-2 expression by 15(S)-HETE in human amnion fibroblasts. Activation of NF-κB was determined by examining the phosphorylation of the p65 subunit of NF-κB. Consistently, 15(S)-HETE (10 ng/ml) failed to affect p65 phosphorylation on its own, but it significantly enhanced the phosphorylation of p65 by LPS (10 ng/ml) at 1, 3, and 6 h, IL-1β (1 ng/ml) at 1 and 3 h, and SAA1 (50 ng/ml) at 1 h in human amnion fibroblasts (Fig. 5A–C). Correspondingly, siRNA-mediated knockdown of RELA (P65) significantly attenuated the induction of COX-2 expression not only by LPS (10 ng/ml), IL-1β (1 ng/ml), and SAA1 (50 ng/ml) themselves but also the potentiated induction by 15(S)-HETE (10 ng/ml) (Fig. 5D–F). Our data presented above indicate that 15(S)-HETE derived from the ALOX15/15B pathway may be an amplifier of COX-2 expression and PGE2 production in human amnion fibroblasts in the inflammatory process of parturition. Given the self-reinforcing role of PGE2 in membrane activation through induction of its own synthetic enzyme COX-2 expression in human amnion fibroblasts (40), we postulated that PGE2 may also be a stimulator of ALOX15 and 15B expression. We found that PGE2 (0.01–1 μM; 6 h) increased ALOX15 and 15B mRNA and protein abundance in amnion fibroblasts in a concentration-dependent manner with concomitant increased 15(S)-HETE production (Fig. 6A–C), which was blocked by either a PGE2 EP2 receptor antagonist PF-04418948 (10 μM) or a PKA inhibitor PKI 14–22 amide (5 μM) (Fig. 6 D–I). These data suggest that PGE2 and 15(S)-HETE may form a feed-forward loop in amnion fibroblasts in the inflammatory process of parturition. Evidence gathered in human studies indicates a possible pivotal role of 15(S)-HETE in the initiation of parturition, which prompted us to investigate whether administration of 15(S)-HETE could indeed induce preterm birth in mice. Given that ALOX15 is the only enzyme catalyzing the formation of 15(S)-HETE in mice, we first examined the expressional profile of ALOX15 in the mouse placenta and fetal membranes, both of which are important contributors of PGs in parturition in the mouse (41, 42). Immunohistochemical staining showed that ALOX15 was present both in the yolk sac mesodermal cells of the fetal membranes and in the junctional zone of the placenta (Fig. 7A, B), which contrasted to the hardly detectable levels of ALOX15 and 15B in human placenta. Moreover, the abundance of 15(S)-HETE, ALOX15, and COX-2 in the mouse placenta and fetal membranes increased in a gestational age-dependent manner from 14.5 to 18.5 dpc (Fig. 7C–F). Notably, subcutaneous injection of 15(S)-HETE (50 μg; once half day) on 16.5, 17, and 17.5 dpc induced preterm birth by 0.5–2 days (Fig. 8A–C), along with increased COX-2 and PGE2 abundance in the placenta and fetal membranes (Fig. 8D–H). In this study, we have provided evidence for the first time that 15(S)-HETE synthesized in human amnion plays a pivotal role in the initiation of parturition by bolstering the activation of NF-κB by proinflammatory mediators including LPS, IL-1β, and SAA1 with consequently amplified COX-2 expression and PGE2 production in amnion fibroblasts. Given parturition being a process of inflammation of gestational tissues (17, 18, 43) and the important role of LPS, IL-1β, and SAA1 in infection and noninfection-induced inflammatory reaction (14, 44), our findings may be fitted into both normal and infection-induced parturition. The important role of 15(S)-HETE in parturition was endorsed by increased ALOX15/15B and 15(S)-HETE abundance in human amnion in spontaneous TL and PL as well as by the finding that 15(S)-HETE administration induced preterm birth in the mouse. Previous studies have demonstrated that 15(S)-HETE might be associated with several events in reproduction, including ovulation, embryo implantation, pre-eclampsia, etc. (45, 46, 47, 48). Increased 15(S)-HETE levels have been found in amniotic fluid and maternal blood in both term and preterm birth (34, 35, 36, 37). However, neither the source nor the role of 15(S)-HETE in parturition has ever been specified. An early study investigated AA metabolism by ALOX pathways in human intrauterine tissues and found that none of AA metabolites showed significant changes with labor in rates of formation, including 15-HETE in the amnion (49). However, in this study, we found that the amnion expressed much more ALOX15/15B than the chorion/decidua and placenta, and, moreover, only the amnion but not the chorion/decidua and placenta exhibited significant increases in ALOX15/15B and 15(S)-HETE abundance at parturition, suggesting that increased 15(S)-HETE levels in amniotic fluid in parturition may reflect the increased synthesis of 15(S)-HETE in the amnion. The human amnion is known to be particularly rich in AA content, which is greatly depleted during parturition (22). Considering that PGE2, PGF2α, and 15(S)-HETE are the only three eicosanoids increased in the amnion in parturition as revealed by AA-targeted metabolomics in this study, we believe that AA is mobilized in the amnion mainly for the synthesis of PGE2, PGF2α, and 15(S)-HETE in parturition. In this study, we demonstrated that 15(S)-HETE promoted parturition through potentiation of the induction of COX-2 expression and PGE2 production by proinflammatory mediators as illustrated in both human amnion fibroblasts and mice studies. Our findings coincidently reconciled the findings in colonic myofibroblasts and follicular dendritic cell-like cells that 15(S)-HETE also enhances COX-2 expression by IL-1β (50, 51). COX-2 is highly inducible by proinflammatory mediators at sites of inflammation, which is responsible for the production of abundant PGs in inflammation. PGs produced in gestational tissues, specifically PGE2 and PGF2α, are conferred with specific parturition-pertinent effects, that is, stimulation of myometrial contraction and cervical ripening as well as membrane activation (11, 12, 13). As such, the regulatory mechanism underlying COX-2 expression in gestational tissues has been intensively investigated. It is known that the expression of COX-2 is under the tight control of the classical proinflammatory transcription factor NF-κB (52, 53). Here in this study, we also showed that 15(S)-HETE bolstered the induction of COX-2 expression by proinflammatory mediators though potentiation of NF-κB activation in human amnion fibroblasts. Considering that the classical role NF-κB in mediation of the expression of a wide array of proinflammatory mediators in inflammation, we speculate that there may be other inflammatory mediators whose expression can be amplified by 15(S)-HETE in the amnion fibroblasts during the inflammatory process of parturition. Undoubtedly, it should be an interesting issue to explore with in the future. A number of potential receptors have been suggested for 15(S)-HETE including peroxisome proliferator-activated receptor γ (PPARγ), leukotriene B4 type-2 receptor, and transient receptor potential vanilloid subfamily member 1 (54, 55, 56). PPARγ has been reported to be a receptor for 15(S)-HETE in macrophages (57). However, concomitantly, opposite changes in PPARγ and COX-2 have been reported in the fetal membranes at the onset of labor (58), suggesting that PPARγ is unlikely a receptor mediating the effect of 15(S)-HETE in the amnion. We are unclear at the current stage which putative receptor mediates the amplification of proinflammatory mediators-induced NF-κB activation by 15(S)-HETE in human amnion fibroblasts. Notably, we discovered in this study that PGE2 was a stimulator of ALOX15 and 15B expression in human amnion fibroblasts. This finding is worth particularly emphasizing because the induction of ALOX15 and 15B by PGE2 filled a gap in the feed-forward loop between 15(S)-HETE and PGE2 production in amnion fibroblasts. We believe that this feed-forward loop is an important strategy adopted in pregnancy to ensure the adequate production of 15(S)-HETE and PGE2 for parturition, which appears to fit well into the feed-forward mechanism of parturition (59). There are four PGE2 receptors, namely, EP1, 2, 3, and 4 (60). Among them, EP2 and EP4 are more abundantly expressed in human amnion fibroblasts (40). Our recent study has shown that the expression of EP2 increases, whereas the expression of EP4 decreases in the amnion tissue in parturition despite that both receptors are reportedly coupled with the cAMP/PKA pathway (40). Moreover, we have demonstrated that EP2 receptor mediates the induction of COX-2 by PGE2 in amnion fibroblasts, an effect that can be attenuated by the PI3K pathway coupled with the more complicated EP4 receptor (40), which may explain why these two PGE2 receptors manifested opposite changes in the amnion in parturition. Here in this study, we demonstrated that EP2 receptor also mediated the induction of ALOX15 and 15B by PGE2 in amnion fibroblasts, suggesting again that the EP2 receptor plays a crucial role in parturition by mediating the induction of both COX-2 and ALOX15/15B expression in human amnion fibroblasts. Our findings provide further evidence that PGE2 is an important activator of membrane activation resulting in the surges of both PGE2 and 15(S)-HETE syntheses. In conclusion, 15(S)-HETE synthetized by ALOX15 and ALOX15B plays an important role in parturition by forming a feed-forward loop with the COX-2/PGE2 pathway through potentiation of inflammation-induced NF-κB activation. Interruption of this feed-forward loop may be of therapeutic value for the treatment of preterm birth. The original data and materials presented in the study are available from the corresponding authors upon reasonable request. This article contains supplemental data. The authors declare that they have no competing interests.

PMC9646708

Cheng Wei,Xiaoyang Zhang,Dazhao Peng,Xu Zhang,Haizhen Guo,Yalin Lu,Lin Luo,Bo Wang,Zesheng Li,Yingjie He,Xuezhi Du,Shu Zhang,Hao Liang,Shenghui Li,Sheng Wang,Lei Han,Jianning Zhang

LncRNA HOXA11-AS promotes glioma malignant phenotypes and reduces its sensitivity to ROS via Tpl2-MEK1/2-ERK1/2 pathway

09-11-2022

CNS cancer,Long non-coding RNAs,Prognostic markers

Our previous studies showed that dysregulation of the long noncoding RNA (lncRNA) HOXA11-AS plays an important role in the development of glioma. However, the molecular mechanism of HOXA11-AS in glioma remains largely unknown. In this study, we explore the molecular mechanisms underlying abnormal expression and biological function of HOXA11-AS for identifying novel therapeutic targets in glioma. The expression of HOXA11-AS, and the relationship between HOXA11-AS and the prognosis of glioma patients were analyzed using databases and glioma samples. Transcriptomics, proteomics, RIP, ChIRP, luciferase, and ChIP assays were used to explore its upstream and downstream targets in glioma. The role of HOXA11-AS in regulating the sensitivity of glioma cells to reactive oxygen species (ROS) was also investigated in vitro and in vivo. We found that HOXA11-AS was significantly upregulated in glioma, and was correlated with the poor prognosis of glioma patients. Ectopic expression of HOXA11-AS promoted the proliferation, migration, and invasion of glioma cells in vitro and in vivo. Mechanistically, HOXA11-AS acted as a molecular sponge for let-7b-5p in the cytoplasm, antagonizing its ability to repress the expression of CTHRC1, which activates the β-catenin/c-Myc pathway. In addition, c-Myc was involved in HOXA11-AS dysregulation via binding to its promoter region to form a self-activating loop. HOXA11-AS, functioned as a scaffold in the nucleus, also recruited transcription factor c-Jun to the Tpl2 promoter, which activates the Tpl2-MEK1/2-ERK1/2 pathway to promote ROS resistance in glioma. Importantly, HOXA11-AS knockdown could sensitize glioma cells to ROS. Above, oncogenic HOXA11-AS upregulates CTHRC1 expression as a ceRNA by adsorbing let-7b-5p, which activates c-Myc to regulate itself transcription. HOXA11-AS knockdown promotes ROS sensitivity in glioma cells by regulating the Tpl2-MEK1/2-ERK1/2 axis, demonstrating that HOXA11-AS may be translated to increase ROS sensitivity therapeutically.

LncRNA HOXA11-AS promotes glioma malignant phenotypes and reduces its sensitivity to ROS via Tpl2-MEK1/2-ERK1/2 pathway Our previous studies showed that dysregulation of the long noncoding RNA (lncRNA) HOXA11-AS plays an important role in the development of glioma. However, the molecular mechanism of HOXA11-AS in glioma remains largely unknown. In this study, we explore the molecular mechanisms underlying abnormal expression and biological function of HOXA11-AS for identifying novel therapeutic targets in glioma. The expression of HOXA11-AS, and the relationship between HOXA11-AS and the prognosis of glioma patients were analyzed using databases and glioma samples. Transcriptomics, proteomics, RIP, ChIRP, luciferase, and ChIP assays were used to explore its upstream and downstream targets in glioma. The role of HOXA11-AS in regulating the sensitivity of glioma cells to reactive oxygen species (ROS) was also investigated in vitro and in vivo. We found that HOXA11-AS was significantly upregulated in glioma, and was correlated with the poor prognosis of glioma patients. Ectopic expression of HOXA11-AS promoted the proliferation, migration, and invasion of glioma cells in vitro and in vivo. Mechanistically, HOXA11-AS acted as a molecular sponge for let-7b-5p in the cytoplasm, antagonizing its ability to repress the expression of CTHRC1, which activates the β-catenin/c-Myc pathway. In addition, c-Myc was involved in HOXA11-AS dysregulation via binding to its promoter region to form a self-activating loop. HOXA11-AS, functioned as a scaffold in the nucleus, also recruited transcription factor c-Jun to the Tpl2 promoter, which activates the Tpl2-MEK1/2-ERK1/2 pathway to promote ROS resistance in glioma. Importantly, HOXA11-AS knockdown could sensitize glioma cells to ROS. Above, oncogenic HOXA11-AS upregulates CTHRC1 expression as a ceRNA by adsorbing let-7b-5p, which activates c-Myc to regulate itself transcription. HOXA11-AS knockdown promotes ROS sensitivity in glioma cells by regulating the Tpl2-MEK1/2-ERK1/2 axis, demonstrating that HOXA11-AS may be translated to increase ROS sensitivity therapeutically. Malignant glioma is the most common primary intracranial tumor, the second leading cause of death among patients under the age of 34 years, and the third leading cause of death among patients aged 35–54 years [1]. The standard treatment is maximum safe surgical resection of the tumor, combined with postoperative chemotherapy and radiotherapy, but the therapeutic effect achieved is still unsatisfactory [2, 3]. The concept of reactive oxygen species (ROS) was first proposed in the 1950s and has gradually become a hot topic in cancer research [4]. Cancer cells have high ROS levels, and overexpress antioxidant enzymes to protect themselves from the consequent oxidative stress [5, 6]. Therefore, increasing the ROS levels in cancer cells, while inhibiting their antioxidant capacity to aggravate their oxidative stress, is a promising anticancer strategy [7–9]. Long noncoding RNAs (lncRNAs) are a class of noncoding RNAs more than 200 nucleotides (nt) in length [10]. Approximately 93% of the DNA in the human genome is transcribed into RNA, of which only 2% is protein-coding mRNA and the remaining 98% is noncoding RNA [11]. More than 28,000 lncRNAs have been identified in the human genome [12]. Recent studies have shown that lncRNAs are involved in epigenetic regulation, transcriptional regulation, post-transcriptional regulation, translation, post-translational modification, and chromosomal remodeling, by acting as guides, baits, scaffolds, and signal transducers [13, 14]. They are closely associated with the occurrence and progression of many major diseases, including cancer [15]. In addition, abnormal interaction between lncRNAs and signal transduction pathways might be the primary reason behind the resistance of glioma to ROS treatment. In our previous studies, we noted a positive correlation between lncRNA HOXA11-AS (HOXA11 antisense RNA) expression, and the grade of glioma patients. HOXA11-AS could regulate glioma cell cycle progression, and maintain the tumor cell stemness [16]. There is accumulating evidence that HOXA11-AS can promote the proliferation, migration, and invasion of a variety of tumor cells through molecular scaffolds, molecular sponges, and other mechanisms [17, 18]. However, there are few studies on the molecular mechanism of HOXA11-AS in glioma. In this research, we studied the expression of HOXA11-AS in glioma using online databases and glioma samples, and analyzed the relationship between HOXA11-AS expression and the prognosis of glioma patients. The biological functions of HOXA11-AS in glioma have been verified in vitro and in vivo. Through mRNA and small RNA sequencing, the ectopic expression mechanism of HOXA11-AS in glioma was explored. In addition, we determined that HOXA11-AS acts as a key regulator of glioma sensitivity to ROS through transcriptomics, proteomics, and ROS sensitivity assays. Finally, using ROS-producing nanoparticles (NPs), we demonstrated that HOXA11-AS could regulate the ROS sensitivity of glioma cells in vitro and in vivo. Our findings revealed the potential of HOXA11-AS as a therapeutic target to mediate ROS sensitivity in glioma. The clinical data and RNA sequencing data about 33 tumors and corresponding normal tissues were downloaded from TCGA and GTEx databases via UCSC Xena (https://xenabrowser.net/datapages/). In the analysis of glioma alone, 255 cases of normal brain tissue from GTEx database and 529 cases of low-grade glioma (LGG, WHO II-III), and 173 cases of glioblastoma (GBM, WHO IV) from TCGA database were included. The online database, GEPIA2 (http://gepia2.cancer-pku.cn/#index), was used for prognosis analysis of patients with multiple tumors [19]. The HOXA11-AS mRNA expression profiles in four subtypes (Classical, Mesenchymal, Neural, and Proneural) and the related prognoses of the glioma patients were analyzed through four databases (CGGA, TCGA, REMBRANDT, and GSE16011). Based on median values of RNA expression levels, glioma patients were classified into high and low-expression groups, and survival curves and log-rank tests were used for validation. The subcellular localization of HOXA11-AS in different cell lines was predicted in the lncATLAS database (https://lncatlas.crg.eu/) [20]. The COX analysis of HOXA11-AS in glioma from TCGA database was analyzed on SangerBox portal (http://SangerBox.com/Tool). Analysis of HOXA11-AS related genes in CGGA database was performed using R package. The positively or negatively correlated genes with HOXA11-AS expression were screened in CGGA database. TCGA dataset was used to screen HOXA11-AS positively (Top 50) or negatively (Top50) correlated genes in LinkedOmics (http://www.linkedomics.org/login.php). SangerBox portal was used to analyze and map the results of GO and KEGG analyses. Transcription factors that could bind to the Tpl2 promoter region were predicted via the PROMO database. The c-Jun binding peaks of Tpl2 promoter region and c-Myc binding peaks of HOXA11-AS promoter region were predicted via UCSC portal. The binding of c-Jun to the Tpl2 promoter region was predicted via Tartaglialab website (http://www.tartaglialab.com/). All of the primary human glioma specimens were obtained from patients who underwent surgery at the First Affiliated Hospital of Zhengzhou University, and each sample contained 80% tumor cells, as confirmed by microscopic examination. The tissue samples were graded by the neuropathologist in accordance with the World Health Organization (WHO) criteria and stored in liquid nitrogen. The glioma specimens included grade II (17 samples), grade III (8 samples), and grade IV (16 samples). This study was approved by the institutional review boards of the hospitals, and written informed consent was obtained from all patients. The histology and clinical data of the glioma samples are available in Supplementary Table 1. The human astrocytes, U87, LN229, U251, TG905, and LN308 cell lines, which were all cultured in the Tianjin Neurological Institute of Tianjin Medical University General Hospital, and mycoplasma detection and STR cell identification were carried out. The human astrocytes were grown in AM medium (Astrocyte Medium, Sciencell, USA). The U87, LN229, U251, and LN308 were grown in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum (FBS, BI serum, Israel). The TJ905 cell line were cultured with DMEM/F12 medium (Gibco, USA). Two patient-derived primary GBM cells (primary GBM cell 1 and primary GBM cell 2) were obtained from glioma tissues surgically removed from WHO grade IV glioma patients in Tianjin Medical University General Hospital, which were approved by the ethics committee, and informed consent was signed. Cells were grown in serum-free medium (DMEM-F12/Neurobasal [1:1 mix] with 1% B27 and 2 mM L-glutamine and supplemented with 20 ng/mL each of eEGF and FGF2). All cells were grown at 37 °C in a humidified atmosphere (95% humidity) with 5% CO2. The lentivirus carrying the HOXA11-AS cDNA sequence (Lv-HOXA11-AS) were used to upregulate HOXA11-AS expression in U87 (MOI = 5) and LN229 (MOI = 10) cell lines, and control lentivirus (Lv-NC) was used to construct control cell lines. In addition, the lentivirus carrying the HOXA11-AS shRNA sequence (Lv-shHOXA11-AS) were used to knockdown HOXA11-AS expression in U87 (MOI = 5) and LN229 (MOI = 10) cell lines, and control lentivirus (Lv-NC) was used to construct control cell lines. Both Lv-NC, Lv-HOXA11-AS, and Lv-shHOXA11-AS were purchased from Genechem Co., Ltd. (Shanghai, China). The pGL3-Tpl2 plasmid was purchased from Hanbio Biotechnology Co., Ltd. (Tianjin, China). The pGL3-Tpl2 plasmid was used to upregulate Tpl2 expression. The pcDNA3.1-HOXA11-AS plasmid was purchased from Genewiz (Beijing, China) and used to upregulate HOXA11-AS expression. The c-Jun plasmids (pcDNA3.1-Flag-Jun-full length (FL), pcDNA3.1-Flag-Jun-TAD, pcDNA3.1-Flag-Jun-241-254, and pcDNA3.1-Flag-Jun-DBD) were purchased from Genewiz (Beijing, China) (The schematic diagram of c-Jun plasmids was shown in Fig. 5D). The plasmids were transfected with Lipofectamine 3000 (L3000015, Thermo Fisher Scientific, USA) according to the manufacturer’s instructions. The above plasmids were confirmed by agarose gel electrophoresis and DNA sequence analysis. The overexpression efficiency was detected by qPCR and western blotting as described below. The siRNAs, let-7b-5p mimics, and inhibitors used in this study were all purchased from GenePharma Co., Ltd. (Shanghai, China). SiRNAs and let-7b-5p mimics/inhibitors were transfected into cell lines using Lipofectamine RNAiMax (13778150, Thermo Fisher Scientific, USA) according to the manufacturer’s instructions. The siRNAs and let-7b-5p mimics/inhibitors sequences were shown in Supplementary Tables 2 and 3. The knockdown or overexpression efficiency was detected by qPCR and western blotting as described below. Total RNA was extracted using TRIzol reagent (Takara, Japan). The mRNA and lncRNA (3 µg) were reverse transcribed for the synthesis of cDNA using a GoScript Reverse Transcription system (A5001, Promega, USA). The miRNA RT assay was conducted using the Hairpin-it microRNA and U6 snRNA Normalization RT-PCR Quantitation Kit (GenePharma, China). The expression status of mRNA and lncRNA were measured on an ABI QuantStudio 3 using GoTaq® qPCR Master Mix (A6001, Promega, USA), and the expression of GAPDH was used as an internal control. In addition, the expression status of miRNA was measured on an ABI QuantStudio 3 using Hairpin-it microRNA and U6 snRNA Normalization RT-PCR Quantitation Kit, and the expression of U6 was used as an internal control. For mRNA and lncRNA, the PCR system was 20 μL, and the following procedures were performed: 40 cycles were performed at 95 °C for 3 min, 95 °C for 15 s, and 60 °C for 1 min. The dissolution curve program is: 95 °C 15 s, 60 °C 1 min, 95 °C 1 s. For miRNA, the following procedures were performed: 40 cycles were performed at 95 °C for 3 min, 95 °C for 12 s, and 62 °C for 40 s. The relative expression level of the target gene was calculated by 2−△△CT. The primer sequences showed in Supplementary Table 4. The cells to be detected were seeded into 96-well plates in advance, generally ranging from 2 × 103 to 1 × 104 cells. The test was started 24 h later and lasted for 5 consecutive days. The medium in the well was absorbed and discarded, and CCK-8 reagent (APExBIO, USA) was mixed with serum-free medium in a 5 ml tube in advance and added into 96-well plates with 100 μL per well (CCK8 reagent: Serum-free medium = 10 μL: 90 μL per well). After incubation at 37 °C for 1 h, OD value was detected on spectrophotometric measurements at 450 nm. EdU assay Kit (Cell-Light EDU Apollo488 In Vitro Kit) was purchased from Ribobio Co., Ltd. (Guangzhou, China). The experiment was carried out in strict accordance with the kit instructions. For wound healing experiment: according to the density of 2 × 105 cells per well, U87 and LN229 cells were inoculated in the 6-well plate. After 24 h, siRNA transfection was carried out. After 24 h, 200 μL pipetting head was used to scratch the cells in the plate, and the serum-free medium was changed. Images at 0 h, 12 h, and 24 h were collected under an inverted microscope (IX81, Olympus Company, Japan) and the cell migration rate was calculated and analyzed using ImageJ software. Each assay was replicated three times. For Transwell experiment: the upper chamber of Transwell was coated with a layer of Matrigel matrix glue (matrix glue: serum-free medium = 1:4 v/v ratio) (Corning, USA). After solidification for 40 min at 37 °C in cell culture incubator, the upper chamber was placed in a 24-well plate, and the cells were resuspended and counted using serum-free medium. 5 × 104 cells were seeded into the upper chamber with 200 μL serum-free medium, and the lower chamber was added with 500 μL of full medium containing 10% fetal bovine serum. After 24 h of culture, 4% paraformaldehyde was used to fixate cells for 10 min, 1% crystal violet was used to staining cells for 10 s, the remaining cells in the upper chamber were slightly wiped off, and images were collected under a positive microscope (BX53, Olympus Company, Japan) and the number of gelled cells was counted. Each assay was replicated three times. Cell slides were made in advance, and 2 × 104 to 5 × 104 cells were planted on each 24-well cell slide. After 24 h, the medium was discarded, and the cells were fixed with 4% formaldehyde for 15 min. PBS was used to wash cells for three times, 3 min each time. 0.5% Triton X-100 was used to incubate for 20 min at room temperature, and the cells was washed with PBS for 3 times, 3 min each time. PBS was absorbed and dried with absorbent paper. Then, a sufficient amount of diluted β-catenin (CST, 8480, 1:100) or Alexa FluorTM 594 phalloidin (A12381, Thermo Fisher Scientific, USA) was added to each well. The solution was placed into a cartridge and incubated overnight at 4 °C. The cells were washed with PBST for 3 times, 3 min each. The diluted fluorescent secondary antibody was added and incubated for 1 h in 37 °C without light. The cells were washed with PBST for 3 times, 3 min each time. Each hole was dyed with 100 μL DAPI working solution for 10 min, and cleaned with PBST for 4 times, 5 min each time. The cover glass was dried with absorbent paper and laced upside down on the slide with anti-fluorescence attenuation sealing agent (Sigma, USA), then the fluorescence image was collected. After the cells were prepared, the medium was absorbed and discarded, washed twice with PBS, and the cells were scraped off with 400 μL precooled PBS. Then, transferred into 1.5 ml tube, centrifuged at 10,000 rpm at 4 °C for 10 s, and the supernatant was discarded. Resuspend the cells with 400 μL PBS containing RNA enzyme inhibitor (N2615, Promega, USA) (RNA enzyme inhibitors: PBS = 1:20 v/v ratio) and 0.1% NP-40 (85124, Thermo Fisher Scientific, USA) (NP-40: PBS = 1:1000 v/v ratio), put it in ice for 5 min, then vortexed for 5 s. After centrifugation at 10,000 rpm at 4 °C for 20 s, the supernatant was extracted from cytoplasm and transferred into a new tube. At this time, the supernatant was cytoplasm extract, and the precipitate was nucleus. The nuclei were resuspended by 200 μL PBS containing 0.5% NP-40 and RNA enzyme inhibitor (RNA enzyme inhibitors: PBS = 1:20 v/v ratio), and were blown for 10 times, placed on ice for 10 min, and centrifuged for 20 s at 13,000 rpm at 4 °C. The supernatant was the nuclear extract, and nuclear extract and cytoplasmic extract were used for subsequent RT-qPCR experiments according to the above steps. GFP-labeled HOXA11-AS probes were obtained from GenePharma Co., Ltd. (Shanghai, China). Hybridizations were carried out using FISH Kit (GenePharma) according to the manufacturer’s instructions. 4% paraformaldehyde was used to fix the glioma cells followed by the treatment of 0.5% Triton. Then, cells were cultured with specific probe overnight. All fluorescence images were captured using IX81 fluorescence inverted microscope (Olympus, Japan). The sequence for HOXA11-AS probe is: 5′-AATGCGAGACTCCAGGAGAATGCGGATCAGTGACAAACCGGAG GAGGGAGTTTCTCCAGAGGCTGTGGAAAGAAGCGTA-3′. The 3D structure of Ago2 was from https://www.rcsb.org/. The HOXA11-AS model was generated from the MC-Fold/MC-Sym program and analyzed for energy optimization using TINKER. Subsequently, we executed molecular docking between Ago2 and HOXA11-AS employing the SYBYL-X 2.0 program. The Magna RIP™ RNA-Binding Protein Immunoprecipitation Kit (17-700, Merck Millipore, USA) was used according to the manufacturer’s instructions. In c-Jun or Ago2 RIP experiments, 4 × 107 U87 cells were harvested and lysed in RIP lysis buffer. Each lysate was further divided into three groups for anti-c-Jun (anti-Ago2 or anti-Flag), anti-IgG (negative control), and Input (positive control). Either c-Jun, Ago2, Flag, or IgG antibody was added to each sample to enrich RNA binding protein (RBP). Subsequently, the RBP of interest, together with the bound RNA, were collected using dynabeads. After washing off unbound material, the RBP was digested by Proteinase K, and the RNA bound to immunoprecipitated RBP was purified and reverse-transcribed into cDNA. Then, qPCR assay was performed to measure the % Input of HOXA11-AS in each group. The primer sequences and antibody information used for RIP analysis were provided in Supplementary Tables 4 and 5. For ChIRP assay, ChIRP Kit (BersinBio, China) was used to verify the binding of HOXA11-AS to let-7b-5p and all the experimental steps were carried out in accordance with the kit instructions. A 3′ end Biotin modified-DNA probe targeting HOXA11-AS was synthesized and provided by BersinBio. 1.2 × 108 glioma cells were cross-linked with 1% formaldehyde and sonicated for the hybridization reaction. After the chromatin was sheared into 100–500 bp fragments, the cell lysates were incubated with the biotinylated DNA probe solution for 3 h at 37 °C. The binding complex was covered with streptavidin-conjugated magnet beads. RNA was finally eluted and purified from the magnet beads for RT-qPCR analysis. The sequences of the probes are available in Supplementary Table 6. The sequences of HOXA11-AS cDNA targeted by let-7b-5p were inserted into the upstream of the firefly luciferase in the pSICHECK2 vector to obtain pSICHECK2-HOXA11-AS reporter plasmid (Fig. 2H) and CTHRC1 3′-UTR were inserted into the upstream of the firefly luciferase in the pSICHECK2 vector to obtain pSICHECK2-CTHRC1 reporter plasmid (Hanbio, China) (Fig. 3B). The Tpl2 luciferase reporter plasmid (pTpl2-luc) was purchased from Hanbio Biotechnology Co., Ltd. (Tianjin, China) in order to detect the transcriptional activity of Tpl2. The c-Myc luciferase plasmid (pMyc-TA-luc) were purchased from Beyotime Biotechnology (Shanghai, China) to detect the transcriptional activity of c-Myc. The TOP/FOP luciferase reporter plasmids were obtained from Merck Millipore (USA) to detect the transcriptional activity of β-catenin. The promoter sequence of Tpl2, including A (−500/0), B (−1000/0), C (−1500/0), and D (-2000/0), were PCR-amplified from the genomic DNA of U87 cells, which were then inserted into the HindIII-NheI sites upstream of the firefly luciferase in the pGL3-Basic vector (Genewiz, China) (Fig. 5F). The luciferase plasmids were transfected with Lipofectamine 3000 and luciferase activity was measured with a Bright-Glo™ Luciferase Assay System (E2620, Promega, USA) according to the instructions. The luciferase expression was detected with a microplate reader (Synergy2, BioTek, USA). The cells were lysed by RIPA lysis buffer (Solarbio, China) containing a protease and phosphatase inhibitor cocktail (Sigma) and the protein concentration was detected with a BCA kit (Solarbio, China) according to the manufacturer’s instructions. Proteins were separated by 10% SDS-PAGE gels and transferred with 0.22 μm PVDF membranes (Millipore, USA). The membranes were blocked and incubated with specific antibodies overnight at 4 °C. The membranes were then incubated with the corresponding HRP-conjugated secondary antibody. The protein bands were visualized and detected by the enhanced chemiluminescence system (Bio-Rad, Hercules, EDA USA). The relevant antibodies used are shown in Supplementary Table 5. U87 and LN229 cells were cultured to 90% confluence. Cells were harvested for chromatin immunoprecipitation (ChIP) by ChiP kit (56383, CST, USA), according to manufacturer’s protocols. First, solubilized chromatin was prepared from a total of 2 × 107 cells. The chromatin solution was diluted 10‐fold with ChIP dilution buffer and precleared with protein A beads and preimmune serum. The precleared chromatin solution was divided and utilized in immunoprecipitation assays with anti‐c-Jun, anti-c-Myc, or anti‐IgG antibody. Following, the antibody‐protein‐DNA complex was eluted from the beads. After cross‐linking, protein and RNA were removed and the purified DNA was subjected to PCR with primers specific for the Tpl2 promoter region containing the c-Jun binding sites or HOXA11-AS promoter region containing the c-Myc binding sites. The primer sequences and antibody information were shown in Supplementary Tables 5 and 7. U87 cells infected with Lv-NC or Lv-HOXA11-AS were used for transcriptomics and proteomics. Based on the TMT labeling-based proteomics, we comparatively quantified the host proteome of Lv-NC and Lv-HOXA11-AS cells. Each group contained 3 duplicate samples. The transcriptomic analysis (including mRNA and small RNA sequencing) in this study was conducted at Gene Denovo Biotechnology Co., Ltd. (Guangzhou, China), and the proteomic analysis was conducted at Zhongke New Life Biotechnology Co., Ltd (Shanghai, China). The criteria for determining differential genes in transcriptomics were |fold change|(FC) > 1.5, P < 0.05. The proteomic criteria were |fold change|>1.2, P < 0.05 (Fig. 4A). To assess the half-life of Tpl2 mRNA, actinomycin-D (Act D, Sigma, A4262) was used to block mRNA synthesis. The Lv-HOXA11-AS were infected into U87 and LN229 cells. After 24 h, ActD was added to the culture medium, followed by incubation for 0 h, 2 h, 4 h, 6 h, 8 h, and 12 h. Total RNA was collected at different time points and Tpl2 mRNA stability in the ActD treatment group was analyzed by qPCR. U87 and LN229 cells were transfected with either si-HOXA11-AS or si-NC. After 48 h, cells were collected and cell lysates were prepared. Cellular ROS level were measured by fluorescence plate reader using Reactive Oxygen Species Assay Kit (Solarbio, China) according to the manufacturer’s instructions. For oxidative stress treatment in vitro assay, H2O2 was purchased from Sigma (29.4 μmol/μL, USA). Firstly, 10,000 cells were planted in 96-well plates per well in order to detect the IC50 (half maximal inhibitory concentration) of H2O2 in U87 and LN229 cell lines. The absorbance (OD) of each well was measured by CCK-8 at 8 h after dosing and the survival rate and IC50 of different concentration groups was calculated. The pH-sensitive nanoparticles (NPs) and pyropheophorbide-a (PPa) labeled NPs were prepared according our previous reports [21, 22]. In brief, methyl linoleate hydroperoxide (MLH) (40 μL, 100 mM in N,N-Dimethylformamide) was mixed with a Tetrahydrofuran (THF) solution of poly (ethylene glycol)-block-poly (diisopropylaminoethyl methacrylate) (PEG-PDPA) (3 mL, 8 mg). The MLH and PEG-PDPA were prepared by ourselves [21]. After dropwise addition of 8 mL of pure water, the solution containing nanomedicine was evaporated to remove organic solvent. To prepare PPa-labeled NPs, PEG-PDPA (4 mg) and PEG-PDPA-PPa (4 mg) were added during preparation. In addition, dynamic light scattering (DLS) experiment were performed with a Zetasizer Nano instrument (Malvern Instruments Ltd., United Kingdom) equipped with a 10-mW helium-neon laser (λ = 632.8 nm) and thermoelectric temperature controller to detect the particle size of NPs. The IC50 of NPs was measured in U87, LN229, and primary GBM cells at 24 h, 48 h, and 72 h after the addition of NPs and calculated at each time point using spectrophotometer. All animal procedures were conducted in accordance with protocols approved by the Tianjin Medical University Animal Care and Use Committee and followed guidelines for animal welfare. Four-week-old BALB/c female nude mice were purchased from Beijing HFK Bioscience Co., LTD. For subcutaneous xenograft models, 5 × 105 cells were suspended in 50 µL of PBS and implanted into the flanks of nude mice. For siRNA-mediated knockdown of HOXA11-AS in vivo, when the tumor volume reached 100 mm3, the animals were randomized into three groups with 7 mice in each group: si-NC group, si-HOXA11-AS#1 group and si-HOXA11-AS#2 group. For siRNA-mediated knockdown of Tpl2 in vivo, when the tumor volume reached 100 mm3, the animals were randomized into two groups with 10 mice in each group: si-NC and si-Tpl2 groups. After that, tumor volumes were measured every 2 days and si-HOXA11-AS or si-Tpl2 were injected into the knockdown group at a dose of 10 μL of siRNA versus 10 μL of Lipofectamine TM 3000 per nude mouse. After 17 or 21 days, the nude mice were sacrificed, subcutaneous tumors were removed, images were collected. The tumor volume was calculated with the formula Volume = (length × width2)/2. For biodistribution assay, U87 cells were infected with lentiviruses of GFP-luciferase (GenePharma, China) to establish a stable GFP-luciferase (GFP-luc) overexpression GBM cell model. 5 × 105 U87-GFP-luc cells were injected into the intracranial striatum of nude mice with a stereotactic instrument and a microinfusion pump (Stoelting Co., USA). And orthotopic glioma-bearing mice implanted with U87-GFP-luc cells were randomly divided into two groups (4 mice per group) and received one-time i.v. injection of Free-PPa or PPa-NPs (all at a dose equivalent of 4.0 mg/kg Free-PPa or PPa-NPs per mouse). The fluorescence intensity of the Free-PPa and PPa-NPs groups were monitored by the assistance of fluorescence imaging on hours 4, 12, 24, 48, and 72 via the IVIS imaging system (perkinelmer, USA) after injection of Free-PPa or PPa-NPs. After 72 h, the major organs were removed for fluorescence imaging again, and then the brain tissue was fixed, dehydrated, and sectioned into 10-μm slices for IF. For in vivo therapeutic model, shRNA lentiviruses (or scrambled lentivirus, GenePharma, China) were used to construct U87 cell lines with HOXA11-AS knockdown stably. 5 × 105 control or HOXA11-AS knockdown U87 cells were injected into the intracranial striatum of nude mice with a stereotactic instrument and a microinfusion pump (Stoelting Co., USA). The animals were divided into 4 groups (control, NPs, Lv-shHOXA11-AS, Lv-shHOXA11-AS + NPs), with 10 mice in each group. Starting on day 14 after tumor cell implantation, NPs (13.33 μmol/kg, iv) was given every other day for 4 times. Other animals in control and Lv-shHOXA11-AS groups were treated with an equal volume of PBS alone. In order to obtain tumor growth status in live animals of different treatment groups by bioluminescent imaging, the mice were anesthetized and injected intraperitoneally with D-luciferin (150 mg/kg, beetle luciferin, potassium salt, E1605, Promega) 10 min prior to imaging with the IVIS imaging system (PerkinElmer, USA) for 10–120 s. Eight weeks post implantation, the surviving nude mice in each group were sacrificed. Survival analysis was determined using Kaplan–Meier survival curve. The major organs were collected and sectioned into 10-μm slices for hematoxylin and eosin (H&E) and immunohistochemistry (IHC) staining. In order to conduct histological analysis, tumor tissues were fixed in 10% neutral buffered formalin for HE staining and IHC analysis. For HE staining, the HE staining kit (Solarbio, China) was used according to the manufacturer’s instructions. Pictures were taken using an Olympus upright BX53 microscope. For IHC analysis, 8 µm slides were dewaxed in xylene and then rehydrated through graded alcohols to distilled water (dH2O). Antigen retrieval was performed using sodium citrate (pH=6) buffer at 97 °C for 20 min. After washing in PBS, slides were blocked with 5% blocking serum for 30 min at room temperature. Next, the slides were incubated at 4 °C overnight with primary antibodies against β-catenin, Tpl2, p-MEK, and p-ERK before being incubated with a biotin-labeled secondary antibody (1:100 dilution) for 1 h at 37 °C, followed by incubation with diaminobenzidine (DAB). The slides were then counterstained with hematoxylin and mounted. Pictures were taken using an Olympus upright BX53 microscope. For IF assay, brain tissues were fixed in 4% paraformaldehyde and then dehydrated in 15% and 30% sucrose before optimal cutting temperature (OCT) compound embedding and cutting into 10 µm slides. After washing in PBS, slides were blocked with 3% blocking serum (contain 0.3% Triton X-) for 60 min at room temperature. The slides were incubated at 4 °C overnight with primary antibodies against NeuN. Then, the diluted fluorescent secondary antibody was added and incubated for 1 h in room temperature without light. Each slide was dyed with 100 μL DAPI working solution for 10 min, and cleaned with PBST for 3 times, 10 min each time. The cover glass was dried with absorbent paper and laced upside down on the slide with anti-fluorescence attenuation sealing agent (Sigma, USA), then the fluorescence image was collected by an Olympus FV1200 laser scanning confocal microscope. Antibodies information are shown in Supplementary Table 5. The image processing software used in this study was Photoshop CS6, and the image production and statistical calculation software was GraphPad Prism 8. In this study, unpaired t-test was used for the difference comparison between the two groups involved, one-way ANOVA was used for the comparison between the multiple groups, and two-way ANOVA was used for the comparison of OD values between the multiple groups of CCK-8. The log-rank test was used for survival analysis. P < 0.05 was considered statistically significant. Our previous studies showed a positive correlation between lncRNA HOXA11-AS (HOXA11 antisense RNA) expression and the grade of glioma patients. Moreover, HOXA11-AS could regulate glioma cell cycle progression, and maintain the tumor cell stemness [16]. Then, we analyzed the expression and prognosis of HOXA11-AS in glioma patients. Analysis of the TCGA and GTEx databases revealed that the expression levels of HOXA11-AS in glioblastoma (GBM) were higher than those in low-grade glioma (LGG, WHO II-III) and normal tissues (Fig. 1A). The receiver operating characteristic (ROC) curve also indicated that the expression of HOXA11-AS differed between normal brain tissue and glioma (Fig. 1B). To validate these findings, we performed real-time quantitative PCR (RT-qPCR) analysis of RNA expression in 41 glioma samples and found much higher levels of HOXA11-AS in WHO III-IV grade than that in WHO II grade (Fig. 1C). The subtype analyses were applied in CGGA, TCGA, REMBRANDT, and GSE16011 databases, and results showed that HOXA11-AS expression was closely associated with subtypes of glioma patients in CGGA, which may be related to the race (Fig. S1A–D). The analyses of CGGA, TCGA, REMBRANDT, and GSE16011 databases also revealed that the expression level of HOXA11-AS was closely related to the prognosis of glioma (Fig. S1E–H). Cox regression analysis in TCGA database showed that HOXA11-AS was an independent prognostic factor for glioma. (Fig. S1I–J). Furthermore, we further analyzed HOXA11-AS expression levels and prognosis in multiple tumors. The TCGA and GTEx databases showed elevated levels of HOXA11-AS in 16 tumor types, compared to their corresponding normal tissues (Fig. S2A). Prognostic analysis using the GEPIA database also indicated that high HOXA11-AS levels in a variety of tumors, including adrenocortical cancer (ACC), kidney papillary cell carcinoma (KIRP), LGG, and pancreatic cancer (PAAD), were associated with poor overall survival (OS) and recurrence-free survival (RFS) (Fig. S2B–C). These findings suggest that HOXA11-AS is an oncogenic lncRNA associated with poor prognosis in multiple tumors, including glioma. Therefore, we focused on HOXA11-AS and investigated its biological functions and molecular mechanisms in glioma. The expression of HOXA11-AS was examined in a panel of glioma cell lines via RT-qPCR, and the U87 and LN229 GBM cell lines were selected for subsequent experiments because of their elevated levels of HOXA11-AS (Fig. 1D). We knocked down and overexpressed HOXA11-AS in these two cell lines using siRNA and lentivirus of HOXA11-AS, respectively (Fig. S3A–B). CCK-8 and EdU assays showed that HOXA11-AS knockdown significantly inhibited cell proliferation, whereas its ectopic expression promoted cell growth (Figs. 1E–F and S4A–D). In addition, overexpression of HOXA11-AS promoted cell migration (Fig. S4E–F) and invasion (Fig. S4G–H) whereas its depletion reduced cell migration (Fig. S5A–B) and invasion (Fig. S5C–D). HOXA11-AS also enhanced the formation of filopodia in glioma cells (Fig. S5E–F). Next, we investigated the effects of HOXA11-AS knockdown on glioma growth in vivo. Xenograft mouse models were generated by the subcutaneous injection of U87 cells into nude mice. Seven days after implantation, the mice were treated with scramble siRNA or si-HOXA11-AS (Two siRNAs), respectively. The experiment was terminated after 17 days of treatment. The mice in si-HOXA11-AS#1 or si-HOXA11-AS#2 treatment group showed much lower tumor growth rate, smaller tumor size, and lighter tumor weight, as compared to those of the mice in the si-NC group (Fig. 1G–I). Collectively, these results indicate that elevated HOXA11-AS levels promote glioma cell proliferation, migration, invasion, and tumor growth. To define the molecular mechanisms of HOXA11-AS in glioma, we initially examined the cellular localization of HOXA11-AS in glioma. Analysis of the LncATLAS database indicated that HOXA11-AS was primarily localized in the nucleus (Fig. S6A), and RT-qPCR and FISH experiments on U87 and U251 cells revealed that HOXA11-AS was partially localized in the cytoplasm, but primarily localized in the nucleus (Fig. 2A–B), implying that it functions in both the cytoplasm and the nucleus. Previous studies have shown that many of the cytoplasmic lncRNAs act as molecular sponges (ceRNAs). Therefore, we next investigated whether cytoplasmic HOXA11-AS functions as a ceRNA in glioma. Molecular docking analysis predicted that HOXA11-AS binds to the Ago2 protein (Fig. 2C), which acts as the catalytic engine of the RNA-induced silencing complex (RISC) and plays a significant role in miRNA guided post-transcriptional gene silencing. RNA immunoprecipitation (RIP) analysis further revealed that HOXA11-AS forms a complex with Ago2 (Fig. 2D). Next, we performed mRNA and small RNA sequencing in U87 cells infected with Lv-HOXA11-AS or Lv-NC and found significant differential expression of 615 mRNAs (404 upregulated and 211 downregulated mRNAs in Lv-HOXA11-AS-infected cells) and 83 miRNAs (14 upregulated and 69 downregulated miRNAs in Lv-HOXA11-AS-infected cells), between Lv-HOXA11-AS- and Lv-NC-infected U87 cells (Fig. S6B–D). Then, using association analysis, we constructed a ceRNA network of HOXA11-AS in glioma to identify its target miRNAs, which included let-7b-5p, miR-504-5p, miR-1180-3p, miR-16-5p, miR-24-3p, miR-125b-5p, miR-140-3p, and miR-1343-3p (Fig. 2E). The CGGA database search showed reduced expression of let-7b-5p, miR-504-5p and miR-1180-3p in HGG compared with LGG, with poor overall survival (Fig. S6E–K). Since HOXA11-AS levels were elevated in HGG, we next investigated whether HOXA11-AS is the ceRNA of let-7b-5p, miR-504-5p, and miR-1180-3p. RT-qPCR showed that let-7b-5p expression was mostly decreased in response to the ectopic expression of HOXA11-AS (Fig. 2F). The ChIRP assay revealed that let-7b-5p binds to HOXA11-AS (Figs. 2G and S7A–B). HOXA11-AS was co-localized with let-7b-5p in U87 and LN229 cells (Fig. S7C). Furthermore, dual-luciferase assay using U87 and LN229 cells indicated that HOXA11-AS is a direct target of let-7b-5p (Fig. 2H). These results indicate that HOXA11-AS serves as a ceRNA for let-7b-5p in glioma. As the HOXA11-AS/let-7b-5p axis was active in glioma, we next explored its downstream targets. Analyses of eight databases (PITA, RNA22, miRmap, miRNAMap, miRanda, TargetScan, miRDB, and Starbase) revealed 71 mRNAs as possible downstream targets of let-7b-5p (Fig. 3A, left). In comparison with our mRNA sequence results, which showed 404 upregulated mRNAs in Lv-HOXA11-AS-infected U87 cells, we found that only CTHRC1, a critical gene promoting tumor progression and metastasis including glioma, overlapped with the let-7b-5p target set (Fig. 3A, right). Thus, we examined whether CTHRC1 was the downstream target of HOXA11-AS/let-7b-5p. Our previous studies showed that CTHRC1 was overexpressed in various cancer types and functions as an important oncogene that may promote tumorigenesis and development through different mechanisms including glioma [23]. Analysis of CTHRC1 expression in 41 glioma samples confirmed these results (Fig. S8A). RT-qPCR revealed that CTHRC1 expression dramatically decreased after transfection of let-7b-5p mimics into U87 and LN229 cells (Fig. S8B–D). To further demonstrate the direct regulation of CTHRC1 by let-7b-5p, we constructed luciferase reporter plasmid system with the targeting sequences of wild-type and mutated CTHRC1-3′UTRs (Fig. 3B, top), and transfected them into U87 and LN229 cells. Luciferase activities of wild-type, but not mutant CTHRC1 luciferase plasmid, were significantly suppressed by let-7b-5p mimics (Fig. 3B, bottom). Since HOXA11-AS acts as a ceRNA for let-7b-5p, we observed a significant increase in CTHRC1 mRNA levels following the ectopic expression of HOXA11-AS (Fig. S9A). RT-qPCR analysis of 41 glioma samples revealed a positive correlation between HOXA11-AS expression and CTHRC1 expression (Fig. S9B). In addition, we demonstrated that the overexpression of HOXA11-AS reversed the inhibitory effect of let-7b-5p mimics on CTHRC1 expression (Fig. S9C–D). These results indicated that CTHRC1 is a direct target of HOXA11-AS/let-7b-5p. CTHRC1 activates β-catenin pathway by inducing its nuclear localization [24, 25]. As HOXA11-AS increases CTHRC1 expression by serving as a ceRNA for let-7b-5p, we next investigated whether HOXA11-AS regulates the β-catenin pathway. Cell fractionation and immunofluorescence staining showed that ectopic expression of HOXA11-AS promoted the translocation of β-catenin from the cytoplasm into the nucleus (Fig. S9E–F). As β-catenin is a co-activator of TCF/LEF transcription factors that initiate the transcription of various genes, including c-Myc, cyclin D1, and c-Jun, we further determined whether CTHRC1 regulates c-Myc transcription [26–28]. Luciferase reporter assays were performed after transfection of c-Myc promoter-Luc construct and CTHRC1 siRNA into U87 and LN229 cells (Fig. S10A–B). We found that the knockdown of CTHRC1 significantly inhibited c-Myc transcription activity (Fig. S10B). Furthermore, the effect of HOXA11-AS and CTHRC1 on β-catenin co-activator activity was measured using the TOP/FOP luciferase reporter system, that has wild-type (TOP) or mutant (FOP) TCF binding sites upstream of a minimal c-fos promoter. TOP, but not FOP, luciferase activity was reduced after depletion of HOXA11-AS or CTHRC1 (Figs. 3C and S10C). Collectively, these results suggest that HOXA11-AS and CTHRC1 activate the β-catenin/c-Myc cascade. We subsequently investigated whether CTHRC1 mediates HOXA11-AS activity in the β-catenin/c-Myc pathway. We performed c-Myc and TOP/FOP luciferase reporter assays by transfecting U87 and LN229 cells with Lv-HOXA11-AS and CTHRC1 siRNA, individually and together. As shown in Figs. 3D and 3E, knockdown of CTHRC1 completely abrogated HOXA11-AS stimulated c-Myc and β-catenin transcription activities, and the introduction of siRNA-CTHRC1 alone repressed the basal c-Myc promoter-Luc and TOP-Luc activity in both cell lines. Since HOXA11-AS serves as a ceRNA for let-7b-5p to induce CTHRC1, we further examined the effect of let-7b-5p on HOXA11-AS-regulated β-catenin/c-Myc pathway. As shown in Fig. 3F, the HOXA11-AS knockdown reduced c-Myc transcriptional activity and depletion of let-7b-5p largely reversed the inhibitory effect of HOXA11-AS knockdown on c-Myc transcriptional activity (Figs. 3F and S10D). Taken together, these findings indicate that HOXA11-AS can activate the β-catenin/c-Myc pathway by preventing let-7b-5p from downregulating CTHRC1 expression. Studies have shown that c-Myc is an important transcription factor that regulates the transcription of multiple oncogenes [25]. The binding site of c-Myc was found in the HOXA11-AS promoter region in MCF-7 breast cancer cell lines from UCSC (Fig. S10E), indicating that c-Myc may bind to the promoter region of HOXA11-AS in glioma. ChIP-PCR experiments further verified that c-Myc could bind to the promoter region of HOXA11-AS, and the main binding site was in the −1500bp to −1000bp region of the HOXA11-AS promoter in U87 and LN229 cells (Fig. 3G). These results suggest that HOXA11-AS can regulate let-7b-5p/CTHRC1/β-catenin/c-Myc pathway to mediate its own transcription and form a unique self-activation loop. These results also confirmed the molecular mechanism underlying the abnormal expression of HOXA11-AS in glioma. As HOXA11-AS is predominantly located in the nucleus (Fig. 2A–B), we performed transcriptomics and proteomics analyses to identify its nuclear targets in U87 cells, following their transfection with Lv-HOXA11-AS or the Lv-NC control (Fig. 4A). Transcriptomics and proteomics analyses revealed that 615 genes (FC > 1.5, P < 0.05) and 136 proteins (FC > 1.2, P < 0.05) were significantly differentially expressed between U87-Lv-HOXA11-AS and U87-Lv-NC cells (Fig. S11A–B). Next, we performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of these 615 differentially expressed genes (DEGs) and 136 differentially expressed proteins (DEPs), individually (Fig. S11C–F) and collectively (Fig. 4B–C). GO and KEGG analysis revealed that the DEGs were primarily enriched in signal transduction, regulation of signal transduction, and the TNF and TGF-β signaling pathways (Fig. S11C–D), whereas the DEPs primarily affected chromatin silencing, chromosome organization and TNF signaling pathway (Fig. S11E–F). Notably, integrated analysis of 615 DEGs and 136 DEPs revealed that, while multiple terms were common in GO enrichment analysis (Fig. 4B), only the TNF-α pathway was shared by the DEGs and DEPs in KEGG enrichment analysis (Fig. 4C), indicating that the TNF-α pathway could be an important nuclear target of HOXA11-AS. In addition, to further identify the function of HOXA11-AS involvement in glioma, Pearson correlation and cluster analysis were performed on CGGA and TCGA cohorts according to the expression pattern of HOXA11-AS. Heatmaps indicated that a large number of genes were positively or negatively correlated with HOXA11-AS in CGGA and TCGA databases (Fig. S12A–C). KEGG pathway analysis revealed that HOXA11-AS was highly correlated with TNF-α pathway in CGGA and TCGA cohorts (Fig. S12B–D), indicating that the TNF-α pathway might be an important downstream pathway for HOXA11-AS. Next, we performed RT-qPCR and identified 17 DEGs in the TNF-α pathway through transcriptomics analysis, 13 of which were upregulated and the others were downregulated, in Lv-HOXA11-AS infected U87 and LN229 cells (Fig. 4D–E). Among the 13 genes upregulated by HOXA11-AS, tumor progression locus 2 (Tpl2, also known as COT or MAP3K8) is a hub gene of the TNF-α pathway [29, 30]. Tpl2 regulates the occurrence and progression of tumor cells by activating the MEK/ERK pathway [31]. To investigate the role of Tpl2 and its association with HOXA11-AS in glioma, we demonstrated that the protein and phosphorylation levels of Tpl2, and activation of MEK1/2-ERK1/2, were induced by the ectopic expression of HOXA11-AS in U87 and LN229 cells (Fig. 4F–G). Furthermore, GTEx and TCGA database analyses revealed that Tpl2 expression in GBM was higher than that in LGG and normal brain tissues (Fig. S13A). Moreover, analyses of the CGGA, TCGA, REMBRANDT, and GSE16011 databases indicated that Tpl2 expression was closely correlated with the WHO grade and prognosis of glioma (Fig. S14). Correlation analysis in TCGA database and 41 glioma tissues showed that HOXA11-AS expression was positively correlated with Tpl2 expression (Fig. S13B–C). Glioma patients with high levels of both HOXA11-AS and Tpl2 expression had a poorer prognosis than those with overexpression of either alone (Fig. S13D–E). Next, we evaluated the effect of Tpl2 on glioma growth and progression. CCK-8, wound healing, and transwell assays showed that ectopic expression of Tpl2 promoted, while knockdown of Tpl2 inhibited glioma cell proliferation, migration, and invasion (Figs. S13F–K and S15A–H). Furthermore, we established a glioma xenograft mouse model using U87 cells. We found that Tpl2 siRNA treatment significantly reduced tumor growth, weight, and size (Fig. 4H–J). Notably, the proliferation, migration, and invasion of glioma cells induced by the ectopic expression of HOXA11-AS were largely inhibited by the knockdown of Tpl2 (Fig. S16A–F). These results indicate that Tpl2 is a key mediator of HOXA11-AS and plays a pivotal role in glioma growth. To elucidate the mechanism by which HOXA11-AS induces Tpl2 mRNA expression, we performed an actinomycin D chase assay and found that HOXA11-AS had no effect on Tpl2 mRNA stability (Fig. 5A). However, Tpl2 promoter luciferase assay showed that the depletion of HOXA11-AS inhibited, whereas ectopic expression of HOXA11-AS stimulated, Tpl2 transcription activity (Fig. 5B). It is well documented that lncRNAs can recruit transcription factors (TFs) to the promoter region of target genes [32]. Therefore, we hypothesized that HOXA11-AS recruits TF to the Tpl2 promoter region. PROMO database analysis revealed TFs that can bind to the Tpl2 promoter (Fig. S17A). Among these TFs, c-Jun was the most important, as it is a major downstream TF of the MEK-ERK pathway [33, 34]. Further analysis of the UCSC database revealed c-Jun binding sites within the Tpl2 promoter in A549 lung cancer cells (Fig. S17B). In addition, analysis of the Tartaglialab database (http://www.tartaglialab.com/) revealed possible binding of HOXA11-AS to c-Jun (Fig. S17C). These findings strongly suggested that HOXA11-AS might recruit c-Jun to the Tpl2 promoter. To confirm these predictions, we performed RIP experiments in U87 and LN229 cells using a c-Jun antibody for immunoprecipitation, and found that HOXA11-AS bound to c-Jun (Fig. 5C). We then created Flag-c-Jun deletion mutants, including Jun-transactivation domain (TAD), Jun-241-254 and Jun-DNA binding domain (DBD) (See Fig. 5D for the schematic diagram of the c-Jun deletion mutants). The binding of HOXA11-AS to four different domains of c-Jun was verified by RIP assay in U87 and LN229 cells. The results showed that the TAD of c-Jun interacted with HOXA11-AS (Fig. 5D). Subsequently, ChIP assay revealed that c-Jun binds to the Tpl2 promoter, and the regions corresponding to primers A, D and H are the location where c-Jun is most likely to bind Tpl2 (Fig. 5E). To determine the region of the Tpl2 promoter that interacts with c-Jun/HOXA11-AS, we constructed luciferase plasmids with four different Tpl2 promoter regions, −500bp/0, −1000bp/0, −1500bp/0 and −2000bp/0 respectively. The luciferase reporter assay demonstrated that c-Jun/HOXA11-AS bound to the −1000 to −1500bp region in the Tpl2 promoter (Fig. 5F–G). In addition, the ChIP assay revealed that the binding of c-Jun to the Tpl2 promoter was largely abrogated by knockdown of HOXA11-AS (Fig. 5H). Furthermore, we investigated whether the induction of Tpl2 by HOXA11-AS depended on the presence of c-Jun. After infection of U87 and LN229 cells with Lv-HOXA11-AS alone and together with c-Jun siRNA, RT-qPCR and western blotting analyses showed that HOXA11-AS-induced Tpl2 mRNA and protein expression was significantly reduced by c-Jun depletion (Figs. 5I–J and S17D). Collectively, these results indicate that HOXA11-AS recruits c-Jun to the Tpl2 promoter to activate Tpl2/MEK/ERK pathway. Cancer cells are more sensitive to the accumulation of ROS because they have higher basal levels of ROS, owing to the enhanced antioxidant capacity [35–37]. Increasing intracellular ROS levels to further aggravate oxidative stress in tumor cells can help cross the toxicity threshold in cancer cells before normal cells, and thus selectively kill cancer cells. The MEK-ERK pathway can affect the antioxidant capacity of cancer cells, including glioma [38, 39]. The above results indicate that HOXA11-AS regulates the activity of the Tpl2-MEK1/2-ERK1/2 pathway. Therefore, we hypothesized that HOXA11-AS may affect the sensitivity of glioma cells to ROS. To test this hypothesis, we first verified whether HOXA11-AS expression could affect ROS levels in glioma, and found that of HOXA11-AS knockdown did not affect intracellular ROS levels in U87 and LN229 cells (Fig. S17E). Then, we used hydrogen peroxide(H2O2) as a model drug to verify whether HOXA11-AS affects the sensitivity of glioma cells to ROS. The IC50 of H2O2 was found to be 27.55 to 30.27 nM in U87 cells, and 10.21 to 10.98 nM in LN229 cells, through CCK-8 assay (Fig. S18A). HOXA11-AS knockdown was found to increase the sensitivity of glioma cells to H2O2, while HOXA11-AS overexpression reduced it (Figs. 6A and S18B). For future translational research, we developed a nanoparticle (NP) platform that could promote ROS production in cancer cells. This NPs could exist stably and decompose into PEG-PDPA and MLH under the action of hydrogen ions after entering cells. Iron ions (labile iron pool, LIP) could react with MLH and promote the decomposition of MLH into RO· (peroxide). The peroxide compounds released from NPs produce intracellular ROS and kill the tumor cells (Fig. 6B) [21]. The particle size of NPs was about 50 nm verified by DLS experiment (Fig. S18C). Two GBM cell lines and two patient-derived primary GBM cells were used to study the role of HOXA11-AS in increasing the resistance of cancer cells to ROS. In vitro ROS sensitization experiments showed that HOXA11-AS knockdown increased the sensitivity of glioma cells to ROS produced by NPs, and vice versa. (Figs. 6C and S18D–G). These results indicated that HOXA11-AS could regulate the sensitivity of glioma cells to ROS released from NPs in vitro. To further verify HOXA11-AS could regulate the sensitivity of glioma cells to ROS released from NPs in vivo, the ability of NPs to cross the blood-brain barrier (BBB) was verified. We inoculated U87-GFP-luc cells into the brain of Balb/c nude mice to form an orthotopic glioma model, and injected Free-PPa and PPa-NPs into mice through the tail vein. Fig. S19A showed that PPa-NPs could cross the BBB compared with Free-PPa. In vivo distribution experiments showed that Free-PPa and PPa-NPs were mainly enriched in the liver and kidney (Fig. S19B). Furthermore, these PPa-NPs were able to accumulate at tumor sites (Fig. 19 C) rather than normal brain tissue (Fig. 19D) or neurons (Fig. 19E) To investigate the ability of HOXA11-AS to regulate ROS sensitivity in vivo, xenograft tumors were established through the injection of U87 cells stably expressing bioluminescent reporter luciferase or HOXA11-AS shRNA, into the forebrain striatum and treated with NPs. The results showed that both NP treatment and HOXA11-AS knockdown alone modestly improved survival and reduced tumor burden (Fig. 6D–E). However, the combination of NP treatment and HOXA11-AS knockdown significantly reduced the tumor burden and improved survival (Fig. 6F–G). The median survival results showed that the co-treatment with NPs and HOXA11-AS knockdown group (all ten nude mice were alive at the end of the experiment on day 58) lived significantly longer than the control (38 days), NP treatment (44 days), and HOXA11-AS knockdown (six nude mice were still alive at the end of the experiment on day 58) groups (Fig. 6G). In addition, IHC analysis showed that the combination of NP treatment and HOXA11-AS knockdown significantly reduced the signals of β-catenin in nuclear, Tpl2, p-MEK and p-ERK compared with control or NPs groups (Fig. S20). Histological analysis of the mice did not reveal any deleterious effects of cotreatment of HOXA11-AS knockdown and NP in the main organs, including the heart, liver, spleen, lung, and kidneys (Fig. S21). Notably, we did not observe any physical or behavioral differences between the treatment groups and the control group. Specifically, there were no statistically significant differences in constitutional signs, including animal weights, in any of the treatment groups compared to the control group. In summary, HOXA11-AS knockdown sensitized GBM cells to ROS in xenograft models, drastically impairing tumor growth and prolonging survival. Glioma is the most common malignant tumor of the central nervous system, accounting for 80% of all primary brain tumors. Despite advances in understanding the molecular and cellular biology of glioma, there have been no significant changes in treatment strategies [2]. Increasing evidence shows that many long noncoding RNAs (lncRNAs) in glioma are closely associated with tumorigenesis and prognosis [40]. HOXA11-AS is located on chromosome 7p15.2, and the length of the HOXA11-AS gene is 3,885 bp, whereas that of the HOXA11-AS transcript is 1628 nt [18]. It has been reported that HOXA11-AS could act as either an oncogene or tumor suppressor gene in many types of tumors. For example, HOXA11-AS can act as an oncogene in non-small cell lung cancer (NSCLC), hepatocellular carcinoma (HCC), glioma, breast cancer (BC), gastric cancer (GC), kidney cancer (RC), uveal melanoma (UM), laryngeal squamous cell carcinoma (LSCC), cervical cancer (CC), esophageal squamous cell carcinoma (ESCC), and osteosarcoma [16, 41–47]. In contrast, HOXA11-AS exerts a tumor suppressor effect in epithelial ovarian cancer (EOC) [48]. Our previous studies showed that oncogenic HOXA11-AS regulates glioma cell cycle progression and tumor cell stemness[16]. Based on bioinformatics analyses of glioma databases, we found that high expression of HOXA11-AS was significantly correlated with high-grade and poor prognosis in glioma. CCK-8, EdU, wound healing, and Transwell assays showed that HOXA11-AS promoted the proliferation, migration, and invasion of glioma cells. Subcutaneous xenograft experiments also verified the role of HOXA11-AS in promoting glioma growth. These results indicated that HOXA11-AS is highly expressed, and promotes the growth and progression of glioma, confirming its oncogenic function. Subcellular location assay has shown that HOXA11-AS is mainly located in the nuclear region and a small part is located in the cytoplasm. Most lncRNAs located in the cytoplasm likely function as ceRNAs. For example, Zhan et al. found that HOXA11-AS could act as a ceRNA by absorbing miR-214-3p in HCC, while miR-214-3p could directly inhibit enhancer of zeste homolog 2 (EZH2) transcription [49]. In glioma, HOXA11-AS could regulate the expression of EZH2 by sponging miR-214-3p [50]. Since HOXA11-AS could bind to Ago2 via RIP assay, it was speculated that HOXA11-AS could function as a ceRNA. Furthermore, eight target miRNAs that might be regulated by HOXA11-AS were identified using small RNA sequencing. Through luciferase reporter assay, let-7b-5p was identified as the direct target of HOXA11-AS, and CTHRC1, the downstream target of let-7b-5p, was also discovered. which indicated that CTHRC1 is a direct target of HOXA11-AS/let-7b-5p. We further found that HOXA11-AS could regulate the β-catenin/c-Myc pathway through the let-7b-5p /CTHRC1 axis, and c-Myc could bind to the HOXA11-AS promoter. These results indicate the presence of the self-activation loop, which also explains the molecular mechanism underlying the abnormal expression of HOXA11-AS in glioma. Since HOXA11-AS is mainly located in the nucleus of glioma cell, we continued to explore the main mechanism of HOXA11-AS in the nucleus. Studies have reported that lncRNAs located in the nucleus may function as molecular scaffolds, recruiting transcription factors to target gene promoters and thus promoting the transcription of target genes. For example, Chen et al. found that HOXA11-AS could interact with DNA (Cytosine-5)-methyltransferase 1 (DNMT1) and enhancer of zeste homolog 2 (EZH2), and recruit DNMT1 and EZH2 to the promoter region of miR-200b, thereby regulating the transcriptional activity of miR-200b. Integrated analysis of transcriptomics and proteomics showed that TNF-α pathway might be an important downstream pathway for HOXA11-AS. The key node gene Tpl2 in this pathway has attracted our attention. Through ChIP, RIP, and rescue experiments, we found that HOXA11-AS promoted Tpl2 transcription by recruiting c-Jun to its promoter, thus activating the Tpl2-MEK1/2-ERK1/2 pathway. Moreover, HOXA11-AS regulates the proliferation, migration, and invasion of glioma cells by activating the Tpl2-MEK1/2-ERK1/2 pathway. By increasing intracellular ROS levels and inhibiting the antioxidant capacity of cancer cells to further aggravate oxidative stress, cancer cells can reach the toxicity threshold before normal cells, leading to their selective destruction. The MEK-ERK pathway can affect the antioxidant capacity of cancer cells [37]. Therefore, we speculated that HOXA11-AS might affect the sensitivity of glioma cells to ROS by regulating the Tpl2-MEK1/2-ERK1/2 pathway. Through ROS experiments, we found that HOXA11-AS increased the tolerance of glioma cells and primary GBM cells to ROS through the Tpl2-MEK1/2-ERK1/2 pathway. In vivo experiments, HOXA11-AS knockdown combined with adjuvant therapy using ROS-producing nanoparticles significantly increased the sensitivity of glioma cells to ROS. These results suggest that HOXA11-AS regulates the sensitivity of glioma cells to ROS through the Tpl2-MEK1/2-ERK1/2 pathway. Figure 7 shows the mechanism of the core pathway delineated the entire manuscript. Taken together, we found that HOXA11-AS could play an oncogenic role in glioma and is associated with poor prognosis. Cytoplasmic HOXA11-AS could upregulate the expression of CTHRC1 by adsorbing of let-7b-5p, thus activating β-catenin/c-Myc to form a self-activation loop, which may be the reason for the abnormal expression of HOXA11-AS. In addition, we also confirmed that nuclear HOXA11-AS promoted Tpl2 transcription by recruiting c-Jun to the promoter region of Tpl2, thereby activating the Tpl2-MEK1/2-ERK1/2 pathway, affecting the sensitivity of glioma to ROS. Our data suggest that HOXA11-AS can act as a promising prognostic biomarker and new therapeutical target to mediate ROS sensitivity in glioma. A reproducibility checklist Supplementary materials(Figure S1-S21) Supplementary Table 1. Clinical data of glioma patients. Supplementary Table 2. The sense and antisense siRNA sequences for mRNA or lncRNA knockdown. Supplementary Table 3. The mimics and inhibitor sequences for let-7b-5p overexpression or knockdown. Supplementary Table 4. The forward and reverse primers for qRT-PCR. Supplementary Table 5. Antibody informations. Supplementary Table 6. The odd and even probe sequences for ChIRP assay. Supplementary Table 7. The forward and reverse primer sequences for ChIP assay. Original full length western blots

PMC9646923

Marianne Dölz,Marko Hasiuk,John D. Gagnon,Mara Kornete,Romina Marone,Glenn Bantug,Robin Kageyama,Christoph Hess,K. Mark Ansel,Denis Seyres,Julien Roux,Lukas T. Jeker

Forced expression of the non-coding RNA miR-17∼92 restores activation and function in CD28-deficient CD4+ T cells

17-10-2022

Biological sciences,molecular mechanism of gene regulation,immunology

Summary CD28 provides the prototypical costimulatory signal required for productive T-cell activation. Known molecular consequences of CD28 costimulation are mostly based on studies of protein signaling molecules. The microRNA cluster miR-17∼92 is induced by T cell receptor stimulation and further enhanced by combined CD28 costimulation. We demonstrate that transgenic miR-17∼92 cell-intrinsically largely overcomes defects caused by CD28 deficiency. Combining genetics, transcriptomics, bioinformatics, and biochemical miRNA:mRNA interaction maps we empirically validate miR-17∼92 target genes that include several negative regulators of T cell activation. CD28-deficient T cells exhibit derepressed miR-17∼92 target genes during activation. CRISPR/Cas9-mediated ablation of the miR-17∼92 targets Pten and Nrbp1 in naive CD28−/− CD4+ T cells differentially increases proliferation and expression of the activation markers CD25 and CD44, respectively. Thus, we propose that miR-17∼92 constitutes a central mediator for T cell activation, integrating signals by the TCR and CD28 costimulation by dampening multiple brakes that prevent T cell activation.

Forced expression of the non-coding RNA miR-17∼92 restores activation and function in CD28-deficient CD4+ T cells CD28 provides the prototypical costimulatory signal required for productive T-cell activation. Known molecular consequences of CD28 costimulation are mostly based on studies of protein signaling molecules. The microRNA cluster miR-17∼92 is induced by T cell receptor stimulation and further enhanced by combined CD28 costimulation. We demonstrate that transgenic miR-17∼92 cell-intrinsically largely overcomes defects caused by CD28 deficiency. Combining genetics, transcriptomics, bioinformatics, and biochemical miRNA:mRNA interaction maps we empirically validate miR-17∼92 target genes that include several negative regulators of T cell activation. CD28-deficient T cells exhibit derepressed miR-17∼92 target genes during activation. CRISPR/Cas9-mediated ablation of the miR-17∼92 targets Pten and Nrbp1 in naive CD28−/− CD4+ T cells differentially increases proliferation and expression of the activation markers CD25 and CD44, respectively. Thus, we propose that miR-17∼92 constitutes a central mediator for T cell activation, integrating signals by the TCR and CD28 costimulation by dampening multiple brakes that prevent T cell activation. T cells are critical to protect mammals from infections and tumors. T cell activation, a key event for adaptive immunity, relies on two signals: T cell receptor (TCR) stimulation as well as costimulation by specialized receptors. While the TCR signal provides specificity, costimulation by antigen-presenting cells (APCs) provides the quantitiative and qualitative support for T cell activation (Acuto and Michel, 2003; Esensten et al., 2016). One of the best-studied and prototypical costimulatory molecules is CD28. It promotes multiple processes required for T cell biology such as T cell activation, proliferation, survival, metabolic adaptation, and Interleukin-2 (IL-2) production (Esensten et al., 2016; Riha and Rudd, 2010). These form the basis for clonal expansion and T cell differentiation into a variety of effector T cells that are necessary to mount effective immune responses. Recent data further demonstrates that CD28 expression is not only required for T cell priming but also days later for effector CD4+ T cell responses during infection (Linterman et al., 2014). Due to its centrality for T cell and immune responses more generally, CD28 is an important target in therapeutic immunology (Sansom and Walker, 2013; Esensten et al., 2016; Edner et al., 2020). CD28 blockade by CTLA4-Ig is clinically used to prevent renal allograft rejection and to treat rheumatic disease (Esensten et al., 2016). In contrast, vaccine adjuvants induce the activation of innate immune cells to express ligands that trigger costimulatory molecules on T cells. Furthermore, immune-activating CTLA-4 blocking antibodies represent the foundation of cancer immunotherapy (Leach et al., 1996) and CD28 is required for cancer immunotherapy with PD-1 blocking antibodies (Kamphorst et al., 2017; Hui et al., 2017; Homet Moreno et al., 2016). Finally, CD28 intracellular signaling domains can provide the required costimulatory signal in second and third generation chimeric antigen receptor (CAR) T cell constructs for adoptive cellular therapies (Gross and Eshhar, 2016). Thus, CD28 is a key clinically relevant immunoregulatory receptor and a precise understanding of molecular events induced by CD28 costimulation has direct therapeutic relevance. However, despite intense research, the understanding of the molecular consequences of CD28 costimulation remains incomplete (Esensten et al., 2016; Tian et al., 2015; Liu et al., 2018). CD28 costimulation acts through pleiotropic effects; it promotes the phosphatidylinositol 3-kinase (PI3K) pathway, amplifies the TCR signal, stabilizes the transcriptome induced by TCR stimulation, and enhances calcineurin/NFAT signaling (Riha and Rudd, 2010; Esensten et al., 2016; Martinez-Llordella et al., 2013). Collectively, CD28 stimulation ultimately leads to transcriptional changes mediated by the transcription factors (TF) NF-κB, AP-1, and NFAT (Esensten et al., 2016). In addition, CD28 also acts through many non-transcriptional mechanisms such as mRNA stabilization and altered mRNA splicing (Esensten et al., 2016). Thus, due to the complexity of CD28 costimulation, substantial controversy remains about the importance of various molecular mechanisms and the relative importance of quantitative versus qualitative CD28-mediated signals (Esensten et al., 2016; Acuto and Michel, 2003; Riha and Rudd, 2010; Sansom and Walker, 2013). Since a protein-centric view prevailed in most studies, here we investigated the role of the microRNA cluster miR-17∼92, a non-coding RNA, in CD28 costimulation and T cell activation. MicroRNAs (miRNA) are arguably the best-studied class of non-coding genes. These short RNA molecules of ∼22nt length are highly conserved gene repressors that mainly act through base pairing with the 3′ untranslated region (UTR) of target RNAs resulting in their decreased abundance and/or translational inhibition (Baumjohann and Ansel, 2013). Together with transcription factors (TFs), miRNAs are the most important trans-regulators of gene expression, regulating most mRNAs (Bartel, 2018). Target RNAs can be bound by multiple miRNAs and individual miRNAs can bind to and repress multiple genes, often genes found in the same pathway (Baumjohann and Ansel, 2013). Canonical base-pairing is largely determined by miRNA nucleotide positions 2–7, called the “seed” region (Bartel, 2018) but non-canonical miRNA targeting is widespread and can be equally effective (Loeb et al., 2012; Hsin et al., 2018). The interaction of a miRNA and its target mostly results in mild gene repression, often only reducing protein concentration by less than 2-fold. Nevertheless, miRNA-mediated gene regulation is highly consequential for T cell differentiation and function (Xiao and Rajewsky, 2009; O'Connell et al., 2010; Baumjohann and Ansel, 2013; Jeker and Bluestone, 2013). We noticed that key functions attributed to CD28 such as T cell proliferation, survival, and TFH differentiation are also regulated by miR-17∼92. miR-17∼92, a polycistronic transcript induced by CD28 costimulation (de Kouchkovsky et al., 2013), gets processed into 6 mature miRNAs representing 4 different seed families (Xiao and Rajewsky, 2009). Much like CD28, miR-17∼92 promotes T cell proliferation, survival, and differentiation (Xiao et al., 2008; Jeker and Bluestone, 2013). Moreover, T cell-specific miR-17∼92-deficiency results in severely impaired TFH differentiation and impaired GC formation (Baumjohann et al., 2013; Kang et al., 2013), reminiscent of impaired GC formation observed in CD28-deficient mice (Ferguson et al., 1996). On the contrary, miR-17∼92 overexpression results in a systemic lupus-like syndrome with increased GC formation and autoantibody production (Xiao et al., 2008) most likely due to enhanced TFH generation (Kang et al., 2013; Baumjohann et al., 2013). Here, we show that miR-17∼92 was necessary to repress genes in CD28−/− T cells and forced miR-17∼92 expression was sufficient to restore an important fraction of the impaired transcriptional regulation and function of CD28-deficiency in murine CD4+ T cells. We used transcriptome analysis, computational predictions and biochemical miRNA/target RNA interaction maps to define a high-confidence set of 68 empirically validated direct miR-17∼92 target genes in T cells. CRISPR/Cas9-mediated deletion of individual miR-17∼92 target genes restored distinct functions in naive CD28−/− T cells. We propose that miR-17∼92 acts as an important mediator of T cell activation/CD28-costimulation by repressing multiple inhibitory proteins that restrain T cells from becoming activated. miR-17∼92 expression has previously been linked to T cell activation/CD28 costimulation (Sandberg et al., 2008; de Kouchkovsky et al., 2013) and the CD28 ligands CD80 and CD86 have a dose-dependent effect on miR-17 expression (Wang et al., 2015). However, the relative contribution of TCR versus CD28 is not well understood. To further investigate this relationship for miR-17∼92 induction, we stimulated naive murine CD4 T cells with various combinations of anti-CD3/anti-CD28 monoclonal antibodies (Figure 1A). TCR stimulation alone increased miR-17 expression while isolated CD28 stimulation did not. Compared to low TCR stimulation, high TCR stimulation had a little additional effect on miR-17 but further increased CD69/CD25 expression (Figures 1A and 1B). Adding even a low concentration (0.2 μg/mL) of anti-CD28 antibody to TCR stimulation increased miR-17 expression at both timepoints (24 and 48 h). The induction of miR-17 progressed from 24 h to 48 h. At any concentration of anti-CD3 mAb stimulation and timepoint, CD28 costimulation resulted in a positive interaction for miR-17 expression. At both timepoints, the combination of low anti-CD3 (0.5 μg/mL) and low anti-CD28 (0.2 μg/mL) resulted in higher induction of miR-17 than the highest anti-CD3 stimulation alone (5 μg/mL). Finally, at 48 h, low TCR stimulation combined with high CD28 costimulation increased miR-17 but not CD25/69 expression in comparison to low TCR/low CD28 costimulation. In summary, TCR signaling and CD28 costimulation are intricately linked to miR-17 expression (as a surrogate miRNA for the miR-17∼92 cluster). Although TCR stimulation alone induced miR-17 expression, combined TCR and CD28 stimulation synergistically increased miR-17 resulting in stronger expression than through either signal alone. These results are consistent with and extend previous experiments using natural ligands that demonstrated a graded control of miR-17∼92 by CD28 (Wang et al., 2015). In order to further investigate the functional connection between CD28 and miR-17∼92, we analyzed the consequences of T cell-specific loss and gain of miR-17∼92 on key processes regulated by CD28. We compared samples from mice that lack miR-17∼92 in T cells (CD4cre.miR-17∼92lox/lox, designated T1792Δ/Δ hereafter), wildtype (wt) mice, and mice overexpressing miR-17∼92 in T cells (CD4cre.Rosa26loxSTOPloxCAG-miR-17∼92Tg, designated T1792tg/tg hereafter). In comparison to wt T cells, proliferation as well as the production and secretion of the CD28-dependent cytokine interleukin-2 (IL-2) was impaired in T1792Δ/Δ T cells and increased in T1792tg/tg T cells (Figures S1A–S1C) confirming previous findings (Baumjohann et al., 2013; Kang et al., 2013; Steiner et al., 2011). Thus, miR-17∼92-deficiency was reminiscent of phenotypes observed in CD28-deficient mice. In contrast, transgenic miR-17∼92 had the opposite effect. Thus, the results demonstrated that the non-coding RNA miR-17∼92 exerted a dose-dependent regulation of CD4+ T cell proliferation and IL-2 production which are known to be CD28-dependent. We hypothesized that miR-17∼92 could be a downstream mediator or integrator of T cell activation/CD28 costimulation. To test this hypothesis we crossed B6.CD28−/− (CD28−/−) mice (Shahinian et al., 1993) with T1792tg/tg mice, resulting in B6.CD28−/−.CD4cre.Rosa26loxSTOPloxCAG-miR-17∼92Tg designated “rescue” hereafter. T cells in these mice lack CD28 but constitutively express transgenic miR-17∼92. If miR-17∼92 physiologically supported CD28 costimulation then we expected that transgenic miR-17∼92 expressions could restore some of the CD28 defects. First, we investigated how “rescue” cells behaved in vitro. We compared wt, CD28−/−, and “rescue” naive CD4+ T cells stimulated with plate-bound anti-CD3 mAb alone or a combination of anti-CD3 and anti-CD28 mAb. As expected, wt cells blasted (Figure 1C) and increased proliferation (Figure 1D) in response to costimulation compared to anti-CD3 stimulation alone. Compared to wt cells, CD28−/− T cells showed reduced size and proliferation and were unable to respond to anti-CD28 stimulation (Figures 1C and 1D). In contrast, “rescue” T cells stimulated with anti-CD3 alone or combined anti-CD3/anti-CD28 blasted and proliferated like wt T cells fully stimulated with anti-CD3 and anti-CD28 mAb (Figures 1C and 1D). Next, we turned our attention to surface markers whose expression is either TCR- or CD28-dependent. The early activation marker CD69 is rapidly upregulated during T cell activation and reflects TCR signaling strength (Shang et al., 2018). As a second marker, we used the high affinity IL-2 receptor alpha subunit CD25, a well-known CD28-dependent gene. The relative number of CD69+CD25+ cells and CD69 expression per cell (Figure 1E) was comparable for all genotypes, as predicted for a TCR-dependent marker. In contrast, the signal intensity of CD25 was clearly CD28-dependent. Wildtype T cells increased CD25 MFI after CD28 costimulation while CD28−/− T cells displayed lower CD25 MFI than wt cells and were unable to respond to CD28 stimulation (Figure 1E). In contrast, CD25 expression was fully restored in “rescue” T cells, even after anti-CD3 stimulation alone (Figure 1E). Finally, CD44 expression was also highly CD28-dependent. In concert with the other CD28-dependent parameters (blasting, proliferation, IL-2, and CD25), wt T cells responded to CD28 ligation with CD44 expression but CD28−/− cells lacked CD44 upregulation. In contrast, the frequency of CD44hiCD62Llo “rescue” T cells was at least comparable to fully costimulated (anti-CD3/anti-CD28) wt T cells (Figure 1F). Thus, the miR-17∼92 transgene appeared to efficiently replace CD28 function in vitro, restoring the expression of the CD28-dependent markers CD25 and CD44. Given the remarkable capacity of the miR-17∼92 transgene to restore discrete functions in CD28-deficient T cells, we measured miR-17 expression upon activation in various genotypes (Figure S1D). T1792Δ/Δ T cells were unable to express miR-17 except for a faint signal at 48 h, likely reflecting incomplete deletion. In contrast, wt T cells increased miR-17 at 24 and 48 h while CD28−/− T cells displayed impaired miR-17 expression. Rescue T cells expressed increased miR-17 already in naive T cells and expression further increased with activation. Expressing one copy of the miR-17∼92 transgene in CD28-sufficient T cells increased miR-17 expression compared to wt cells and adding a second transgene copy further increased miR-17 expression. Thus, at 24 h, rescue T cells displayed slightly increased miR-17 expression compared to wt cells and similar expression to wt cells at 48 h. At 48 h, rescue T cells, T1792 wt/tg and T1792tg/tg T cells overexpressed miR-17∼92. Overall, these results support the suitability of the chosen experimental system. Finally, since IL-2 production constitutes another functionally important consequence of CD28 costimulation and miR-17∼92 correlated strongly with IL-2 production (Figures S1B and S1C), we analyzed this cytokine next. As with blasting or proliferation, CD28-deficient T cells produced less IL-2 but “rescue” T cells produced even supraphysiologic amounts of IL-2 upon activation (Figure 1G). Thus, transgenic miR-17∼92 was sufficient to replace CD28 for several costimulation-dependent processes in vitro. Next, we investigated whether transgenic miR-17∼92 could also substitute CD28 function in vivo. CD28−/− mice display a severe defect in TFH development and GC formation (Shahinian et al., 1993; Ferguson et al., 1996; Linterman et al., 2009; Walker et al., 1999). Therefore, we infected wt, CD28−/− and “rescue” mice with lymphocytic choriomeningitis virus (LCMV) Armstrong to induce an acute viral infection leading to TH1 and TFH differentiation as well as GC B cell formation. Confirming previous literature, we found severely impaired CD44 upregulation, TFH differentiation, and GC B cell formation in CD28−/− mice compared to wt littermates (Figures 2A–2D). In contrast, all these parameters were restored in “rescue” mice (Figures 2A–2D). This is remarkable given the complexity of TFH differentiation (Crotty, 2011). In addition, rescued T cells not only phenotypically resembled TFH cells through their expression of CXCR5, PD-1, Bcl-6, and ICOS (Figures 2B and 2C) but they were functional because they induced GC B cell formation which reflects TFH/B cell crosstalk. Furthermore, the spleen of infected “rescue” mice, but not CD28−/− mice, featured organized GCs containing GL7+ B cells and CD4+ T cells (Figure 2E) demonstrating the restoration of another hallmark defect found in CD28−/− mice (Ferguson et al., 1996). Finally, we analyzed TH1 responses and found that transgenic miR-17∼92 restored the defect in TH1 differentiation observed in CD28−/− mice (Figures 2F and 2G). We noticed that fewer CD28−/− cells expressed Tbx21 compared to wt cells (Figure 2F). However, some of those cells that did express Tbx21 co-expressed IFNγ, even in CD28−/− cells. Analyzing the ratio of Tbx21+IFNγ+/Tbx21+ T cells confirmed that the missing costimulatory signal mainly resulted in defective Tbx21 induction rather than IFNγ production (Figure 2H). These results suggest that the CD28−/− defect acts during T cell activation, i.e. before TH1 differentiation. Accordingly, the miR-17∼92 transgene appears to restore T cell activation signals. T1792tg/tg T cells displayed increased proliferation and IL-2 secretion compared to wt cells (Figures S1A–S1C) and intracellular IL-2 was not only restored but even higher in “rescue” T cells than wt T cells (Figure 1G), suggesting that the effect of miR-17∼92 depended on its abundance. Therefore, we investigated the effect of a single copy of the miR-17∼92 transgene in CD28−/− cells. In addition, we directly compared the effect of the miR-17∼92 transgene in CD28-deficient and CD28-sufficient T cells in vivo to test if CD28 triggering and the miR-17∼92 transgene were additive. The effect of various genotypes on miR-17 expression is shown in Figure S1D. We infected CD28−/−, T1792Δ/Δ, wt, het rescue, rescue, and T1792tg/tg mice with LCMV Armstrong. “het rescue” were CD28−/− that only carried one copy of the miR-17∼92 Tg while “rescue” mice were the rescue mice used above (Figures 1 and 2) with two copies of the miR-17∼92 transgene. The relative number of CD44+ T cells was lowest in CD28−/− and highest in T1792tg/tg mice (Figure S2A). Moreover, CD28−/− and T1792Δ/Δ mice exhibited similar defects compared to wt mice. In contrast, even one copy of the miR-17∼92 Tg was sufficient to rescue the CD28−/− phenotype of TFH and GC B cell formation (Figures S2B–S2D). In summary, we found an unexpectedly complete the restoration of CD28 costimulatory function and T cell activation exerted by transgenic miR-17∼92 expression in vitro as well as in vivo. Although CD28’s main function is on T cells, we sought to formally test whether the miR-17∼92-mediated rescue effect was cell intrinsic. We crossed MHC class II-restricted CD4+ TCR transgenic mice specific for LCMV (SMARTA; Vα2+Vβ8.3+) to wt, CD28−/− and “rescue” mice. We adoptively transferred (AT) naive CD4+ T cells to CD28−/− host mice followed by acute LCMV infection. Eight days post-infection we isolated spleen, mesenteric, and peripheral lymph nodes (LN). In all three organs the frequency and absolute number of Vα2+Vβ8.3+ CD28−/− cells was strongly reduced compared to Vα2+Vβ8.3+ CD28 w/w cells. In contrast, the miR-17∼92 transgene restored relative and absolute numbers of Vα2+Vβ8.3+ CD28−/− T cells (Figures 3A, S3A, and S3B). Furthermore, among Vα2+Vβ8.3+ T cells, fewer CD28−/− cells upregulated CD44 than in wt cells, a defect that was entirely restored in rescue cells (Figures 3B, S3C, and S3D). Thus, these data unequivocally demonstrate that transgenic miR-17∼92 cell intrinsically compensated for CD28 deficiency. To unravel the molecular mechanism underlying miR-17∼92-mediated function during T cell activation we performed RNA-sequencing on naive and in vitro activated (24 and 48 h) CD4+ T cells from T1792Δ/Δ, wt, and T1792tg/tg mice. Principal component analysis (PCA) revealed that the transcriptomes of naive T cells from all three genotypes were very similar (Figure 4A, 0 h). T cell activation induced major changes in gene expression (PC1, 56.7% of variance explained and PC2, 14% of variance explained) and also made the genotypes separate at 24 h and even more clearly at 48 h after activation (Figures 4A and S4A) (PC1 and PC3, 7.2% of variance explained). Since miRNAs often repress individual genes only mildly (Bartel, 2018), we compared the most extreme genotypes, i.e. T1792Δ/Δ to T1792tg/tg to increase the power of differential gene expression analysis, at each time point. At a false discovery rate (FDR) of 1%, the number of differentially expressed genes (DEG) increased over time (830 genes up-regulated and 789 genes down-regulated at 0 h, 2,493 up and 2,370 down at 24 h, and 3,173 up and 3,242 down at 48 h). Unsupervised hierarchical clustering of DEG 24 h after activation revealed a nuanced pattern of gene clusters (Figure 4B). As expected from the PCA (Figure 4A), gene expression across genotypes was very similar in naive T cells and the magnitude of expression differences increased after activation (Figure 4B). According to their expression profile, we highlighted 4 different groups of genes (Figure 4B): cluster I genes were induced over time and enhanced in T1792tg/tg compared to wt but reduced or delayed in T1792Δ/Δ. Cluster II genes decreased with time and miR-17∼92 supported their repression. Overall cluster III gene expression increased after activation but expression per time point inversely correlated with the genotype (T1792Δ/Δ > T1792tg/tg). Thus, miR-17∼92 limited the maximal expression of genes in this group after induction. Finally, genes displaying the most obvious inverse correlation with the genotype were grouped in clusters IVa and IVb. To disentangle the gene regulation modalities of the different clusters, we used exon-intron split analysis (EISA) to discriminate between transcriptional versus posttranscriptional regulation (Gaidatzis et al., 2015a). In addition, we employed computational target gene predictions for miR-17∼92 from the Targetscan (“TS”) database (Agarwal et al., 2015) and used a dataset of biochemically detected direct miRNA:mRNA interactions in T cells defined by Argonaute 2 high-throughput sequencing of RNA isolated by crosslinking immunoprecipitation (“AHC”) (Gagnon et al., 2019). For each gene, we quantified the read coverage on TS seed matches for each of the miR-17∼92 cluster seed families. These predictors of transcriptional (“DE intron,” for differential expression pattern detected at the nascent transcripts) and posttranscriptional regulation (“TS,” “AHC”), annotated on the heatmap (Figure 4B) revealed different patterns of regulation for the 4 groups of genes highlighted above. Cluster I genes were enriched for transcriptional regulation (both DE and DE intron) and displayed few TS sites or AHC reads (Figure 4B, box I). Thus, they were mainly induced by increased gene transcription, and miR-17∼92 promoted this transcriptional activity. Examples include Cd44, IL-21, Tbx21, and IFNγ (Figure 4C). In contrast, genes from clusters IVa and IVb (Figure 4B, boxes IVa, IVb), which displayed a colinear reduction of expression levels across genotypes, contained few transcriptionally regulated genes but were enriched for TS sites (p value = 0.001037; Fisher test) and experimentally determined AHC reads (p value = 0.003598; Fisher test) (Figure 4B). Thus, these clusters were mainly regulated posttranscriptionally and were likely enriched for direct miR-17∼92 target genes. To further characterize direct miR-17∼92 target genes we focused on naive T cells and the 24 h time point since indirect effects likely increased after this time point. To visualize the effect of individual miR-17∼92 cluster miRNAs on their target genes we compared the expression of genes identified by TS and with >5 AHC reads for each miRNA seed family to all genes without any seed match for that family. As illustrated by the miR-17 seed family, the miR-17∼92 transgene repressed miR-17 target genes in naive T cells (Figure 4D, left panel) but the absence of miR-17∼92 had no effect on the expression of miR-17 target genes (Figure 4D, right panel). In contrast, after T cell activation miR-17 target genes were repressed in T1792tg/tg T cells (Figure 4E, left panel) and derepressed in T1792Δ/Δ T cells (Figure 4E, right panel). Similar effects were observed for all seed families although to a lesser extent for the miR-18 seed family (Figures 4B and 4C). We defined genes as empirically validated miR-17∼92 targets if they fulfilled the following criteria: i) significant derepression in T1792Δ/Δ vs wt and significant repression in T1792tg/tg vs wt at 24 h ii) predicted TS match iii) > 5 AHC reads and iv) posttranscriptional regulation based on EISA. Applying these criteria across the 4 seed families from miR-17∼92 defined a set of 68 empirically supported direct miR-17∼92 target genes (Table S1). These genes included previously described miR-17∼92 targets validated in T cells, such as Phlpp2 (Kang et al., 2013) and Cyld, validated in B cells (Jin et al., 2013) (Figure 4F). In addition, this approach identified many less studied genes (e.g. Rcan3, Nrbp1, Rnf38, and Rnf167) (Figure 4F). Notably, several of the target genes negatively regulate pathways important for T cell activation. For instance, Phlpp2 is a PI3K inhibitor (Kang et al., 2013) and Cyld is an NF-κB inhibitor (Reiley et al., 2007). In addition, we identified the putative calcineurin inhibitor Rcan3 (Mulero et al., 2007) as a new miR-17∼92 target and validated its RNA-Seq data by qPCR on independent biologic replicates (Figure S4D). Its 3′UTR contains a conserved miR-17-5p 8mer binding site and AHC confirmed a single discrete peak at this site, evidence for a direct interaction of a miRNA with the Rcan3 3′UTR in primary T cells (Figure S4E and (Loeb et al., 2012; Gagnon et al., 2019)). Together, these data empirically validate Rcan3 as a miR-17 target in T cells. Thus, using a combination of experimentally validated differential gene expression (T1792Δ/Δ, wt, and T1792tg/tg T cells), evidence of posttranscriptional gene regulation and biochemical detection of miRNA binding we defined a high confidence set of miR-17∼92 target genes in T cells. miR-17∼92 became functionally relevant after T cell activation and shaped the transcriptome in intricate ways. Our data demonstrate that miR-17∼92 can promote and inhibit gene expression, through posttranscriptional gene regulation, enhancing gene silencing or dampening expression of induced genes. Thus, miR-17∼92-mediated gene repression is important to shape the T cell transcriptome during T cell activation. To identify which molecular pathways were regulated by miR-17∼92, we analyzed curated gene sets enriched for differentially expressed genes between T1792tg/tg and T1792Δ/Δ. At 24 h, the gene sets with the highest statistical significance and largest average fold change were related to cytokines, inflammation, and T cell differentiation (Figure S5A) while at 48 h many metabolic pathways were altered (Figure S5B). Next, we performed enrichment analysis on DoRothEA regulons (Garcia-Alonso et al., 2019) to identify TF activity that could explain the regulated pathways. Regulons were defined using any existing documented interaction with a particular TF. At 24 h, the five most significantly enriched TF regulons with the highest fold change contained two NFAT members (NFATC2, NFATC3) as well as RELA, NF-κB1, and GATA3 (Figure 5A). Since NFAT TFs are important for T cell activation and differentiation (including TFH differentiation (Martinez et al., 2016)) but miR-17∼92 is not known to promote NFAT activity in T cells, we focused on the calcineurin/NFAT axis. Genes belonging to regulons NFATC2 and NFATC3 include many T cell lineage-defining TFs, cytokines, and cytokine receptors and most of them – including IL-21, Tbx21, and IFNγ (Figure 4C) – were regulated by miR-17∼92 (Figure 5B). This confirmed that miR-17∼92 constitutes a central regulator of T cell activation and suggested that transgenic miR-17∼92 enhanced canonical pathways that resulted in the functional substitution of CD28 for the differentiation of TFH and TH1 in vivo (Figure 2). Since miR-17∼92 promoted the expression of signature TF and cytokines defining multiple T cell subsets, we tested if miR-17∼92 more generally could replace CD28. To this end, we differentiated TH1, TH17, and iTreg cells in vitro. We confirmed that also in this setup transgenic miR-17∼92 was sufficient to compensate for the absence of CD28 and functionally corrected the defects of CD28−/− T cells to differentiate into all 3 subsets (Figure S6). While miR-17∼92 predominantly promoted inflammatory pathways at 24 h, at 48 h several metabolic pathways were regulated by miR-17∼92 (Figures S5A and S5B). Thus, we hypothesized that initial transcriptional changes could lead to altered metabolism. It was recently shown that cell cycle entry of quiescent T cells was controlled by store-operated Ca2+ entry (SOCE) and calcineurin/NFAT through control of glycolysis and oxidative phosphorylation (Vaeth et al., 2017). Therefore, we analyzed T cell metabolism in naive and activated T1792Δ/Δ, wt, and T1792tg/tg T cells. In line with the transcriptome results (Figure 4), metabolic flux analysis demonstrated comparable glycolytic and respiratory activity of naive T cells in T1792Δ/Δ, wt, and T1792tg/tg T cells (Figures S5C and S5D). In contrast, 48 h after activation, glycolytic and respiratory activity positively correlated with the miR-17∼92 genotype (Figure S5E and S5F). Furthermore, genes associated with the TCA cycle and respiratory electron transport positively correlated with miR-17∼92 at 48 h but not before, supporting the notion that miR-17∼92 indirectly promoted this metabolic activity (Figure S5G). Since transcriptome analysis identified NFAT as a key pathway promoted by miR-17∼92 (Figures 5A and 5B), we designed experiments to functionally validate this increased activity. We activated wt, CD28−/− and “rescue” T cells in the presence of cyclosporin A (CsA), a drug known to inhibit calcineurin/NFAT, and quantified CD69/CD25 expression. Compared to wt cells, CD28−/− cells were 4-fold more sensitive to CsA (Figure 5C). At a CsA concentration that did not affect wt cells (arrow in Figure 5C), T cell activation of CD28−/− T cells was clearly inhibited (Figures 5C and 5D). Thus, compared to wt cells CD28−/− T cells are hypersensitive to CsA. In contrast, CD28−/− T cells with forced miR-17∼92 expression (rescue cells) were normally activated (Figures 5C and 5D). In fact, at higher CsA concentrations the “rescue” T cells were even more resistant to CsA inhibition than wt T cells (Figure 5C). These findings were further corroborated by the analysis of cell size which revealed that in the presence of low dose CsA the blasting defect of CD28−/− T cells was compensated for by transgenic miR-17∼92 (Figure 5E). Finally, we assessed the nuclear translocation of activated NFATC2 as a direct readout of calcineurin activity. ImageStream analysis of T cells activated in the presence of 6.25 ng/mL CsA showed T cells with cytoplasmic (top row) or nuclear (lower row) NFATC2 (Figure 5F). Quantification of this data demonstrated that the presence of a low CsA concentration reduced nuclear NFATC2 translocation in CD28−/− T cells but was restored in “rescue” cells (Figure 5G). Thus, the miR-17∼92 transgene replaced CD28-enhanced NFAT signals for blasting, CD69/CD25 upregulation, and nuclear translocation of NFATC2. Next, we examined whether miR-17∼92 was physiologically required to shape the molecular program triggered by CD28 engagement. We repeated transcriptome analysis with RNA-seq on naive and activated (24 h) CD4+ T cells from T1792Δ/Δ, wt, and T1792tg/tg mice but added CD28−/− and “rescue” T cells. The correlation of miR-17∼92 target gene expression levels with the first RNA-seq experiment (Figure 4) was very high (Figure S7A). A PCA showed that the transcriptomes of naive T cells of all 5 genotypes were closely related (Figure 6A). However, after T cell activation (PC1, 67.6% of variance) the 5 genotypes formed 5 distinct groups that were separated on PC2 (5.6% of variance). Notably, the “rescue” samples were located between CD28−/− and T1792Δ/Δ, wt, and T1792tg/tg T cells (Figure 6A). This implied that transgenic miR-17∼92 partially restored the genetic networks dysregulated by CD28 deficiency, supporting the notion that the phenotypic rescue (Figure 1, Figure 2, Figure 3) was not a transgene artifact. Moreover, compared to all genes without a miR-17 seed match, the miR-17 target gene set based on the same criteria specified earlier but using a second dataset differential gene expression analysis was overall derepressed in CD28−/− T cells compared to wt cells (Figures 6B and S7B). It is important to note that these cells have an untouched miR-17∼92 locus and therefore should repress miR-17∼92 target genes similar to wt cells. However, the absence of CD28 resulted in increased expression of those genes. This result demonstrated that CD28-dependent effects normally rely on miR-17∼92 to repress dozens of genes during T cell activation. Remarkably, the expression of target genes was corrected in “rescue” cells (Figures 6C and S7B). Thus, transgenic miR-17∼92 repressed the physiologic targets that were derepressed in CD28−/− T cells. However, the molecular rescue effect on the entire transcriptome was incomplete (Figure 6A). We, therefore, analyzed which transcripts were restored and whether they were regulated transcriptionally or posttranscriptionally. Similarly to Figure 4B, we selected genes differentially expressed in the T1792Δ/Δ to T1792tg/tg comparison at 24 h, and performed unsupervised hierarchical clustering using their gene expression profiles across all 5 genotypes. At 24 h, one gene cluster stood out in which “rescue” T cells were more similar to wt and T1792tg/tg cells than CD28−/− and T1792Δ/Δ cells (Figure 6D, box). Genes in this cluster contained several NFAT-dependent transcripts including IL-4, IL-12a, IL12rb2, IL-21 and IFNγ (Table S2, Figure 5B). Importantly, Cd44, IL-21, Tbx-21, and IFNγ transcripts, also contained in this cluster, were reduced in CD28−/− compared to wt cells (Figure 6E). In contrast, transgenic miR-17∼92 partially or completely restored these transcripts in “rescue” cells to wt levels. Furthermore, T1792tg/tg T cells expressed supraphysiologic mRNA levels (Figure 6E). Thus, the phenotypic rescue of CD44 and IFNγ in CD28−/− T cells (Figure 1, Figure 2, Figure 3) could at least partially be attributed to increased expression of these genes driven by the miR-17∼92 transgene in “rescue” cells. Conversely, as illustrated above (Figure 6B) many direct targets were not only derepressed in T1792Δ/Δ but also in CD28−/− cells. Several genes (e.g. Rcan3, Nrbp1, Rnf38, and Rnf167) were similarly derepressed in CD28−/− and T1792Δ/Δ cells, expression was restored in “rescue” cells to levels similar to wt and even further repressed in T1792tg/tg cells (Figure 6F). Others, such as Phlpp2 and Cyld were clearly regulated by miR-17∼92 but their expression differed in CD28−/− and T1792Δ/Δ cells (Figure 6F). This suggests that these targets are sensitive to regulation by miR-17∼92 and in multiple cases, their derepression observed in CD28−/− T cells is largely attributed to reduced repression by miR-17∼92. However, functional validation of candidate targets will be necessary. To understand the functional relevance of the empirically validated miR-17∼92 targets (Table S1) we reasoned that genetically inactivating targets should have phenotypically similar effect as miR-17∼92-mediated repression. We therefore chose to ablate candidate target genes in CD28−/− T cells using CRISPR/Cas9. In order to be able to analyze a candidate gene’s role during T cell activation we sought to delete candidate genes in naive T cells, i.e. before activation. To this end, we adapted a protocol to use CRISPR/Cas9 in naive CD4+ T cells (Seki and Rutz, 2018) (Figure 7A). We used a non-targeting control (NTC) crRNA as a negative control, included the well-validated miR-17∼92 target Pten as a positive control and chose Phlpp2, Cyld, Rcan3, Nrbp1, Rnf38, and Rnf167 (Figures 4F and 6F) to test our hypothesis. As readouts for potential phenotypic rescue we analyzed proliferation, CD25, CD44, and ICOS since these parameters were clearly restored in “rescue” T cells (Figures 1D–1F and 2B). In line with a previous report that genetic Pten-ablation in T cells removed the requirement for CD28 costimulation for proliferation (Buckler et al., 2006), we observed increased proliferation in cells electroporated with a Pten-targeting gRNA but not any of the other gRNAs (Figure 7B). This effect was clearly evident at 48 h but could no longer be detected at 72 h. Ablation of nuclear receptor binding protein 1 (Nrbp1) resulted in increased expression of CD25 after 72 h while the ablation of the other genes did not affect CD25 expression (Figure 7C). Furthermore, CD44 expression was increased by Pten-ablation at 48 and 72 h and by Nrbp1-ablation at 72 h. Finally, contrary to expectations, Pten-ablation resulted in decreased ICOS expression at 72 h. Thus, CRISPR/Cas-mediated ablation of individual target genes resulted in distinct phenotypic consequences. Deleting two individual miR-17∼92 target genes in naive CD28−/− CD4+ T cells increased 3 of the 4 tested parameters that were also rescued by transgenic miR-17∼92 expression, suggesting that their repression by transgenic miR-17∼92 in “rescue” T cells contributed to the phenotypic rescue observed. In contrast, the deletion of Pten resulted in two expected phenotypic consequences and one opposite to expectations. This suggests that ICOS upregulation observed in “rescue” T cells is not mediated by repression of Pten but rather an as yet unidentified miR-17∼92 target gene. Together, these results support CRISPR/Cas-based gene deletion as a means to functionally validate candidate miRNA target genes and strongly suggest that miR-17∼92-mediated repression of these targets is functionally relevant during T cell activation. T cell activation depends on TCR and CD28 engagement to trigger complex molecular mechanisms including the activation of the PI3K, NF-κB, and calcineurin/NFAT pathways. Intense research in the past decades uncovered many molecules that transmit TCR and CD28 signals (Esensten et al., 2016; Tian et al., 2015; Liu et al., 2018). Most studies focused on proteins as signaling intermediates but we and others previously reported that the combined engagement of TCR and CD28 also alters the expression of non-coding RNAs. Most miRNAs are downregulated after T cell activation but a few, including miR-17∼92, remain relatively constant or are slightly induced (Bronevetsky et al., 2013; de Kouchkovsky et al., 2013). This suggests that miR-17∼92 might be functionally relevant during T cell activation. We previously found that miR-17∼92 was induced after combined stimulation of TCR and CD28 in vitro but not after TCR stimulation alone (de Kouchkovsky et al., 2013). Here, we found that TCR stimulation alone does induce miR-17 but that costimulation through CD28 synergistically further increased miR-17 expression. Even adding low CD28 costimulation resulted in higher miR-17 induction than high TCR stimulation alone. These findings are consistent with experiments using stimulation by the natural ligands CD80/CD86. Heterozygosity or absence of CD80/CD86 on B cells resulted in a dose-dependent reduction in miR-17 induction in co-cultured T cells while deficiency or blockade of CTLA-4 resulted in increased miR-17 expression (Wang et al., 2015). Collectively, these studies demonstrate that TCR stimulation and costimulation/coinhibition by CD28 or CTLA-4 are intimately linked to miR-17∼92 expression. Furthermore, stimulating T cells with antibodies directed to CD3 is sufficient to induce proliferation in T1792tg/tg T cells suggesting that transgenic miR-17∼92 renders T cells CD28 costimulation independent (Xiao et al., 2008). Therefore, we set out to formally investigate if and how transgenic miR-17∼92 was sufficient to substitute for the absence of CD28 and whether miR-17∼92 was required for CD28-mediated T cell activation. We found an unexpectedly potent rescue effect both in vitro and in vivo. Many defects of CD28−/− T cells were functionally compensated for by the miR-17∼92 transgene. This is notable for two reasons. First, miR-17∼92 is a non-coding RNA and second, miRNAs are negative regulators. Thus, overexpression of an inhibitory non-coding RNA was sufficient to enable T cell activation in the absence of CD28 costimulation. To analyze the molecular mechanism by which miR-17∼92 enabled T cell activation of CD28−/− CD4+ T cells we first defined miR-17∼92 target genes in CD28-sufficient CD4+ T cells before and after activation. RNA-seq analysis of T cells with miR-17∼92 loss- or gain of function and sampled over a time course revealed that miR-17∼92 mainly influenced the transcriptome after T cell activation, a finding consistent with the phenotypic analysis of ex vivo characterized naive T cells. There is an emerging notion that evidence of miRNA binding combined with differential gene expression in primary cells constitutes a precise approach to empirically define miRNA:target relationships (Gagnon et al., 2019; Hsin et al., 2018). We, therefore, employed this approach but refined it by combining EISA with AHC to discriminate whether differentially expressed gene clusters were primarily regulated transcriptionally or posttranscriptionally. This analysis provides a highly granular view of miRNA-mediated gene regulation. Specifically, pathway and regulon enrichment analysis revealed that miR-17∼92 enhanced the calcineurin/NFAT pathway. Consistent with this, a gene cluster enriched for genes that positively correlated with miR-17∼92 was mainly transcriptionally regulated and contained many genes known to be regulated by NFAT TF including genes for various CD4+ T cell subsets such as TFH (IL-21) and TH1 (Tbx-21 and IFNγ). Experiments with pharmacologic CNIs, e.g. CsA, confirmed that miR-17∼92 functionally promoted the calcineurin/NFAT pathway. Importantly, we demonstrated that the expression of genes we validated empirically as miR-17∼92 targets were elevated in stimulated CD28−/− T cells. In contrast, transgenic miR-17∼92 partially restored the molecular program that was defective in CD28−/− T cells. In particular, the expression of key NFAT-regulated genes found to be driven by miR-17∼92 in CD28-sufficient T cells were restored by the transgene in CD28−/− T cells. We conclude that during T cell activation miR-17∼92 is required to repress this set of genes. With the list of stringently defined miR-17∼92 target genes, we set out to investigate the functional relevance of identified miRNA target genes. This is generally challenging because miRNAs bind to and regulate many genes and because the per gene repression is mostly modest (Baumjohann and Ansel, 2013). We previously addressed this using siRNA-mediated gene repression (Pua et al., 2016). Here, we reasoned that the disruption of genes that physiologically are to be repressed by miR-17∼92 during T cell activation could partially restore defects of CD28-deficient T cells resulting in the gain of function (rescue) phenotypes. Indeed, some parameters that were rescued by transgenic miR-17∼92 were also restored by the disruption of a single miR-17∼92 target gene (Pten: proliferation; Nrbp1: CD25; see model in Figure S8) while others were restored by the disruption of more than one target (Pten, Nrbp1: CD44). For yet other targets we could not detect any functional relevance for the measured parameters (Phlpp2, Cyld, Rcan3, Rnf38, Rnf167). Importantly, due to the known dependency on the cellular context, the absence of a change in the assessed parameters does not exclude that repression of these genes may be relevant in a different context, timepoint, or for a function not assessed here (Lu et al., 2015; Hsin et al., 2018). For instance, it’s interesting to note that CRISPR/Cas9-mediated disruption of Pten restored proliferation at the earlier time point but the effect waned with time. Similarly, we previously found that removing one copy of Pten was able to restore the defect observed in T1792Δ/Δ cells during early TFH differentiation but not during later stages (Baumjohann et al., 2013). In addition, the disruption of Pten resulted in the opposite effect (decrease) than miR-17∼92 transgene expression (increase) for ICOS expression. Therefore, although Pten is an important miR-17∼92 target, others must be functionally relevant and remain to be identified. For instance, our limited CRISPR/Cas9 validation revealed that disrupting Nrbp1 increased CD25 and CD44 expression suggesting that it regulates these important proteins. Interestingly, Nrbp1 is a known tumor suppressor and Nrbp1-disruption increases CD44 and c-myc in the intestine of conditional KO mice (Wilson et al., 2012). These results suggest that Nrbp1 is another negative regulator of T cell activation. Together, these results clearly demonstrate that a single target is very unlikely to explain the phenotypic rescue observed in CD28−/− T cells expressing transgenic miR-17∼92. Rather, we propose that miR-17∼92 represses a network of genes fulfilling distinct functions. A scaled up, targeted CRISPR/Cas9 screen could be used to identify regulators of T cell function. For instance, our data suggest that ICOS expression may be restrained by yet unknown miR-17∼92 targets. In addition, not all target genes have binding sites for each of the miR-17∼92 cluster’s miRNAs. Therefore, such a screen could help to identify which miRNA of the cluster affects which function. As an example, we identify Nrbp1 as a functionally relevant miR-17∼92 target. Since it contains binding sites for miR-17 and miR-19 it is likely that one or both of these miRNAs are functionally relevant for its repression and consequently CD25 and CD44 regulation. However, investigation of the relevance of the respective binding sites needs separate validation experiments. Thus, our empirically validated target list provides a blueprint for an arrayed CRISPR/Cas9 screening approach to reveal new biologic insight. Expanding such a screen to additional readouts, e.g. IL-2 or Tbx21, might reveal the regulation of distinct functions by unexpected genes. Moreover, CRISPR/Cas9 screening in naive T cells could be useful beyond miRNA research, particularly in CD28−/− T cells, e.g. to identify T cell negative regulators. Our results are in line with limited experimental evidence that gene deletion can partially rescue CD28 deficiency. For instance, genetic deletion of Casitas B lymphoma-b protein (Cbl-b) or TRAF6 is sufficient to restore IL-2 production and proliferation in CD28−/− T cells (Bachmaier et al., 2000; Chiang et al., 2000; King et al., 2006). However, neither IFNγ, IL-4 or ICOS expression nor GC formation was restored by Cbl-b deletion (Bachmaier et al., 2000; Chiang et al., 2000). Thus, while loss of Cbl-b uncoupled T cells from the strict requirement for CD28 costimulation, it could not restore all aspects of CD28-mediated costimulation suggesting that additional genes restrain T cell activation. As a group of genes, negative regulators impose a requirement for a costimulatory signal to actively remove these brakes to allow a productive T cell response. CD28 costimulation, therefore, serves a dual purpose by enhancing TCR signaling and simultaneously overcoming T cell repression (Paolino and Penninger, 2010; Buckler et al., 2006; Martinez-Llordella et al., 2013). Using Pten-deficient T cells it was previously shown that PTEN imposes a requirement for CD28 costimulation by setting a threshold for activation (Buckler et al., 2006). Here, we show that CRISPR/Cas-mediated Pten disruption can partially overcome defective proliferation and CD44 upregulation of CD28−/− CD4+ T cells but negatively affects ICOS expression. In addition, we identify the tumor suppressor Nrbp1 as an additional gene whose removal enables the the expression of important proteins in CD28−/− CD4+ T cells. Moreover, our list of empirically validated miR-17∼92 target genes contains several other known or suspected tumor suppressor genes. The growing list (e.g. Pten, Phlpp2, Cyld, Nrbp1) of tumor suppressors/negative regulators whose expression is controlled by miR-17∼92 is noteworthy and suggests that these could all contribute to setting a threshold for T cell activation. Although it’s remarkable that removing individual genes in CD28−/− T cells is sufficient to restore specific functions, it’s important to note that during T cell activation the inhibitors are not completely eliminated. Therefore, although experimental deletion can demonstrate that a given gene has the potential to act as a powerful negative regulator, its physiologic function is more delicate to uncover and complete deletion likely overestimates the contribution of the gene under investigation. To date, no single gene or pathway can explain the function of T cell activation and/or CD28 costimulation. More likely, multiple pathways need to be induced and multiple negative regulators need to be removed. It is conceivable that precise control of genetic programs is particularly important for an exponential process like clonal T cell expansion. Even minor changes in setting the T cell activation threshold or the rate of proliferation or survival could result in major consequences for the host organism. Since miRNAs fine-tune expression of many genes they are ideally suited to mediate controlled release from multiple restraining proteins preventing T cell activation (Bartel, 2018). Our findings greatly expand the list of high-confidence miR-17∼92 targets with a suspected function in T cell activation and illustrate that miR-17∼92 can exert complex regulation of the T cell transcriptome through direct and indirect gene regulation. In summary, we propose a model (graphical abstract) in which miR-17∼92 acts as a downstream mediator, modulator, and/or amplifier of the molecular program triggered by combined TCR/CD28 engagement serving as a gatekeeper of a network of restrainers of T cell activation and function. In this model, non-coding RNA-mediated direct repression of inhibitors indirectly supports the transcriptional induction of genes necessary for further T cell differentiation and function (e.g. CD44, IL-21, Tbx21, IFNγ). Importantly, we not only show that transgenic miR-17∼92 is largely sufficient to substitute for CD28-deficiency but that miR-17∼92 is physiologically required to shape the transcriptome after CD28 costimulation and that miR-17∼92 overexpression in CD28-sufficient T cells results in “super-costimulation.” In support of the proposed model, constitutive transgenic miR-17∼92 expression leads to a lupus-like systemic autoimmune syndrome (Xiao et al., 2008), not unlike the ones observed in mice deficient in Cbl-b, PTEN, TRAF6, or Cyld (Bachmaier et al., 2000; Chiang et al., 2000; Suzuki et al., 2001; King et al., 2006; Reiley et al., 2007). Our results support the consideration of miR-17∼92 or its target genes for pharmacologic intervention or for its integration into engineered cellular therapies. Our study is limited in scope and by technical constraints concerning the mechanistic link between i) CD28, miR-17∼92 and T cell activation, ii) miR-17∼92 target validation as well as iii) relevance for other cells. We cannot definitively discern the relative functional relevance of miR-17∼92 induced by TCR stimulation alone versus combined TCR and CD28 costimulation. This study did not address the molecular mechanism of miR-17∼92 regulation in T cells. Although the presence of myc binding sites is suggestive of transcriptional regulation (O'Donnell et al., 2005), miRNA biogenesis can be controlled by extracellular cues and maturation of miR-17∼92 is known to be regulated posttranscriptionally (Du et al., 2015). It will be important to identify the individual miRNAs of the cluster that regulate specific functions and to determine whether the expression of miR-17 (which we measured) corresponds to other miRNAs of the cluster, such as miR-18a, miR-19a, and miR-19b-1 that reportedly have an important function in T cells (Montoya et al., 2017; Simpson et al., 2014; Jiang et al., 2011). Together, future studies addressing these open questions will untangle the molecular connection between TCR stimulation/CD28 costimulation/T cell activation and miR-17∼92 expression/function. For instance, such studies could address whether miR-17∼92-deficient T cells receiving both TCR and costimulatory signal become anergic similar to costimulation-deficient T cells. With regards to miR-17∼92 target validation the study did not address whether miR-17∼92 increased calcineurin/NFAT activity through repression of a direct calcineurin inhibitor, e.g. Rcan3, or indirectly, e.g. through repression of a PI3K inhibitor, e.g. Pten or Phlpp2. Furthermore, a more comprehensive analysis of the repression of single or combinations of targets will be required but this will remain challenging since our results suggest a context and time-dependent regulation of multiple genes as observed for other miRNAs. Technically, the deletion efficiencies of the CRISPR/Cas9 approach varied, ranging from 13.4 to 28% (Phlpp2) to 65.5–84% (Rnf167) but 30–49% was sufficient to see phenotypic effects (Nrbp1) in this experiment. Interpretation of the results further needs to take into account that the inhibitors are not completely eliminated by miRNAs during T cell activation. Therefore, complete deletion likely overestimates the contribution of the gene under investigation. Finally, future studies need to determine whether T cell subsets that rely less on CD28 than naive T cells (e.g. memory and CD8+ T cells) display differential sensitivity to miR-17∼92-mediated gene regulation or express more miR-17∼92. Further information and requests for resources should be directed to and will be fulfilled by the lead contact, Lukas Jeker (lukas.jeker@unibas.ch). This study did not generate new unique reagents. All animal work was performed in accordance with the federal and cantonal laws of Switzerland. Protocols were approved by the Animal Research Commission of the Canton of Basel-Stadt, Switzerland. Most of the mouse lines were imported from the JAX laboratory as indicated in the key resources table. T1792Δ/Δ mice were imported from the laboratory of Dr. Bluestone (UCSF). Rescue SM+ mice were obtained by crossing the rescue strain to other transgene lines in house so that their precise genetic origin is difficult to determine. As for the transfer cells, we used SM CD45.1+ wt cells imported from SWIMR. All other mouse lines were crossed to CD45.2 expression. Cre negative littermates (from T1792tg/tg or T1792Δ/Δ) were used as wt controls, and cre negative littermates from the rescue strain were used as CD28ko. 6–8 week old females and males were used for all experiments. Organs were obtained after CO2 euthanization and kept on ice until processing. Mesenteric lymph nodes (LN), peripheral LN (inguinal, axillary, brachial, six cervical) and spleen were taken for most of the experiments. Spleens were injected with 0.5 mL ACK lysis buffer (0.155 M NH4Cl, 200 μL 0.5 M EDTA pH = 8.0, 0.012 M NaHCO3 pH 7.2) for erythrolysis before processing. The organs were meshed with 0.4 μm filters to obtain single cell suspensions which were then washed with FACS buffer (2% heat-inactivated FCS in PBS, for stainings add 0.02% NaN3). Cells were centrifuged at 4°C, 5 min at a speed of 370 g for most washing procedures. Naïve CD4+ T cells were isolated from cell suspensions with pooled lymph nodes and spleen. Isolation was performed with StemCell mouse naïve CD4+ T cell isolation kit according to manufacturer’s instructions. In brief, the cell suspensions were incubated with rat serum and CD4+ isolation antibody for 7.5 minutes, then with memory depletion antibody for 2.5 minutes, and in the end with magnetic beads for another 2.5 minutes before incubating with the isolation magnet for 2.5 minutes. The resulting untouched naïve CD4+ T cells were then washed with FACS buffer, and purity was routinely checked with a staining for CD4+, CD44− and viability. Plates were coated over night with 0.2 μg anti-CD28 and 0.5 μg anti-CD3 per mL PBS for most of the experiments (low stimulation as according to (Baumjohann et al., 2013)). We used 1 mL/well for 24 well plates and 0.2 mL/well for 96 well plates. Before plating of the cells, plates were washed with PBS. We plated 2∗105 naive CD4+ T cells per well in 96 well flat-bottom in 200 μL medium. For 24 well plates, 2∗106 naïve CD4+ T cells per mL medium in 1 mL medium were plated. Complete T cell medium (RPMI-1640 Medium, 10% FCS, 1% HEPES, 1% non-essential amino acids, 1% Glutamax, 0.1% 2-Mercaptoethanol) was supplemented with 50U IL-2/mL. Cells were cultured at 37°C, 5% CO2 for 24 h or longer depending on the purpose of the experiment as indicated in figure legends. Freshly isolated naïve CD4+ T cells were washed with PBS. 1 μL of Cell Trace stock solution (dissolved in DMSO according to the manufacturer’s instructions) was then used per mL PBS for 10∗106 cells. Cells were incubated at 37°C for 20 minutes, then 5x the original staining volume of normal T cell culture medium was added for 5 minutes to remove residual dye. Cells were washed and plated in complete culture medium supplied with 50U IL-2 per mL for 48 h. IL-2 secretion was addressed in 48 h culture supernatants from cells that were plated in pre-coated wells in complete T cell medium without IL-2. IL-2 ELISA was performed with the BioLegend ELISA MAX mouse IL-2 set according to the manufacturer’s instructions. After the last washing step, TMB substrate was added for the readout. Absorbance was measured with an ELISA plate reader (Synergy H1 Hybrid Reader, BioTek) at 450 nm as well as 570 nm wavelength, and normalized to wild type control samples that were run in the same experiment. Generally, cells were stained for viability with viability dye eFluor780 in PBS for 20 min at 4°C and then washed with PBS or FACS buffer. Non-specific binding was blocked with anti-CD16/anti-CD32 0.5 mg/mL on ice for 10 minutes. Surface staining was performed in FACS buffer for 20–30 minutes at 4°C. In intracellular staining or LCMV experiments, cell fixation was performed with Fix-Perm for 20 minutes on 4°C (1 h for LCMV experiments). Intracellular staining was done in permeabilization buffer for 1 h at 4°C. Activation status of the cells was assessed by staining and gating for singlets/lymphocytes/viable cells/CD4+ and early activation marker CD25/CD69 as well as CD44/CD62L expression. Blasting of lymphocytes was addressed by pre-gating on singlets/viable cells. For cytokine staining after in vitro differentiation or ex vivo e.g., for IL-2 staining, cells were stimulated with 50 ng/mL PMA, 500 ng/mL Ionomycin and 10 μg/mL Brefeldin A (BFA) for 3 h at 37°C before staining. Data was acquired with an LSR Fortessa (BD) and analyzed with FlowJo. Mice were infected with 2∗105 PFU LCMV-Armstrong strain i.p. with U-100 insulin syringes (0.30 mm (30G) x 8 mm). Eight days post infection the spleens were harvested for staining. We gated for singlets/lymphocytes/viable cells/CD3+CD4+ to look at CD44 expression, and moreover we addressed TFH cells by the co-expression of key markers Bcl-6, PD-1, ICOS and CXCR5. We gated for singlets/lymphocytes/viable cells/CD19+B220+ to look at GC B cells expressing Fas and GL-7. Re-stimulation of splenocytes was performed in flat bottom 96 well plates with 1 μg/mL LCMV-specific peptide GP-64 in comparison to polyclonal 50 ng/mL PMA, 500 ng/mL Ionomycin stimulation for one hour, then 10 μg/mL Brefeldin A was added for another three hours before staining. We then gated for TH1 cells using singlets/lymphocytes/viable cells/CD3+CD4+ and finally looking at Tbx21/IFNγ expression. All in vivo experiments were performed blinded with assignment of animal numbers after data analysis. Spleens were embedded in cryo embedding medium and frozen on dry ice before storage at −80°C. Sections were cut at a thickness of 6 μm and dried on air. Single sections were then fixed with acetone for 5 minutes and circled with PAP pen. Staining for CD19, CD4 and GL-7 was performed in FACS buffer with anti-CD16/anti-CD32 in a wet chamber at 4°C overnight. Slides were then washed with PBS on a shaker for 15 minutes before drying and mounting. Imaging was performed with a 20x objective on a Nikon Ti2 microscope and analyzed with ImageJ version 2.0.0. Naïve SMARTA+ CD4+ T cells from wt, CD28−/− and rescue mice were isolated transferred into CD28−/− recipients. Each recipient received 1∗105 cells in 100 μL PBS i.v. The recipients were infected with 2∗105 PFU LCMV Armstrong i.p. two days after cell transfer. Eight days after infection, the mesenteric and peripheral LN as well as the spleen were analyzed separately for the presence of Vα2+Vβ8.3+ T cells (pre-gated on singlets/lymphocytes/viable cells/CD4+), and these cells were further characterized for their CD44 expression. One CD28−/− mouse that did not receive donor cells was used as a negative control in each experiment to display the recipient-intrinsic Vα2+Vβ8.3+ population. For any experiment involving RNA, cells were washed with PBS before counting and the RNA was kept on ice during the experiments, storage at −80°C. All pipetting was performed with filter-tips and RNAse-free tubes. All procedures for the extractions were performed at the facilities with materials, protocols and supervision of the facility experts. For RNA sequencing, 2.5∗105 cells were washed with PBS and resuspend in 200 μL TRI Reagent. RNA was extracted from Trizol-samples with a Zymo Direct-zol kit which includes DNAse treatment. Quality control was run with a Bioanalyzer. RNA quality was assessed with a Fragment Analyzer (Advanced Analytical) and RNA-seq library preparation was performed using Illumina Truseq stranded kit. Sequencing was performed on an Illumina NexSeq 500 machine to produce single-end 76-mers reads. All steps were performed at the Genomics Facility Basel (ETH Zurich). Read quality was assessed with the FastQC tool (version 0.11.5). Reads were mapped to the mouse genome (UCSC version mm10) with STAR (version 2.5.2a) (Dobin et al., 2013) with default parameters, except filtering out reads mapping to more than 10 genomic locations (outFilterMultimapNmax = 10), reporting only one hit in the final alignment for multimappers (outSAMmultNmax = 1) and filtering reads without evidence in the spliced junction table (outFilterType = "BySJout"). All subsequent gene expression data analysis was performed using the R software (version 3.5). Read alignment quality was evaluated using the qQCReport function of the R Bioconductor package QuasR (version 1.18). Gene expression was quantified using the qCount function of QuasR (Gaidatzis et al., 2015b) as the number of reads (5′ends) overlapping with the exons of each gene assuming an exon union model (using the UCSC knownGenes annotation downloaded on 2015-12-18). To quantify intronic expression levels, exonic coordinates were extended by 10 bp on each side of the exons, and for each gene the resulting read count was subtracted to the read count obtained on the whole gene (extended by 10 bp on each side). The R Bioconductor package edgeR (version 3.28) (Robinson et al., 2010) was used for differential gene expression analysis. Between samples normalization was done using the TMM method (Robinson and Oshlack, 2010). Only genes with CPM (counts per million reads mapped) values more than 1 in at least 4 samples (the number of biological replicates) were retained. Principal component analysis (PCA) was performed on the log-transformed normalized CPM values. An generalized linear model including a genotype effect, an activation effect, and a replicate effect (nested within genotype) was fitted to the raw counts (function glmFit), and differential expression was tested using likelihood ratio tests (function glmLRT). p-values were adjusted by controlling the false-discovery rate (Benjamini-Hochberg method) and genes with a FDR lower than 1% were considered differentially expressed. To select a restricted list of most likely miRNA targets for each seed family, we restricted the genes to those with a conserved 3′UTR seed match (using TargetScan (Friedman et al., 2009)), and a coverage of more than 5 AHC reads. We additionally restricted the list by using differential expression criteria from the first RNA-seq dataset: genes should be significantly differentially expressed between T1792tg/tg and wt, as well as between T1792Δ/Δ and wt, with fold-changes in the expected directions; using intronic read counts, genes should not be significantly differentially expressed in the same comparison, at an increased FDR threshold of 10%. Gene set enrichment analysis was performed with the function camera (Wu and Smyth, 2012) from the limma package (version 3.42; using the default parameter value of 0.01 for the correlations of genes within gene sets) using gene sets from the curated gene set collection (C2) of the Molecular Signature Database (MSigDB v7.0) (Subramanian et al., 2005) with a special focus on gene sets from pathway databases, including KEGG (Kanehisa et al., 2017), Biocarta (http://cgap.nci.nih.gov/Pathways/BioCarta_Pathways), PID (Schaefer et al., 2009) and Reactome (Fabregat et al., 2018). We tested a total of 1,576 gene sets containing more than 10 genes and those with a false discovery rate (FDR) lower than 1% were considered significant. Regulon enrichment analysis was performed similarly to pathway enrichment analysis using camera from the limma package. DoRothEA v2 regulons (Fabregat et al., 2018) were downloaded from https://github.com/saezlab/DoRothEA. More specifically, we used human TOP10score regulons in VIPER format, containing the targets with the highest quality score possible (and at least 10 genes) per transcription factor. Mouse regulons were obtained by orthology: mouse genes with a 1-to-1 or Many-to-1 relationship to human genes of each DoRothEA regulon were obtained using Ensembl Compara (release 97); only orthology relationships annotated to taxonomic levels "Boreoeutheria", "Eutheria", "Euarchontoglires", "Theria", or "Amniota" were retained. We tested a total of 1,307 regulons including at least 10 mouse genes and those with a false discovery rate (FDR) lower than 1% were considered significant. Sample preparation and read depths on seed regions of genes targeted by different miRNA seed families were obtained as previously described (Loeb et al., 2012; Gagnon et al., 2019). Briefly, libraries were constructed using CD4+ T cells where nuclei were eliminated. AGO2 immunoprecipitation was performed using a monoclonal antibody (Wako; clone 2D4). Libraries were multiplexed and sequenced on an Illumina HiSeq 2500 system. Reads were binned based on individual barcodes and aligned to the mm10 reference genome using Bowtie (Langmead et al., 2009). Maximum read depths across mature miRNAs and miRNA targets were generated using the samtools package (Li et al., 2009). TH1 differentiation conditions were generated with 50U IL-2, 5 ng/mL IL-12 and 10 μg/mL anti-IL-4 per mL T cell medium (Vigne et al., 2011). iTregs were differentiated with retinoic acid (0.9 mM), 250U IL-2, 0.75 ng/mL TGFβ, 10 μg/mL anti-IFNγ and 10 μg/mL anti-IL-4 (Barrat et al., 2002). TH17 were generated with 50 ng/mL IL-6, 3 ng/mL TGFβ, 5 μg/mL anti-IFNγ and 10 μg/mL anti-IL-4 per mL T cell medium (Chen et al., 2003). For the differentiation, 2∗105 naïve CD4+ T cells were plated on a pre-coated 96-well flat bottom plate (coated over night with 0.2 μg anti-CD28, 0.5 μg anti-CD3 per mL PBS) and harvested at 24 h, 48 h or 72 h after plating. Calibration plates were coated over night with 200 μL calibrant. Cell plates were coated with 18 μL 0.1 M NaHCO3 pH8.0 6.67% CellTak (Seahorse XF96 flux pack, Bucher Biotech, CH). The following day, cell plates were washed with H2O and left for drying during cell preparation. Compounds were prepared for a final in-well concentration of 1 μM for Oligomycin, 2 μM for FCCP and 11 μM for Rotenone. CD4+ T cells (naïve or activated) were harvested with glucose-free, unbuffered RPMI, washed and counted multiple times before plating 3∗105 cells per well. Mitochondrial perturbation was performed by sequential injection of glucose (80 mM stock), oligomycin, FCCP and rotenone. Measurements of oxygen consumption rate (OCR, pMoles/min) and extracellular acidification rate (ECAR; mpH/min) were performed with a Seahorse XF96 flux analyzer (Seahorse Bioscience, USA). Data analysis was performed using Prism (Version7.0d), mitochondrial parameters were calculated as described by Gubser et al. (2013). The sensitivity of CD28−/− and rescue cells to compounds interfering with Ca2+ signaling was tested. Increasing amounts of CsA were added to the cultures during activation: 2∗105 naïve CD4+ T cells were plated in 100 μL complete T cell medium in pre-coated 96 well plates. 100 μL of a serial dilution of CsA were then added to result in in-well concentrations of 100 ng/mL, 50 ng/mL, 25 ng/mL, 12.5 ng/mL, 6.5 ng/mL, 3.125 ng/mL, 1.5625 ng/mL or 0 ng/mL. Cells were then activated in the presence of these CsA concentrations for 48 h before harvesting and staining for viability and activation markers. Naïve CD4+ T cells were isolated and activated for 48 h in the presence of 6.25 ng/mL CsA. They were then harvested and washed with PBS before fixation for 20 min at 4°C. Intracellular staining for NFATc2 was performed in a two-step staining with first 1 h at room temperature with anti-NFATc2 in permeabilization buffer, and subsequently 1 h at room temperature with goat anti-mouse IgG1 in permeabilization buffer. Nuclei were stained with DAPI in the last 5 min of incubation. Acquisition was run on an ImageStreamX Mark2 Imaging flow cytometer (Amnis), and data analysis was performed with the IDEAS software (v6.2). 5∗105 cells were washed resuspend in 400 μL TRIreagent. RNA isolation was then performed according to the isolation protocol from TRIreagent supplier (SIGMA). In brief, 0.1 mL of 1-bromo-3-chloropropane per mL of TRI Reagent was added, samples were mixed by vigorous shaking, incubated for 15 min at room temperature and then centrifuged at 12’000 g for 15 min at 4°C for phase separation. The aqueous phase was then mixed with 0.5 mL of isopropanol per mL of TRI Reagent used, again centrifuged for 10 min for RNA precipitation. RNA was then washed with 70% Ethanol and finally resuspend in RNAse-free water. RNA concentration and purity was determined with a Nanodrop2000 Spectrophotometer (ThermoScientific). For Rcan3 qPCR, reverse transcription was performed using the SIGMA MMLV kit on 1 μg RNA according to the manufacturer’s instructions. qPCR was run with TaqMan FAST Universal PCR master mix on an Applied Biosystems® Real-Time PCR System. 18S was used as a reference gene. For miRNA17 qPCR, reverse transcription was performed using the TaqMan MicroRNA Reverse Transcription Kit (ABI) and a miRNA-specific reverse stem-loop primer according to the manufacturer’s instructions (Moltzahn et al., 2011). qPCR was run with TaqMan FAST Universal PCR master mix on an Applied Biosystems® Real-Time PCR System. SNO234 was used as a reference gene. Protospacer sequences were taken from the Brie library (Doench et al., 2016) and ordered as CRISPR RNAs (crRNAs) from IDT. Cas9 ribonucleoproteins (RNPs) were prepared as described (Dolz et al., 2021). During preparation, 1.44 μL of 100 mg/mL of poly-L-glutamic acid (PGA) solution (Sigma) was added to gRNAs to improve cutting efficiency (Nguyen et al., 2020). Naïve CD4+ T cells were isolated from CD28−/− mice as described above, labelled with CTV and cultured for 24 h in 24-well plates at a density of 3∗106/mL in T cell medium supplemented with 5 ng/mL IL-7. Prior to electroporation, 1.8–2∗106 cells/electroporation were washed with PBS, counted and resuspended in 20 μL P4 buffer (Lonza). Cells were electroporated with Lonza Nucleofector 4D in 16-well strips using pulse code DS137. After electroporation, cells were cultured in IL-7 supplemented medium for 5 days and activated with plate-bound anti-CD3/anti-CD28 as described above for low stimulation, i.e. 0.2 μg anti-CD28, 0.5 μg anti-CD3 per mL PBS. Cells were harvested and stained for flow cytometry after 48 and 72 hours of activation. Leftover naïve cells were washed with PBS, resuspended in QuickExtract solution (Lucigen), vortexed for 1 min, incubated at 60°C for 6 minutes, vortexed for 1 min and incubated at 98°C for 10 minutes to extract genomic DNA. Genomic DNA was used for PCRs that were sequenced to measure editing efficiency using TIDE (https://tide.nki.nl/). Statistical analysis was performed with GraphPad Prism (v7.0d, Graphpad Software). Normal distribution was not assumed, and non-parametric tests were chosen individually depending on the type of experiment as indicated in the figure legends. The overall statistical significance was set to 5% (α = 0.05), and error bars represent standard deviation (SD). N corresponds to number of biological replicates. We used Kruskal-Wallis tests for most of the experiments with more than two unpaired groups and one factor (e.g., %Fas+GL7+ population in three genotypes), followed by Dunn’s multiple comparison test or Dunnet’s multiple comparison test for CRISPR experiments. We used two-way Anova for experiments including two factors (e.g., %CD44hiCD62lo population in three genotypes activated with or without anti-CD28), followed by Tukey’s multiple comparison test.

PMC9647057

Mi Lin,Ning-Zi Lian,Long-Long Cao,Chang-Ming Huang,Chao-Hui Zheng,Ping Li,Jian-Wei Xie,Jia-Bin Wang,Jun Lu,Qi-Yue Chen,Ya-Han Li,Zhu-Huai Peng,Xiao-Yu Zhang,Yi-Xian Mei,Jian-Xian Lin

Down-regulated expression of CDK5RAP3 and UFM1 suggests a poor prognosis in gastric cancer patients

27-10-2022

gastric adenocarcinoma,CDK5RAP3,UFM1,AKT pathway,prognosis

Purpose The relationship between the CDK5RAP3 and UFM1 expression and the prolonged outcomes of patients who underwent gastric cancer (GC) surgery was investigated. Methods Single-sample gene set enrichment analysis (ssGSEA), unsupervised clustering and other methods were used to verify the relationship between CDK5RAP3 and UFM1 in GC through public databases. Additionally, CDK5RAP3 and UFM1 expression in cancerous and paracancerous tissues of GC was analysed in the context of patient prognosis. Results CDK5RAP3 and UFM1 expression was downregulated synchronously, the interaction was observed between the two proteins, and UFM1 and CDK5RAP3 expression was found to be inversely associated to AKT pathway activation. Prognostic analysis showed that the prognosis is poorer for low CDK5RAP3 and UFM1 patients, than for high CDK5RAP3 and/or UFM1 (p<0.001) patients, and this expression pattern was an independent predictor for overall survival of GC. Coexpression of CDK5RAP3 and UFM1 combined with TNM staging can improve the accuracy of prognosis prediction for patients (p <0.001). Conclusions It is confirmed in our findings that a combination of CDK5RAP3 and UFM1 can produce a more precise prediction model for GC patients’ survival.

Down-regulated expression of CDK5RAP3 and UFM1 suggests a poor prognosis in gastric cancer patients The relationship between the CDK5RAP3 and UFM1 expression and the prolonged outcomes of patients who underwent gastric cancer (GC) surgery was investigated. Single-sample gene set enrichment analysis (ssGSEA), unsupervised clustering and other methods were used to verify the relationship between CDK5RAP3 and UFM1 in GC through public databases. Additionally, CDK5RAP3 and UFM1 expression in cancerous and paracancerous tissues of GC was analysed in the context of patient prognosis. CDK5RAP3 and UFM1 expression was downregulated synchronously, the interaction was observed between the two proteins, and UFM1 and CDK5RAP3 expression was found to be inversely associated to AKT pathway activation. Prognostic analysis showed that the prognosis is poorer for low CDK5RAP3 and UFM1 patients, than for high CDK5RAP3 and/or UFM1 (p<0.001) patients, and this expression pattern was an independent predictor for overall survival of GC. Coexpression of CDK5RAP3 and UFM1 combined with TNM staging can improve the accuracy of prognosis prediction for patients (p <0.001). It is confirmed in our findings that a combination of CDK5RAP3 and UFM1 can produce a more precise prediction model for GC patients’ survival. As a well-known malignant tumor, gastric cancer leads to high lethality of patients worldwide, which makes it rolling as a leading cause to death. The latest epidemiological survey showed that gastric cancer ranks fifth and third global incidence and mortality rates, respectively, among malignant tumours. Globally, more than one million new gastric cancer cases are diagnosed every year, and approximately 800,000 people die of gastric cancer (1). At diagnosis and therapy, majority of patients are already at an advanced stage because of low specificity of early gastric cancer symptoms. Advanced gastric cancer patients have an unfavourable prognosis, with just around a 15% 5-year survival rate (2).. Accurate prognostic assessment helps to formulate reasonable treatment plans and follow-up plans. The TNM staging system is the foremost predictor for gastric cancer prognosis. However, even with the same TNM stage, the prognosis of patients is not the same. In 2014, data from the Gastric Cancer Genome Atlas Research Network confirmed the molecular heterogeneity of gastric cancer (3). Therefore, the prognostic evaluation of the biological potential of gastric tumours has attracted increased attention. It has vital theoretical and clinical significance for the prognostic evaluation of gastric cancer to explore the molecular markers for early identification of gastric cancer and the important role of molecular targeted therapy. CDK5RAP3, known as C53, is an activation binding protein of cyclin-dependent kinase 5; it contains 506 amino acid residues and has a zinc-leucine zinc finger structure (4). CDK5RAP3 plays a key role in the formation and evolution of various malignant tumours (5). In our previous researches, we found that CDK5RAP3 can inhibit the phosphorylation of AKT in gastric cancer (6), thereby inhibiting the GSK-3β mediated phosphorylation, degrading β-catenin and acting as a tumour suppressor in the occurrence and progression of gastric cancer (7). UFM1, a amall ubiquitin protein that contains 85 amino acids, was first discovered by Komatsu in 2004. UFM1 is first activated by UBA5 and is then converted into UFC1 and UFL1. UFL1 recognizes and helps UFM1 to bind the target protein. Finally, UFM1 processes and modifies the target protein to perform its biological vital activities. UFM1 and its modification system participate in different pathophysiological and biological processes, including the cell cycle, fatty acid β oxidation, cell survival, and hypoxia tolerance (8–10). Research has demonstrated that the development of breast cancer involves UFM1 (11). In previous studies, we found that UFM1 can also negatively regulate PI3K/AKT signalling by increasing the ubiquitination of PDK1 to inhibit the invasion and metastasis of gastric cancer (12). The Akt-related signal transduction pathway is a complex signalling network mediated by growth factor receptors (GFRs) (13). Activation of this pathway suppresses cell apoptosis triggered by different stimuli, increases progression and proliferation of the cell cycle, participates in the neovascularization, plays an important role in the formation of tumours, and participates in invasion and metastasis of tumours (14–16). Thereby, we considered that CDK5RAP3 and UFM1 may play a coordinated role in inhibiting the gastric cancer invasion and metastasis. Although some studies have suggested that UFM1 binds to CDK5RAP3, the expression of the two proteins and their effects on the prolonged survival in gastric cancer have not been documented yet. Therefore, we investigated the correlation between UFM1 and CDK5RAP3 expression and the prognosis of gastric cancer using public databases. We also detected the expression of the two indicators in 215 gastric cancer tissue samples using IHC, Western blotting and qPCR. To improve the accuracy of judging prognosis in gastric cancer, the relationship between expression of these two proteins and relevant clinical and pathological characteristics, as well as long-term survival in patients was analysed. We searched the published gastric cancer gene expression database systematically, including those with complete clinical information and excluding those with no survival information. Finally, we gathered The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) cohorts and 17 Gene Expression Omnibus (GEO) cohorts of samples from patients with GC for this study (GSE54129, GSE65801, GSE35809, GSE51105, GSE13861, GSE27342, GSE29272, GSE63089, GSE19826, GSE79973, GSE13911, GSE51575, GSE118916, GSE122401, GSE130823, GSE15459, GSE66229) and the TCPA database for analysis. The original data were collected and downloaded from GEO (http://www.ncbi.nlm.nih.gov/geo/), TCGA (https://portal.gdc.cancer.gov/), and TCPA (https://www.tcpaportal.org/tcpa). The tissues in this study were selected from gastric adenocarcinoma tissue specimens of 215 patients undergoing radical gastrectomy for gastric cancer in our center from January 2013 to December 2014. All patients were newly diagnosed and before surgery they had not received chemotherapy or radiation treatment. The patients were pathologically confirmed to have gastric adenocarcinoma after surgery with comprehensive clinicopathological information. The data were analyzed retrospectively. This study was approved by the Fujian Medical University Union Hospital Ethics Committee and written permission was obtained from every relevant patient. We obtained 3 GFR gene sets (KRAS_SIGNALING_UP and AKT_UP. V1_DN and MTOR_UP. V1_DN) from C6 (oncogenic gene sets) of MSigDB (https://www.gsea-msigdb.org/). Using the R software package “GSVA” (gene set variation analysis for microarray and RNA-seq data), we scored each sample in the TCGA cohort by ssGSEA (method = “ssgsea”, ssgsea.norm = TRUE, verbose = TRUE). Unsupervised clustering methods (K-means) were used to classify the TCGA cohort into different clusters based on the enrichment of GFR pathways. The clustering factors were the ssGSEA scores of the three GFR gene sets. These scores were first converted to z scores to improve the accuracy of clustering. We determined the final number of clusters according to the algorithm provided by the R software package “NbClust”. Finally, the TCGA queue was accurately divided into 3 clusters defined as Cluster A, Cluster B, and Cluster C. We performed GSEA on the TCGA and GEO datasets (GSE54129, GSE65801, GSE35809, and GSE51105). First, we used the mean ± standard deviation (SD) of the CDK5RAP3 expression value as the cut-off point to divide each data set into three groups: the group of high, moderates and low. Next, we compared the high and low expression group to obtain differentially expressed genes. Additionally, the R package “clusterProfiler” (v3.12.0)0 (https://guangchuangyu.github.io/software/clusterProfiler) was applied to perform GSEA on these differential genes. MSigDB provided us with all of the hallmark and oncogenic gene sets (https://www.gsea-msigdb.org/). Tumour specimens containing enough formalin-fixed and embedded by paraffin were sliced into 4-μm serial sections and mounted for immunohistochemical analysis on silane-coated glass slides. The sections were dewaxed, rehydrated, antigen repaired, blocked and then incubated with appropriate antibodies. The rabbit anti-human CDK5RAP3 (ab24189; 1:200; Abcam) or UFM1 (ab109305; 1:200; Abcam) antibody was used as the primary antibody. Two experienced pathologists independently assessed IHC-stained tissue slices and scored them based on the intensity of cell staining and the positive ratio of the stained tumour cells. The proportion and intensity of CDK5RAP3-positive and UFM1-positive cells in random selection visual areas were evaluated to indicate the protein expression level. The following were the staining score standards for CDK5RAP3 and UFM1: no staining was indicated by a score of 0; the light yellow was defined as mild staining with a score of 1; the yellowish brown was defined as moderate staining with a score of 2; the brown was defined as significant staining with a score of 3. The following were the proportional score standards for stained tumor cells: when less than or equal to 5 percent cells were positive, the score was 0; when the positive cells were range from 6 to 25 percent, the score was 1; when the positive cells were range from 26 to 50 percent, the score was 2; when the positive cells were greater than or equal to 50 percent, the score was 3. ( Figure S1 ). The final score ranging from 0 to 9 for the expression of CDK5RAP3 and UFM1, was obtained by multiplying the staining score and proportional score. The low-expression group was defined as patients having a final score <4. The high-expression group included the remaining patients. We cut fresh soy-sized gastric cancer tissue and paracancerous tissue pieces into a shaking tube. Next, lysis solution was added (1 mg of tissue plus 6 µl of lysis solution). The lysis solution comprising RIPA lysis solution (Thermo Fisher Scientific, Waltham, MA, USA) + PMSF solvent + Cocktail (Roche, South San Francisco, CA, USA) was prepared (100:1:1). The tubes were then placed in the oscillator at 5 m/s for 30 s. Thereafter, the samples were subjected to shaking after 12000 rpm 4 times, followed by centrifugation for 5 min. The supernatant was then pipetted into a new EP tube. The protein concentration was measured by the BCA method, and the protein sample (loading volume per well 40 μg) was separated by 10% SDS-PAGE and transferred to a PVDF membrane. Subsequently, the membrane was blocked with 5% skim milk for 1 hour at room temperature. Next, the membrane was incubated with primary antibodies (CDK5RAP3, UFM1 and GAPDH) at 4°C overnight. After that, the membrane was washed with washing buffer (TBS-T) 3 times, 5 min each time, and then incubated with HRP secondary antibody (Cell Signaling Technology) for 1 h at room temperature. GAPDH was used as an internal control. Finally, the membrane was washed with TBS-T for 30 min and the protein bands were detected by enhanced chemiluminescence (Amersham Corporation, Arlington Heights, IL, USA). The following antibodies were used by Western blots: CDK5RAP3 (ab24189; 1:1000 dilution; Abcam, Cambridge, MA, USA), UFM1 (ab109305; 1:1000 dilution; Abcam, Cambridge, MA, USA), p-AKT (serine 473) (ab81283, 1:1000 dilution; Abcam, Cambridge, MA, USA) and GAPDH (#5174; 1:2000 dilution; Cell Signaling Technology). Total RNA from gastric cancer and paracancerous tissues was extracted using Invitrogen’s TRIzol kit according to the manufacturer’s instructions and used to obtain cDNA using Takara’s reverse transcription system. The copy numbers of GAPDH, CDK5RAP3 and UFM1 were detected using qPCR. The following were the detailed primer sequences: CDK5RAP3 Forward primer: 5′-GCTGGTGGACAGAAGGCACT-3′ Reverse primer: 5′-TGTCCTGGATGGCAGCATTGA-3′ UFM1 Forward primer: 5′-GTCCCC AGCACACTAGAGGA-3′ Reverse primer: 5′-GGA AAAGAGCGGGAG AGAGT-3′ GAPDH Forward primer: 5′-GAAGGTGAAGGTCGG AGT-3′, Reverse primer: 5′-GAAGATGGTGATGGGATTTC-3′ GAPDH was used as an internal reference, and the ΔΔCt method was used for analysis. Protein was extracted from stably transfected cells (HGC-27) overexpressing UFM1, and the BCA method was used to determine the protein concentration. A small amount of protein solution was saved and boiled with 2× SDS sample buffer and then frozen at -20°C for Western blot analysis. Next, an appropriate amount of UFM1 antibody was added to the remaining protein solution at a ratio of 100 µg of protein/1 µg antibody and incubated at 4°C with gentle shaking overnight. Protein A/G agarose beads (20 µl) were incubated at 4°C for 2–4 h and centrifuged at 4°C at 3000×g for 3 min. It discarded the supernatant and washed the agarose beads on 5 times with a buffer of 1 ml lysis. After the final removal of the supernatant, 20 µl of 2× SDS was added to the pellet, followed by boiling in water for 5 min. Finally, the CDK5RAP3 antibody was used for Western blot. According to the institutional follow-up protocol, qualified doctors monitored all patients by outpatient clinics, phone calls, emails, letters or visits. The first 2 years of follow-up were completed every 3 months. The next 3 years of follow-up were completed every 6 months. Then they were followed up annually until death or after 5 years. Most of the patients had undergone physical exams, laboratory tests, imageological examinations and annual gastroscopy. The time from operation to last follow-up or death was defined as the overall survival time. The follow-up rate of the whole group was 93.56%, and the median follow-up time was 57 months (range, 2–83 months). All statistical analyses were performed using the Social Science Statistical Software Package (SPSS) version 23.0 for Windows (IBM, Chicago, IL, USA) or R software (version 3.6.2). If not specified, the results were shown as percentages or means ± SD. As needed, the data were analysed by chi-square test, Fisher’s exact test or Student’s t test. The survival rate was evaluated by Kaplan-Meier method and log-rank test. The Cox proportional hazards model was used for univariate and multivariate prognostic analysis. Multivariate analysis was performed on factors with p<0.05 in univariate analysis. Statistical significance was indicated when the P value was less than 0.05. Pearson’s correlation or Spearman’s correlation was used to estimate the correlation coefficient (p <0.05). Additionally, the protein interaction network was constructed using GeneMANIA (http://www.genemania.org/). A receiver operating characteristic (ROC) curve and the area under the curve (AUC) were computed to assess discriminative ability. First, we used unsupervised clustering methods to classify 375 tumour samples from The Cancer Genome Atlas (TCGA) database into three molecular subgroups (Cluster A, Cluster B, and Cluster C) based on the three characteristic pathways of GFRs: KRAS, AKT, and MTOR. The heat map showed that the downstream signalling pathway-related genes GFR signature, GF and GFR were inhibited in patients in Cluster A, while they were activated in patients in Cluster C ( Figure 1A ). By analysing the related proteins of the GFR pathway from The Cancer Proteome Atlas (TCPA) database, we observed that the GFR pathway-related proteins SYK, PDK1, P90RSK, 4EBP1, and BIM were found to be highly expressed in Cluster A, PREX was found to be highly expressed in Cluster B, and CKIT, AMPKALPHA, PKCALPHA_pS657, BAD_pS112, PKCALPHA, PACDELTA_pS664, SHP2542, TUBERIN_pT1462, and IRS1 were found to be highly expressed in Cluster C, with significant differences ( Figure 1B ). Survival analysis also indicated that the overall survival of the patients from Cluster C was lower than that of the patients from Cluster A (p = 0.043) ( Figure 1C ). To explore which genes played a key regulatory role in the GFR pathway, we compared the genetic changes in patients in Clusters B vs. A, C vs. A, and C vs. B. The Venn diagram showed that Clusters B vs. A, C vs. A and C vs. B had 507 common downregulated genes ( Figure 1D and Table S1 ), and 1,536 common upregulated genes ( Figure 1E and Table S2 ). Analysing the co-downregulated genes and CDK5RAP3-interacting proteins in the string database, we found that the CDK5RAP3 and UFM1 genes were included in the 507 common downregulated genes, and the mRNA levels of CDK5RAP3 and UFM1 in patients of category C were lower than those in patients of categories A and B. The log fold-change of CDK5RAP3 was -0.741 in Cluster C vs. Cluster A and -0.567 in Cluster C vs. Cluster B. The log fold-change of UFM1 was -0.636 in Cluster C vs. Cluster A and -0.423 in Cluster C vs. Cluster B. ( Figures 1F, G ). Additionally, an interaction was observed between the two proteins ( Figure 1H ). We further performed pathway enrichment analysis of patients with high and low CDK5RAP3 expression in the TCGA and GEO databases. The mountain map, heat map and GSEA enrichment analysis map all indicated that CDK5RAP3 expression negatively correlated with AKT pathway activation ( Figures 2A–C ), a finding that was consistent with previous research results (6). Additionally, the correlation analysis of four GEO databases (GSE13861, GSE27342, GSE29272, GSE63089) and the TCGA database revealed that the expression levels of UFM1 and CDK5RAP3 were significantly correlated ( Figures 2D–H ). Co-IP experiments confirmed that UFM1 had a direct binding effect with CDK5RAP3 ( Figure 2I ). Therefore, we knocked down and overexpressed UMF1 and CDK5RAP3 in the HGC cell line to verify that UFM1 and CDK5RAP3 negatively correlated with AKT pathway activation. The results showed that knocking down UFM1 caused a decrease in CDK5RAP3 expression and reduced the inhibition of AKT phosphorylation, while the overexpression of UFM1 caused an increase in CDK5RAP3 to enhance the inhibition of AKT phosphorylation ( Figure 2J ). However, the UFM1 didn’t change when CDK5RAPS was knocked down or overexpressed ( Figure 2K ). Analysis of 7 GEO databases (GSE13861, GSE54129, GSE19826, GSE79973, GSE13911, GSE51575, GSE29272) showed that CDK5RAP3 expression was low in gastric cancer ( Figure 3A ). CDK5RAP3 expression levels in cancerous and paracancerous tissues from 15 cases in GSE118916, 80 cases in GSE122401, and 47 cases in GSE130823 were found to be low ( Figure S2 ), as was UFM1 expression in cancerous and paracancerous tissues from 15 patients in GSE118916 ( Figure 3B ). Furthermore, we used samples from the internal centre for verification. IHC staining of cancerous and paracancerous tissues from gastric cancer patient showed that CDK5RAP3 and UFM1 protein expression in cancerous samples were both lower than that in paracancerous ( Figure 3C ). IHC staining score was used to analyse CDK5RAP3 and UFM1 protein expression in paraffin-embedded gastric cancer samples from 124 patients. CDK5RAP3 was found to be lowly expressed in 102 patients (82.3%) and had high expressions in 22 patients (17.7%). The expression levels of UFM1 were found to be low in 93 patients (75. 5%) and high in 31 patients (25.0%). Spearman’s correlation analysis indicated that CDK5RAP3 and UFM1 expression was significantly correlated ( Figure 3D ). We also used Western blotting to detect CDK5RAP3 and UFM1 expression in the cancerous and paracancerous tissues of 43 gastric cancer patients ( Figure 3E ) and simultaneously detected the mRNA levels of CDK5RAP3 and UFM1 in the tumour tissues of 48 patients with gastric cancer. Pearson’s correlation analysis showed that the expression of the two mRNA levels was positively correlated ( Figure 3F ). The overall survival was reduced dramatically in patients with low CDK5RAP3 expression compared with patients with high CDK5RAP3 in the 3 GEO databases (GSE13861, GSE15459 and GSE66229) and the TCGA database ( Figure S3 ). Similarly, the overall survival rate was significantly worse among patients with low UFM1 than in patients with high UFM1 ( Figure S4 ). In the GSE66229 database, the patients with low CDK5RAP3 expression had a significant lower disease-free survival rate than that of patients with high CDK5RAP3 expression, and the patients with low UFM1 expression also had a significant lower disease-free survival rate than those with high UFM1 expression ( Figure S5 ). Regarding the internal centre data, the 3-year overall survival rate was 66.9% with median 57 months follow-up for the entire group. According to survival analyses, the 3-year cumulative overall survival rate of high CDK5RAP3 expression patients was significantly higher than that of low CDK5RAP3 patients (81.8% vs. 62.7%, p < 0.05, Figure 4A ); those with low UFM1 expression exhibited a lower 3-year overall survival rate than patients with high UFM1 expression (58.1% and 90.3%, respectively; p 0.05; Figure 4B ). We further analysed the prognostic value of the combination of CDK5RAP3 expression and UFM1 expression by Kaplan–Meier analysis. In comparison to the other groups of patients, patients with low expression levels of CDK5RAP3 and UFM1 had a poorer 3-year cumulative survival rate—only 54.9%—which was substantially below CDK5RAP3 high and/or UFM1 high expression patients ( Figure 4C ). After combing the groups, we found that patients with low CDK5RAP3 and UFM1 expression had a significantly worse prognosis than those with high CDK5RAP3 and/or UFM1 expression (88.1%) (p < 0.001; Figure 4D ). Analysis of factors associated with the expression of CDK5RAP3 and UFM1 in gastric cancer tissues showed that the CDK5RAP3 and UFM1 expression significantly correlated with BMI, lymph node metastasis, depth of invasion and pathological TNM stage ( Table 1 ). Combining the low CDK5RAP3 and UFM1 expression, analysis of related factors showed that the low expression level of the two was related to tumour size, depth of invasion, lymph node metastasis and TNM staging ( Table 2 ). BMI, tumour size and TNM staging were further included in the logistic regression model. The results of multivariate analysis suggested that TNM staging was an independent factor related to the low expression of CDK5RAP3 and UFM1 (I+II vs. III: 95% CI: 1.128–5.755, p = 0.023). Cox regression analyses were used to clarify the prognostic value of CDK5RAP3 and UFM1 expression. Based on the univariate analysis, overall survival was related to BMI, tumour size, TNM staging and combined CDK5RAP3 and UFM1 expression ( Table 3 ). Multivariate analysis indicated that the coexpression level of CDK5RAP3 and UFM1, as well as TNM stage were both independent predictive variables for patient prognosis with gastric cancer ( Table 3 ). We compared the accuracy of CDK5RAP3 or UFM1 expression, as well as combined CDK5RAP3 and UFM1 expression and TNM staging, in predicting gastric cancer survival using ROC curve analysis. The combination expression of CDK5RAP3 and UFM1 was more accurate in predicting patient survival than either CDK5RAP3 or UFM1 expression on its own (AUC was 0.638, 0.584, and 0.596; 95% CI was 0.532–0.740, 0.473–0.688, and 0.490–0.702; p = 0.021, 0.172, and 0.104 for CDK5RAP3 + UFM1, CDK5RAP3 and UFM1 respectively). Additionally, combined CDK5RAP3 and UFM1 expression had a prognostic value that was similar to TNM staging (AUC: 0.651, 95% CI: 0.601–0.786, p = 0.001; Figure 5A ). Furthermore, compared with CDK5RAP3 or UFM1 combined with or without TNM staging, the coexpression of CDK5RAP3 and UFM1 combined with TNM staging further improved the prognostic prediction accuracy of patients (p < 0.001, Figure 5B ). Thus, the combination of CDK5RAP3 and UFM1 expression had a higher prognostic ability for overall survival in GC patients.You may insert up to 5 heading levels into your manuscript as can be seen in “Styles” tab of this template. These formatting styles are meant as a guide, as long as the heading levels are clear, Frontiers style will be applied during typesetting. Gastric cancer remains the third leading cause of death in China despite improvements in diagnosis and therapy in recent years (5, 17). To better guide diagnosis and therapy, identifying specific biomarkers linked to gastric cancer prognosis may help improve the accuracy of gastric cancerprognostic assessment (18–20). Based on our previous study and an examination of public databases, this study found that the expression of the UFM1 and CDK5RAP3 genes are downregulated synchronously in gastric cancer patients with poor prognosis and that an interaction occurs between the UFM1 and CDK5RAP3 proteins. Therefore, we chose to evaluate UFM1 as a prognostic factor with CDK5RAP3. GFRs and their abnormal signal transduction are important mechanisms of tumorigenesis and development, and they have become hot topics of research in recent years (10, 21). Many studies have shown that the abnormal function of growth factors and their receptors is an important cause of tumour occurrence and development. Such growth factor receptors have tyrosine kinase activity and can regulate the activity of downstream signalling pathways through phosphorylation (22, 23). The PI3K/Akt signalling pathway plays an important antiapoptotic role. Abnormalities in Akt-related signalling pathways are also associated with the occurrence of various tumours (13, 24). Therefore, we used public databases to search for proteins related to the GFR signalling pathway and CDK5RAP3 and attempted to identify biological prognostic indicators for gastric cancer. It was suggested that UFM1 was positively correlated with CDK5RAP3 and its low expression was associated with poorer prognosis of gastric cancer. Previous studies have shown that multiple proteins related to CDK5RAP3 and UFM1 and their modification systems (such as UFC1 and UFL1) are closely related (14, 15, 25). The correlation analysis of multiple public databases in this study also proved that the CDK5RAP3 and UFM1 expression were found to be substantially linked. To date, few studies have investigated the combined expression levels of CDK5RAP3 and UFM1 and its prognostic significance in gastric cancer. Therefore, in patients with gastric cancer, we assessed the relationship between relevant clinicopathological parameters and overall survival by detecting the CDK5RAP3 and UFM1 expression levels. In univariate analysis, low CDK5RAP3 expression was linked to a poor prognosis, and high UFM1 expression was linked to a better survival rate in gastric cancer patients, indicating that both CDK5RAP3 and UFM1 play a tumour suppressor role in gastric cancer. Further analysis of related factors showed that the CDK5RAP3 and UFM1 coexpression was strongly linked to the invasive depth, lymph node metastasis and TNM stage, indicating that the two proteins are closely related to tumour invasion and migration in gastric cancer. Additionally, we found that the functions of CDK5RAP3 and UFM1 in gastric cancer were positively correlated. Patients with low CDK5RAP3 and UFM1 expression had the worst prognosis; if either of the two proteins showed high expression, patient’s prognosis was dramatically better. We considered that because CDK5RAP3 and UFM1 both played a role as tumour suppressor proteins, when one of the two proteins was highly expressed, the tumour suppressor effect in gastric cancer results in no difference in survival. When both proteins were expressed at a low level, the inhibition of the tumour was relieved, resulting in the poorest prognosis of all groups. Further analysis showed that the accuracy of prognostic analysis using CDK5RAP3 and UFM1 expression was closer to the accuracy of TNM staging prognostic analysis and higher than that of using CDK5RAP3 or UFM1 expression alone. Therefore, combination of the CDK5RAP3 and UFM1 expression can improve the capacity to forecast the survival outcomes of patients with gastric cancer. The TNM staging system has been identified as a major prognostic factor for the gastric cancer. It’s also a valuable foundation for the formulation of gastric cancer treatment. However, differences in the prognosis of the same stage patients persist. In this study, we combined the coexpression of CDK5RAP3 and UFM1 with TNM staging for prognostic analysis. In comparison to the conventional TNM staging’s forecast accuracy, combining CDK5RAP3 and UFM1 expression with TNM greatly improved the accuracy of predicting gastric cancer patient survival. This finding indicated that the coexpression level of CDK5RAP3 and UFM1 could increase the accuracy of gastric cancer prognostic evaluation. Maybe it is possible to build a more precise model combining CDK5RAP3, UFM1 and TNM staging to predict 5-year survival of gastric cancer after surgery. It is worth exploring in the subsequent research. As a result, in clinical practice, the coexpression of CDK5RAP3 and UFM1 can be used in cooperation with TNM staging to effectively guide treatment and follow-up of patients with gastric cancer. This study mainly explored the impact of the coexpression level of UFM1 and CDK5RAP3 on the clinicopathological parameters of gastric cancer patients and its prognostic significance, providing a preliminary basis for further research. Further investigation of how UFM1 and CDK5RAP3 regulate AKT pathway and whether CDK5RAP3 and UFM1 are associated with metastasis would be highly significant. Therefore, the elucidation of related mechanisms warrant further study. The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/, TCGA-STAD, GSE54129, GSE65801, GSE35809, GSE51105, GSE13861, GSE27342, GSE29272, GSE63089, GSE19826, GSE79973, GSE13911, GSE51575, GSE118916, GSE122401, GSE130823, GSE15459, GSE66229 https://www.tcpaportal.org/tcpa, TCPA https://www.gsea-msigdb.org/, GSEA. The studies involving human participants were reviewed and approved by Fujian Medical University Union Hospital Ethics Committee. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article. ML, L-LC, and J-XL conceived of the study; ML, N-ZL, and L-LC conducted the experiment and performed the major analysis; ML and N-ZL prepare the manuscript; J-XL, C-MH, C-HZ, PL, J-WX, J-BW, JL, Q-YC, Y-HL, Z-HP, X-YZ, and Y-XM helped to collect the data and revise the manuscript critically for important intellectual content; All authors contributed to the article and approved the submitted version. This study was funded by Construction Project of Fujian Province Minimally Invasive Medical Center (NO.[2021]662). Scientific and technological innovation joint capital projects of Fujian Province (No. 2018Y9008). Fujian Provincial Health Technology Project (2018-1-40). China Scholarship Council (202108350068). We are thankful to Ju-Li Lin, Hua-Long Zheng, Guang-Tan Lin and Fujian Medical University Union Hospital for managing the gastric cancer patient database. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling editor DP declared a past collaboration with the author L-LC. All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

PMC9647095

Li Chen,Xintong Kang,Xiujuan Meng,Liang Huang,Yiting Du,Yilan Zeng,Chunfeng Liao

MALAT1-mediated EZH2 Recruitment to the GFER Promoter Region Curbs Normal Hepatocyte Proliferation in Acute Liver Injury

15-04-2022

MALAT1,EZH2,GFER,H3K27me3,Methylation,Acute liver injury

Background and Aims The goal of this study was to investigate the mechanism by which the long noncoding RNA MALAT1 inhibited hepatocyte proliferation in acute liver injury (ALI). Methods Lipopolysaccharide (LPS) was used to induce an ALI cellular model in HL7702 cells, in which lentivirus vectors containing MALAT1/EZH2/GFER overexpression or knockdown were introduced. A series of experiments were performed to determine their roles in liver injury, oxidative stress injury, and cell biological processes. The interaction of MALAT1 with EZH2 and enrichment of EZH2 and H3K27me3 in the GFER promoter region were identified. Rats were treated with MALAT1 knockdown or GFER overexpression before LPS induction to verify the results derived from the in vitro assay. Results MALAT1 levels were elevated and GFER levels were reduced in ALI patients and the LPS-induced cell model. MALAT1 knockdown or GFER overexpression suppressed cell apoptosis and oxidative stress injury induced cell proliferation, and reduced ALI. Functionally, MALAT1 interacted directly with EZH2 and increased the enrichment of EZH2 and H3K27me3 in the GFER promoter region to reduce GFER expression. Moreover, MALAT1/EZH2/GFER was activated the AMPK/mTOR signaling pathway. Conclusion Our study highlighted the inhibitory role of reduced MALAT1 in ALI through the modulation of EZH2-mediated GFER.

MALAT1-mediated EZH2 Recruitment to the GFER Promoter Region Curbs Normal Hepatocyte Proliferation in Acute Liver Injury The goal of this study was to investigate the mechanism by which the long noncoding RNA MALAT1 inhibited hepatocyte proliferation in acute liver injury (ALI). Lipopolysaccharide (LPS) was used to induce an ALI cellular model in HL7702 cells, in which lentivirus vectors containing MALAT1/EZH2/GFER overexpression or knockdown were introduced. A series of experiments were performed to determine their roles in liver injury, oxidative stress injury, and cell biological processes. The interaction of MALAT1 with EZH2 and enrichment of EZH2 and H3K27me3 in the GFER promoter region were identified. Rats were treated with MALAT1 knockdown or GFER overexpression before LPS induction to verify the results derived from the in vitro assay. MALAT1 levels were elevated and GFER levels were reduced in ALI patients and the LPS-induced cell model. MALAT1 knockdown or GFER overexpression suppressed cell apoptosis and oxidative stress injury induced cell proliferation, and reduced ALI. Functionally, MALAT1 interacted directly with EZH2 and increased the enrichment of EZH2 and H3K27me3 in the GFER promoter region to reduce GFER expression. Moreover, MALAT1/EZH2/GFER was activated the AMPK/mTOR signaling pathway. Our study highlighted the inhibitory role of reduced MALAT1 in ALI through the modulation of EZH2-mediated GFER. Acute liver injury (ALI) is a pernicious clinical condition marked by rapid hepatocyte dysfunction and defects in patients with or without a history of liver disease.1 Hepatitis viruses, drugs, immunologic injury and other hepatotoxic factors cause significant hepatocyte death, ultimately inducing ALI or even acute liver failure (ALF).2 Increases in aspartate aminotransferase (AST) and alanine aminotransferase (ALT) are used to identify the likelihood of ALI.3,4 Currently, immunosuppressors, antiviral agents, bioartificial livers, and liver transplantation are available treatment options for ALI.5 For patients with ALF and acute-on-chronic liver failure, the only definitive option is liver transplantation when there is a poor prognosis.6 Of note, the prognosis is often made worse by ineffective treatment, high cost, risk of organ rejection, limited liver donors, and severe treatment-related adverse effects.5,7 Under the circumstances, it is imperative to develop novel treatment strategies to prevent ALI. Long noncoding (lnc)RNAs are transcripts >200 nucleotides that are dysregulated in liver disease and are considered biological markers for the diagnosis, prognosis, and treatment.8,9 Abnormal expression of MALAT1 has been identified in rodent models and patients with acute kidney injury.10 Interestingly, downregulation of MALAT1 blocks hypoxia/reoxygenation-induced hepatocyte apoptosis and limits the release of lactic dehydrogenase (LDH).11 Knockdown of MALAT1 improves the outcome of lipopolysaccharide (LPS)-induced acute lung injury and suppresses apoptosis of human pulmonary microvascular endothelial cells.12 Moreover, MALAT1 recruits the histone methyltransferase EZH2 to the microRNA (miR)-22 promoter region, thus inhibiting the expression of miR-22.13 Furthermore, MALAT1 recruits EZH2 to the promoter region of ABI3BP to downregulate its expression and modulate H3K27 methylation in gallbladder cancer cells.14 Notably, regulation of methylation plays an essential role in the deterioration and management of ALI.15,16 In addition, GFER encodes augmenter of liver regeneration (ALR) a protein that specifically supports liver regeneration.17 Transient knockdown of GFER has been found to promote cell death and reduce cell proliferation in liver tissue.18 We hypothesized that GFER is a downstream gene regulated by MALAT1 in ALI. The study aim was to elucidate the role and potential mechanism of MALAT1 in LPS-induced ALI. To that end, animal and cellular models of ALI were established using LPS, and the severity of ALI, apoptosis and proliferation were assessed. This study included 26 patients with acute drug-induced liver injury (ADILI) who were hospitalized in the liver disease department of The Third Xiangya Hospital of Central South University and 19 healthy people from the physical examination clinic of The Third Xiangya Hospital of Central South University. The diagnostic criteria of ADILI were based on the guidelines for the diagnosis and treatment of drug-induced liver injury of the 17th National Conference on Viral Hepatitis and Hepatology of the Chinese Medical Association in 2015 and a Roussel Uclaf Causality Assessment Method (RUCAM) scale score of >8 points.19 Patients were excluded if they had viral hepatitis B or C or other types of viral hepatitis, autoimmune liver disease, alcoholic liver disease, nonalcoholic fatty liver disease, cholestatic disease, or inherited metabolic liver disease. Peripheral blood was selected for clinical research in this study because liver biopsies were not performed. Approximately 4 mL of peripheral blood was collected in the morning after overnight fasting, placed in a serum tube, centrifuged at 4,000 rpm at 4°C for 10 m, and stored at −80°C until. use. This study complied with the Declaration of Helsinki and was approved by the ethics committee of The Third Xiangya Hospital of Central South University. All patients signed an informed consent form (No. 22014). Patient information is shown in Table 1. HL7702 human hepatocyte cells were provided by Cell Bank of Chinese Academy of Science and maintained in Dulbecco’s modified Eagle’s medium (Gibco, Grand Island, NY, USA) containing 10% fetal bovine serum plus 1% streptomycin-penicillin and cultured in a 37°C incubator with 5% CO2. Hepatocytes were induced by 1 µg/mL LPS (Sigma-Aldrich, St Louis, MO, USA) for 16 h in a 5% CO2 atmosphere at 37°C to induce ALI. In some cases, hepatocytes were cultured with an adenosine monophosphate-activated protein kinase (AMPK) inhibitor (10 µM, Compound C; Sigma-Aldrich) for 1 h before LPS induction. HL7702 cells were seeded in culture plates and transfected with MALAT1 overexpression vector (oe-MALAT1), MALAT1 knockdown vector (sh-MALAT1, 20 µL, viral titer 5×108 TU/mL), EZH2 overexpression vector (oe-EZH2, 20 µL, viral titer 5×108 TU/mL), EZH2 knockdown vector (sh-EZH2, 20 µL, viral titer of 5×108 TU/mL), GFER overexpression vector (oe-GFER, 20 µL, viral titer of 3×108 TU/mL), GFER knockdown short hairpin vector (sh-GFER, 20 µL, viral titer of 5×108 TU/mL) or the corresponding negative controls (oe-NC and sh-NC) (20 µL, 3×108 TU/mL). Lentivirus vectors used for gene overexpression (LV5-GFP) or knockdown (pSIH1-H1-copGFP) were provided by GenePharma (Shanghai, China). Each experiment was conducted in triplicate. LPS induction was performed in HL7702 cells 24 h after transfection. The animal procedures in this study were approved by the Ethics Committee of The Third Xiangya Hospital of Central South University (No. 22014) and followed the guidelines of the National Institutes of Health. Forty-two Sprague-Dawley rats 7–8 weeks of age, and 200–250 g (Shanghai Laboratory, Animal Research Center, Chinese Academy of Science) were fed under pathogen-free conditions and kept in a 12 h light/dark cycle and 60–65% humidity. The rats were allowed free access to food and water. The rat ALI model was induced by intraperitoneal injection of 10 mg/kg LPS (Sigma-Aldrich), with normal saline treatment as the control. The model was maintained for 6 h before sh-MALAT1 (5×108 TU/mL, 300 µL/rat) or oe-GFER (3×108 TU/mL, 300 µL/rat) was introduced into rats through intravenous injection in tails for MALAT1 knockdown or GFER overexpression, with sh-NC (3×108 TU/mL, 300 µL/rat) or oe-NC (3×108 TU/mL, 300 µL/rat) as the negative control. The 42 rats were randomly divided into seven groups of six rats each, including normal controls, and saline (administration of an equal volume of normal saline), LPS (intraperitoneal injection of 10 mg/kg LPS for 6 h modelling), LPS + sh-NC group (intravenous injection of sh-NC in the tail vein 42 h before LPS induction, followed by intraperitoneal injection of 10 mg/kg LPS for 6 h modelling), LPS + sh-MALAT1 (injection of sh-MALAT1 in the tail vein 42 h before LPS induction, followed by intraperitoneal injection of 10 mg/kg LPS for a 6-h modelling), LPS + oe-NC group (injection of oe-NC in the tail vein 42 h before LPS induction, followed by intraperitoneal injection of 10 mg/kg LPS for a 6-h modelling), and LPS + oe-GFER (injection of oe-GFER in the tail vein 42 h before LPS induction, followed by intraperitoneal injection of 10 mg/kg LPS for a 6-h modelling). All animals were sacrificed to collect serum and liver tissue 48 h after modelling for 6 h and injection of lentivirus vectors. RNA extraction of cells or tissues was carried out using TRIzol (Invitrogen, Carlsbad, CA, USA) followed by detection of RNA concentration and purity. Qualified RNA samples were adjusted to an appropriate concentration and then reverse-transcribed using random primers and a reverse transcription kit (TaKaRa, Tokyo, Japan) following the manufacturer’s instructions. Gene expression was quantified by fluorescence qRT-PCR (LightCycler 480; Roche, Indianapolis, IN, USA), and was carried out following the manufacturer’s instructions (SYBR Green Mix; Roche Diagnostics). In brief, cDNA templates were predenatured at 95°C for 5 m, denatured at 95°C for 10 s, annealed at 60°C for 10 s and finally extended at 72°C for 20 s, followed by 40 cycles of cycling. Each qPCR assay was performed with three replicates. The relative expression of GFER and MALAT1 was determined by the 2−ΔΔCt method with GAPDH as the reference gene. The primer sequences for GAPDH, MALAT1, and GFER are shown in Table 2. Cells or tissues were lysed in RIPA lysis buffer and centrifuged for protein extraction. Protein concentration was detected with a bicinchoninic acid assay kit (Beyotime Biotechnology, Shanghai, China) to ensure equal loading volume of protein. The corresponding volume of protein was homogenized with loading buffer (Beyotime) and then denatured for 3 m in a boiling water bath. Proteins were separated on 10% sodium dodecyl-sulfate polyacrylamide gel electrophoresis (SDS-PAGE) gels following the kit (Beyotime) manufacturer’s instructions. Briefly, the protein was electrophoresed at 80 V, and then the voltage was switched to 120 V for 1–2 h. After protein separation, membrane transfer was performed in an ice bath at 300 mA for 60 m. Then, the membranes were rinsed in a washing solution for 1–2 m, followed by blocking at room temperature for 60 m or at 4°C overnight. Incubation with primary antibodies against rabbit anti-human GAPDH (1:1,000; Cell Signaling, Boston, MA, USA), GFER (1:500; Santa Cruz Biotechnology, Dallas, TX, USA), p-mTOR (Ser2448 1:1,000; Cell Signaling Technology), mTOR (1:1,000; Cell Signaling Technology), p-AMPK (Thr172 1:1,000; Cell Signaling Technology), AMPK (1:1,000; Cell Signaling Technology), H3K27me3 (1:1,000; Abcam, Cambridge, UK), or EZH2 (1:500; Abcam) performed at room temperature on a shaking table for 1 h. After incubation, the membranes were washed three times for 10 m each and then incubated with horseradish peroxidase-labeled goat anti-rabbit IgG (1:5,000; Beijing ComWin Biotech Co., Ltd., Beijing, China) for 1 h at room temperature. Before color development, the membranes were washed three times for 10 m each. A chemiluminescence imaging system (Bio-Rad, Hercules, CA, USA) was used to visualize the membranes. The culture supernatant of HL7702 cells or rat serum was collected for liver function testing. AST, ALT, and LDH were assayed with kits following the manufacturer’s (Sigma-Aldrich, Merck KGaA, Darmstadt, Germany) instructions. Twenty-four hours after transfection, 100 µL of the cell suspension was seeded into a 96-well plate, with three replicates for each sample. Cells were cultured in an incubator for 24, 48, or 72 h, 10 µL of CCK-8 reagent (Dojindo, Tokyo, Japan) for 3 h, and absorbance was determined at 450 nm. Cells (1×105 cells/well) were transferred to 96-well plates and cultured for 2 h with 100 µL EdU stain (5 µM; Sigma-Aldrich, Merck KGaA). The cells were then immobilized in 50 µL fixation buffer for 30 m. After removing the buffer, the cells were incubated with 50 µL glycine (2 mg/mL) for 5 m and washed with 100 µL phosphate buffered saline (PBS). After culture with 100 µL of permeabilization buffer and washing in 100 µL PBS, the cells were incubated with 100 µL of 1× Apollo solution in the dark for 30 m. Finally, the cells were cultured with 100 µL diamidino-phenylindole at room temperature for 5 m away from light and washed in 100 µL PBS, followed by observation by fluorescence microscopy (Olympus, Tokyo, Japan). Collected cells were fixed in 4% paraformaldehyde for 30 m and then in 70% cold alcohol for 15 m. The cells were then incubated with PBS containing 0.3% Triton X-100 at room temperature for 5 m and incubated with TUNEL solution (Beyotime) for 60 m at 37°C. After washing in PBS three times, the cells were blocked with an antifade reagent and observed by fluorescence microscopy. The cell nuclei were stained with diamidino-phenylindole and apoptosis (%)=(TUNEL-positive cells/total cells)×100. Rats were sacrificed to collect liver tissues, which were fixed in 10% neutral buffer formalin (Beijing Solarbio Science & Technology Co., Ltd., Beijing, China) for 24 h, dehydrated, and embedded in paraffin. After being sliced into 4 µm serial sections, the tissue was dewaxed with xylene and dehydrated in an alcohol gradient. A TUNEL detection kit (ZK-8005; ZSGB-Bio, Beijing, China) was used to determine the apoptosis rate in rat liver tissues. Five random fields in each section were evaluated by light microscopy (Olympus Optical Co. Ltd., Tokyo, Japan). Apoptotic cells were brown or brownish in color and with apoptotic cell morphology. Apoptosis was reported as the apoptosis index, and apoptosis (%)=(TUNEL-positive cells/total cells)×100. Assay kits were used to determine MDA, SOD (Abcam), and GSH (Sigma-Aldrich) levels in cultured cells and liver tissues following manufacturer’s instructions. Magna RIP RNA-binding protein immunoprecipitation kits (Millipore Corp, Billerica, MA, USA) was used for the RIP assay. Briefly, HL7702 cells were lysed in 100 µL lysis buffer containing protease and RNase inhibitors, and then the protein lysate was incubated with rabbit anti-human EZH2 antibody (ab186006 1:500; Abcam) at 4°C for 30 m or anti-IgG antibody (ab109489, 1:100; Abcam) as the control. Subsequently, 10–50 µL of protein A/G beads were added and incubated with the cells at 4°C overnight. After incubation, the protein A/G-bead-antibody complexes were washed 3–4 times in 1 mL lysis buffer, and RNA was extracted and purified using the RNA extraction method. qRT-PCR was carried out with a MALAT1-specific primer to identify the interaction between EZH2 and MALAT1. The ChIP assay was performed with SimpleChIP Plus sonication chromatin IP kits following the manufacturer’s (Cell Signaling Technology) instructions. HL7702 cells were fixed in 1% formalin to crosslink DNA and proteins. Then, the cells were lysed in lysis buffer and nuclear lysis buffer and ultrasonicated to generate 200–300 bp chromatin segments. The cell lysate was immunoprecipitated with protein A-beads conjugated with the corresponding antibodies, including anti-EZH2 antibody (ab228697; Abcam) and anti-histone 3 antibody (trimethyl-K27, ab6002; Abcam). Anti-IgG antibody (ab171870; Abcam) was added as a negative control. Protein-DNA crosslinking was reversed the RNA was purified, and enrichment of the DNA segment was detected by qRT-PCR. Rat liver tissues were collected, fixed in 10% neutral buffered formalin (Beijing Solarbio Science & Technology Co., Ltd.) for 24 h, dehydrated, embedded in paraffin, cut into 4 µm serial sections, and stained with H&E (Beijing Solarbio Science & Technology Co., Ltd.). Tissue histology was evaluated by optical microscopy (Olympus Optical, Tokyo, Japan). Collected liver tissues were fixed in 4% paraformaldehyde for 48 h, embedded in paraffin, and sectioned at 4 µm. The sections were baked for 20 m, dewaxed in xylene, and washed once in distilled water. After washing three times in PBS, the sections were placed in 3% H2O2 for 10 m and subjected to antigen retrieval. After washing with PBS three times, the sections were blocked in goat serum at room temperature for 20 m. Excess serum was discarded, and the primary antibody against Ki-67 (ab16667, 1:200; Abcam) was added to the sections for incubation (4°C, overnight). Afterwards, the sections were incubated with secondary antibody at room temperature for 1 h and washed three times in PBS. Color development was sustained for 1–3 m using diaminobenzidine solution, and H&E was then used for 3 m for nuclear staining. The sections were then dehydrated, permeabilized, and cover slipped for observation. The percentage of positive cells in five randomly selected fields was reported (positive cells (%)=(positive cells/total cells)×100. GraphPad 7.0 software was used for the statistical analysis, and data were reported as means ± SD. The significance of between-group differences was determined by t-tests. Multiple comparisons was carried out with one-way analysis of variance followed by Tukey’s multiple comparisons test. P-values <0.05 were considered statistically significant. To explore the expression of MALAT1 and GFER in patients with ALI, we included 26 patients with ADILI and 19 healthy individuals. There were no significant differences in sex or age between the two groups. Compared with the normal group, the serum levels of ALT, AST, γ-GT, alkaline phosphatase (ALP), and TBil, and the RUCAM scores of patients in the ADILI group increased significantly (Table 2; p<0.05). The results of qRT-PCR and western blotting showed that, compared with the normal group, the expression of MALAT1 in the serum of patients in the ADILI group increased, and the expression of GFER decreased (Fig. 1A, B; p<0.05). The results indicated that MALAT1 was highly expressed but GFER was weakly expressed in the serum of patients with ALI. Human hepatocytes HL7702 were treated with LPS to induce an ALI cellular model, and ALT, AST, and LDH levels were assayed in the culture supernatant. LPS induction increased ALT, AST, and LDH levels (Fig. 2A, C; p<0.05). The CCK-8 assay found that proliferation was decreased in the LPS group compared with the control group (Fig. 2D; p<0.05), which was confirmed by EdU staining (Fig. 2E; p<0.05). TUNEL staining found that the cell apoptosis rate was increased in the LPS group compared with the control group (Fig. 2F, G; p<0.05). Assays of MDA, SOD, and GSH in cells found that after LPS treatment, MDA increased significantly and SOD and GSH decreased significantly (Fig. 2H, J; p<0.01). Moreover, qRT-PCR and western blot assays found that LPS increased MALAT1 expression (Fig. 2K; p<0.001) and decreased GFER expression (Fig. 2L, M; p<0.05) in HL7702 cells compared with the control group. Overall, LPS induction enhanced MALAT1 expression and reduced GFER expression in HL7702 cells. HL7702 cells were transfected with sh-MALAT1, oe-MALAT1, sh-GFER, or oe-GFER for 24 h, followed by treatment with LPS for 16 h. The transfection efficiency was validated by qRT-PCR and western blot analysis (Fig. 3A–D; p<0.05). In addition, MALAT1 overexpression inhibited GFER expression and MALAT1 knockdown enhanced GFER expression (Fig. 3B–D; p<0.05). However, overexpression or knockdown of GFER did not influence MALAT1 expression (Fig. 3A). Introduction of MALAT1 overexpression or GFER knockdown substantially increased the levels of ALT, AST and LDH, while MALAT1 knockdown or GFER overexpression had the opposite effects (Fig. 3E–G; p<0.05). Compared with the LPS + oe-MALAT1 group, the LPS + oe-MALAT1 + oe-GFER group had decreased ALT, AST and LDH levels (Fig. 3E–G; p<0.05). Moreover, MALAT1 overexpression or GFER knockdown inhibited HL7702 cell proliferation and induced cell apoptosis and oxidative stress injury, but MALAT1 knockdown or GFER overexpression increased the proliferation rate and decreased the apoptosis rate and oxidative stress injury (Fig. 3H–M; p<0.05). In the LPS + oe-MALAT1 + oe-GFER group, HL7702 cells possessed increased proliferative ability and decreased apoptosis rate and oxidative stress injury than those in the LPS + oe-MALAT1 group (Fig. 3H–M; p<0.05). The data indicate that downregulation of MALAT1 inhibited hepatocyte apoptosis and oxidative stress injury but promoted cell proliferation by regulating GFER. GFER significantly reduced ALT and AST in a mouse ALI model, and alleviated liver injury caused by ischemia-reperfusion.20,21 The University of California Santa Cruz (UCSU) genome browser (http://genome-asia.ucsc.edu/) predicted the presence of H3K27me3 methylation peak in the GFER promoter region (Fig. 4A). Inhibition of MALAT1 reduces liver ischemia-reperfusion injury,11 and MALAT1 changes the progression of liver fibrosis by regulating SIRT1.22 A previous study reported that MALAT1 recruited histone methyltransferase EZH2 to the pri-miR-22 promoter region to inhibit miR-22 expression.13 In this study, MALAT1 expression was negatively associated with GFER expression in the cellular ALI model, and the regulation of HL7702 cell proliferation and apoptosis by MALAT1 was involved in GFER. We hypothesized that MALAT1 recruited EZH2 to the GFER promoter region to suppress GFER expression. As expected, the RIP assay identified the interaction between MALAT1 and EZH2 (Fig. 4B; p<0.001). A ChIP assay was performed to confirm whether EZH2 regulated GFER expression via H2K27me3 methylation. EZH2 and H3K27me3 were found to be more abundant in the GFER promoter region in the oe-MALAT1 group than in the oe-NC group (p<0.01); enrichment of EZH2 and H3K27me3 in the GFER promoter region was reduced in the sh-MALAT1 group when compared with the sh-NC group (Fig. 4C; p<0.05). HL7702 cells were transfected with oe-EZH2 or sh-EZH2, and western blots of EZH2 expression demonstrated that transfection of oe-EZH2 promoted EZH2 and H3K27me3 expression and reduced GFER expression. sh-EZH2 treatment resulted in contrary findings (Fig. 4D; p<0.05). Overall, the results show that MALAT1 inhibited GFER expression by recruiting EZH2 to the GFER promoter region and promoting H3K27me3 methylation. HL7702 cells were transfected with sh-MALAT1, oe-MALAT1, sh-EZH2, oe-EZH2, sh-GFER and oe-GFER for 24 h, followed by treatment with LPS for 16 h. The levels of phosphorylated proteins active in the AMPK/mTOR signaling pathway were assayed by western blotting. MALAT1/EZH2 overexpression or GFER knockdown significantly upregulated phosphorylated AMPK levels and downregulated phosphorylated mTOR levels in HL7702 cells;. MALAT1/EZH2 knockdown or GFER overexpression had the opposite effects (Fig. 5A–C; p<0.05). After HL7702 cells were transfected with oe-MALAT1 lentiviral vector and its control (oe-NC) for 24 h, they were treated with the AMPK inhibitor Compound C (CC) for 1 h, induced by LPS for 16 h, and then collected for subsequent assays. Western blot assays demonstrated that compared with the LPS + oe-NC group, phosphorylated AMPK level was decreased and phosphorylated mTOR expression was increased in the LPS + oe-NC + CC group. HL7702 cells in the LPS + oe-MALAT1 group had the reverse responses; phosphorylated AMPK level was reduced and that of mTOR was increased in the LPS + oe-MALAT1 + CC group compared with the LPS + oe-MALAT1 group (Fig. 5D–F; p<0.01). In the LPS + oe-NC + CC group, the levels of ALT, AST and LDH were decreased (Fig. 5G–I; p<0.05), HL7702 cell proliferation was increased (Fig. 5J, K; p<0.05), and the apoptosis rate and oxidative stress injury were inhibited (Fig. 5L–O; p<0.05) compared with those in the LPS + oe-NC group. HL7702 cells in the LPS + oe-MALAT1 group had increased ALT, AST, and LDH levels (Fig. 5G–I; p<0.05) concurrent with decreased cell proliferation (Fig. 5J, K; p<0.05) and enhanced apoptosis rate and oxidative stress injury (Fig. 5L–O; p<0.05), compared with those in the LPS + oe-NC group. In the LPS + oe-MALAT1 + CC group, the levels of ALT, AST and LDH in HL7702 cell supernatant were suppressed (Fig. 5G–I; p<0.05) along with increased proliferation (Fig. 5J, K; p<0.05) and reduced apoptosis rate and oxidative stress injury (Fig. 5L–O; p<0.05). The data indicate that MALAT1/EZH2/GFER activated the AMPK/mTOR signaling pathway. Rats were intravenously injected with sh-MALAT1 or oe-GFER in the tail vein for 42 h, followed by treatment of LPS to induce ALI. We demonstrated that the levels of ALT, AST and LDH in rat serum were increased in the LPS group (p<0.01, vs. the saline group), while those of the LPS + sh-MALAT1 group and the LPS + oe-GFER group were lower than those in the LPS + sh-NC group and the LPS + oe-NC group (Fig. 6A–C). As shown by H&E staining, rats in the normal group and the saline group had normal histology with clear hepatic lobules and regular hepatic sinusoids. In contrast, rats in the LPS group had significant liver injury, manifested as cellular edema, hematolysis, diffuse necrosis, and inflammatory cell infiltration (Fig. 6D). However, LPS-induced injury was alleviated in the liver tissues of rats in the LPS + sh-MALAT1 and LPS + oe-GFER groups (Fig. 6D). qRT-PCR and western blotting revealed an increase in MALAT1 expression (Fig. 6E; p<0.01) and a decrease in GFER expression (Fig. 6F, G; p<0.05) in the LPS group compared with those in the saline group. In the LPS + sh-MALAT1 group, MALAT1 expression was significantly reduced (Fig. 6E; p<0.01) and GFER expression was upregulated (Fig. 6F, G, p<0.05) in liver tissues, compared with the LPS + sh-NC group. Moreover, increased GFER expression (p<0.001) and unchanged MALAT1 expression (p > 0.05) were found in the LPS + oe-GFER group compared with the LPS + oe-NC group (Fig. 6E–G). Taken together, downregulation of MALAT1 promoted GFER expression in ALI-model rats. Rats were injected with sh-MALAT1 or oe-GFER in the tail vein for 42 h, followed by LPS induction for modelling, and then immunohistochemistry was performed. Mice in the LPS group had fewer Ki-67-positive liver cells than the saline group, but MALAT1 knockdown or GFER overexpression increased the percentage of Ki-67-positive cells in liver tissues (Fig. 7A; p<0.05). In addition, TUNEL staining indicated increased apoptosis rate in rat liver tissue from the LPS group (p<0.01, vs. the saline group) and decreased hepatocyte apoptosis in the LPS + sh-MALAT1 or the LPS + oe-GFER group (both p<0.05) (Fig. 7B). We also found that MDA was increased and SOD and GSH were decreased in liver tissue from the LPS group relative to the saline group. In the LPS + sh-MALAT1 group or the LPS + oe-GFER group, MDA level was decreased and SOD and GSH levels were increased (Fig. 7C–E; p<0.01). In addition, liver tissues in the LPS group were found to have upregulated levels of phosphorylated AMPK (Fig. 7F, G; p<0.01) and downregulated levels of phosphorylated mTOR (Fig. 7F. H; p<0.05). However, MALAT1 knockdown or GFER overexpression reduced phosphorylated AMPK and increased phosphorylated mTOR levels in liver tissue (Fig. 7F–H; p<0.05). Thus, these data proved the ameliorative effect of MALAT1 downregulation or GFER overexpression on ALI in vivo. ALI is a severe, acute disease with a high mortality. It is characterized by acute hepatocyte necrosis, and there have not been any treatment breakthroughs in the last few decades.23,24 Recently, various lncRNAs, including DINO, NEAT1, and XIST, and their downstream genes have been investigated to explain the progression and improve the effectiveness of ALI treatment.16,25,26 LPS, which was first found in the outer membrane of gram-negative bacteria, has been widely used in vivo and in vitro to mimic the pathology of ALI.27–29 In this study, we successfully established cellular and animal models of ALI using LPS, and MALAT1 was found to inhibit cell proliferation and promote apoptosis in LPS-induced rats and hepatocytes. MALAT1 has been found to limit proliferation and induce apoptosis of human renal tubular epithelial cells in the presence of LPS,30 and MALAT1 knockdown was found to block LPS-induced acute lung injury by inhibiting apoptosis and improving cell viability.31 The apoptosis-promoting effect of MALAT1 has been previously reported in hepatocytes.31 However, Li et al.32 reported that MALAT1 overexpression was required for accelerating hepatocyte proliferation and liver regeneration. Our in vivo and in vitro experiments showed that MALAT1 was upregulated after LPS exposure and identified as an ALI-promoting gene. GFER, also known as ALR, protects against chemical- or toxin-related ALI,33,34 ischemia reperfusion-induced liver and kidney injury.35,36 Deletion of ALR accelerates steatohepatitis and hepatocellular carcinoma.37 Therefore, we examined the expression of GFER in LPS-induced hepatocytes, which showed a decrease in GFER expression. Overexpression of GFER alleviated hepatocyte apoptosis, excessive hepatocyte proliferation, and improved liver function after LPS insult. Intriguingly, MALAT1 was negatively associated with, and significantly regulated, GFER expression at the cellular and animal levels. In addition, the ALI-promoting effect of MALAT1 was offset by GFER upregulation, indicated by decreases in serum AST/ALT, hepatocyte apoptosis, and enhanced proliferation of hepatocytes. The results indicated that MALAT1 regulated GFER expression to aggravate ALI. We then focused on elucidating the mechanism of MALAT1 regulation of GFER in ALI. MALAT1 has oncogenic activity through EZH2-mediated suppression of miR-217 expression in lung carcinogenesis.38 In the presence of MALAT1, silencing of EZH2 has been shown to reduce apoptosis and enhance cardiac function.13 Increased MALAT1 recruits EZH2 to its downstream gene to promote H3K27me3 expression for specific transcription inhibition.39 EZH2 can modify the enrichment of its catalyzed H3K27me3 and contribute to the pathogenesis of liver failure by triggering the release of tumor necrosis factor (TNF) and other pro-inflammatory cytokines.40 Inhibition of EZH2 blunts H3K27me3 and restrains the activities of serum ALT and AST.41 We predicted an H3K27me3 methylation peak in the GFER peak, indicating that MALAT1 might regulate GFER expression through methylation. Our functional experiments identified an interaction between MALAT1 and EZH2, and further demonstrated that MALAT1 overexpression significantly enhanced the enrichment of EZH2 and H3K27me3 in the GFER promoter region. The study firstly demonstrated that MALAT1 suppressed GFER expression by recruiting EZH2 to the GFER promoter region, enhancing H3K27me3 methylation, and aggravating ALI. AMPK regulates energy homeostasis and metabolism, and mTOR is an enzyme downstream of AMPK.42 Activation of the AMPK/mTOR signaling pathway has been shown to be involved in liver diseases, including nonalcoholic fatty liver disease and ALI.43–45 More important, Pu et al.46 found that deletion of ALR induced increased AMPK phosphorylation and decreased mTORC1 phosphorylation, and increased both autophagy flux and apoptosis. In this study, phosphorylated AMPK was increased, and phosphorylated mTOR was reduced in ALI model rats. At the same time, MALAT1 inhibition or GFER overexpression decreased the expression of p-AMPK and promoted p-mTOR expression. Inhibiting the phosphorylation of AMPK by Compound C inhibited ALT, AST, and LDH, and hepatocyte apoptosis, and improved hepatocyte proliferation. Knockdown of MALAT1 or overexpression of GFER had a similar effect to that of Compound C in ALI. The AMPK/mTOR pathway is a typical regulator of autophagy.47 Both endogenous and exogenous interleukin-37 protect against ischemia reperfusion-induced hepatic injury by restraining excessive autophagy and apoptosis by regulating the AMPK/mTOR signaling pathway.48 Therefore, much attention should be paid in future studies to whether MALAT1/EZH2/GFER participates in ALI by activating the AMPK/mTOR pathway to affect hepatocyte autophagy. This study revealed that MALAT1 upregulation was associated with decreased proliferation, enhanced apoptosis, and aggravation of liver injury, which sheds light on the prevention and treatment of ALI. Notably, we showed that MALAT1 inhibited GFER by recruiting EZH2 to the GFER promoter region and enhancing H3K27me3 methylation, thus resulting in deterioration of ALI. Activation of the AMPK/mTOR signaling pathway was also linked to the ALI-promoting effect of MALAT1. The study thus characterized a possible regulatory mechanism of MALAT1 exacerbating ALI, which contributes to understanding ALI progression and provides a novel approach for ALI treatment. Further studies are required to explain the regulation and methylation of the molecules by MALAT1 in ALI to boost the application of their therapeutic benefits in clinical practice.

PMC9647128

Amrendra Kumar,Kanak Raj Kanak,Annamalai Arunachalam,Regina Sharmila Dass,P. T. V. Lakshmi

Comparative transcriptome profiling and weighted gene co-expression network analysis to identify core genes in maize (Zea mays L.) silks infected by multiple fungi

27-10-2022

transcriptome analysis,WGCNA,CAZymes,R-gene,transcription factor,fungal,maize silks

Maize (Zea mays L.) is the third most popular Poaceae crop after wheat and rice and used in feed and pharmaceutical sectors. The maize silk contains bioactive components explored by traditional Chinese herbal medicine for various pharmacological activities. However, Fusarium graminearum, Fusarium verticillioides, Trichoderma atroviride, and Ustilago maydis can infect the maize, produce mycotoxins, hamper the quantity and quality of silk production, and further harm the primary consumer’s health. However, the defense mechanism is not fully understood in multiple fungal infections in the silk of Z. mays. In this study, we applied bioinformatics approaches to use the publicly available transcriptome data of Z. mays silk affected by multiple fungal flora to identify core genes involved in combatting disease response. Differentially expressed genes (DEGs) were identified among intra- and inter-transcriptome data sets of control versus infected Z. mays silks. Upon further comparison between up- and downregulated genes within the control of datasets, 4,519 upregulated and 5,125 downregulated genes were found. The DEGs have been compared with genes in the modules of weighted gene co-expression network analysis to relevant specific traits towards identifying core genes. The expression pattern of transcription factors, carbohydrate-active enzymes (CAZyme), and resistance genes was analyzed. The present investigation is supportive of our findings that the gene ontology, immunity stimulus, and resistance genes are upregulated, but physical and metabolic processes such as cell wall organizations and pectin synthesis were downregulated respectively. Our results are indicative that terpene synthase TPS6 and TPS11 are involved in the defense mechanism against fungal infections in maize silk.

Comparative transcriptome profiling and weighted gene co-expression network analysis to identify core genes in maize (Zea mays L.) silks infected by multiple fungi Maize (Zea mays L.) is the third most popular Poaceae crop after wheat and rice and used in feed and pharmaceutical sectors. The maize silk contains bioactive components explored by traditional Chinese herbal medicine for various pharmacological activities. However, Fusarium graminearum, Fusarium verticillioides, Trichoderma atroviride, and Ustilago maydis can infect the maize, produce mycotoxins, hamper the quantity and quality of silk production, and further harm the primary consumer’s health. However, the defense mechanism is not fully understood in multiple fungal infections in the silk of Z. mays. In this study, we applied bioinformatics approaches to use the publicly available transcriptome data of Z. mays silk affected by multiple fungal flora to identify core genes involved in combatting disease response. Differentially expressed genes (DEGs) were identified among intra- and inter-transcriptome data sets of control versus infected Z. mays silks. Upon further comparison between up- and downregulated genes within the control of datasets, 4,519 upregulated and 5,125 downregulated genes were found. The DEGs have been compared with genes in the modules of weighted gene co-expression network analysis to relevant specific traits towards identifying core genes. The expression pattern of transcription factors, carbohydrate-active enzymes (CAZyme), and resistance genes was analyzed. The present investigation is supportive of our findings that the gene ontology, immunity stimulus, and resistance genes are upregulated, but physical and metabolic processes such as cell wall organizations and pectin synthesis were downregulated respectively. Our results are indicative that terpene synthase TPS6 and TPS11 are involved in the defense mechanism against fungal infections in maize silk. Maize (Zea mays L.), also called the “queen of cereals”, ranks third in the world after wheat and rice production. About 5.5% of maize (corn) is used as human food from all energy sources of food (51%), which comes from rice (20%), wheat (20%), and other cereals or grains (6%). It is also one of the most widely grown grain crop and is being cultivated in more than 166 countries. The United States produces most of the maize (30%), followed by China (23%), Brazil (9%), Argentina (5%), and India (2%) (Crop et al., 2021). Maize is primarily grown for food and feed intent for human and animal nutrition. In addition, maize has found extensive applications in beauty and drug industries, too. All plant parts in Z. mays can be used to generate revenue. The silk from Z. mays has been used to treat different illnesses as it is being applied in the Indian system of medicine and Chinese traditional medicine (Zhao et al., 2012). In fact, in India, between 2,500 BCE and 500 BCE, the ayurvedic concept saw Z. mays as an essential herb, especially the silk part (stigma maydis), for healing and controlling many diseases (Pandey et al., 2013). This traditional knowledge of the significance of the use of maize silk was eventually lost with the advent of allopathy in the 18th century. Recent research has shown that maize silk exhibits powerful health-promoting effects. This is also true because the silk contains bioactive compounds such as flavonoids, proteins, carbohydrates, vitamins, steroids, tannins, alkaloids, mineral salts, and polysaccharides (Zhao et al., 2012; Guo et al., 2017). These compounds may help protect against cancer, hypertension, diabetes, hepatic, cardiovascular, and other age-related diseases. Researchers are exploring ways to lower body weight and blood glucose levels, increase serum insulin secretion, improve glucose intolerance in type 2 diabetic mice, and control hyperglycemia (Mada et al., 2020). Rahman and Wan Rosli (2014) opined that maize silk provided an ideal environment for fungal propagules as a nutrient-rich, soft, and moisture-laden tissue within the husks. The corn silk serves as an ideal place for fungal propagules to reside and multiply within the cob environment. The fungal spores adhere and germinate into hyphal structures, which spreads into the maize silk, infects the ovules, and creates an imbalance of the hormones (Li et al., 2018). The parenchymatous cells of the maize silk serves as a suitable place for hyphae to grow especially for fungi like Aspergillus, Fusarium, Penicillium, and Ustilago species which cause diseases like ear rot, corn smut, and brown spot (Miller et al., 2007) and an economic loss of 5–42% yield per year (Thompson and Raizada, 2018). The silk of Z. mays is also said to have an effect against Trichoderma species (Gong et al., 2014; Contreras-Cornejo et al., 2016). A study in 2019 reported that maize silk has the genes and transcription factors that code for the callose of the papillae, which prevent fungi from growing (Shi et al., 2019) within the cobs. Fusarium species like Fusarium graminearum (Fg) and Fusarium verticillioides (Fv) usually infect the outer layer of the maize silk in Z. mays in order to draw nourishment for hyphal growth. On the other hand, infection caused by Ustilago maydis was found to affect the entire length of maize silk. These fungi produce mycotoxins, carcinogenic substances that cause esophageal and liver inflammation in humans (Marín et al., 2004). It is therefore important to understand the mechanism of the plant–fungal interaction in the infection process. Transcriptome studies have been extensively used to study specific genes expressed during the infection process. Hence, multiple fungal systems from the families Nectriaceae (F. verticillioides—Fv and F. graminearum—Fg), Hypocreaceae (Trichoderma atroviride—Ta), and Ustilaginaceae (U. maydis—Um) were used to study the expression pattern in Z. mays silk. Agostini et al. (2019) chose the datasets from the experimental studies on the combination of these fungi to examine and figure out the essential genes involved in many molecular and biological processes. Furthermore, we looked at the co-expression in different networks using weighted gene co-expression network analysis (WGCNA) to build the networks based on the pairwise co-expression between gene expression levels. Since WGCNA builds a scale-free network based on similarities in gene expression profiles that may be linked to the phenotypes of interest, this method was used to find groups of genes that work well together (Bakhtiarizadeh et al., 2018; Abbassi-Daloii et al., 2020; Xu et al., 2021). The goal of the comparative study based on statistical estimates like the DEG and WGCNA modules was to investigate and study more about how different fungi infect Z. mays silk. F. graminearum, F. verticillioides, and U. maydis are all partially biotrophic parasites that can also eat dead organisms (Incremona et al., 2014; Pei et al., 2019; Pandian et al., 2020). F. verticillioides is an endophyte which competes with the fungal pathogen F. graminearum and is antagonistic to U. maydis (Lee et al., 2009; Rodriguez Estrada et al., 2012). This study evaluates how fungal stress resistance and yield can improve maize silk through molecular breeding and biotechnology. Two transcriptome datasets of fungus-infected silk of Z. mays were obtained from the National Centre for Biotechnology Information-Sequence Read Archive (NCBI SRA) database. Each dataset consisted of samples (18) infected by different fungi belonging to the families of Hypocreaceae, Nectriaceae, and Ustilaginaceae, with BioProject accession numbers PRJNA13048 (A) (https://www.ncbi.nlm.nih.gov/bioproject/PRJEB13048) and PRJNA382306 (B) (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA362306). Each data set had three biological replicates comprising of silk samples affected with two fungi, F. graminearum and U. maydis in A, while B was infected with F. verticillioides and T. atroviride along with the control in each dataset (Agostini et al., 2019) ( Supplementary Table S1A ). The quality of both datasets was computed using the FASTQC tool (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/), and the raw read sequences of both datasets were mapped to the latest reference sequence of Z. mays B73 (V5.0) (http://ftp.ebi.ac.uk/ensemblgenomes/pub/release-51/plants/fasta/zea_mays) using the HISAT2 tool (Kim et al., 2015). The read count of each gene mapped to the reference genome was calculated using the FeatureCount tool (Liao et al., 2014). Pairwise differential gene expression analysis was performed using control A and B datasets based on the experimental design ( Supplementary Table S1B ). DESeq2 of Bioconductor R package (Love et al., 2014) was used to perform differential expression calculation, and significantly differentially expressed genes (DEGs) were identified by applying the cutoff value of log2 fold change of ≥|1.5| with P-value cutoff <0.05. The expected significant common DEGs between control A and B datasets were filtered out for further analysis. Weighted gene co-expression network analysis (WGCNA) (Langfelder and Horvath, 2008) of the R package was performed on the complete read count matrix of common DEGs identified (12,447 genes across 18 samples). The matrix between each pair of genes across all the samples was calculated using Pearson’s correlation. It generated an adjacency matrix by default soft power and computed the topological overlap matrix (TOM) along with the corresponding dissimilarity (1-TOM) values. Gene modules were detected using the dynamic cutting algorithm with a minimum module size of 30 and a default cutoff height (0.99), and the gene modules were arranged in the dendrogram depending on their shape (Langfeldera et al., 2007) The correlation between module eigengenes and the gene expression of genes related to biotic stress was analyzed among the significantly correlated modules of interest associated with biotic stress in Z. mays silk. A heat map was used to represent the correlation values. The module membership (MM) is the association between each module eigengene and its gene expression as gene significance (GS), defined as the correlation between each trait and its gene expression (Tian et al., 2021). The MM and GS were determined to closely correlate the genes in a module with a cutoff value of MM >0.8 and a GS >|0.2| (Farhadian et al., 2021). The modules were correlated with the most significant DEGs common among relevant specific traits. All modules were considered core genes. A protein–protein interaction (PPI) network was constructed for the more significant common gene accession using the STRING database (v11) (Szklarczyk et al., 2019) by applying an interaction filter score >0.4 (medium confidence) and further visualized through Cytoscape (Doncheva et al., 2019). Within the networks, the parameters K-mean cluster score = 2, degree cutoff = 2, and maximum depth = 100 (Bandettini et al., 2012) were set. A subnetwork analysis was performed using molecular complex detection (MCODE) for the clustering connected. For the identification of key/essential genes from the PPI network, the CytoHubba (Chin et al., 2014) plugin of Cytoscape was used, which extracted the top 100 genes with the selected four scoring methods of CytoHubba, namely, maximal clique centrality (MCC), maximum neighborhood component (MNC), edge percolated component (EPC), and node–connect degree, respectively. To recognize and identify the functions of the significantly expressed genes, Biomart, ShinyGo, and UniProt (Kinsella et al., 2011; Consortium, 2015; Ge et al., 2020) were employed. The information was generated and further verified through BLASTx (Camacho et al., 2009) against PlantTFdb (Guo et al., 2007) and PRGdb version 3.0 (Osuna-Cruz et al., 2018) for the recognition of genes encoding for resistance, transcription factors, and specific pathways related to them. The DRAGO (PRGdb tool) pipeline also classified the R-gene classes and domain. In contrast, dbCAN2 with default parameters integrated with automated annotation tools of Diamond, HMMER, and Hotpep (Finn et al., 2011; Buchfink et al., 2015; Osuna-Cruz et al., 2018) enabled us to identify the polysaccharide degradation enzymes, also known as CAZymes. To acquire a better understanding of the essential genes that are involved in the defense mechanisms in Z. mays silk against different fungal infections, a comparative study of the publicly accessible transcriptome information was carried out. The current study used DEG and WGCNA analyses to identify the core set of genes modulated in infected maize datasets. The core genes were further subjected to PPI for essential gene indication, followed by examining biotic stress determinants like TF, R-genes, and CAZymes. Supplementary Figure S1A provides a schematic of the analytical process. Supplementary Figure S1B displays the alignment data for each sample, and Supplementary Figure S1C illustrates the process by which readings were allocated to the genic feature count. The FastQC statistics showed that all the samples were of good quality, with above 80% of the samples lined up with the reference genome, and, further, that more than 70% of the reads were uniquely assigned to genic features. The two datasets were analyzed for differential expression using DESeq2, and genes with a p-value of 0.05 and |log2FC|>1.5 were determined to be significant DEGs. Approximately 33% of genes (14,694 and 14,808 of 44,303 genes) of datasets PRJEB13048 and PRJNA362306 ( Supplementary Table S2 ) were differently expressed in pairwise differential expression analysis of fungus-infected versus control samples ( Table 1 ) ( Supplementary Figure S1D1–4 ). In the DEGs, approximately 28% (12,447) of genes appeared in both datasets ( Supplementary Figure S1E ). The number of DEGs varied in the fungal infections based on the comparison between up- and downregulated genes within controls. A comparative analysis of the DEG infections exhibited about 9,558 (22%) genes being expressed uniquely, among which roughly 5,043 (11%) genes were recognized to have been duplicated. Figure 1 displays that 19.4% (8,577) of Z. mays silk genes were significantly expressed in Fg, 11.7% (5,146) were described in Um, 1.7% (723) were expressed in Fv, and 0.3% (155) were expressed in Ta infections, respectively ( Supplementary Table S3 ). It is thus important to investigate which gene among them is responsible in regulating the expression under different fungal perturbations. The rationality variation in the value of the DEGs seemed to be caused by fungal infection (Yang et al., 2022), and this perhaps could be reasoned out with the differences observed in the sample sizes of the datasets, where the control of dataset A (Ca) had 7.5 GB, while the control of dataset B (Cb) had 2.5 GB. The concept well defends this examination that varied sample sizes and the spatiotemporal specificity of samples could influence the drastic variations in the different data sets (Wang, et al., 2021). Furthermore, a Venn diagram of total DEGs comparison between up- and downregulated genes within controls was examined in the interaction analysis of DEGs expressed in different fungal infections. In total, 21 (0.2%) genes were represented across the four different fungal infection settings. In the intersection of three fungal infection situations and specifically in Fg, Fv, and Ta infections, four genes (0.0%) were expressed. Additionally, in Fg, Ta, and Um infections, 17 genes (0.2%) were expressed. Fg, Fv, and Um infections expressed 479 (5.0%) genes. Two genes (0.0%) were expressed in Fv, Ta, and Um. The intersection between two fungal infection conditions, namely, Ta and Um, expressed 10 genes (0.1%). In Fv and Ta, 13 genes were expressed. Twenty genes (0.2%) were expressed under Fv and Um infection circumstances. Only Fg and Um expressed 3,838 (40.2%) genes. In the presence of both Fg and Fv infections, 77 (0.8%) genes were expressed. In Fg and Ta, 21 (0.2%) genes were expressed. Upon infection with a single fungal isolate of Fg, 4,120 (43%) genes were expressed, while 760 (8%) genes were expressed during the Um infection, and 108 (1.1%) and 68 (0.7%) genes were expressed in Fv and Ta infections, respectively ( Figure 2 ). These genes involved different physiological activities in Z. mays, including hormone response, secondary metabolism, phosphorylation, photosynthesis, cell wall organization, control, replication, and response to stimuli. Therefore, our basic premise is that these genes might have different gene expression patterns and counts depending on their natural genetic makeup. While some processes overlapped in samples of other fungal infections, Fg infection displayed higher gene expression levels in the Z. mays silk. Weighted gene co-expression network analysis of 12,447 common genes was carried out to identify the core genes involved in fungus defense. The WGCNA analysis resulted in 11 modules, with 58 genes in the small modules to 5,415 genes in the most significant modules ( Figure 3A ). A correlation of module eigengenes to disease trait data was performed, with a cutoff value of significance |GS| >0.5 and P-value <0.05. Out of 11 modules, purple, yellow, and green-yellow modules were associated with both Ca and Ta, the green module with Ca, red and turquoise modules with Fg, black with Cb, pink being positively correlated with Um, and magenta, blue, brown, and black modules being negatively correlated with Fg ( Figure 3B ). Interestingly, none of the modules was significantly associated with Fv. A further intra-modular analysis based on the gene significance (GS) and module membership (MM) of genes identified vital genes in the six modules for the Fg trait. A filter of |MM >0.8 and GS >|0.2| was applied for essential gene identifications. The up- and downregulated genes were compared to the genes in modules with relevant specific traits (Yang et al., 2020). We were able to infer that the up- and downregulated core genes in modules 3,817 (607 and 3,210, red and turquoise modules) and 2,851 (1,949, 564, 212, and 126; blue, brown, black, and magenta modules) were highly stable with DEGs in F. graminearum affected silk ( Figure3C 1-2). It can also be seen that the average (“logFC value of control a + control b/2) log2 fold change value of differently expressed genes was used to construct the complex heat map ( Figure 3D ). The complicated heatmap representation of 6,668 core genes, logFC (DEGs), and GS (WGCNA) exhibited approximately equal Fg fungal infection ( Supplementary Table S4 ). We extracted the protein–protein interaction network with a medium confidence score >0.4 of F. graminearum-affected Z. mays silk 6,668 core genes that matched with 3,325 STRING genes ( Supplementary Table S5 ). Network visualization and analysis were performed using Cytoscape, which identified 2,879 genes as nodes and 29,918 edges. No interaction was observed for the remaining 446 genes. Out of 86 clusters, only 12 MCODE scores were more significant than four of the MCODE clustering ( Tables 2 , S6 ). The PPI discovered 65 essential genes to be common in all four scoring techniques, such as MCC, MNC, EPC, and node–connect degree of CytoHubba ( Figure 4A ). Cluster 1 (35 nodes and 1,096 edges) contained downregulated genes, had the highest MCODE score (30.444), and overlapped the CytoHubba scoring. The enrichment analysis of the different biological processes of these essential genes was evaluated using the online enrichment tool ShinyGO. Substantial genes were enriched in photosynthesis, generation of precursor metabolites and energy, light reaction, and response to light stimulus ( Figure 4B ), indicating that a maximum number of genes were involved in the photosynthesis process. A total of 14,601 DEGs were functionally identified in several fungus-affected samples. Overall, 773 high functional categories with 46.6, 45.7, 21.5, and 8.8% genes conforming to biological processes, molecular functions, cellular components, and pathways were identified, respectively ( Table 3 ). In F. graminearum (Fg) infections to the Z. mays silk, the up-regulated genes gene ontology (GO) enrichment analysis revealed biological process categories with the following GO terms: protein phosphorylation (347 genes of the DEGs), defense response (133 genes of the DEGs), phosphorylation (423 genes of the DEGs), phosphate-containing compound metabolic process (543 genes of the DEGs), cell surface receptor signaling pathway (50 genes of the DEGs), phosphorus metabolic process (544 genes of the DEGs), and response to biotic stimulus (83 genes of the DEGs), response to external biotic stimulus (75 genes of the DEGs). Upregulated genes likewise contain 179 biological processes, 149 molecular functions, eight cellular components, and nine functional pathway categories ( Supplementary Table S7 .FgU). The downregulated genes GO enrichment analysis revealed biological process categories with the following GO terms: polysaccharide metabolic process (124 genes of the DEGs), carbohydrate metabolic process (261 genes of the DEGs), cellular glucan metabolic process (79 genes of the DEGs), glucan metabolic process (80 genes of the DEGs), cellular polysaccharide metabolic process (91 genes of the DEGs), polysaccharide biosynthetic process (72 genes of the DEGs), photosynthesis, light harvesting in photosystem I (16 genes of the DEGs), cellular carbohydrate metabolic process (108 genes of the DEGs), cell wall organization or biogenesis (104 genes of the DEGs), photosynthesis, light reaction (40 genes of the DEGs), likewise, in down-regulated genes contain 149 of biological processes, 49 of molecular functions, 53 of cellular components, and 15 pathway functional categories ( Supplementary Table S7 .FgD). In Um infections to the Z. mays silk, GO enrichment analysis of the DEGs identified biological process categories with the following GO terms: regulation of RNA biosynthetic process (242 genes of the DEGs), regulation of RNA metabolic process (245 genes of the DEGs), regulation of nucleobase-containing compound metabolic process (247 genes of the DEGs), defense response (47 genes of the DEGs), regulation of cellular macromolecule biosynthetic process, regulation of macromolecule biosynthetic process (250 genes of the DEGs), regulation of cellular biosynthetic process, regulation of biosynthetic process (251 genes of the DEGs), protein phosphorylation (181 genes of the DEGs), and nucleic acid-templated transcription (246 genes of the DEGs). In the upregulated genes,110 genes were involved in biological process, 103 genes performed molecular functions, eight genes were responsible for cellular component, and four genes are responsible for pathways ( Supplementary Table S7 .UmU), and in downregulated genes, 95 were detected for biological process, 38 genes for molecular functions, 56 and 19 for cellular components, and 19 pathways, respectively ( Supplementary Table S7 .UmD). In Fv infections of the Z. mays silk, GO enrichment analysis of the DEGs identified biological process categories with the following GO terms: defense response (28 genes of the DEGs), cell surface receptor signaling pathway (13 genes of the DEGs), response to biotic stimulus (17 genes of the DEGs), response to bacterium (11 genes of the DEGs), defense response to other organisms (15 genes of the DEGs), protein phosphorylation (48 genes of the DEGs), response to oxidative stress (17 genes of the DEGs), response to external biotic stimulus, defense response to fungi (nine genes of the DEGs), cell wall polysaccharide catabolic process, xylan catabolic process (four genes of the DEGs), photosynthesis, light-harvesting (four genes of the DEGs), cell wall macromolecule catabolic process (four genes of the DEGs), and phenol-containing compound biosynthetic process (four genes of the DEGs). Upregulated genes contain 126 biological process, 93 molecular process, six cellular components, and five pathways ( Supplementary Table S7 .FvU), downregulated gene contain 31 biological processes, nine molecular functions, and 14 cellular components ( Supplementary Table S7 .FvD). GO terms were significantly enriched in Ta infections caused in Z. mays silk. The following GO terms were identified: response to the stimulus (six genes of the DEGs), response to stress (five genes of the DEGs), catabolic process (four genes of the DEGs), response to endogenous stimulus (three genes of the DEGs), regulation of metabolic process and regulation of cellular process (three genes of the DEGs), cellular response to stimulus (three genes of the DEGs), cell wall organization or biogenesis (two genes of the DEGs), regulation of cellular process (18 genes of the DEGs), regulation of metabolic process (16 genes of the DEGs), developmental process (seven genes of the DEGs), response to stimulus (five genes of the DEGs), cellular component organization (three genes of the DEGs), cell cycle process (three genes of the DEGs), and cellular component organization or biogenesis (two genes of the DEGs). Upregulated genes likewise contain 32 biological process, 21 molecular functions, and 14 genes for cellular components as identified ( Supplementary Table S7 .TaU), and the downregulated genes contain 33 biological processes, 21 molecular functions, and 17 cellular components with high-level GO categories ( Supplementary Table S7 .TaD). The functional enrichment analysis of core genes with a false discovery rate <0.05 showed 704 higher-level GO categories in which 3,082, 3,254, 1,360, and 582 genes were involved in biological processes, molecular functions, cellular components, and pathways ( Supplementary Table S8 ). Core genes with highly enriched GO terms positively included biological processes to stimulus–response, defense responses against fungus, and phosphorylation ( Figure 5A ). The GO annotations of downregulated genes showed that they belong to different biological processes ( Figure 5B ) in Fg infection of Z. mays silk. Among the 45 transcription factor (TF) classes, WRKY, NAC, ethylene-responsive factor (ERF), MYB, C2H2, basic helix–loop–helix (bHLH), and GRAS TFs were highly differentially expressed in infected silk. The heat shock transcription factor, transcription activator-like effectors, RAV, M-type-MADS, and ZF-HD showed up- and downregulation ( Figure 6A ). Out of 520 TFs from core genes, 410 TFs matched with ShinyGo functional annotation of DEGs. Three TFs, namely, bHLH-0, DRE-binding protein3/ERF, and MYB-110, were upregulated in all the infected samples. AP2-EREBP-115, C2C2-Dof-26, and Homeobox-59 were upregulated in Fg, Ta, and Um infections. Five WRKY, four NAC, and MYB, three bHLH, two AP2-EREBP, G2-like, and one bZIP TF family were upregulated, and two TFs, namely, bHLH-161 and Homeobox-60/71, were downregulated in Fg, Fv, and Um infections. In total, 228 TF genes were expressed in Fg and Um infections, while 147 TF genes were expressed in a Fg infection ( Supplementary Table S9 ). Core genes contained 257 genes for carbohydrate-active enzymes (CAZYme), while dbCAN2’s diamond, hmmer, and hotpep databases equally shared 169 and 88 up- and downregulated genes. Specific expressions of 16 and 10 modules were found in up- and downregulated genes, respectively, while 24 modules were found in both ( Figure 6B ). These genes belong to different modules, namely, glycosyl transferase (GT), glycoside hydrolase (GH), carbohydrate-binding modules (CBM), auxillary activities (AA), and carbohydrate esterases (CE). A further comparison with DEGs from other fungi (Fv, Ta, and Um) was conducted. The 20 CAZyme-related genes were detected in three fungus-infected silk samples (Fg, Fv, and Um), 123 genes in Fg and Um infections, and two genes in Fg and Fv infections; 112 CAZyme-related genes were only expressed in Fg infections of Z. mays silk ( Supplementary Table S10 ). Upon screening of resistance (R) genes from significant core genes, 346 and 174 up- and downregulated R-genes ( Supplementary Table S11 ) were found. These 520 R-genes are divided into 13 classes and seven domains. The majority of kinase and transmembrane (TM) domains comprised of the KIN class ( Figure 6C ), and the other classes were receptor-like kinases (RLK), receptor-like protein (RLP), receptor-like proteins consisting of an LRR repeat (RLP), contains coiled-coil and kinase (CK), nucleotide-binding site (N), CC-NBS-LRR (CNL), NL (NBS-LRRs), etc. Compared with Fv-, Ta-, and Um-infected silk, the putative DUF26-domain receptor-like protein kinase family protein showed a positive expression in all fungal infections. In the Fg, Fv, and Um conditions, 48 R-genes were considerably expressed. In the Fg and Um infections, 222 R-genes were expressed, with three R-genes in Fg and Fv infections and 227 R-genes expressed in Fg condition. The receptor-like serine/threonine-protein kinase, putative leucine-rich repeat receptor-like protein kinase family protein, and protein kinase superfamily were all present in more significant amounts in Fg and Um than in Fv and Ta conditions. These expression patterns played a crucial role during signal transduction and other biological functions. Fungi are the second major biotic factor that reduce crop yield after insects. Some of the major fungal diseases of maize are Gibberella ear rot, Fusarium ear rot, corn smut, brown spot, etc., which cause considerable yield losses up to 42% (Thompson and Raizada, 2018). In addition, fungi produce many mycotoxins, leading to poisoning and quality deterioration (Agostini et al., 2019). However, there is a lack of research information regarding the direct comparative studies with respect to multiple fungal infections (Fg, Fv, Ta, and Um) in maize silk and identification of abundant genes in these four fungal infections. The current study is based on computational approaches of the publicly available transcriptome data of Z. mays silk infected with multiple fungi focused on core genes identification by differential expression analysis followed by co-expression analysis through WGCNA. We identified 14,694 and 14,808 DEGs of control datasets of A and B ( Supplementary Table S2 ). In further simplification, 4,519 and 5,125 genes were determined by comparing the up- and downregulated genes within controls. The up- and downregulated genes were compared to genes in the modules of WGCNA with relevant specific traits, and core genes were identified as described in the method and represented in Figure 3C . Our comparative study found that, in Fg infection conditions, more genes were affected compared to other Fv, Ta, and Um fungal infections. Twenty-one (21) most prominent genes identified in this study were expressed in all four fungal infections of maize silk. Many significant genes were identified, which were common to conditions caused by three and two fungi. Moreover, 4,120, 108, 68, and 760 genes were uniquely expressed in Fg-, Fv-, Ta-, and Um-affected silk ( Figure 2 ). Twenty-one (21) DEGs were identified in all four infections ( Figure 2 ) which showed different expression values and functions in maize silk. The analysis of four fungus-affected samples revealed that these genes showed a higher significance in Fg infection than in other fungal infections. The upregulated expression of 21 genes was found except for the downregulation of CYP 450 in Fg, Fv, and Um and BP3, CRINKLY 4, OSM 34, DUF 26, and benzoxazinone in Ta along with CRINKLY 4 in Fv infections ( Figure 7 ). According to Li and co-workers, the peroxidase (POD) enzyme controls the lengthening of germ tubes to shield maize kernels from fungal diseases (Li et al., 2018). It is a fact that POD genes were found to be upregulated in all samples of maize silks infected by fungi, with logFC values of 11.02 in Fg infections, 8.7 in Fv infections, and 5.59 and 6.7 in Ta and Um infections, respectively. These are suggestive that the POD genes that we detected in our study also have a similar function. Interestingly, Um had a higher expression of the DBP3 protein gene than the other fungal infections in maize silks. Reports suggest that multiple steps downstream of the ABA-independent route show its significance in the regulation of abiotic stress (Joshi et al., 2016). Conversely, our research indicates that DREB protein synthesis promotes a defensive activity against fungal infections, especially Um. The expression of benzoxazinone is highest in the Fg and Fv tissues, followed by Um, and lowest in Ta infections. The current study coincides with the impact of another report which suggests that it has an effect on pests and antifungal activity (Cantillo et al., 2017). A DEG analysis revealed that, in Ta, the cytochrome p450 (CPY450) gene is upregulated despite showing downregulation in other fungal infections. This gene plays a variety of roles in plant defense, including the biosynthesis and catabolism of phytohormones and other secondary compounds (Xu et al., 2015; Lambarey et al., 2020; Li and Wei, 2020; Pandian et al., 2020). In addition, the expression pattern of CRINKLY4, a kinase family protein, showed a variable expression in fungus-affected silk. These proteins influence the shape of the cell size and the epidermal development in maize leaf (Becraft et al., 1996). CAT1 expression increases during microbial infections and hinders plant growth, according to a previous study (Yang et al., 2014). Our study demonstrated that fungal pathogen assaults on maize silks activated CAT1-related genes. The results also suggest that it could change the metabolic activity during fungal invasion in maize silks (Vina-Vilaseca et al., 2011; Yang et al., 2014). It is reported that SKIP19 protein genes influence and respond to biotic and abiotic stress and play a key role in soybean pollen tube germination and salt and drought tolerance (Chen et al., 2008; Yang et al., 2008; Chang et al., 2009; Ren et al., 2020). There is variable expression in Fg and Um infections, but the strong expression in Fv and Ta infections confirms the SKIP19 gene’s role in Z. mays Fv and Ta defense. The osmotic-like protein (OSM34) in plants, animals, and fungi improves host defense and immune defense against biotic and abiotic stress (de Jesús-Pires et al., 2020). Our research found that OSM34 protein genes were upregulated in Fg, Fv, and Um infections but downregulated in Ta infections in accordance with the roles mentioned. DUF26, which is upregulated in all fungal infections under study, except Ta, belongs to the receptor-like protein kinase sub-family; its domain plays a crucial role in stress resistance and antifungal defense (Liu et al., 2021). Putative RING zinc finger domain superfamily proteins have ubiquitin–protein ligase activity and help plant growth and development in A. thaliana (Gao et al., 2015; Kim et al., 2019). Small auxin-up RNA is a member of the auxin-responsive gene family that is upregulated in all the fungal infections considered in the present inquiry with logFC 11.5 in Fg, 8.5 in Fv, 4.7 in Ta, and 9.8 in Um. Previous research using microarray data profiling identified these genes as highly expressed in the roots and leaves but less in seeds, which is essential in plant growth and development (Chen et al., 2014; Zhang et al., 2021). S-norcoclaurine synthase proteins exhibit a defense response and have a signaling receptor activity, and four miscellaneous RNA genes were identified. Within the Fg, Fv, and Um fungal infection conditions, 479 expressed genes out of 502 are intersectionally connected. These genes were relatively high in defense responses, photosynthesis, detoxification, and secondary metabolic processes. In any case, the Fg conditions have a higher expression value than those observed with Fv and Um infections ( Supplementary Table S12 ). Seventeen genes are highly expressed in Fg, Ta, and Um samples; these genes are involved in the DNA-binding transcription factor activity and are upregulated in these Fg and Um samples but downregulated in Ta infections ( Supplementary Table S12 ). Several genes implicated in the light reaction of photosynthesis were discovered to be highly expressed during abiotic stress conditions (McNinch et al., 2020). This finding clearly indicates that the presence of fungal pathogen in the Z. mays silk may be a crucial factor controlling the photosynthesis processes, functions of the plant cell surface receptor signaling pathway, and hydrogen peroxide catabolic process. In our study, the terpene synthase genes TPS6 (Ensembl id: Zm00001eb412960) and TPS11 (Ensembl id: Zm00001eb412980) were significantly upregulated in Fg, Fv, and Um infections caused in maize silk ( Supplementary Table S12 ). A group of researchers (Huffaker et al., 2011) observed that terpene synthase (TPS6 and TPS11) proteins involve the plant pathogen’s defense. TPS6 and TPS11 are transcribed only in the leaves and roots of Z. mays. TPS6/TPS11 function in terms of resistance to Um infections and tumor formations (van der Linde et al., 2011) and have a role in the production of several antibiotics (Huffaker et al., 2011). During Fg and Um infections, a total of 3,838 were intersectionally connected. Seventy-seven genes (77) were found to be commonly expressed in Fg and Fv infections, while 20, 21, and 13 genes were common between Fg and Ta, Fv and Um, and Fv and Ta infections, respectively ( Supplementary Table S13 ). These genes were highly expressed in response to stimulus and stress. As suggested by Thompson and Raizada (2018), the maize silk have a defense mechanism against fungal infections, with wounds being the most susceptible to damage caused by Fg. Apart from maize silk, studies conducted by Reid et al. (1992) and du Toit and Pataky (1999) also identified that Fg and Um almost take the same time period for a successful infection in ear heads. Yang et al. (2018) suggested that E3 ligase under drought tolerance of Z. mays plays a crucial role in enabling plants to effectively and efficiently cope with environmental stress. In our results, RING-type E3 ubiquitin transferase was positively expressed under Fg- and Um-infected silk of Z. mays ( Supplementary Table S13 ). Photosynthesis, chlorophyll a-b binding protein, and light reaction photosynthesis I and II reaction center genes were highly downregulated. HVA22-like protein was downregulated, wherein HVA22 specifically inhibits GA-induced PCD/vacuolation of aleurone cells in barley (Guo and Ho, 2008). Glucanendo-1,3-beta-glucosidases (β-1,3-glucanases) protein genes have negative regulation, and these proteins play a significant role against the fungal pathogen by degradation of the cell wall. Lozovaya et al. (1998) found a positive correlation between the Aspergillus flavus fungus-infected kernel of maize silk and β-1,3-glucanases. Gao et al. (2017) found that GDSL esterase/lipase participates in immunity through lipid homeostasis in rice. In Fg and Um infection conditions, 10 GDSL genes were downregulated, and three upregulated genes were found. Huo et al. (2020) reported that 10 genes strongly contribute to male fertility, such as immature tassels, meiotic tassels, and others. Maize silk infected with Fg activated more genes and was involved in phosphorylation ( Supplementary Table S14 ). This suggests that phosphorylation may be one of the initial events in a putative signal transduction pathway leading to the post-translational modification of a protein that controls cell cycle, development, growth, and stress responses. The research group of Palmer et al. (1993) reported that blue light induces phosphorylation in Z. mays plant mediated by an enzyme which belongs to the Ser/Thr class of kinases (Luan, 2002). Furthermore, a large number of genes were found to be involved in carbohydrate metabolic process when affected with the Fg pathogen, with the carbohydrate metabolism genes being downregulated during the process. When maize silk was infected with the Um pathogen, most genes responded to stimulus, stress, oxidation–reduction (redox) reactions, and biological processes like cell cycle (de la Torre et al., 2020). Genes involved in the cell cycle process were downregulated. It has been found that the cell cycle regulation and appressorium morphologenesis are delicately linked. The given primary function of the appressorium is to aid in the invasion of the plant tissue and the subsequent proliferation inside the host (de la Torre et al., 2020). Significant expression patterns were not observed with Fv and Ta infections. In summary, the Fg infections cause more damaging effects compared to other fungal infections. The analysis revealed that 4,355 were interconnected during intersectional studies ( Figure 2 ) with Fg and Um pathogen conditions belonging to Nectriaceae (Fg) and Ustilaginaceae (Um). During biotic stress, it was observed that genes associated with photosynthesis were downregulated as reported by researchers (Doke et al., 1996; Zhu and Li, 2015), which was in agreement with our results. In Fg infections, variations were observed with cell wall-related genes ( Supplementary Table S14 ), as fungal pathogens are known to secrete pectinases, xylanases, cellulases, and ligninases (Sharma, 2016) which can cause plant cell wall degradation during the infection process. We identified 3,325 proteins from the string databases of core genes. The network was simplified into 12 highly sub-connected clusters ( Table 2 ) and identified essential proteins in the network based on the CytoHubba scoring method. The PPI network revealed that cluster 1 has 35 downregulated core proteins that infected the maize silk plant and were involved in biological processes ( Figure 4B ), such as photosynthesis. A research team (Horst et al., 2008) proposed that Um infection to Z. mays leaves reduced the photosynthetic rate and maintained the nutrients as well as influenced the chlorophyll content on a time scale (Kshirsagar et al., 2001). Wang et al. (2021) proposed the light harvesting in photosystem 1, a biosynthetic/metabolic process positively expressed in Gibberella stalk rot disease in Z. mays plant. Moreover, in other clusters 2 and 3, UMP pyrophosphorylase protein is involved in UMP biosynthesis via salvage and L-tryptophan biosynthesis. It has an intermediate role in benzoxazinoid biosynthesis with indole-3-glycerol phosphate in the chloroplast (Richter et al., 2021). Photorespiration, the pathway used to regenerate 2-phosphoglycolate metabolism, plays an essential role in photosynthesis in higher plants and is localized in chloroplasts (Eisenhut et al., 2008; Bräutigam and Gowik, 2016). Trehalose-6-phosphate synthase protein has a role in sugar-induced signaling pathway, and its function has different stages in the plant on growth and development. Iordachescu and Imai (2008) found that plant trehalose levels are typically low. They can change in response to shoot drought, salt, and cold stress challenges in roots and shoots. Another group of researchers (Henry et al., 2015) also reported that trehalose pathway genes were highly affected under saline conditions. Furthermore, these genes were downregulated with the involvement of fructose-bisphosphate aldolase (FBA) protein in various pathways, namely, glycolysis, carbohydrate degradation, and other physiological and biochemical processes. These biological processes include plant defense, response to biotic stress, plant growth, plant development, regulation of secondary metabolites, signal transduction, and Calvin cycle (Lv et al., 2017) and have been documented in other plant species including Z. Mays, A. thaliana, and Oryza sativa (Mininno et al., 2012) under abiotic stress conditions like salt, drought, heat and cold conditions. Our study finds FBA protein in cluster 2 and is downregulated in Fg infections in maize silk but upregulated in wheat to improve the enzyme activity and CO2 concentrations in green plant tissues during development (Lv et al., 2017). In cluster 4, upregulated genes involved phosphotransferase, which has a vital role in the hexose metabolism pathway, which is part of carbohydrate metabolism, to generate glucose-6-phosphate for glycolysis. GRMZM2G076075_P02, glucose-6-phosphate isomerase, is also involved in the glyconeogenesis process, whereas GRMZM2G161245_P01; malate dehydrogenase, is an enzyme that participates in the citric acid cycle from the conversion of malate into oxaloacetate (using NAD+) and also has a vice versa reaction (Takahashi-Íñiguez et al., 2016). In Pisum sativum, a 280% increase in malate dehydrogenase enzyme activity was observed with respect to Fusarium wilt diseases in comparison to control pea plants (Reddy and Stahmann, 1975) and downregulated proteins GRMZM2G074158_P01 and GRMZM2G085577_P01; α-1,4-glucan phosphorylase belongs to the glucosyltransferase family, and these enzymes have an important role in starch and metabolism pathway given the reversible transfer of glucosyl units from glucose-1-phosphate to the non-reducing end of α-1,4-d-glucan chains with the release of phosphate (Rathore et al., 2009). Cluster 6 has 45 nodes with 295 edges and one hub node, which is a cover scoring method. The expression of the protein GRMZM2G137151_P01 (1-deoxy-D-xylulose 5-phosphate synthase, DXS) genes was mainly in Artemisia annua leaf and flowering buds (Zhang et al., 2018a). In our study, the expression of this protein is upregulated in Fg fungal infections in the maize silks. A similar result was reported by Cordoba et al. (2011) who demonstrated plastid localization in Z. mays leaves. DXS catalyzes the first reaction that converts pyruvate and glyceraldehyde-3-phosphate to 1-deoxy-D-xylulose 5-phosphate in the methylerythritol phosphate pathway (Tambasco-Studart et al., 2005; Zhang et al., 2020). The remaining cluster 5 and nodes, namely, 7, 10, 11, and12 ( Table 2 ), do not cover up the pathways under the high scoring method within the top 100 ( Supplementary Table S15 ). In our current endeavors, while analyzing the transcriptome data from different fungal infections and infections caused in corn silks, several TFs, R-gene, and CAZymes have been identified from core genes that serve as a molecular switch to interact with cis-acting transcription factor binding sites and directly control the transcriptional regulation of plant genes (Kimotho et al., 2019; Soni et al., 2020). In our study, 45 families were identified from 367 and 153 up- and downregulated transcripts of plant genes respectively ( Figure 6A ), such as WRKY, NAC, AP2/ERF, MYB, C2H2, bHLH, bZIP, etc. ( Supplementary Table S9 ). One of the significant plant-specific transcription factors is encoded by the WRKY gene family, discovered in several plant species (Ma et al., 2021) which have highly expressed genes. A team of researchers from the Louisiana State University (Fountain et al., 2015) reported similar results in maize with respect to the resistance and susceptibility to A. flavus fungal infection. WRKY plays a vital role in biotic stress and is involved in PAMP signaling and multiple defense responses through mitogen-activated protein kinase (MAPK) signaling, especially in sensing pathogen effectors or PAMP, and also interacts with resistance (R) protein (Meng and Zhang, 2013). Numerous studies have shown that a significant proportion of WRKY TFs are involved in disease response via the jasmonic acid (JA) signaling pathway (Ma et al., 2021). These TFs act as repressors or activators of basal defense responses (Windram et al., 2012). Similarly, NAC TFs participate in gene transcription regulations (Journot-Catalino et al., 2006), development, and stress response (Farhadian et al., 2021) and are the second highly expressed TFs in endosperm and kernels than in roots and stems that were known to regulate starch synthesis (Olsen et al., 2005; Xiao et al., 2021). Our study observed that NAC TFs were highly expressed in Fg infection of silk of Z. mays compared to other fungal infections such as Fv, Ta, and Um. Many plants have seen ERF TFs involved in disease resistance with phytohormone-mediated fungal defense (Luo et al., 2019), such as ERF activity in JA-mediated defense responses (Grennan, 2008; Jin et al., 2017). In A. thaliana, DREB TFs represent a large part of the AP2/ERF superfamily (Agarwal et al., 2017; Hrmova and Hussain, 2021). An MYB transcription factor is a more prominent family involved in multiple biological functions in the plants, such as primary and secondary metabolite reactions, regulating the plant growth and development, cell morphogenesis, and response to biotic and abiotic stress (Cao et al., 2020; Duan et al., 2021). However, in plants, MYB TF works as an activator for transcription that triggers G2/M-specific gene expression (2011; Haga et al., 2007). Our findings indicated that MYB-related genes might be involved in the Z. mays pathogen response since the expression profile of the MYB-related gene family in Z. mays and soybeans exhibits a wide range of variation with time following a Um infection (Du et al., 2013). Researchers studied the expression of bHLH TFs in the young leaf, root, and auricular tissue of Z. mays which have a high expression while being involved in plant development (Murre et al., 1994; Zhang et al., 2018b). After about a decade, the bHLH TF was identified in a study conducted on A. thaliana that acts as the target of JAZ protein and negatively regulates JA-mediated plant defense and development (Song et al., 2013). Wei et al. (2012) reported that 125 bZIP genes were found, which encode 170 proteins in Z. mays tissue; 18 bZIPTF were significantly upregulated as expressed in silk. These TFs regulate different biological processes such as floral development, seed formation, response to biotic and abiotic stress (Katagiri et al., 1989), starch synthesis in rice endosperm and maize kernel, and they saw that starch synthesis genes have a similar expression pattern (Wang et al., 2013). The invasion of fungal pathogens through penetration to the silk of Z. mays enhanced the defense mechanism against pathogens through activation of the gene encoding cell wall-associated proteins, of which UGTs (UDP-glycosyltransferases) have been found in Z. mays and other species in investigations (Duan et al., 2021). It is involved in the production of phytohormones, metabolites, growth, development, and biotic and abiotic stress (Li et al., 2014; Rehman et al., 2018). UDP members of the GT1 family catalyze, help the biosynthesis of oligo- and polysaccharides, and transfer sugar residues from nucleotide donor substrates to receptor substrates or a developing carbohydrate chain (Hoffmeister et al., 2001; Jayaprakash et al., 2021). GT1-related genes were substantially up-regulated in the Fg-affected silk of Z. mays. Cao et al. (2008) have reported that the GT1 family is the most prominent family in all three species. Aside from the GT1 family, rice’s top five GT families include the GT2, GT4, GT8, GT31, and GT47 families in A. thaliana and Populus species (poplar). GT8 and GT47 classes were highly downregulated in Fg fungal infections (Kong et al., 2019), and GT8 family was found to play an essential role in plant cell wall formation which is considered critical for growth and development. The cold and saline conditions significantly caused upregulation to aid in salt stress tolerance. Cao et al. (2008) distinguish that most GT47 genes have a low expression in the different development stages of rice. Similarly, the second most highly expressed CAZyme family was GH ( Supplementary Table S10 ), which is involved in the carbohydrate metabolic process (GO:0005975) to cleave glycosidic bonds in various forms of glucan, glycosides, and glycoconjugates. These also have industrial use and biotechnological applications to develop bio-fuel (xylanases, cellulases, etc.) and are useful in pharmaceutical research (Roy et al., 2020). This study also identified CAZymes like CBM, CEs, and AA enzyme to be involved in lignin catabolic process with less gene expression in differential expression analysis ( Figure 6B ). We were also successful in identifying 13 classes of R-genes which consist of seven different domains from core genes. In this particular analysis, the KIN class was found to be a major class of R-genes consisting of kinase (KIN) and kinase transmembrane KIN-TM domains ( Figure 6C ). Similarly other major R-gene classes identified were receptor-like kinases (RLK), receptor-like protein (RLP), and receptor-like-protein consisting of a leucine-rich repeat (LRR), which play a crucial role in plant development as well as response to biotic and abiotic stress (De Hoff et al., 2009). Resistance genes are also known as adult plant resistance genes or quantitative resistance genes. R-genes have been identified within host and pathogen cells (Jones et al., 2014). The defense response in plants depends on the pathogen’s attack, such as phytohormones involved in defense responses and salicylic acid which controls the biotrophic pathogens (Kelley et al., 2012). Jasmonic acid (JA)-dependent and ethylene (ET)-dependent signaling pathways regulate the necrotrophic pathogens (Bari and Jones, 2009; Birkenbihl and Somssich, 2011). Most plant disease R proteins have been seen to contain a series of LRRs, a nucleotide-binding site (NBS), and a putative amino-terminal signaling domain. Thus, NBS–LRR protein activations are a phenomenon that changes the structure as well as nucleotide-binding status (DeYoung and Innes, 2006). DUF26 is a subfamily receptor-like protein kinase. It has 90% identity with the cysteine-rich receptor (CRR)-like protein kinase; its domain has antifungal activity and an essential role in stress resistance (Liu et al., 2021). It has been discovered in Arabidopsis plant that the CRR RLKs (CRKs) include two DUF26, CRK9, CRK26, and four DUF6 domains. These domains are involved in ABA signaling via regulating the ABA responses to seed germination, development, abiotic stress, and potential antifungal agents (Quezada et al., 2019). DUF26 domain protein genes were positively expressed in our study in all infected fungal conditions. Two proteins of DUF26 domain—AFP1 and AFP2—were found in Um-infected Z. mays apoplastic fluid (Ma et al., 2018) which are upregulated and act as an antifungal, thus increasing the resistance to fungal pathogens. In total, 48 R-genes are significantly expressed in Fg, Fv, and Um conditions, 222 R-genes have been expressed in Fg and Um conditions, three R-genes were found in Fg and Fv infections, and 227 R-genes are described in Fg condition. Subsequently, R-genes such as protein kinase superfamily proteins, receptor-like serine/threonine-protein kinase (EC 2.7.11.1), and putative leucine-rich repeat receptor-like protein kinase family protein were more significantly expressed in Fg and Um compared to other fungal infections (Fv and Ta). Alam et al. (2010) reported that LRR–RLK gene expression was significantly low in a salt-stress condition compared to other abiotic stresses. Additionally, putative receptor-like protein kinase, leucine-rich repeat protein kinase family protein, disease resistance RPP13-like protein 4, L-type lectin-domain containing receptor kinase IX.1, and LRR family proteins were significantly expressed only in Fg and Um ( Supplementary Table S11 ). It is known that the LRR sequence participates in a strong PPI. A crucial clue to biotic variables affecting the Z. mays plant was discovered after the available silk of Z. mays transcriptome information was unified on NCBI and re-examined. Based on a comparative study using statistical estimates such as the DEG, the expression level of genes and the behavior of similar coding proteins have been found very distinct during multiple fungal infections in the silk of Z. mays. The 21 DEGs have been found in all four fungus-infected silk of Z. mays. The up- and downregulated genes were compared to the genes in the modules of WGCNA with relevant specific traits and identified core genes. In the current study, 520 from TFs, 169 from CAZyme, and 520 from R-genes among 6,668 core genes are involved in Fg infection in Z. mays silk, but not in the other fungal systems examined (Fv, Ta, and Um) for their transcriptomic datasets of Z. mays silk. The present study supports that the immunity stimulus and resistance genes are upregulated, while the downregulated genes are involved in photosynthesis, cell wall organization, pectin metabolism process, and response to auxin in the silk of Z. mays. We know that Fg and Fv belong to Nectriaceae family, but gene expression is very different, with 723 in Fv and 8577 in Fg. It is reported that Fg has an antagonistic relationship with Um, but the current study supports the probable similar function of the expressed genes during a fungal infection. Based on the transcriptome data analysis, we found that Ta does not affect the infected maize silk. Publicly available datasets were analyzed in this study. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material. AK, KRK, and PTVL designed the research. AK and KRK performed the analysis and analysed the results. AK wrote the initial manuscript. PTVL, RSD, and AA majorly reviewed and analysed the draft and finalized the manuscript. All authors contributed to the article and approved the submitted version. The fund for this study was received from Pondicherry University. The authors are thankful to the Department of Bioinformatics, Pondicherry University for providing all the necessary infrastructure for this work. AK is grateful to Pondicherry University for providing a Non-National Eligibility Test fellowship. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

PMC9647495

36321792

Pier Paolo Leoncini,Patrizia Vitullo,Sofia Reddel,Valeria Tocco,Valeria Paganelli,Francesca Stocchi,Elena Mariggiò,Michele Massa,Giovanni Nigita,Dario Veneziano,Paolo Fadda,Mario Scarpa,Martina Pigazzi,Alice Bertaina,Rossella Rota,Daria Pagliara,Pietro Merli

MicroRNA profiling of paediatric AML with FLT-ITD or MLL-rearrangements: Expression signatures and in vitro modulation of miR-221-3p and miR-222-3p with BRD4/HATs inhibitors

31-10-2022

acute myelogenous leukaemia,microRNAs,epigenetic drugs,biomarkers,children

Novel therapeutic strategies are needed for paediatric patients affected by Acute Myeloid Leukaemia (AML), particularly for those at high-risk for relapse. MicroRNAs (miRs) have been extensively studied as biomarkers in cancer and haematological disorders, and their expression has been correlated to the presence of recurrent molecular abnormalities, expression of oncogenes, as well as to prognosis/clinical outcome. In the present study, expression signatures of different miRs related both to presence of myeloid/lymphoid or mixed-lineage leukaemia 1 and Fms like tyrosine kinase 3 internal tandem duplications rearrangements and to the clinical outcome of paediatric patients with AML were identified. Notably, miR-221-3p and miR-222-3p resulted as a possible relapse-risk related miR. Thus, miR-221-3p and miR-222-3p expression modulation was investigated by using a Bromodomain-containing protein 4 (BRD4) inhibitor (JQ1) and a natural compound that acts as histone acetyl transferase inhibitor (curcumin), alone or in association, in order to decrease acetylation of histone tails and potentiate the effect of BRD4 inhibition. JQ1 modulates miR-221-3p and miR-222-3p expression in AML with a synergic effect when associated with curcumin. Moreover, changes were observed in the expression of CDKN1B, a known target of miR-221-3p and miR-222-3p, increase in apoptosis and downregulation of miR-221-3p and miR-222-3p expression in CD34+ AML primary cells. Altogether, these findings suggested that several miRs expression signatures at diagnosis may be used for risk stratification and as relapse prediction biomarkers in paediatric AML outlining that epigenetic drugs, could represent a novel therapeutic strategy for high-risk paediatric patients with AML. For these epigenetic drugs, additional research for enhancing activity, bioavailability and safety is needed.

MicroRNA profiling of paediatric AML with FLT-ITD or MLL-rearrangements: Expression signatures and in vitro modulation of miR-221-3p and miR-222-3p with BRD4/HATs inhibitors Novel therapeutic strategies are needed for paediatric patients affected by Acute Myeloid Leukaemia (AML), particularly for those at high-risk for relapse. MicroRNAs (miRs) have been extensively studied as biomarkers in cancer and haematological disorders, and their expression has been correlated to the presence of recurrent molecular abnormalities, expression of oncogenes, as well as to prognosis/clinical outcome. In the present study, expression signatures of different miRs related both to presence of myeloid/lymphoid or mixed-lineage leukaemia 1 and Fms like tyrosine kinase 3 internal tandem duplications rearrangements and to the clinical outcome of paediatric patients with AML were identified. Notably, miR-221-3p and miR-222-3p resulted as a possible relapse-risk related miR. Thus, miR-221-3p and miR-222-3p expression modulation was investigated by using a Bromodomain-containing protein 4 (BRD4) inhibitor (JQ1) and a natural compound that acts as histone acetyl transferase inhibitor (curcumin), alone or in association, in order to decrease acetylation of histone tails and potentiate the effect of BRD4 inhibition. JQ1 modulates miR-221-3p and miR-222-3p expression in AML with a synergic effect when associated with curcumin. Moreover, changes were observed in the expression of CDKN1B, a known target of miR-221-3p and miR-222-3p, increase in apoptosis and downregulation of miR-221-3p and miR-222-3p expression in CD34+ AML primary cells. Altogether, these findings suggested that several miRs expression signatures at diagnosis may be used for risk stratification and as relapse prediction biomarkers in paediatric AML outlining that epigenetic drugs, could represent a novel therapeutic strategy for high-risk paediatric patients with AML. For these epigenetic drugs, additional research for enhancing activity, bioavailability and safety is needed. Acute Myeloid Leukaemia (AML) constitutes 20% of all paediatric leukaemia and is responsible for substantial mortality. Despite progress made over the past years in diagnosis, risk stratification and treatment of AML, survival remains suboptimal with a success rate of 60–70% (1–3), with relapse being the leading cause of death. Risk stratification in patients with AML is of paramount importance in order to deliver tailored therapy, enabling treatment intensification in high-risk patients (for example, allogenic stem cell transplantation in high-risk in first complete remission). Moreover, novel therapies are needed in order to further improve prognosis in these patients. Among prognostic factors, cytogenetic and molecular abnormalities, together with Minimal Residual Disease and treatment response, play a pivotal role in defining AML prognosis and treatment (4,5). Rearrangements involving Histone-lysine N-methyltransferase 2A (KMT2A) gene, formerly known as myeloid/lymphoid or mixed-lineage leukaemia 1 (MLL1) gene, as well as Fms like tyrosine kinase 3 (FLT3) internal tandem duplications (FLT3-ITD) represent useful prognostic factors and, possibly, therapeutic targets. Indeed, they still define an intermediate to high risk AML (1–3,6). In particular, FLT3-ITD mutations are associated with higher risk of relapse and dismal prognosis (1,3,7), whereas MLL-rearranged AML is an heterogeneous group of diseases with more than 100 rearrangements being described and with different outcome largely dependent on the fusion partner (8). FLT3-ITD and MLL rearrangements have been functionally linked to dysregulation of expression of microRNAs (miRNAs or miRs) (9). miRs are small non-coding RNA molecules (~18-22 nucleotides long), involved in several cellular processes (10,11). In cancer, they are implicated both in promoting carcinogenesis (oncomiRs) and in suppressing tumour transformation (12). Their role in AML have been extensively investigated over the past years reviling promising data on diagnosis, prognostic stratification and, possibly, treatment in AML patients (13–15). Despite extensive research performed to understand the role of miRs in AML, the majority of studies are focused on adult patients, while a precise characterization of miRNAs expression in paediatric AML is less documented. Moreover, data on the role of different miRs are conflicting because of the variability of genetic abnormalities found in AML (16). In the present study, it was aimed to identify AML specific miR signatures in a cohort of patients harbouring molecular lesions (FLT3-ITD and MLL rearrangement), studying the expression of distinct miR sets in relation to relapse risk. Epigenetic networks, including histone modification mechanisms, are involved in the regulation of both miRs expression and function. An increasing interest in the field of cancer therapeutic drugs is focused on small molecular compounds targeting epigenetic regulation (17). Bromodomain and extra-terminal domain family of proteins (BET) and histone acetyl transferase (HAT) inhibitors proteins are the best characterized ones. BET are effective in preventing Bromodomain Containing protein 4 (BRD4) associated transcription of several oncogenes, reducing proliferation and increasing apoptosis in AML (18–22). BRD4 is a member of BET family proteins, characterized by the presence of functional structures called bromodomains which bind specific acetylated residues on histone tails to modulate transcription of target gene (23–25), enhancing transcription of several oncogenes (26). Among BRD4 inhibitors, JQ1 was used as its activity on modulation of miRs was previously described (27). JQ1 [(S)-tert-butyl-2-(4-(4-chlorophenyl)-2,3,9-trimethyl-6H-thieno[3,2-f][1,2,4]triazolo[4,3-a][1,4]diazepin-6-yl)acetate] is a small molecule belonging to the thienotriazolodiazepine group and it prevents the binding of BRD4 to acetylated residues on histone H3 tails, particularly H3AcK14 (20,28–30). Furthermore, among HAT inhibitors, curcumin, a natural compound extracted from the root of Curcuma Longa has been shown to inhibit acetylation of histone tails, blocking the activity of the HAT p300 even causing its proteasomal degradation (31). This results in a global decrease of acetylation on histone tails and a consequent modulation of gene transcription (32,33). BRD4 inhibitors have exhibited only moderate results in clinics and novel ways to increase their antitumour activity are needed (34). It was therefore hypothesized that the association with curcumin would increase JQ1 efficacy. The BET family are ‘readers’ of chromatin acetylation whereas HAT could be classified as a ‘writer’ of histone acetylation (34) thus offering a theoretical basis for JQ1 and curcumin synergic activity. Moreover, it was previously showed that also JQ1, like curcumin, blocks p300-mediated acetylation (25,35). Thus, it was investigated in vitro whether a combination of BRD4 and HAT inhibitors have an effect in terms of modulation of miRs and antitumour effects on different AML cell-lines harbouring mutations resembling those present in our patients. A total of 23 patients aged 1 to 18 years, who received a diagnosis of AML harbouring FLT3-ITD or MLL rearrangement (Table SI). Although not mutually exclusive, these rearrangements are not frequently found together. In the present study, none of the patients had both the rearrangements. Bone marrow (BM) samples were collected from January 1st 2010 to December 31st 2016 at Bambino Gesù Children's Hospital in Rome and at Department of Paediatrics, University in Padua, at diagnosis and at disease recurrence from the 13 patients who underwent relapse (REL-D and REL-R groups, respectively) and at diagnosis from the 10 patients who did not display relapse (NREL group). A total of 8 frozen age-matched BM samples from healthy children (HD) (unused aliquots from healthy BM donors) were retrieved from the tissue bank at Bambino Gesù Children's Hospital as a control population. Informed consent was obtained from either parents or legal guardians according to the Declaration of Helsinki (2008). The present study was approved by the Institutional Review Boards of Bambino Gesù Children's Hospital (Rome, Italy). Mononuclear cells were isolated by density gradient centrifugation at 400 g and 20°C for 30 min, diluted in 90% fetal bovine serum (FBS) plus 10% dimethyl sulfoxide (DMSO) and stored in liquid nitrogen. CD34+ cells from BM samples of three patients randomly selected in our cohort, were magnetically separated using MACS CD34+ microbead kit (Miltenyi Biotech GmbH). In particular, the molecular analysis of these patients revealed a FLT3-ITD with normal karyotype and two MLL rearrangements [t(9;11) and t(10;11)]. The identity of CD34 cells was validated by flow cytometry using FACSCantoII equipped with FACSDiva 6.1 CellQuest software (Becton, Dickinson and Company) using 20 µl of CD34 PerCP antibody (cat. no 340666; BD Biosciences) with an incubation of 30 min at 4°C. Total RNA was extracted using TRIzol® reagent (Invitrogen; Thermo Fisher Scientific, Inc.) and purified using RNA Cleanup and Concentration kit according to the manufacturer's protocol (Norgen Biotek Corp.). RNA quantification was performed using Nanodrop 2000 at 260 nm wavelength (Thermo Fisher Scientific, Inc.) and RNA integrity and purity was assessed with RNA Bioanalyzer kit according to the manufacturer's protocol (Agilent Technologies, Inc.). miR expression profile was performed using the nCounter Human v2 miRNA Expression Assay and nCounter Nanostring platform according to manufacturer's protocol (NanoString Technologies). AML cell lines THP-1 (MLL-MLLT3; MLL-AF9), MOLM-13 (MLL-MLLT3; MLL-AF9; FLT3-ITD) and MV-4-11 (MLL-AFF1; MLL-AF4; FLT3-ITD) were obtained from DSMZ and cultured at 37°C using RPMI-1640 medium (Euroclone SpA) supplemented with 10% FBS (Thermo Fisher Scientific, Inc.) and 1% Penicillin-Streptomycin (Thermo Fisher Scientific, Inc.). Mycoplasma testing was performed for the cell lines used. Cell lines were treated with 250 nM JQ1 and 10 µM curcumin singularly alone or in association, for 48 h. JQ1 and Curcumin were obtained from Selleck Chemicals and resuspended in DMSO, following the manufacturer's protocol. Whole-cell lysates were prepared with RIPA lysis buffer (Thermo Fisher Scientific, Inc.) supplemented with protease and phosphatase inhibitors (Thermo Fisher Scientific, Inc.). Cells were lysed by sonication, incubated for 30 min at 4°C and then obtained cells lysates were centrifuged at 13,000 × g for 20 min at 4°C. The protein concentration of the resulting supernatant was estimated by BCA assay. Then, 40 µg of sample was separated on Criterion TGX Precast Gels 4–20% (BioRad Laboratories, Inc.) and transferred to Hybond ECL nitrocellulose membranes (Amersham; Cytiva). Membranes were blocked at room temperature for 1 h in 5% non-fat milk in Tris buffered saline and 0,05% Tween-20 (TBS-T). Membranes were incubated at 4°C overnight with rabbit polyclonal anti-human CDKN1B (1:500; cat. no. sc-528; Santa Cruz Biotechnology, Inc.) and 1 h at room temperature with rabbit monoclonal anti-human GAPDH (1:1,000; cat. no. D16H11; Cell Signaling Technology, Inc.) primary antibodies. After incubation they were washed three times in TBS-T, then incubated with HRP-labelled goat anti-rabbit (1:5,000; cat. no. sc-2004) and goat anti-mouse (1:5,000; cat. no. sc-2005; both from Santa Cruz Biotechnology, Inc.) IgG secondary antibodies, respectively at room temperature for 1 h. Subsequently, they were washed an additional three times with TBS-T and then developed with ECL reagent (Western Lightning Plus; PerkinElmer, Inc.). Expression levels of hsa-miR-221-5p, hsa-miR-222-5p and U6 were measured using TaqMan microRNA assays (cat. nos. 000524, 002276 and 001973; Thermo Fisher Scientific, Inc.). Reverse transcription (RT) primer, preformulated forward/reverse primer and MGB probes for each assay were provided by the manufacturer. The TaqMan MiR Reverse Transcription kit was used for cDNA synthesis from 10 ng total RNA template according to the manufacturer's protocol. QuantStudio 12K Flex Real Time PCR System (Thermo Fisher Scientific, Inc.) was used for qPCR reactions with the following conditions: Enzyme activation 95°C for 20 sec and 40 cycles of denaturation (95°C for 1 sec) and annealing/extension (60°C for 20 sec) steps. miRNA expression data were normalized to U6 using the 2−ΔΔCq (36) method by the Relative Quantification module of Thermo Fisher Cloud Data Analysis Apps. At least two independent amplifications were performed for each probe on triplicate samples. Following treatment with 250 nM BRD4 and 10 µM curcumin, cells were washed twice with ice cold PBS and stained for 15 min at room temperature in calcium-binding buffer with PE-conjugated Annexin V (AnnV) and 7-Aminoactinomycin D (7-AAD) using the AnnV apoptosis detection kit (BD Pharmingen; BD Biosciences) according to the manufacturer's recommendations. Samples were analysed within 1 h by a fluorescence-activated cell sorting using a FACSCantoII equipped with FACSDiva 6.1 CellQuest software (Becton, Dickinson and Company). MicroRNA profiling normalization was performed using the nSolver Analysis Software (NanoString Technologies) as recommended by NanoString. P-values were calculated using the LIMMA (v.3.46.0) package (37) from the Bioconductor R (v.4.0.5) project. The P-values were adjusted for multiple testing using the Benjamini and Hochberg method to control the False Discovery Rate. An independent normalization phase for each comparison was performed, considering only the samples present in such comparison (for example, NREL vs. HD). Then, the miRNA expression of the HD group was specifically normalized in the comparisons in which the HD group was taken into consideration. Validated targets of miRs were reported in Table I according to miRWalk 2.0 online software analysis (http://mirwalk.umm.uni-heidelberg.de/). One-way ANOVA and post hoc comparison using Tukey's HSD Post Hoc or Dunnett's test were performed using SPSS software v19 (IBM Corp.) and GraphPad Prism v6 (GraphPad Software, Inc.). The heatmap was generated by using GenePattern tool (38), with Euclidean and Spearman correlation distances in columns and rows, respectively. Venn diagrams were created using web tool (39). CD34+ cells were cultured using MethoCult H4434 methylcellulose medium (Stem Cell Technologies) supplemented with 250 nM JQ1 and 10 µM curcumin. A miR profiling analysis was first performed to verify whether the two distinct molecular subsets of the cohort of our patients (MLL rearranged and FLT3-ITD) showed different miR expression fingerprints. Comparing both MLL rearranged and FLT3-ITD sets with healthy donors (HDs), 4 and 16 significantly deregulated miRs were identified, respectively (Table I). miR-196b-5p and miR-34a-5p resulted upregulated in both AML sets. The comparison between the two molecular AML sets showed 3 differentially regulated miRs with miR-10a-5p and miR-99a-5p significantly higher in FLT3-ITD and miR-9a-5p with enhanced expression in MLL-rearranged sets (Table I). Other miRs such as miR-451a, miR-520d-5p/527/518a-5p, miR-574-5p and miR-192-5p were uniquely dysregulated in FLT3-ITD or MLL sets with respect to HDs (Table I). A miR expression profiling analysis was then performed based onto clinical outcomes of patients to identify those miRs associated with relapse. The hierarchical clustering results of miRNAs expression of REL-D vs. NREL groups is revealed in Fig. 1. A total of 18 miRs were broadly upregulated in the REL-D patients set with respect to the NREL set and 48 miRs displayed the opposite trend (Fig. 1A and Table II). To further identify and refine a signature that was associated with relapse, among these differentially expressed 66 miRs, only those shared in the REL-D vs. HD and NREL vs. HD comparison were subsequently considered (Fig. 1B and Table II). The resulted signature associated to relapse and not-relapse is listed in Table III. Validated targets of miRs were identified using miRWalk 2.0 online software (40) and are reported in Table III. CDKN1B, a key regulator of cell cycle which has been previously reported to be associated with prognosis in AML, resulted as primary target of miR-221-3p and miR-222-3p (41). To evaluate if the signature of miRs associated with relapse at diagnosis was maintained over time (if it is present also in the REL-R group), expression of miRs at diagnosis and at disease recurrence was compared between those patients who relapsed (REL-R vs. REL-D) and no significant differences were detected (Table II). Since our goal was to identify miRs associated to relapse with a significant prognostic value, the expression level of both miRs resulted overexpressed in REL-D vs. HD group and REL-D vs. NREL was first evaluated in the AML cells lines by qPCR (data not shown). High variability was obtained in the different cell lines. This result prompted the authors to focus only on hsa-miR-221-3p and hsa-miR-222-3p, presenting the same trend of expression in all the cell lines, as well as in vivo in the patients. It was first analysed whether the combination of JQ1 and curcumin could modulate miR-221-3p and miR-222-3p expression in AML cell lines. RT-qPCR experiments (Fig. 2A), revealed a trend towards downregulation of the two miRs in all the cell lines, featuring an enhanced effect with the drug combination. The expression of miR-221-3p expression showed a clear trend towards downregulation, reaching statistical significance in MV-4-11 cells when treated with JQ1, curcumin or the combination of both (Fig. 2A, upper panel). miR-222-3p demonstrated a trend towards downregulation with coupled treatment in all cell-lines, but it did not reach statistical significance (Fig. 2A, lower panel). It was then analysed whether the combination of JQ1 and curcumin could modulate the CDKN1B protein level, that it is a miR-221-3p and miR-222-3p target, as before mentioned. As revealed in Fig. 2B, the treatment determined an increase in CDKN1B expression both in THP-1 and in MOLM-13 cells while in MV-411 cells the effect of drug combination on the upregulation of CDKN1B expression was comparable to that obtained by single treatment with JQ1. It was assessed whether these treatments could drive the cell lines toward an apoptotic response. A significant increase in AnnV positive cell percentage was identified in all the cell lines while treated with the drug combination compared with DMSO (Figs. S1 and 3). At a deeper glance, THP-1 and MV-4-11 cells, showing a minor increase in CDKN1B expression, displayed a milder apoptotic response compared with MOLM-13, characterized instead by higher changes in term of expression of CDKN1B (Figs. 2 and 3). miR-221-3p and miR-222-3p expression was analysed in CD34+ cells isolated from BM samples from three randomly selected patients from our cohort. The purity of CD34+ cells after isolation was 89% (Fig. S2). After CD34+ cells were cultured with MethoCult methylcellulose medium supplemented with JQ1 and curcumin, RNA was extracted and miR-221-3p, miR-222-3p expression was evaluated. In all the samples analysed miR-221-3p and miR-222-3p were modulated when treated with JQ1 and curcumin. In particular, the double treatment led to a significant downregulation of their expression (Fig. 4). Despite improvements in the treatment of paediatric patients affected by AML, novel therapeutic strategies are needed, particularly for paediatric high-risk AML, characterized by high relapse incidence. miRs have been extensively studied as potential biomarkers in adult AML and, recently, expression signatures of miRs in paediatric samples have been proposed (14,19,42). Notably, few miRNA-based prognostic models have been proposed for paediatric cytogenetically normal AML (18,43), t(8;21) RUNX1-RUNX1T1 AML (44) and AML without considering the cytogenetics (18,45). However, conflicting results on the prognostic value of different miRs were reported, possibly due to the vast variability of miRs expression among different cytogenetic and molecular subtypes of AML (15). It was therefore decided, first, to characterize signature of miRs in patients harbouring FLT3-ITD and MLL rearrangement to verify whether distinct AML molecular subsets could affect different miR expression fingerprints. Moreover, to further narrow our miRs signature (identifying only AML associated miRs), HDs were used as a control group. Data on expression of miRs among different AML molecular subtypes are available (16). In accordance with literature data (46), miR-9-5p and miR-10a-5p were upregulated in MLL-rearranged and FLT3-ITD sets, respectively, even compared with HDs. In addition, miR-99a-5p was upregulated in FLT3-ITD patients as compared with MLL patients, and was already described in literature as a potential oncomiR in paediatric AML (42). miR-196b-5p and miR-34a-5p were both upregulated in MLL and FLT3-ITD with respect to HDs and they are possibly involved in the pathogenesis of both AML variants. miR-34a is a widely studied miR as a promising target and it has been considered as a preclinical and clinical model for the treatment of solid tumours, myeloma and B-cell lymphoma (47,48). Despite the limited number of patients, to the best of our knowledge, this is the first study investigating the role of miRs as predictive biomarker of relapse in paediatric AML patients with FLT3-ITD or MLL rearrangement. A total of 28 miRs differentially expressed were identified between patients who relapsed and those who did not, being possibly associated with prognosis. Among them, the vast majority of miRs have already been reported as related to cancer. Comparing the signature of our miRs to those previously reported, a common dysregulation of mir-155 was identified, but displaying an opposite behaviour with a protective effect in the present study as opposed to a relapse association in others (19,49). miR-155 has led to conflicting results in previous studies, with certain groups reporting an anti-leukemic role (50) and others (13,51–53) reporting a role as oncomiR in AML. This is possibly explained by a different level of expression of miR-155, acting as a tumour suppressor when highly expressed and as an oncomiR when overexpressed to an intermediate level (54). miR-34a-5p expression was positively correlated with relapse. It was already described that patients with low miR-34a expression showed shorter overall and recurrence-free survival (55). Numerous of the identified miRs, have also been independently considered as promising tool to develop novel therapies; these include miR-200c (47,56), which was revealed to be associated to NREL patients. miR-221-3p and miR-222-3p, both associated to relapse, gained the attention of the authors as they have been broadly reported in literature as oncomiRs both in hematologic malignancies such as chronic lymphocytic leukemia (57), myelodisplastic syndrome (58), acute lymphoblastic leukemia (59) and AML (60,61) as well as in solid tumours. These miRs have as their primary target CDKN1B, a master regulator of the cell cycle. CDKN1B is a well-known cyclin-dependent kinase inhibitor which regulates cell cycle progression at G1 stage, preventing the activation of cyclin E-CDK2 or cyclin D-CDK4 complexes, resulting in a blockade of cell division cycle (62). BET and HAT inhibitors have already been associated with modulation of miRs in hematological malignances (63,64). The present findings revealed, for the first time, that JQ1 determines a clear trend towards downregulation of miR-221-3p expression in both MOLM-13 and MV-4-11AML cell lines with a synergic effect when associated with curcumin; reaching statistical significance in the second one. This is interesting also considering that for FLT3-ITD rearrangement MOLM-13 expresses both mutated and wild-type allele, while MV-4-11 expresses mutated allele only (65). It could be hypothesized that FLT3-ITD in both mutated alleles renders MV-4-11 cells more sensitive to the effect of drug on miR-221-3p modulation. Moreover, following the combined treatment, an increase was demonstrated in CDKN1B expression and in apoptotic response in our AML cell lines. The present results were confirmed in cultures with primary leukemic cells, showing a marked reduction of miR-221-3p and miR-222-3p expression. These results supported the idea that BET inhibitors, along with curcumin, could regulate not only coding RNA transcription, but also non-coding RNA such as miRs. Although the combination of JQ1 and curcumin synergistically reduced miR-221 and miR-222 expression and increased apoptosis in AML cells, a limitation to the present study was represented by insufficient patient samples. Direct regulation of CDKN1B by miR-221-3p and miR-222-3p should be confirmed by further experiments including western blot analysis to verify expression levels of CDKN1B in samples of patients and HDs and the use of inhibitors of miR-221 or miR-222 in a bigger cohort of patients. In conclusion, the present study identified fingerprints of miRs related to relapse and non-relapse in paediatric FLT3-ITD- or MLL-rearranged AML. Numerous of these miRs are known to be involved in pathogenetic mechanisms of several haematological malignancies as well as solid tumours and represent both good candidates for targeted treatments and therapeutic tools in different neoplasms. The use of the well-known BRD4 inhibitor JQ1, as well as novel BRD inhibitors in the care of leukaemia could be potentiated by epigenetic drugs such as HATs inhibitors, antagomiR or miR mimic and could expand the therapeutic arsenal in HR-AMLs, particularly for paediatric patients.

PMC9647623

Peng Wei,Zhifeng Dong,Ming Lou

Lncrna FGD5-AS1 Aggravates Myocardial Ischemia-Reperfusion Injury by Sponging Mir-129-5p

01-10-2022

Myocardial ischemia,Reperfusion injury,Cardiology

Background: LncRNA FGD5-AS1 regulates the pathogenesis of many human diseases. We aimed to elucidate the function of lncRNA FGD5-AS1 and the regulatory mechanism of lncRNA FGD5-AS1/miR-129-5p in myocardial ischemia-reperfusion (I/R) injury. Methods: Myocardial I/R injury mice model and H/R treated H9c2 cells were established. RT-qPCR and Western blot analysis were used to detect the mRNA and protein expression. Cell viability was detected by MTT assay. Dual luciferase reporter assay was applied to confirm the relationship between lncRNA FGD5-AS1 and miR-129-5p. Results: LncRNA FGD5-AS1 was upregulated in myocardial I/R injury mice models and H/R treated H9c2 cells. Functionally, knockdown of lncRNA FGD5-AS1 promoted cell viability and inhibited apoptosis in H/R treated H9c2 cells. In addition, lncRNA FGD5-AS1 directly targets miR-129-5p. Upregulation of lncRNA FGD5-AS1 weakened the protective effect of miR-129-5p on myocardial I/R injury. Conclusion: LncRNA FGD5-AS1 aggravates myocardial I/R injury by downregulating miR-129-5p.

Lncrna FGD5-AS1 Aggravates Myocardial Ischemia-Reperfusion Injury by Sponging Mir-129-5p LncRNA FGD5-AS1 regulates the pathogenesis of many human diseases. We aimed to elucidate the function of lncRNA FGD5-AS1 and the regulatory mechanism of lncRNA FGD5-AS1/miR-129-5p in myocardial ischemia-reperfusion (I/R) injury. Myocardial I/R injury mice model and H/R treated H9c2 cells were established. RT-qPCR and Western blot analysis were used to detect the mRNA and protein expression. Cell viability was detected by MTT assay. Dual luciferase reporter assay was applied to confirm the relationship between lncRNA FGD5-AS1 and miR-129-5p. LncRNA FGD5-AS1 was upregulated in myocardial I/R injury mice models and H/R treated H9c2 cells. Functionally, knockdown of lncRNA FGD5-AS1 promoted cell viability and inhibited apoptosis in H/R treated H9c2 cells. In addition, lncRNA FGD5-AS1 directly targets miR-129-5p. Upregulation of lncRNA FGD5-AS1 weakened the protective effect of miR-129-5p on myocardial I/R injury. LncRNA FGD5-AS1 aggravates myocardial I/R injury by downregulating miR-129-5p. Myocardial ischemia refers to a pathological state in which the blood perfusion of the heart is reduced. It can lead to a decrease in oxygen supply to the heart and abnormal myocardial energy metabolism (1). With the improvement of people's living standards, the incidence of myocardial ischemia in China is increasing (2). Myocardial ischemia-reperfusion (M-I/R) injury indicates a cardiovascular dysfunction after myocardial ischemia (3). Currently, effective treatment is limited to restoring coronary blood flow to prevent myocardial infarction. Therefore, there is an urgent need for new therapeutic strategies to prevent M-I/R injury. Long non-coding RNA (lncRNA) is a type of RNA molecule with a transcript length of more than 200 nt. LncRNAs do not encode proteins, but regulate gene expression at multiple levels (epigenetic, transcription, post-transcriptional regulation) (4). Moreover, lncRNAs play important roles in the pathogenesis of M-I/R injury. For example, knockdown of lncRNA TTTY15 alleviated M-I/R injury through the miR-374a-5p/FOXO1 axis (5). In addition, lncRNA A2M-AS1 has been reported to lessen the injury of cardiomyocytes caused by hypoxia and reoxygenation via regulating IL1R2 (6). Here, the role of lncRNA FGD5-AS1 was investigated in the pathogenesis of M-I/R injury. lncRNA FGD5-AS1 was involved in the development of human cancers. For instance, silencing of lncRNA FGD5-AS1 inhibited the progression of non-small cell lung cancer by regulating the miR-493-5p/DDX5 axis (7). lncRNA FGD5-AS1 promoted tumor growth by regulating MCL1 via sponging miR-153-3p in oral cancer (8). However, the function and regulatory mechanism of lncRNA FGD5-AS1 remains unclear in the pathogenesis of M-I/R injury. lncRNAs have “sponge-like effects” on numerous miRNAs, including lncRNA FGD5-AS1. In this study, miR-129-5p was found to have a binding site with lncRNA FGD5-AS1. Overexpression of miR-129-5p mitigate sepsis-induced acute lung injury by targeting High Mobility Group Box 1 (9). More importantly, miR-129-5p protects H9c2 cardiac myoblasts from hypoxia/reoxygenation injury by targeting TRPM7 and inhibiting NLRP3 inflammasome activation (10). In addition, lncRNA NEAT1 promoted myocardiocyte apoptosis and suppressed proliferation through regulation of miR-129-5p (11). However, little is known about the interaction between lncRNA FGD5-AS1 and miR-129-5p in myocardial I/R injury. In the present study, the expression level of lncRNA FGD5-AS1 was detected in mouse I/R models. Then, the biological effect of lncRNA FGD5-AS1 in H9c2 cell H/R model was investigated. In addition, the regulatory mechanism of lncRNA FGD5-AS1/miR-129-5p in M-I/R injury was discovered. Male c57BL6/J mice (20–24 g, Guangdong Medical Laboratory Animal Center) were feed in a standard pathogen-free environment (25°C, 60% humidity). Food and water were freely provided. All procedures were performed in accordance with the Care and Use of Laboratory Animals issued by the Chinese Association for Laboratory Animal Care and approved by our Hospital. The myocardial I/R injury model was established by ligating the left anterior descending coronary artery. C57BL/6 mice were anesthetized with 3% pentobarbital sodium and a longitudinal incision. The mouse's thoracic cavity was open by left thoracotomy. Ligation was performed at approximately 3 mm from the source of the descending left anterior coronary artery with line 6–0. After 30 min induction of ischemia, the ligature was untied. Then, the mice were reperfused at various time points (6h, 12h, 24h). The cardiomyocytes cell line H9c2 (Chinese Academy of Sciences, China) were cultured in Dulbecco’s Modified Eagle Medium (DMEM) with 10% fetal bovine serum (FBS) in a humid incubator with 5% CO2 at 37 °C. H/R treatment was used to establish an I/R injury model in H9c2 cells. H9c2 cells were exposed for 24 h under hypoxia (5% CO2, 95% N2) and then re-oxygenated (5% CO2, 95% O2) for 12 h at 37°C. Control cells were incubated under normoxic conditions (NC). FGD5-AS1 siRNA and vector or miR-129-5p mimics and inhibitor were designed and synthesized by GenePharma (Shanghai, China). They were transfected into H9c2 cells using Lipofectamine® 2000 transfection reagent, respectively. When the M-I/R injury mice model was established, the levels of CK-MB and LDH were measured by using commercial assay kits (Invitrogen, Carlsbad, CA) in accordance with the manufacturer’s protocol. Total RNA was extracted using TRIzol reagent (Thermo Fisher Scientific, MA, USA). Then, RT was conducted using a RevertAid First Strand cDNA Synthesis kit (K1622; Thermo Fermentas, USA). PCR was performed on an ABI Prism 7900 detection system (Thermo Fisher Scientific, Inc.) using iQ™ SYBR®-Green SuperMix (Bio-Rad Laboratories, Inc., Hercules, CA). GAPDH was applied as internal reference. MiRNA and mRNA expression levels were quantified using the 2−ΔΔCq method. H9c2 cells (2×103cells/well) were seeded into 96-well plates and cultured for 12 h in 5% CO2 at 37°C. Then, cells were subjected to H/R exposure. Next, the cells were incubated with 15 μL/well MTT solution (5 mg/mL, Sigma) at 37 °C for 4 h. The absorbance value was determined at a wavelength of 490 nm by using a Bio-Rad 680 microplate reader (Bio-Rad Laboratories, Inc.). H9c2 cells were collected and lysed by RIPA Lysis Buffer (Beyotime, Shanghai, China). Protein concentration was measured using Enhanced BCA Protein Assay kit (Beyotime, Shanghai, China). Next, protein samples (40 μg) were separated by 10% SDS-PAGE and transferred to PVDF membranes. The membranes were blocked with 5% skimmed milk for 2 h at room temperature and incubated with Bax, Bcl-2 and GAPDH primary antibodies (Abcam, Shanghai, China) overnight at 4°C. After washing, protein samples were incubated with horseradish peroxidase-conjugated secondary antibodies (Abcam, USA) for 2 h. Finally, the blots were detected using an enhanced chemiluminescence (ECL) reagent and analyzed with ImageJ software. Wild-type and mutant FGD5-AS1 containing miR-129-5p binding site were amplified and inserted into the pGL3 vector (Promega) to construct recombinant reporter plasmids WTFGD5-AS1 and MUT-FGD5-AS1. The reporter plasmids and miR-129-5p mimics or miR-NC was co-transfected into H9c2 cells. After 48 h, dual luciferase assay system (Promega, USA) was used to detect luciferase activities. Data were analyzed SPSS 19.0 (IBM Corp., Armonk, NY, USA) and expressed as mean ± SD. Graphs are made by Graphpad Prism 6. Student t-test was adopted to compare the difference between two groups, and multiple comparison was performed by one-way analysis of variance followed by Tukey’s post hoc test. P < 0.05 indicates statistically significant difference. To explore the expression level of lncRNA FGD5-AS1 in myocardial I/R injury, myocardial I/R injury mice models were established. To assess whether the myocardial I/R injury mice mouse model is successfully established, the serum levels of LDH and CK-MB were detected. LDH and CK-MB serum levels were significantly increased in myocardial I/R injury group (Fig. 1A, 1B). Especially, the highest serum levels of LDH and CK-MB were occurred at 12 h after reperfusion (Fig. 1A, 1B). The myocardial I/R injury mice mouse model was successfully established. Next, RT-qPCR showed that lncRNA FGD5-AS1 expression was apparently increased at 6h and 12 h after reperfusion in myocardial I/R injury group compared with Normoxia group (Fig. 1C). To explore the role of lncRNA FGD5-AS1 in myocardial I/R injury, H9c2 cell H/R model was established. RT-qPCR showed that lncRNA FGD5-AS1 expression was upregulated in H9c2 cells treated with H/R compared with Normoxia group (Fig. 2A). After transfection of FGD5-AS1 siRNA, FGD5-AS1 expression was reduced in H/R treated H9c2 cells, but still higher than that in Normoxia group (Fig. 2A). Functionally, cell proliferation was suppressed in H9c2 cells treated with H/R compared to Normoxia group. Downregulation of FGD5-AS1 promoted cell proliferation in H/R treated H9c2 cells. However, H9c2 cell proliferation in H/R + FGD5-AS1 siRNA group was still inhibited compared to Normoxia group (Fig. 2B). Additionally, the effect of lncRNA FGD5-AS1 on apoptosis-related protein (Bcl-2/Bax) was also detected in H9c2 cells. Compared with the Normoxia group, increased expression of Bax and decreased expression of Bcl-2 were identified in H/R group. Compared with H/R group, knockdown of FGD5-AS1 reduced Bax expression and enhanced Bcl-2 expression. The expression of Bax and Bcl-2 in H/R + FGD5-AS1 siRNA group tended to the levels in Normoxia group but could not reach the levels in Normoxia group (Fig. 2C, 2D). Briefly, lncRNA FGD5-AS1 could aggravate myocardial I/R injury by suppressing cell viability and inducing apoptosis. To explain the regulatory mechanism of lncRNA FGD5-AS1 in myocardial I/R injury, the target of lncRNA FGD5-AS1 was searched in the star-Base database (http://starbase.sysu.edu.cn). We found that lncRNA FGD5-AS1 has a binding site with miR-129-5p (Fig. 3A). Dual-luciferase reporter assay showed that miR-129-5p mimics reduced the luciferase activity of wt-FGD5-AS1 (Fig. 3B), indicating that miR-129-5p is a direct target of lncRNA FGD5-AS1. Next, RT-qPCR showed thta miR-129-5p expression was reduced by FGD5-AS1 vector and promoted by FGD5-AS1 siRNA in H9c2 cells (Fig. 3C). At the same time, lncRNA FGD5-AS1 was upregulated in H9c2 cells with miR-129-5p mimics and down-regulated in H9c2 cells with miR-129-5p inhibitor (Fig. 3D). To investigate the interaction between lncRNA FGD5-AS1 and miR-129-5p in myocardial I/R injury, miR-129-5p mimics and miR-129-5p mimics+FGD5-AS1 vector were transfected into H/R treated H9c2 cells. Compared with the Normoxia group, miR-129-5p was downregulated in H/R group. Upregulation of FGD5-AS1 reduced the increased expression of miR-129-5p induced by miR-129-5p mimics. However, miR-129-5p expression was still lower than that in Normoxia group (Fig. 4A). MTT assay showed that miR-129-5p overexpression promoted H/R treated H9c2 cell viability compared with H/R group. However, FGD5-AS1 vector weakened the promoting effect of miR-129-5p overexpression on H/R treated H9c2 cell viability (Fig. 4B). Compared to H/R group, overexpression of miR-129-5p reduced Bax expression and promoted Bcl-2 expression in H/R treated H9c2 cells. However, upregulation of FGD5-AS1 increased Bax expression and reduced Bcl-2 expression in H/R treated H9c2 cells compared with H/R+ miR-129-5p mimics group (Fig. 4C, 4D). These results indicate that overexpression of miR-129-5p ameliorates myocardial I/R injury. lncRNA FGD5-AS1 aggravates myocardial I/R injury by downregulating miR-129-5p. Recently, many lncRNAs and microRNAs have been reported to participate in the pathogenesis of myocardial injury. For example, Downregulation of lncRNA NEAT1 promoted cell proliferation and inhibited cell apoptosis by targeting miR-193a in myocardial I/R injury (12). In this study, lncRNA FGD5-AS1 was upregulated in myocardial I/R injury mice models and H/R treated H9c2 cells. Functionally, knockdown of lncRNA FGD5-AS1 promoted cell viability and inhibited apoptosis in H/R treated H9c2 cells. In addition, miR-129-5p was confirmed to be a target of lncRNA FGD5-AS1. The protective effect of miR-129-5p on myocardial I/R injury was impaired by upregulation of lncRNA FGD5-AS1. These results demonstrate that lncRNA FGD5-AS1 aggravates myocardial I/R injury by down-regulating miR-129-5p. Consistent with our results, other lncRNAs also have been found to regulate myocardial I/R injury. For example, lncRNA FOXD3-AS1 aggravated I/R injury of cardiomyocytes through promoting autophagy (13). However, the role of lncRNA FGD5-AS1 has not been reported in myocardial I/R injury. Most studies reported that lncRNA FGD5-AS1 play important roles in human cancers. For instance, upregulation and carcinogenesis of lncRNA FGD5-AS1 has been detected in colorectal cancer and glioblastoma (14,15). However, lncRNA FGD5-AS1 expression was found to be decreased in oxygen-glucose deprivation and simulated reperfusion (OGD/R)-induced neurons injury. Up-regulation of FGD5-AS1 could recover proliferation and inhibit apoptosis of OGD/R-injured neurons (16). These results are contrary to our results in this study. This difference may be caused by different experimental materials. In the present study, lncRNA FGD5-AS1 directly targeted miR-129-5p and had a negative correlation with miR-129-5p expression in cardiomyocytes. More importantly, overexpression of miR-129-5p ameliorated myocardial I/R injury. Consistent with our study, miR-129-5p alleviates myocardial injury after ischemia/reperfusion (17). miR-129-5p ameliorated ischemia-reperfusion injury by targeting HMGB1 in myocardium (18). All these results indicate that miR-129-5p play a positive effect on myocardial I/R injury. In addition, upregulation of lncRNA FGD5-AS1 impaired the protective effect of miR-129-5p on myocardial I/R injury. lncRNA FGD5-AS1 could aggravate myocardial I/R injury. Interaction between lncRNA FGD5-AS1 and miR-129-5p in myocardial I/R injury has not been found in previous studies. Upregulation of lncRNA FGD5-AS1 is detected in myocardial I/R tissues and cardiomyocytes. Upregulation of lncRNA FGD5-AS1 reduced cell proliferation and induced apoptosis in H/R treated cardiomyocytes. More importantly, lncRNA FGD5-AS1 aggravates myocardial I/R injury by downregulating miR-129-5p. Our results may provide a novel therapeutic or diagnostic target for myocardial I/R injury. Ethical issues (Including plagiarism, informed consent, misconduct, data fabrication and/or falsification, double publication and/or submission, redundancy, etc.) have been completely observed by the authors.

PMC9647653

Yaqin Zhang,Xiangyu Quan,Yingchun Li,Hangyu Guo,Fange Kong,Jiahui Lu,Lirong Teng,Jiasi Wang,Di Wang

Visual detection of SARS-CoV-2 with a CRISPR/Cas12b-based platform

10-11-2022

SARS-CoV-2,CRISPR/Cas12b,Point-of-care,High-throughput test,COVID-19, Corona Virus Disease 2019,SARS-CoV-2, severe acute respiratory syndrome coronavirus 2,RT-PCR, reverse transcription-polymerase chain reaction,POC, point of care,LAMP, loop-mediated isothermal amplification,RPA, recombinase polymerase amplification,RCA, rolling circle amplification,CRISPR, clustered regularly interspaced short palindromic repeats,Cas, CRISPR-associated,AuNP, Gold nanoparticle,TCEP, Tris(2-carboxyethyl) phosphine,DMEM, Dulbecco's modified Eagle medium,TEM, transmission electron microscope,DEPC, Diethypyrocarbonate

The coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic, highlighting the unprecedented demand for rapid and portable diagnostic methods. Clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated (Cas) proteins-based platforms have been used for the detection of pathogens. However, in further applications and research, due to multiple steps needed, many methods showed an increased risk of cross-reactivity. The thermostable Cas12b enables the combination of isothermal amplification and CRISPR-mediated detection, which could decrease the risk of cross-contamination. In this study, we developed a portable and specific diagnostic method that combined the gold nanoparticle (AuNP) with thermal stable CRISPR/Cas12b-enhanced reverse transcription loop-mediated isothermal amplification (RT-LAMP), which is called SCAN, to distinguish the N gene of SARS-CoV-2 from flu gene. We validated our method using RNA from cells transfected by plasmids. We could easily distinguish the positive results by the naked eye based on the strong molar absorption coefficient of AuNP. Moreover, SCAN has the potential for high-throughput tests owing to its convenient operation. In sum, SCAN has broken the site and equipment restrictions of traditional detection methods and could be applied outside of hospitals and clinical laboratories, greatly expanding the test of COVID-19.

Visual detection of SARS-CoV-2 with a CRISPR/Cas12b-based platform The coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic, highlighting the unprecedented demand for rapid and portable diagnostic methods. Clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated (Cas) proteins-based platforms have been used for the detection of pathogens. However, in further applications and research, due to multiple steps needed, many methods showed an increased risk of cross-reactivity. The thermostable Cas12b enables the combination of isothermal amplification and CRISPR-mediated detection, which could decrease the risk of cross-contamination. In this study, we developed a portable and specific diagnostic method that combined the gold nanoparticle (AuNP) with thermal stable CRISPR/Cas12b-enhanced reverse transcription loop-mediated isothermal amplification (RT-LAMP), which is called SCAN, to distinguish the N gene of SARS-CoV-2 from flu gene. We validated our method using RNA from cells transfected by plasmids. We could easily distinguish the positive results by the naked eye based on the strong molar absorption coefficient of AuNP. Moreover, SCAN has the potential for high-throughput tests owing to its convenient operation. In sum, SCAN has broken the site and equipment restrictions of traditional detection methods and could be applied outside of hospitals and clinical laboratories, greatly expanding the test of COVID-19. Over the past two years, coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has emerged as a global pandemic [1]. As of February 20, 2022, there have been over 422 million SARS-CoV-2-confirmed cases worldwide with over 5 million deaths [2]. Although the vaccines and therapeutic agents have been developed, early detection of SARS-CoV-2 is still critical to prevent the infection and control the pandemic. Of note, real-time reverse transcription-polymerase chain reaction (RT-PCR) is still the gold standard of nucleic acid diagnosis due to its sensitivity and specificity [3]. However, RT-PCR needs trained technicians and a thermal cycling controller, which has limited its onsite application. Nowadays, the SARS-CoV-2 rapid antigen test has been applied widely all around the world, which is fast and convenient while with low sensitivity and specificity (106 copies/mL, 77.4–96.8% specificity) [4]. Therefore, there is still an unprecedented need to develop detection methods that are rapid, convenient, sensitive, and portable to use at the point-of-care (POC). As an alternative to the thermal cycling amplification, a plethora of isothermal amplification methods, such as recombinase polymerase amplification (RPA) [5], loop-mediated isothermal amplification (LAMP) [6,7], and rolling circle amplification (RCA) [8], have been applied to detect the SARS-CoV-2 which could get rid of the need of expensive equipment. Recently, clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated (Cas) protein has been applied to molecular diagnostics. These endonucleases guided by crRNA have the target-dependent cis- and trans-cleavage activities, resulting in a high-turnover signal amplification mechanism. Several Cas-based assays have been established, such as Cas12a-based DNA endonuclease-targeted CRISPR trans reporter (DETECTR) [9], Cas13a-based specific high-sensitivity enzymatic reporter unlocking (SHERLOCK) [10], Cas12a-based 1-h low-cost multipurpose highly efficient system (HOLMES) [11]. These Cas-based methods were usually divided into two steps, target nucleic acid amplification and CRISPR recognition, which might bring a risk of cross-reactivity and false positive. Hereupon, numerous one-pot detection methods have been developed to cope with the inaccuracy caused by multistep reactions; Zhang's group developed STOPCovid.v2 to detect SARS-CoV-2 with lateral-flow readout by combining RT-LAMP and CRISPR/Cas12 b in one-pot [12]; J.S. Park et al. developed deCOViD to detect SARS-CoV-2 based on fluorescence which combined RT-RPA and CRISPR/Cas12a [13]; W. Feng et al. developed an integrated RT-RPA/CRISPR/Cas12a assay to detect SARS-CoV-2 with end-point visualization and fluorescence [14]. Most of these methods use fluorescence and microfluidic devices to display the results, which limit the wide application at POC. The Cas12b which came from Alicyclobacillus acidiphilus has been reported to be more thermostable [15]. Compared with the previous methods based on Cas12a [16], the reaction temperature (31 °C–59 °C) of Cas12b protein is compatible with RT-LAMP to initiate the reaction in a homogeneous reaction system. This property could make the RT-LAMP and CRISPR/Cas12b detection be performed isothermally without sophisticated instruments and professional operators. Furthermore, the simplified operation process would make it possible to be an automatic platform. Here, we developed a visual assay for the detection of SARS-CoV-2 N gene RNA by combining the gold nanoparticle (AuNP) with thermal stable CRISPR/Cas12b-enhanced RT-LAMP (SCAN). The visual detection was based on the strong molar absorption coefficient of AuNP [17]. AuNPs were pre-assembled with two different sulfhydryl DNA (DNA1 and DNA2). Since the sequence of linker-ssDNA was complementary to partial sequences of DNA1 and DNA2 at 5′ and 3’ ends respectively, the distance between the two AuNP probes was shortened resulting in a color change (Fig. 1 ). When the sample contained target RNA sequences, SARS-CoV-2 N gene RNA was reverse transcribed and amplified by RT-LAMP. The amplification product was then recognized by Cas12b/crRNA which could activate the trans-cleavage activity of Cas12b to cut linker-ssDNA non-specifically. Therefore, the AuNP probes remained mono-dispersed and red. In the absence of the target nucleic acid, the linker-ssDNA would not be cleaved by Cas12b, and the AuNP-DNA1/2 would be cross-linked by linker-ssDNA resulting in a color change from red to purple. AuNP was purchased from Suzhou Tanfeng Graphene Technology Co., Ltd. (Suzhou, Jiangsu, China). The crRNA (Table S1) was ordered from Bio-Lifesci (Guangzhou, Guangdong, China). Other nucleic acid (Table S1) was ordered from Sangon Biotech (Shanghai, China). NaCl was purchased from Sinopharm Chemical Reagent Co., Ltd. (Beijing, China). Na2HPO4, NaH2PO4 and KCl were purchased from Beijing Chemical Works (Beijing, China). WarmStart® LAMP Kit (DNA & RNA), Isothermal Amplification Buffer Pack, WarmStart® RTx Reverse Transcriptase, Bst 2.0 WarmStart® DNA Polymerase, and MgSO4 were obtained from New England Biolabs (Beijing, China). Tris (2-carboxyethyl) phosphine (TCEP) was obtained from Aladdin (Shanghai, China). AapCas12b was obtained from Magigen Biotechnology Co., Ltd. (Guangzhou, Guangdong, China). BM2000+ DNA Marker was obtained from Biomed (Beijing, China). Diethypyrocarbonate (DEPC)-treated water (DNase/RNase free), Tris-HCl, (NH4)2SO4 and Tween-20 were obtained from Beyotime (Shanghai, China). Dulbecco's modified Eagle medium and fetal bovine were obtained from Procell Life Science&Technology Co., Ltd. (Wuhan, Hubei, China). Tris-EDTA (TE) Buffer, penicillin/streptomycin and GeneJET RNA Purification Kit were acquired from Thermo Fisher Scientific (Shanghai, China). COVID-19 RNA reference materials (high concentration) were obtained from the National Institute of Metrology of China (Beijing, China). Transmission electron microscope (HT7800, Hitachi, Japan); centrifugal machine (MiniSpin®, Eppendorf, Germany); qPCR machine (CFX 96™, Bio-rad, America); Micro UV spectrophotometer (NanoDrop one, Thermo Fisher, China); gel Imaging System (GelDoc Go, Bio-Rad, America); microplate reader (Epoch 2, Bio-Tek, America); metal bath (HB120–S, DLAB Scientific Co., Ltd., China); well plate incubator (HCM100-Pro, DLAB Scientific Co., Ltd., China). The sulfhydryl DNA1 and DNA2 were dissolved in TE Buffer to 100 μM, and the DNA was reduced by TCEP at the ratio of 1:100 (DNA: TCEP) for 30 min at room temperature. The combination of AuNPs and sulfhydryl DNA followed the salt aging method according to previously reported protocols [18]. First, AuNPs were incubated with the sulfhydryl DNA in the ratio of 1:200 (AuNPs: DNA) overnight at room temperature. Thereafter, the mixture was incubated with 2 M sodium phosphate buffer (2 M of NaCl, 200 mM of Na2HPO4 and NaH2PO4, pH 7.4) to reach a final concentration of 200 mM NaCl (the whole process was divided into four steps, and every interval was no less than 1 h). The salt aging process was allowed to incubate overnight at room temperature. At last, the unbound DNA was removed by centrifugation (12,000 rpm, 20 min, three times) using 0.01 M Tris-HCl buffer (pH 7.4). The sulfhydryl DNA modified AuNPs conjugate was resuspended in 0.01 M sodium phosphate buffer (pH 7.4) and stored at 4 °C. The sequence of linker-ssDNA was designed to be partially complementary to the sequences of AuNP-DNA1 and AuNP-DNA2 at 5′ and 3’ ends respectively. To investigate whether the linker-ssDNA could cross-link AuNP-DNA1 and AuNP-DNA2, linker-ssDNA was added to the preassembled AuNP-DNA1 and AuNP-DNA2 mixture to reach the ratio of 50: 1: 1 (linker-ssDNA: AuNP-DNA1: AuNP-DNA2). The 5 M NaCl was added to the mixture to reach the concentration of 200 mM of NaCl to accelerate the speed of the cross-link of linker-ssDNA and AuNP-DNA1/2. The color change of the mixture was observed after incubating at room temperature for 5 min and transmission electron microscope (TEM) images were taken to confirm whether the linker-ssDNA could cross-link AuNP-DNA1/2. To optimize the Cas12b assay, the dsDNA was used to verify the feasibility of the Cas12b/crRNA detection. Firstly. Cas12b was preassembled with crRNA in 1 × buffer (20 mM Tris-HCl, 10 mM (NH4)2SO4, 50 mM KCl, 2 mM MgSO4, 0.1% Tween-20, pH 8.8) for 10 min at 37 °C. The assays were performed with serial dilutions of dsDNA, 400 nM reporter DNA, 1 × buffer, 31.25 nM AapCas12b, and 31.25 nM crRNA in a total volume of 25 μL. The real-time fluorescence was detected by a qPCR machine every 1.5 min at 60 °C. The optimization of the reaction buffer and temperature was conducted by real-time fluorescence detection. For KCl, Cas12b was preassembled with crRNA in 1 × buffer (20 mM Tris-HCl, 10 mM (NH4)2SO4, 2 mM MgSO4, 0.1% Tween-20, pH 8.8) with different concentrations of KCl (0, 25, 50, 100 mM) for 10 min at 37 °C. For MgSO4, Cas12b was preassembled with crRNA in 1 × buffer (20 mM Tris-HCl, 10 mM (NH4)2SO4, 50 mM KCl, 0.1% Tween-20, pH 8.8) with different concentrations of MgSO4 (0, 5, 10, 20 mM) for 10 min at 37 °C. The reaction was carried out as described above. For temperature, the reaction was carried out at different temperatures (50 °C, 55 °C, 60 °C, and 65 °C). 293 T cells (a human renal epithelial cell line, obtained from the American Type Culture Collection, No CRL-11268) was cultured with Dulbecco's modified Eagle medium (DMEM) containing 10% fetal bovine serum and 1% penicillin/streptomycin in a complete humidity incubator with CO2/air (5%/95%) at 37 °C. 293 T cells were seeded into 6-well plates at the density of 3 × 105 cells/well and then transfected by a plasmid with the N gene of SARS-CoV-2 at a dose of 2 μg per well, the negative group was set to be transfected by the plasmid without the N gene of SARS-CoV-2 at the same dose. The RNA was then extracted using an RNA Purification Kit according to the manufacturer's instructions and the concentration was measured by a micro UV spectrophotometer. The amplification of SARS-CoV-2 RNA was performed with the WarmStart® LAMP Kit (DNA & RNA) according to the manufacturer's instructions. Briefly, the reaction, contained 12.5 μL of 2 × WarmStart® LAMP Master Mix reaction premix, 2.5 μL of 10 × primers mixture, 0.5 μL of 50 × fluorescent dye, and 1 μL of target RNA (different concentrations of COVID-19 RNA reference material and the RNA from transfected cells), 8.5 μL of water, was performed using a real-time PCR machine at 65 °C. Then, 2.5 μL of the amplification product was used to activate the Cas12b for fluorescent or visual assay. The fluorescence assay was performed as described above. For visual detection, the reporter DNA was replaced by linker-ssDNA, and the premixed AuNP-DNA1/2 solution was added to the reaction system at the ratio of 50: 1: 1 (linker-ssDNA: AuNP-DNA1: AuNP-DNA2) when the Cas12b/crRNA detection finished (30 min, 60 °C). The RT-LAMP amplification products were then tested and verified using 2% agarose gel electrophoresis. Images were photographed by a gel Imaging System. The SCAN reaction master mix consisted of the following components: 1 × Isothermal Amplification Buffer (20 mM Tris-HCl, 10 mM (NH4)2SO4, 50 mM KCl, 2 mM MgSO4, 0.1% Tween-20, pH 8.8), 1.4 mM dNTPs, 8 units of Bst 2.0 WarmStart® DNA Polymerase, 7.5 units of WarmStart® RTx Reverse Transcriptase, 100 nM Cas12b protein, 100 nM crRNA, 400 nM fluorescent reporter, 0.8 μM FIP/BIP primers, 0.2 μM F3/B3 primers, 0.2 μM LoopF/B primers and 8 mM MgSO4. The detection was performed at 60 °C in a qPCR machine with fluorescent measurements every 1.5 min. For visual detection, the whole reaction was performed at 60 °C on a metal bath for 2 h, then the premixed AuNP-DNA1/2 solution was added to the reaction system as described above, and the phenomenon was recorded by a mobile phone. A 96-well plate was used to detect the N gene of SARS-CoV-2. For each component reaction of SCAN, the 96-well plate was then incubated in a well plate incubator at 65 °C for 30 min (RT-LAMP reaction) and 60 °C for 30 min (CRISPR/Cas12b detection). For the SCAN assay, the 96-well plate was incubated in a well plate incubator at 60 °C for 120 min. Then the premixed AuNP-DNA1/2 solution was added to the 96-well plate after reaction in the visual detection group. The color change was observed and the absorbance of the mixture was detected at 520 nm and 560 nm using a microplate reader. To demonstrate the utility of the AuNPs-based visual detection, the AuNPs were modified by two different sulfhydryl DNAs (v-DNA1 and v-DNA2), which could be cross-linked by a linker-ssDNA. The solution of AuNP-DNA1/2 was red in the absence of linker-ssDNA, and the color changed from red to purple in 5 min when the linker-ssDNA was mixed with pre-assembled AuNP-DNA1/2 due to the shortened distance between AuNPs (Supplementary Fig. 1A). The photograph of TEM has confirmed that the color change was caused by agglomerated AuNPs in the presence of linker-ssDNA (Supplementary Fig. 1B). To reduce the risk of cross-reactivity and increase the possibility of application at POC, the SCAN assay was designed to combine the RT-LAMP with CRISPR-mediated detection. It is necessary to optimize the reaction conditions which could satisfy the need for each reaction (Fig. 2 A). The trans-cleavage activity of CRISPR/Cas12b was evaluated by a fluorescent assay using the synthetic dsDNA as the target. As shown in Fig. 2B, Cas12b could be activated by target dsDNA at concentrations of at least 1 nM and 10 nM dsDNA was chosen for the following optimization. To acquire stable and efficient reaction conditions, we next optimized the reaction buffer in which RT-LAMP and CRISPR-mediated detection could occur simultaneously. The isothermal amplification buffer of RT-LAMP was used as a basis for the optimization because it has a similar composition to the buffer for CRISPR/Cas12b detection. It was shown that 50 mM KCl was the most efficient concentration for the Cas12b assay system (Fig. 2C). For Mg2+, the best signal quality was obtained at 10 mM and 20 mM (Figs. 2D), and 10 mM MgSO4 was chosen as the final reaction concentration considering costs. Since the most suitable temperature for Cas12b to maintain the highest trans-cleavage activity is from 31 to 59 °C according to the previous report [15], and the optimum reaction temperature for RT-LAMP is 60–65 °C, we then investigated the temperature of the CRISPR/Cas12b assay from 50 to 65 °C with an interval of 5 °C. Notably, the best trans-cleavage activity of Cas12b could be observed at 55 °C (Fig. 2E). In addition, the Cas12b at 50 and 60 °C also showed strongly trans-cleavage activity, but dropped significantly at 65 °C. Considering the optimum temperature of RT-LAMP, we selected 60 °C as the final temperature for the further experiment. The diluted COVID-19 RNA reference materials were used to validate the SCAN assay. We performed the assay step by step, containing RT-LAMP, CRISPR/Cas12b activation, and visual detection. First, flu virus RNA was used as the negative control to demonstrate the feasibility of RT-LAMP. The amplification curve of serially diluted SARS-CoV-2 N gene RNA from 4 × 103 copies/μL to 4 × 100 copies/μL rose sharply and plateaued in 20 min (Fig. 3 A), and the amplification products were further confirmed by gel electrophoresis (Supplementary Fig. 2), indicating the SARS-CoV-2 RNA could be successfully amplified by RT-LAMP. We next tested if the amplification products could activate the trans-cleavage activity of Cas12b. As shown in Fig. 3B, the increased fluorescence could be observed in the presence of the amplification products, indicating the amplification products were specific. Finally, a difference in visual signal could be noticed between SARS-CoV-2 and flu virus (Fig. 3C). The multistep SCAN has also been performed in a 96-well plate with a temperature control device to verify the potential for high-throughput applications. As shown in Fig. 3D, the positive and negative samples were successfully discriminated by color change. The NTC and negative group turned to light purple color and the positive group (4 × 100–4 × 103 copies/μL) maintained pink color. The ratio of A520/A560 at 4 × 100 copies/μL of target RNA was consistent with the visual results above (Fig. 3E). Collectively, these results indicated that the target RNA could be successfully detected by multistep SCAN. Since all component reactions in the SCAN assay have been verified and the reaction conditions have been optimized, we combined the RT-LAMP and CRISPR/Cas12b detection in one tube to simplify the workflow. First, the sensitivity and reliability of the one-step SCAN assay were evaluated. The results showed that 40 copies/μL of COVID-19 RNA reference material could be detected in 90 min by fluorescent assay (Fig. 4 A). In contrast, 400 copies/μL could be distinguished against the negative control with a visual assay (Fig. 4B). We also evaluated the sensitivity using a well plate incubator (Fig. 4C). The 96-well plate results correlated well with those observed by the naked eye in tubes, briefly, 400, 1000, and 4000 copies/μL RNA showed an obviously pink color and the other groups turned to purple color (Fig. 4D and E). Collectively, these results showed that the RNA of SARS-CoV-2 could be detected specifically using the one-step SCAN assay. We have confirmed that the COVID-19 RNA reference material could be successfully detected by the SCAN assay. Then we evaluated the clinical feasibility of the SCAN assay by using the cells transfected by a plasmid with the N gene of SARS-CoV-2 to simulate the clinical samples. First, the RNA was detected by a multistep SCAN assay that we performed RT-LAMP, CRISPR/Cas12b detection, and visual detection separately to confirm the feasibility (Supplementary Fig. 3A). Consisted with the fluorescence results (Supplementary Fig. 3B), the presence of purple was observed by the naked eye in tubes (Supplementary Fig. 3C), indicating that the RNA from cells could be detected using a multistep SCAN assay. The color change in the 96-well plate (Supplementary Fig. 3D) and the increase of A520/A560 (Supplementary Fig. 3E) also suggested no cross-reactivity with the negative control group. Next, we performed the one-step SCAN assay directly to detect SARS-CoV-2 RNA extracted from transfected cells, which were serially diluted. The fluorescent results (Fig. 5 A) and visual detection results showed that our assay could distinguish positive samples when the concentration of RNA was more than 1 ng/μL (Fig. 5B). Finally, to demonstrate the feasibility of the assay for high-throughput assay and clinical application, we employed the assay to detect the RNA samples in the well plate. We observed a notable difference in color from pink to purple and the ratio of A520 to A560 between the RNA concentration of more than 1 ng/μL and other groups (Fig. 5C, Supplementary Fig. 4). Since the high-throughput reaction could be performed using a metal bath or a well plate incubator, it suggested the potential POC application. In this study, we have developed a SCAN assay to detect SARS-CoV-2 in both visual-based and fluorescence-based platforms. The RT-LAMP and Cas12b detection are combined in one step owing to the heat-resistant activity of Cas12b. SCAN assay can satisfy several requirements of POC diagnosis. First, the procedure of SCAN is simple, that the RT-LAMP and CRISPR/Cas12b detection were performed in one tube to simplify the whole operation. Second, the SCAN assay is portable with inexpensive equipment, and the whole assay only relies on a metal bath. Furthermore, the results of the SCAN can be easily distinguished by naked eye due to the strong molar absorption coefficient of AuNP. Most important of all, the SCAN assay has a high-throughput potential to detect enormous amounts of virus samples simultaneously since the assay is isothermal and easy to be performed. To test the performance of SCAN in clinical settings, we used the extracted total RNA from cells transfected by SARS-CoV-2 to simulate the real extracted RNA, which showed good specificity and sensitivity. Given many RNA extraction-free methods have been developed to combine with RT-LAMP to detect SARS-CoV-2 [19,20], we expect the SCAN assay could be useful for diagnosis of COVID-19 at POC tests. In short, this method which is based on CRISPR/Cas12b and AuNP has the potential to assist in the rapid diagnosis and screening of patients with COVID-19. Yaqin Zhang: Investigation, Methodology, Visualization, Validation, Formal analysis, Writing - Original Draft. Xiangyu Quan: Methodology, Validation, Investigation. Yingchun Li: Methodology, Validation, Investigation. Hangyu Guo: Validation, Investigation. Fange Kong: Investigation, Writing - Review & Editing. Jiahui Lu: Validation, Data Curation. Lirong Teng: Methodology, Visualization. Jiasi Wang: Conceptualization, Visualization, Supervision, Writing - Review & Editing. Di Wang: Conceptualization, Visualization, Writing - Review & Editing, Funding acquisition. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

PMC9647700

Serena Matis,Anna Grazia Recchia,Monica Colombo,Martina Cardillo,Marina Fabbi,Katia Todoerti,Sabrina Bossio,Sonia Fabris,Valeria Cancila,Rosanna Massara,Daniele Reverberi,Laura Emionite,Michele Cilli,Giannamaria Cerruti,Sandra Salvi,Paola Bet,Simona Pigozzi,Roberto Fiocca,Adalberto Ibatici,Emanuele Angelucci,Massimo Gentile,Paola Monti,Paola Menichini,Gilberto Fronza,Federica Torricelli,Alessia Ciarrocchi,Antonino Neri,Franco Fais,Claudio Tripodo,Fortunato Morabito,Manlio Ferrarini,Giovanna Cutrona

MiR-146b-5p regulates IL-23 receptor complex expression in chronic lymphocytic leukemia cells

14-07-2022

Abstract Chronic lymphocytic leukemia (CLL) cells express the interleukin-23 receptor (IL-23R) chain, but the expression of the complementary IL-12Rβ1 chain requires cell stimulation via surface CD40 molecules (and not via the B-cell receptor [BCR]). This stimulation induces the expression of a heterodimeric functional IL-23R complex and the secretion of IL-23, initiating an autocrine loop that drives leukemic cell expansion. Based on the observation in 224 untreated Binet stage A patients that the cases with the lowest miR-146b-5p concentrations had the shortest time to first treatment (TTFT), we hypothesized that miR-146b-5p could negatively regulate IL-12Rβ1 side chain expression and clonal expansion. Indeed, miR-146b-5p significantly bound to the 3′-UTR region of the IL-12Rβ1 mRNA in an in vitro luciferase assay. Downregulation of miR-146b-5p with specific miRNA inhibitors in vitro led to the upregulation of the IL-12Rβ1 side chain and expression of a functional IL-23R complex similar to that observed after stimulation of the CLL cell through the surface CD40 molecules. Expression of miR-146b-5p with miRNA mimics in vitro inhibited the expression of the IL-23R complex after stimulation with CD40L. Administration of a miR-146b-5p mimic to NSG mice, successfully engrafted with CLL cells, caused tumor shrinkage, with a reduction of leukemic nodules and of IL-12Rβ1–positive CLL cells in the spleen. Our findings indicate that IL-12Rβ1 expression, a crucial checkpoint for the functioning of the IL-23 and IL-23R complex loop, is under the control of miR-146b-5p, which may represent a potential target for therapy since it contributes to the CLL pathogenesis. This trial is registered at www.clinicaltrials.gov as NCT00917540.

MiR-146b-5p regulates IL-23 receptor complex expression in chronic lymphocytic leukemia cells Chronic lymphocytic leukemia (CLL) cells express the interleukin-23 receptor (IL-23R) chain, but the expression of the complementary IL-12Rβ1 chain requires cell stimulation via surface CD40 molecules (and not via the B-cell receptor [BCR]). This stimulation induces the expression of a heterodimeric functional IL-23R complex and the secretion of IL-23, initiating an autocrine loop that drives leukemic cell expansion. Based on the observation in 224 untreated Binet stage A patients that the cases with the lowest miR-146b-5p concentrations had the shortest time to first treatment (TTFT), we hypothesized that miR-146b-5p could negatively regulate IL-12Rβ1 side chain expression and clonal expansion. Indeed, miR-146b-5p significantly bound to the 3′-UTR region of the IL-12Rβ1 mRNA in an in vitro luciferase assay. Downregulation of miR-146b-5p with specific miRNA inhibitors in vitro led to the upregulation of the IL-12Rβ1 side chain and expression of a functional IL-23R complex similar to that observed after stimulation of the CLL cell through the surface CD40 molecules. Expression of miR-146b-5p with miRNA mimics in vitro inhibited the expression of the IL-23R complex after stimulation with CD40L. Administration of a miR-146b-5p mimic to NSG mice, successfully engrafted with CLL cells, caused tumor shrinkage, with a reduction of leukemic nodules and of IL-12Rβ1–positive CLL cells in the spleen. Our findings indicate that IL-12Rβ1 expression, a crucial checkpoint for the functioning of the IL-23 and IL-23R complex loop, is under the control of miR-146b-5p, which may represent a potential target for therapy since it contributes to the CLL pathogenesis. This trial is registered at www.clinicaltrials.gov as NCT00917540. MicroRNAs (miRNAs) represent a family of noncoding RNAs that prevent the translation and promote the degradation of specific mRNAs by binding to their 3′-UTR., Several miRNAs have been implicated in the pathogenesis of chronic lymphocytic leukemia (CLL),3, 4, 5 a disease characterized by the accumulation of monoclonal CD5+CD19+ B cells in lymphoid organs and blood.6, 7, 8, 9 In patients with 13q deletions (del[13q]), the most common cytogenetic lesion of CLL,, the genes encoding the miR-15a/miR-16-1 cluster are targeted by the deletion.,12, 13, 14, 15 The downregulation of these regulatory miRNAs can lead to an increased expression of antiapoptotic molecules, which facilitate clonal expansion, inducing further transforming events.12, 13, 14, 15, 16 MiRNA expression profile studies have disclosed correlations between certain miRNA signatures and cytogenetic features and/or IGHV gene mutational status,17, 18, 19 which represent recognized prognostic markers of CLL. Finally, certain miRNA signatures are associated with disease progression and outcome,,20, 21, 22 or with the onset of a Richter transformation,23, 24, 25 a deadly condition characterized by the development of an aggressive lymphoma in CLL patients., Previously, we reported an inverse correlation between miR-146b-5p concentrations and progression-free survival in a cohort of >200 newly diagnosed Binet stage A patients; cases with the most aggressive clinical course had the lowest miR-146b-5p concentrations. The same inverse correlation was not observed with miR-146a-5p, a paralog of miR-146b-5p, in the same patient cohort. Although not validated by quantitative reverse transcription polymerase chain reaction (qRT-PCR), these differences were substantial and somewhat surprising, given that the 2 miRNAs share many predicted target genes and the same seed sequence. However, the 2 miRNAs are encoded by genes located on different chromosomes (chromosome 5 and 10 for miR-146a-5p and miR-146b-5p, respectively), which may create differences in the posttranscriptional processing associated with the 2 other nucleotides encoded at the 3′ end. Another surprising difference was that the CLL cases with the lowest miR-146b-5p concentrations were also IGHV-unmutated (UM), while this correlation was not observed in the case of miR-146a-5p. Both miR-146a-5p and miR-146b-5p control the proliferation of a variety of cells, particularly because they regulate NF-kB (nuclear factor kappa B) activation, a key transcription factor involved in cell proliferation., Both miR-146a-5p and miR-146b-5p exert a negative regulatory control on the expression of TNFR6 (tumor necrosis factor receptor-6) and IRAK1 (interleukin-1 receptor-associated kinase 1), 2 adaptor molecules that transduce signals delivered via several membrane receptors, such as those of the TNFR and the Toll-like receptor/IL1R superfamilies,30, 31, 32 culminating in NF-kB activation. This function accounts in part for the spontaneous onset of cancers in mice with deletions of miR-146a-5p, and the inverse correlation reported in human cancers between tumor aggressiveness and miR-146b-5p concentrations.35, 36, 37, 38, 39, 40 Inflammatory and autoimmune phenomena observed in mice with deletions of these miRNAs may also be explained by an absent NF-kB regulation.,41, 42, 43, 44, 45 However, the observation that miR-146b-5p is more effective than miR-146a-5p in determining CLL clinical course suggests that miR-146b-5p is implicated in additional mechanisms supporting CLL clonal expansion that are different from TRAF6 (tumor necrosis factor receptor-associated factor 6) and IRAK1 control. Considerable evidence indicates that CLL clonal expansion is promoted by interactions with cells and cytokines from the microenvironment., Moreover, both miR-146a-5p and miR-146b-5p can regulate the release of and the response to cytokines.,, Based on these considerations, we hypothesized that miR-146b-5p was involved in the regulation of the interactions between CLL cells and the microenvironment. We focused on IL-23, a cytokine of the IL-12 cytokine family, released primarily by dendritic cells, which is capable of driving T helper (Th) cell differentiation toward the Th17 cell subset. In a previous study, we found that IL-23 is instrumental in promoting CLL cell proliferation and clonal expansion. Normally, circulating CLL cells express variable concentrations of the IL-23R chain, 1 of the 2 chains forming the heterodimeric IL-23R complex, but are incapable of responding to IL-23 because of the absence of its complementary chain, IL-12Rβ1. Upon appropriate activation signals in vitro, such as the interaction with activated T cells or other CD40L-expressing cells, but not via direct stimulation of the B-cell receptor (BCR), CLL cells express the IL-12Rβ1 chain and begin to secrete IL-23. This initiates an autocrine/paracrine loop (which we have named the IL-23/IL-23R complex loop), whereby CLL cells respond to the IL-23 that they produce. This event promotes leukemic cell proliferation and appears to be very relevant for CLL cell growth/expansion since most leukemic cells in the proliferating centers of lymphoid tissues, infiltrated by CLL cells, produce IL-23 and express a complete IL-23R complex. Moreover, in vivo treatment with antibodies to IL-23p19 (1 of the 2 chains forming the IL-23 molecules) eradicates CLL clones in xenografted mice. Because the expression of the IL-12Rβ1 chain by CLL may represent a key checkpoint for the initiation of the loop, we hypothesized that miR-146b-5p was involved in regulating the expression of this chain. Indeed, the present findings support our hypothesis and show that miR-146b-5p can be a key regulator in controlling CLL cell clonal expansion. The patients investigated were part of the O-CLL1 study (clinicaltrials.gov identifier NCT00917540), an observational cohort of patients with untreated Binet A CLL collected from several Italian institutions enrolled within 12 months from diagnosis., Supplemental Table 1 in the data supplement summarizes the clinical features of the patients investigated.52, 53, 54 In total, samples from 224 CLL cases were studied for expression profiles and single miR expression,; the data are deposited at the NCBI (National Center for Biotechnology Information) GEO (Gene Expression Omnibus) repository (http://www.ncbi.nlm.nih.gov/geo/) and are accessible through GEO Series accession number GSE40533. For CLL cases not included in the miRNome study, we measured miR-146b-5p concentrations by quantitative real-time PCR (RT-qPCR). Also, for these cases, miR-146b-5p expression was significantly correlated with immunoglobulin heavy chain variable region (IGVH) gene mutational status (see supplemental Methods, supplemental Table 2, and supplemental Figure 1). Peripheral blood mononuclear cells from patients with CLL were isolated by Ficoll-Hypaque (Seromed, Biochrom) density gradient centrifugation, and CD19-positive CLL cells were enriched by negative selection as previously reported (see supplemental Methods). Written informed consent was obtained from all patients in accordance with the declaration of Helsinki. The ethics committees from each participating center (listed in the acknowledgments) approved this study. Viable cell counts of CLL samples were conducted before each experiment performed in vitro and in vivo by trypan blue staining and automatic cell counter (Countess, Invitrogen). Values >80% of live cells were considered suitable for the subsequent experimental procedures. MirVana miRNA mimics or inhibitors (Ambion Inc, Thermo Fisher Scientific; Grand Island, NY) were delivered to CLL cells using the Neon Transfection System (Invitrogen, Thermo Fisher Scientific) as described or by the Nucleofector-4D Transfection System (Amaxa), (supplemental Methods). The following miRNA mimics and inhibitors were used: hsa-miR-146b-5p (Assay ID: MC25960; MH25960), hsa-miR-146a-5p (Assay ID: MC10722), miRNA mimic, Negative Control#1 (Cat. no. 4464058), miRNA inhibitor, Negative Control #1 (Cat. no. 4464076). The transfection efficiency was verified by RT-qPCR (see supplemental Methods). Cell surface IL-12Rβ1 and IL-23R chains were detected by flow cytometry. IL-12Rβ1 expression also was analyzed by Western blotting with mouse anti–IL-12Rβ1 monoclonal antibody (mAb) (C-20, sc-658, Santa Cruz Biotechnology, Inc.) and an anti-GAPDH mAb (AM4300, Ambion Inc, Thermo Fisher Scientific) as a loading control. qRT-PCR assessed the IL-12Rβ1 side chain mRNA (see supplemental Methods). MiRNA target reporter vectors were purchased from Origene (IL-12Rβ1, Accession No. NM_153701, transcript variant 2, Cod. SC208722) and Switchgear (IL-23R, Cat. S806498, Accession No. NM_144701). IL-12Rβ1-MUT reporter vector, obtained by deletion of miR-146b-5p seed target site sequence (GTTCTCA [nt328-nt334]), was custom produced by Origene (Figure 2C). 3′ UTR assays are described in the supplemental Methods. HEK293 cells were used for transfection and the luciferase reporter assays. Preliminary tests showed that CLL-related cell lines (MEC-1 and OSU cell lines) were not suitable for testing because of the poor yield of the transfection step. After transfection with the appropriate miRNA, CLL cells were cultured in RPMI 1640 medium with γ-irradiated cells from a stable CD40L-expressing NIH-3T3 (CD40L-TC) murine fibroblast cell line or with the NIH-3T3 cells transfected with the pIRES empty vector (Mock) (1 NIH-3T3 cell: 100 CLL cells) at a concentration of 2 × 106 cells per mL at 37°C in an atmosphere containing 5% CO2. IL-23 cytokine production was measured in cell culture supernatants using the Human Cytokine/Chemokine Panel II and Luminex MAGPIX System (Merck Millipore). These procedures were described previously,,, and additional details are provided in the supplemental Methods. All animal experiments were performed according to the current national and international regulations and were approved by the Licensing and Animal Welfare Body of the IRCCS-Ospedale Policlinico San Martino, Genoa, Italy. The statistical package SPSS for Windows (release 13.0, 2004 software, SPSS UK; Surrey, United Kingdom) was used for all analyses. Statistical comparisons were performed using 2-way tables for the Fisher’s exact test and multiway tables for the Pearson’s χ2 test. Statistical comparisons between related samples were carried out by Wilcoxon or Mann-Whitney U tests. Time-to-first treatment (TTFT) analyses were performed using the Kaplan-Meier method. Statistical significance of associations between individual variables and survival was calculated using the log-rank test. The prognostic impact for the outcome variable was investigated by univariate and multiple Cox regression analysis. Data are expressed as hazard ratio (HR) and 95% confidence intervals (CIs). A value of P < .05 was considered significant for all statistical calculations. Values are given as mean ± SD. First, we confirmed that miR-146b-5p concentrations maintained their prognostic power using a large CLL cohort described previously (O-CLL1 protocol). This comprised 224 Binet stage A patients, 48 of whom met the current diagnostic criteria of clinical monoclonal B-lymphocytosis., As shown in Figure 1A, miR-146b-5p was less expressed in CLL cases with IGHV-UM genes than in those with mutated IGHV (IGHV-M). The majority (41 of 56 [73%]) of cases with the lowest miR-146b-5p concentrations (first quartile) were IGHV-UM (Figure 1A). The median follow-up time in the cohort investigated was 83 months (range, 1-129), and 94 patients had progressed and required therapy at the time of the study censoring. Cases within the quartile with the lowest miR-146b-5p concentrations (first quartile) also had the shortest TTFT (Figure 1B). MiR-146a-5p failed to identify patients with a shorter TTFT (Figure 1C), a finding consistent with the observation that the concentrations of miR-146a-5p were similar in IGHV-M (n = 144, mean ± SD = 49 ± 95) and IGHV-UM cases (n = 80, mean ± SD = 45 ± 47) (supplemental Figure 2A-B). Furthermore, no correlation was observed between the expression of miR-146b-5p and miR-146a-5p, although the differences in expression between quartiles were similar for both miRNAs (supplemental Figure 2C-D). In a Cox multivariate model, together with other prognostic markers (IGHV-UM, CD38-positive, ZAP-70–positive, mutated NOTCH1 gene, RAI stage, FISH del(17p) or del(11q), β2-microglobulin (β2-M) values ≥5 mg/dL, and patients with a peripheral B-lymphocytosis of ≥5000/mm3), low miR-146b-5p expression failed to predict TTFT (supplemental Tables 3 and 4, Model 1). However, following the stratification of cases according to the IGHV mutational status, IGHV-UM cases with the lowest miR-146b-5p concentrations (first quartile) had TTFT curves that were significantly different from those of cases in the remaining quartiles. These differences were not observed in IGHV-M cases (Figure 1D-E). The analysis of the quartiles calculated within each IGHV-M and IGHV-UM group demonstrates the consistent survival association only within the IGHV-UM group (supplemental Figure 3). Cox multivariate analysis, with the variables used above, demonstrated a significant independent association between low concentrations of miR-146b-5p and clinical outcome (HR, 2.0; 95% CI, 1.1-3.9; P = .035) (Figure 1F) in IGHV-UM cases. Observations in 21 pairs of CLL cell samples taken from the same patients at disease onset and progression showed no changes in miR-146b-5p concentrations at disease progression (supplemental Figure 4). To investigate possible mutations and copy number alterations (CNAs) on miR-146b and its putative promoter/enhancer regions possibly responsible for the occurrence of lower concentrations of miR-146b-5p in a subset of patients with CLL, we analyzed a dataset of 551 patients with CLL (CLLE-ES) by ICGC (International Cancer Genome Consortium) Data Portal (release_28), that collects sequencing data from different repositories, including the European genome–phenome archive. No patients with CLL presented somatic mutations in miR-146b genomic region (chr10:104196269-104196341) or putative promoter/enhancer regions predicted by GeneHancer (supplemental Table 5). CNA analysis performed on the same patients with CLL dataset showed the existence of a loss of the genomic region, including miR-146b, in 7 of 551 (1.3%) patients. CNA coordinates and patient characteristics are reported in supplemental Table 6. Similar results were obtained by Leeksma and colleagues, who retrospectively analyzed 2293 arrays for CNA assessment from 13 diagnostic laboratories according to established standards and found 10q losses in 25 of 2293 patients (approximately 1%). About half of these (13 of 2293 [0.6%]) showed 10q deletion encompassing miR-146b at the 10q24.32 locus. Therefore CNA at the miR-146b locus could not account for our observations. We then investigated the possibility that miR-146b-5p expression in CLL could be epigenetically regulated. Methylation status of miR-146b locus was explored in CLL cases, and normal B-cell samples by whole-genome bisulfite sequencing reported in the BluePrint Data Analysis portal (http://dcc.blueprint-epigenome.eu), considering a region spanning 500 bp upstream and downstream the miR-146b locus, respectively (GRCh37.p13 chr10:102436500-102436609, EnsEMBL version: 79). In CLL samples, mean concentrations of hypomethylation or emimethylation were detected in the region upstream or in the 150 bp immediately downstream miR-146b locus, whereas hypermethylation was found in the region encompassing miR-146b and in more downstream regions. In naïve and memory B cells from peripheral blood, respectively, a global pattern of hypomethylation was evidenced in the upstream regions, whereas hypermethylation was observed downstream to miR-146b locus (supplemental Figure 5). Therefore, a wider range and higher methylation concentrations than normal were observed in the upstream region of the miR-146b locus in CLL samples. In addition, the DNA methylation of the miR-146b-5p gene suggests that DNA methylation is directly involved in the regulation of its biogenesis. This regulation could be dependent on activating stimuli received by neoplastic cells in the lymphoid organs. We next investigated whether miR-146b-5p could regulate the expression of IL-23R and/or IL-12Rβ1 chains. CLL clones can be subdivided into those with a low (IL-23R–low) or a high level of IL-23R (IL-23R–high) expression, respectively, when stratified according to a cutoff of IL-23R chain-positive cells lower or greater than 23%. We performed a correlation analysis to ascertain whether miR-146b-5p was lower in cases with higher IL-23R expression in a group of 93 CLL patients (40 cases IL-23R–low and 53 cases IL-23R–high). Although a significant anticorrelation in expression was detected (RHO−0.291; P = .005) (supplemental Figure 6), in vitro luciferase reporter assay failed to demonstrate a significant binding of miR-146b-5p to the IL-23R 3′UTR mRNA (Figure 2A). We used a recently developed web tool named miRabel (http://bioinfo.univ-rouen.fr/mirabel/) to investigate the potential binding of miR-146b-5p to the 3′UTR of the IL-12Rβ1 chain mRNA (for details, see supplemental Methods). This approach predicted a substantial binding capacity (score 0.3572489917218290), which was confirmed experimentally in luciferase reporter assays (Figure 2A), showing an average reduction of the luciferase activity of 35 ± 10.3% (mean ± SD). In contrast, miR-146b-5p did not efficiently bind the IL-12Rβ1 3′UTR–MUT with an average reduction of the luciferase activity of 10 ± 6% vs 33 ± 11% of the 3′UTR WT (mean ± SD; P = .01) (Figure 2C-D) confirming the specificity of the interaction between the miR-146b-5p seed sequence and the complementary sequence on the IL-12Rβ1 chain mRNA. The possible binding of miR-146a-5p to the 3′UTR of the IL-12Rβ1 chain mRNA, predicted by the same algorithms, was not confirmed experimentally (Figure 2). To further confirm the miRNA-mediated regulation IL-12Rβ1 side chain, primary CLL cells were transiently transfected with a specific miRNA inhibitor targeting miR-146b-5p or with a miR-control inhibitor (a random sequence molecule with no identifiable effects on known miRNA functions) and cultured for 48 hours. A consistent upregulation of the IL-12Rβ1 side-chain protein by knocking down miR-146b-5p expression was found by Western blot (Figure 2E-G). The above target validation experiments prompted tests aimed at verifying whether miR-146b-5p inhibition could induce the expression of the Il-12Rβ1 side chain and a functional IL-23R complex on the surface of CLL clones already expressing an IL-23R side chain. Purified CLL cells from 8 IL-23R–high cases (35 ± 11% [mean ± SD] positive cells) (GE1-AG114, GE1-DM210, GC0015, SV1-SA, SR1-ME1077, MG0482, VF0384, and CM18) were transiently transfected with a miR-146b-5p inhibitor or with a miR-CTR inhibitor, cultured for different times, and tested for IL-12Rβ1 and IL-23R side chains expression. Cells transfected with miR-146b-5p inhibitor had a significantly increased expression (P = .0078) of IL-23R complex (average increase value of 57 ± 12% positive cells at 72 hours in culture [mean ± SD]) compared with the control cells (Figure 3A-B). The increased IL-23R complex expression was associated with an upregulation of the IL-12Rβ1 side chain (average increase of 51 ± 9% positive cells at 72 hours [mean ± SD]) that was significantly different (P = .0078) from the control samples; in contrast, the expression of the IL-23R side chain remained virtually unchanged (Figure 3C-D). The cells positive for the chains of the IL-23R complex were identified within the gated populations of viable cells (Figure 3A and supplemental Figure 7). IL-23 released by CLL cells in the culture supernatants was also measured. There were no differences in the IL-23 produced by the miR-146b-5p inhibitor transfected and control cells (Figure 3E). Next, we investigated the functional features of the IL-23R complex expressed by CLL cells. CLL cells purified from the same 8 patients were transiently transfected with the miR-146b-5p inhibitor or with the miR-CTR inhibitor and cultured in the presence or absence of recombinant IL-23 for different time points. Upon exposure to exogenous IL-23 in culture, significant increases (P = .0078) in cell viability (mean ± SD increase at 72 hours, 22.6 ± 10%) (Figure 3F-G) and of cycling cells (mean ± SD increase, 48.7 ± 27%) (Figure 3H-I) were observed in suspensions treated with the miR-146b-5p inhibitor; these effects were abrogated by the addition of a specific IL-23 mAb (αIL-23p19) to the culture supernatant (average inhibition at 72 hours, 37 ± 16%, for cell viability, and 84 ± 15% for cycling cells) (Figure 3F-I). Since stimulation of CLL cells with CD40L in vitro induces the expression of a functional IL-23R complex, we investigated whether the same stimulation caused the downregulation of miR-146b-5p. Purified CLL cells from the 8 patients studied above were either transfected with the miR-146b-5p inhibitor or cultured with CD40L-TC. In both instances, the expression of the IL-12Rβ1 chain (and consequently of the IL-23R complex) was observed in amounts superior to those observed in the respective control cultures (average increase of 46 ± 19% for miR-146b-5p inhibitor treatment and of 77.3 ± 15.6% for CD40L-TC stimulation [mean ± SD], respectively) (Figure 4A-B). To investigate the concentrations of miR-146b-5p following CD40L-TC stimulation, purified CLL cells from 3 different cases with a variable baseline amount of miR-146b-5p were cultured with CD40L-TC and harvested at intervals. Viable cells were measured by flow cytometry by excluding annexin-V/PI-positive cells, whereas activated cells were identified as CD80+ cells (Figure 4C). Cell viability remained high throughout the culture, while there was a progressive acquisition of CD80 expression over time. Exposure to CD40L in vitro caused a substantial downregulation of miR-146b-5p as assessed by RT-qPCR (average inhibition at 48 hours, 70.1 ± 1.7% [mean±SD]) (Figure 4C). In contrast, stimulation of purified CLL cells by coculture with anti-μ and anti-δ Ig-chain–coated beads and IL-4 failed to significantly modify miR-146b-5p expression (supplemental Figure 8 and supplemental Methods). These data also are consistent with our previous findings on the incapacity of cell stimulation via BCR to induce the IL-23R complex expression. Next, purified CLL cells were transfected with the miR-146b-5p inhibitor or to the miR-CTR inhibitor for 6 hours, stimulated with CD40L-TC for 48 hours, and the concentrations of IL-12Rβ1 mRNA determined by RT-qPCR. Preexposure to the miR-146b-5p inhibitor caused a substantial increase of intracellular IL-12Rβ1 mRNA (average increase of 46.5 ± 40% [mean ± SD]) compared with the control samples (Figure 4D) irrespective of the baseline values of miR-146b-5p expression and with a wide variability depending on the propensity to CD40L activation of the different CLL clones. Flow cytometry tests confirmed these observations. Following pretreatment with the miR-146b-5p inhibitor, there was a consistent increase of IL-12Rβ1 (average increase of 25.6 ± 14.6% positive cells [mean ± SD]) and IL-23R complex expression (average increase of 36.4 ± 11.5% [mean ± SD]) compared with control samples (Figure 4E-F). Notably, pretreatment of the purified CLL cells with miR-146b-5p inhibitor before coculturing with CD40L-TC did not cause upregulation of the IL-21R, indicating a selective regulation of the miR-146b-5p on IL-23R complex expression (Figure 4G-H). If miR-146b-5p concentrations regulate the IL-12Rβ1 expression, then a forced increase of intracellular miR-146b-5p should prevent the expression of IL-12Rβ1 following coculture with CD40L-TC. To test this, purified CLL cells from 10 different cases with different baseline miR-146b-5p expression (Figure 5 and supplemental Table 2) were cultured with miR-146b-5p mimic or miR-CTR mimic for 6 hours, CD40L-TC was added, and the cultures continued for 48 hours. Following transfection with miR-146b-5p mimics, lower IL-12Rβ1 mRNA concentrations were detected by RT-qPCR (Figure 5A). Flow cytometry confirmed that transfection with miR-146b-5p mimic prevented the expression of the surface IL-23R complex expression mediated by CD40L activation (average decrease of 55.4 ± 18% [mean ± SD]) (Figure 5B-C) mainly caused by surface downregulation of IL-12Rβ1 side chain (average decrease of 45.2 ± 22 [mean ± SD]) (Figure 5D). Concomitantly, there was a slight decrease in the overall expression of the IL-23R chain (average decrease of 19 ± 16 [mean ± SD] (Figure 5E), while the CLL cells expressing the IL-23R side chain only were increased (average increase of 39 ± 28 [mean ± SD]) (Figure 5F). Finally, a 50 ± 11.5% (mean ± SD) decrease of Ki67+ cells (Figure 5G,I) and of Ki67+ cells expressing the IL-23R complex (Figure 5H) compared with control samples was observed. Pretreatment of the purified CLL cells with miR-146b-5p mimic before coculture with CD40L-TC failed to cause upregulation of the IL-21R, as shown in supplemental Figure 9, confirming a selective effect of the miR-146b-5p mimic. Since miR146b-5p is known to repress TRAF6 and IRAK-1, which play critical roles in NF-kB activation,,, we investigated whether enforced expression of this miRNA caused downregulation of these targets in CLL cells. Since miR146a-5p has similar effects, the 2 miRNAs were tested in parallel. CLL cells were exposed to miR-146a-5p or miR-146b-5p or miR-CTR mimics for 6 hours in vitro and subsequently cocultured with CD40L-TC or mock cells for 48 hours. As shown in supplemental Figure 10A-B, TRAF6 protein was downregulated in CLL cells transfected with either miR-146a-5p or miR-146b-5p mimics compared with the control samples. Some TRAF6 inhibition, although at lower concentrations, was observed in control cocultures with mock cells. Similar results were obtained when IRAK1 expression was tested by flow cytometry in the same culture settings (supplemental Figure 10C-D). Cells from GE1-PM129 and GE1-RO148 CLL cases were cocultured with activated autologous T cells and used to generate xenografts in 10 and 4 NSG mice, respectively. After 4 to 6 weeks, all mice presented circulating human (CD45+CD19+CD5+) cells indicative of successful engraftment. The mice were subdivided into equal groups, and each group of animals was treated with either miR-146b-5p mimic or miR-CTR mimic (1 injection on alternate days for a total of 3 injections). Flow cytometry analyses of samples from peripheral blood, bone marrow, and spleen cells, 3 days after the last miRNA injection, revealed that mice treated with miR-CTR mimic had higher percentages of CD45+CD19+CD5+ CLL cells and lower percentages of CD45+CD19−CD5+ T cells than mice treated with miR-146b-5p mimic (Figure 6A-C and supplemental Table 7). Mice treated with miR-146b-5p mimic presented a higher percentage of apoptotic (annexin-V–positive) CLL cells in the spleen (Figure 6D-E). This finding was consistent with the in situ immunohistochemical (IHC) analysis showing a decrease of spleen infiltration by leukemic (human CD20+) cells after treatment with miR-146b-5p mimics (Figure 6F). Autologous T cells (human CD3+ cells), surrounding remnants of CLL infiltration foci, were still present (Figure 6E-F and supplemental Table 7). In addition, the boundaries of the follicles appeared less evident and were often disrupted by the accumulation of T cells (Figure 7A-B). Engraftment was measured by determining an IHC index derived from the combination of size and numbers of CD20+ follicles in the spleen (supplemental Methods). A significantly lower IHC index was observed in mice treated with the miR-146b-5p mimic compared with control mice (127.6 ± 51 vs 34 ± 31.4 [mean ± SD]; P = .007) (supplemental Table 7). Moreover, in mice treated with the miR-146b-5p mimic, there were fewer Ki67+ cells in the spleen infiltrates (Figure 7C). Double-marker IHC confirmed the presence of fewer cycling CLL cells (human Ki67/CD20+) in the spleen infiltrates of mice treated with the miR-146b-5p mimic compared with the control samples (Figure 7D). Furthermore, staining with specific antibodies showed fewer IL-12Rβ1–expressing cells in mice treated with miR-146b-5p mimic than in the control samples (Figure 7E-F). Notably, a consistent number of cells present in the CLL cell aggregates were stained by anti–IL-23 mAb, indicating that the miR146b-5p mimic treatment did not affect IL-23 cytokine production (Figure 7F). Likewise, there were numerous T cells in the tissues analyzed, indicating that the T-cell compartment was not prominently affected by miR-146b-5p mimic treatment (Figure 7E). The idea for this study stemmed from the consideration that miR-146b-5p had a relevant prognostic impact in CLL and that the IL-23/IL-23R complex loop is important for promoting CLL cell clonal expansion. Since IL-12Rβ1 is expressed following cell activation, this step may represent a relevant checkpoint for the functioning of the loop, and miR-146b-5p could conceivably determine the cell’s susceptibility to IL-23 by regulating IL-12Rβ1 receptor expression. The collected evidence supports the working hypothesis: miR-146b-5p proved capable of binding to the IL-12Rβ1 chain mRNA in an in vitro luciferase assay, whereas miR-146a-5p failed despite sharing the same seed sequence. A partial explanation for this failure could be that the binding of miRNAs associated with the argonaute protein to the relevant mRNA is influenced by sequences flanking the binding sites and by additional noncanonical binding sites. Thus, small sequence variations outside the seed sequence, and the different posttranslational processing of the 2 miRNAs, may cause variations in their binding to target mRNA. The capacity of miR-146b-5p to regulate IL-12Rβ1 expression was confirmed by experiments with specific miR-146b-5p mimics and inhibitors because the former prevented and the latter promoted IL-12Rβ1 expression. This effect was selective given that the expression of IL-21R, which also plays an important role in regulating CLL cell expansion,, was unaffected by miR-146b-5p. Notably, miR-146b-5p did not bind to IL-23 mRNA and did not appear to influence IL-23 production by CLL cells, indicating that IL-12Rβ1 chain expression is a major regulatory step in the IL-23/IL-23R complex loop. NSG mice engrafted with CLL cells and treated with miR-146b-5p mimic presented a reduction of both circulating and tissue-infiltrating leukemic cells compared with mice treated with CTR mimics and a disruption of the leukemic nodules, which had less defined boundaries, inferior numbers of proliferating cells, and appeared infiltrated by T cells. The expression of the IL-12Rβ1 chain by leukemic cells was markedly diminished. The concentrations of human T cells remained apparently unaltered in the engrafted NSG mice upon miR-146b-5p mimic administration, indicating that the treatment did not influence T-cell viability in this setting, although previous reports described a regulatory function of miR-146b-5p in follicular Th cells and regulatory T cells., Whether the T-cell subset distribution is altered remains to be ascertained. The activation of the IL-23/IL-23R complex loop in CLL cells is achieved mainly by stimulation through the surface CD40-dependent, not the BCR-dependent pathway. CD40 is a member of the TNFR family and requires interaction with TRAF6 and IRAK1 to activate NF-kB., Elevated concentrations of miR-146a-5p and of miR-146b-5p cause downregulation of IRAK1 and TRAF6 (supplemental Figure 10) in CLL, in principle, rendering NF-kB activation and stimulation via surface CD40 less effective.,, However, the observation that miR-146a-5p, which downregulates TRAF6 and IRAK1 expression as efficiently as miR-146b-5p, was not associated with prognosis in CLL suggested that the IL-23/IL-23R complex loop had a more critical role in regulating CLL cell growth. Interestingly, mice in which miR-146a-5p or miR-146b-5p is knocked out (KO) both develop lymphomas, although only the lymphomas originated in miR-146b-5p KO mice present a resemblance to human CLL. The reasons for this hierarchy in the mechanisms regulating CLL clonal expansion are far from clear. One possibility is that additional signals delivered by surface molecules different from CD40 and not requiring TRAF6 and IRAK1 adaptors are involved in activating the IL-23/IL-23R loop in vivo. An alternative and not mutually exclusive option could be offered by the redundancy of the TRAF/IRAK family members, whereby other molecules of the same families could substitute for the downregulation of TRAF6 and IRAK1 induced by the miR-146a/b., Notably, other miRNAs can regulate IL-23 stimulatory signals. This is the case of miR-221 and miR-222 that negatively regulate the susceptibility of Th17 cells to IL-23 stimulation by modulation of the IL-23R complex. The issue as to why CLL clones are heterogeneous in the miR-146b-5p concentrations is presently unclear, although it could be related to the different states of activation of the cells from the different CLL clones. This hypothesis is supported by the observations that CLL cell activation with CD40L-TC causes downregulation of miR-146b-5p concentrations in vitro and that the lowest miR-146b-5p concentrations are detected in IGHV-UM cases whose leukemic cells are at the highest activation status determined by surface marker analysis., An alternative and not mutually exclusive hypothesis poses that lesions of the miR-146b gene or regulatory DNA sequences facilitate the maintenance of low miR-146b-5p concentrations in the most aggressive CLL clones. However, this hypothesis is made unlikely by the finding that CNAs were very low in the database analysis we have carried out, and virtually no mutations of the miR-146b-5p locus are detectable in the same database., Alterations in the methylation of the miR-146b-5p locus of CLL cells compared with normal cells have been noticed and are reported in the Blueprint data analysis portal (supplemental Figure 5), a finding that could at least in part explain the heterogeneity of miR-146b expression in CLL, possibly dependent on the activation status of the neoplastic clones. This issue is currently being investigated. The present study has translational relevance as it indicates miR-146b-5p is a potential target through which the susceptibility of CLL cells to IL-23 could be modified. Future studies should investigate a strategy based on increasing intracellular miR-146b-5p concentrations as an application for CLL therapy, either alone or combined with anti–IL-23 mAbs,73, 74, 75 in the attempt to eradicate CLL, which so far has proven virtually incurable. Conflict-of-interest disclosures: The authors declare no competing financial interests.

PMC9647768

Makoto Kurano,Daisuke Jubishi,Koh Okamoto,Hideki Hashimoto,Eri Sakai,Yoshifumi Morita,Daisuke Saigusa,Kuniyuki Kano,Junken Aoki,Sohei Harada,Shu Okugawa,Kent Doi,Kyoji Moriya,Yutaka Yatomi

Dynamic modulations of urinary sphingolipid and glycerophospholipid levels in COVID-19 and correlations with COVID-19-associated kidney injuries

10-11-2022

COVID-19-associated kidney injuries,Urine,Sphingolipids,Glycerophospholipids,Lipidomics

Background Among various complications of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), renal complications, namely COVID-19-associated kidney injuries, are related to the mortality of COVID-19. Methods In this retrospective cross-sectional study, we measured the sphingolipids and glycerophospholipids, which have been shown to possess potent biological properties, using liquid chromatography-mass spectrometry in 272 urine samples collected longitudinally from 91 COVID-19 subjects and 95 control subjects without infectious diseases, to elucidate the pathogenesis of COVID-19-associated kidney injuries. Results The urinary levels of C18:0, C18:1, C22:0, and C24:0 ceramides, sphingosine, dihydrosphingosine, phosphatidylcholine, lysophosphatidylcholine, lysophosphatidic acid, and phosphatidylglycerol decreased, while those of phosphatidylserine, lysophosphatidylserine, phosphatidylethanolamine, and lysophosphatidylethanolamine increased in patients with mild COVID-19, especially during the early phase (day 1–3), suggesting that these modulations might reflect the direct effects of infection with SARS-CoV-2. Generally, the urinary levels of sphingomyelin, ceramides, sphingosine, dihydrosphingosine, dihydrosphingosine l -phosphate, phosphatidylcholine, lysophosphatidic acid, phosphatidylserine, lysophosphatidylserine, phosphatidylethanolamine, lysophosphatidylethanolamine, phosphatidylglycerol, lysophosphatidylglycerol, phosphatidylinositol, and lysophosphatidylinositol increased, especially in patients with severe COVID-19 during the later phase, suggesting that their modulations might result from kidney injuries accompanying severe COVID-19. Conclusions Considering the biological properties of sphingolipids and glycerophospholipids, an understanding of their urinary modulations in COVID-19 will help us to understand the mechanisms causing COVID-19-associated kidney injuries as well as general acute kidney injuries and may prompt researchers to develop laboratory tests for predicting maximum severity and/or novel reagents to suppress the renal complications of COVID-19. Supplementary Information The online version contains supplementary material available at 10.1186/s12929-022-00880-5.

Dynamic modulations of urinary sphingolipid and glycerophospholipid levels in COVID-19 and correlations with COVID-19-associated kidney injuries Among various complications of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), renal complications, namely COVID-19-associated kidney injuries, are related to the mortality of COVID-19. In this retrospective cross-sectional study, we measured the sphingolipids and glycerophospholipids, which have been shown to possess potent biological properties, using liquid chromatography-mass spectrometry in 272 urine samples collected longitudinally from 91 COVID-19 subjects and 95 control subjects without infectious diseases, to elucidate the pathogenesis of COVID-19-associated kidney injuries. The urinary levels of C18:0, C18:1, C22:0, and C24:0 ceramides, sphingosine, dihydrosphingosine, phosphatidylcholine, lysophosphatidylcholine, lysophosphatidic acid, and phosphatidylglycerol decreased, while those of phosphatidylserine, lysophosphatidylserine, phosphatidylethanolamine, and lysophosphatidylethanolamine increased in patients with mild COVID-19, especially during the early phase (day 1–3), suggesting that these modulations might reflect the direct effects of infection with SARS-CoV-2. Generally, the urinary levels of sphingomyelin, ceramides, sphingosine, dihydrosphingosine, dihydrosphingosine l-phosphate, phosphatidylcholine, lysophosphatidic acid, phosphatidylserine, lysophosphatidylserine, phosphatidylethanolamine, lysophosphatidylethanolamine, phosphatidylglycerol, lysophosphatidylglycerol, phosphatidylinositol, and lysophosphatidylinositol increased, especially in patients with severe COVID-19 during the later phase, suggesting that their modulations might result from kidney injuries accompanying severe COVID-19. Considering the biological properties of sphingolipids and glycerophospholipids, an understanding of their urinary modulations in COVID-19 will help us to understand the mechanisms causing COVID-19-associated kidney injuries as well as general acute kidney injuries and may prompt researchers to develop laboratory tests for predicting maximum severity and/or novel reagents to suppress the renal complications of COVID-19. The online version contains supplementary material available at 10.1186/s12929-022-00880-5. Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is associated with various complications. Among them, renal complications, especially acute kidney injury (AKI), are associated with critical conditions. These renal complications are known as COVID-19-associated kidney injuries. A high incidence of AKI has been reported, especially among critically ill patients, and patients with COVID-19-associated kidney injuries reportedly have a higher risk of in-hospital death [7, 13, 14, 40]. Moreover, recent studies have suggested prolonged kidney dysfunction in some patients with COVID-19 [43]. Regarding the etiology of COVID-19-associated kidney injuries, the mechanisms have not been fully elucidated at present, and both direct and indirect mechanisms have been proposed [30]. ACE2 and TMPRSS2, which are key proteins for the entry of SARS-CoV-2 into human cells [15], are highly expressed in podocytes and proximal tubules in the kidney, and SARS-CoV-2 can directly infect and damage the kidney [6, 42, 47]. As indirect mechanisms, complications such as thrombosis and endotheliitis, which are frequently observed in COVID-19 patients [57, 62, 64], can cause renal impairment. In addition, other mechanisms not specific to COVID-19, such as right heart failure [5], cytokines, and nephrotoxins [30], have been proposed to be involved in COVID-19-associated kidney injuries. Since COVID-19-associated kidney injuries are important in terms of clinical outcomes and the underlying mechanisms have yet to be fully elucidated, as described above, investigating biomarkers of COVID-19-associated kidney injuries will be important to understand the pathogenesis of such complications. Urine samples rapidly and accurately reflect renal conditions. Actually, recent studies have revealed that urinary chemical biomarkers (urinary total protein [TP], N-acetyl-β-d-glucosaminidase [NAG], α1-microglobulin [α1-MG], neutrophil gelatinase-associated lipocalin [NGAL], and liver type fatty acid-binding protein [L-FABP]) and urine sediment findings are correlated with both the severity of COVID-19 and COVID-19-associated kidney injuries [16, 19, 35]. Therefore, in the present study, we investigated urinary biomarkers for COVID-19-associated kidney injuries. In this study, we focused on sphingolipids and glycerophospholipids. A series of basic and clinical studies have revealed the importance of these lipids in the pathogenesis of various human diseases including kidney diseases. Among the sphingolipids, the bioactivities of sphingosine 1-phosphate (S1P) and ceramides have been well studied. S1P possesses potent anti-apoptotic and pro-survival properties [27, 33]. A series of basic studies showed that the S1P1 signal might attenuate both acute and chronic kidney diseases through its pro-survival, anti-inflammation, anti-fibrosis, and vasoprotective properties [1, 12, 24, 25, 50], whereas the S1P2 signal might aggravate kidney disease by accelerating fibrosis and inflammation [24, 49]. Ceramides have both pro-apoptosis and pro-inflammation properties [44, 61] and have been shown to accelerate the pathological condition of both chronic and acute kidney diseases in basic studies [41, 56]. Regarding the metabolism of sphingolipids, ceramides are derived from sphingomyelin (SM) and can be converted into sphingosine (Sph). S1P is produced from Sph by S1P kinases [33]. Dihydrosphingosine 1-phosphate (dhS1P), another analog for S1P receptors, is produced from dihydrosphingosine (dhSph) by S1P kinases, and dhSph is processed into ceramides via dihydroceramides [2]. In clinical studies, urinary ceramide levels were reportedly associated with diabetic nephropathy [38, 51], and urinary SM levels are altered in chronic kidney diseases [67]. However, the modulation of urinary sphingolipid levels remains unknown, especially in terms of the pathogenesis of human AKI. Among glycerophospholipids, lysophosphatidic acids (LPA) and lysophosphatidylcholine (LPC) have been well studied in the fields of nephrology. LPA is produced from LPC by autotaxin, and six kinds of LPA receptors have been identified [68]. The roles of LPA in inflammation depend on its receptors. LPA can exacerbate the pathogenesis of chronic kidney diseases, resulting in inflammation and fibrosis [29, 53], while it can also reportedly protect against acute kidney diseases [9, 34]. The urinary LPA levels are elevated in diabetic nephropathy [55], while the urinary autotaxin levels increase in membranous nephropathy [36]. The urinary LPC levels are also positively correlated with kidney dysfunction, and LPC itself might exert lipotoxicity [69]. Lysophosphatidylinositol (LPI) reportedly exacerbates the pathogenesis of sepsis-associated AKI [22]. Regarding other glycerolysophospholipids, such as lysophosphatidylethanolamine (LPE), lysophosphatidylglycerol (LPG), and lysophosphatidylserine (LPS), their roles in kidney injuries remain unknown, and the modulation of urinary lysophospholipids in AKI also remains to be elucidated. Along with LPA, the GPR34, P2Y10, and GPR174 receptors have been shown to be specific for LPS [17], while the GPR55 receptor is specific for LPI and LPG [45]. LPC, LPS, LPE, LPI, and LPG are produced from phosphatidylcholine (PC), phosphatidylserine (PS), phosphatidylethanolamine (PE), phosphatidylinositol (PI), and phosphatidylglycerol (PG), respectively. Although only a limited number of studies are available, PC has been shown to possess protective effects against acute kidney injuries through its antioxidant properties [10, 28], and PS might reduce nephrotoxicity by suppressing inflammation [21]. The roles of other diacylphospholipids in the pathogenesis of AKI have not been reported. Regarding the modulations of urinary diacylphospholipids in humans, a recent study showed that urinary PC levels might be associated with adverse outcomes and mortality in patients with chronic kidney diseases [60]; however, their associations with human AKI are not well known at present. Although several lipidomics analyses using serum or plasma samples from patients with COVID-19 have been performed [23], only one lipidomics study has been conducted using urine samples from a very small number of COVID-19 patients [31]. With this background in mind, we performed lipidomic analyses using urine samples to investigate the mechanisms responsible for COVID-19-associated kidney injuries to understand the pathogenesis of COVID-19 and COVID-19-associated kidney injuries, as well as AKI, better and to help researchers develop reagents capable of preventing severe kidney injuries in the future. In this study, we measured the longitudinal urinary levels of sphingolipids and glycerophospholipids in 272 samples from 91 COVID-19 subjects and 95 samples from 95 control subjects without infectious disease. We collected the residual urinary samples after routine clinical testing from 91 subjects who had been diagnosed as having COVID-19 using an RT-PCR assay between September 2020 and April 2021. The sampling times were classified into the following eight periods: day 1–3, day 4–6, day 7–9, day 10–12, day 13–15, day 16–18, day 19–24, and day 25–40 after symptom onset. Since the timing of RT-PCR testing varied largely among the patients, we used the day after symptom onset as the initial measurement in our investigation of lipid modulation. None of the subjects enrolled in the present study had been vaccinated at the time of sampling. The subjects were classified into three groups according to the maximum severity of COVID-19: maximum severity group 1 (did not require oxygen supplementation), maximum severity group 2 (required oxygen supplementation, but did not require mechanical ventilatory support), and maximum severity group 3 (required mechanical ventilatory support). As a control, we collected 95 urine samples from volunteers without infectious diseases. The current study was performed in accordance with the ethical guidelines established by the Declaration of Helsinki. Written informed consent for sample analysis was obtained from some of the patients. For the remaining participants from whom written informed consent could not be obtained (because they had been discharged or transferred out of the hospital), informed consent was obtained in the form of an opt-out on our institution’s website, as follows. Patients were informed of the study through the website, and those who were unwilling to be enrolled were excluded. The study design was approved by The University of Tokyo Medical Research Center Ethics Committee (2602 and 2020206NI). We measured the levels of the lipid mediators listed below using four independent LC–MS/MS methods and the LC8060 system, consisting of a quantum ultra-triple quadrupole mass spectrometer (Shimadzu, Japan). We simultaneously measured six ceramide species (Cer d18:1/16:0 [C16:0], Cer d18:1/18:0 [C18:0], Cer d18:1/18:1 [C18:1], Cer d18:1/20:0 [C20:0], Cer d18:1/22:0 [C22:0], and Cer d18:1/24:0 [C24:0]), Sph, and dhSph, as previously described [38]. We also measured S1P and dhS1P, as described previously [52]. Furthermore, LPA, LPC, LPS, LPI, LPG, and LPE were also measured, as described previously [37]. We monitored 11 acyl chains (14:0, 16:0, 16:1, 18:0, 18:1, 18:2, 18:3, 20:3, 20:4, 20:5, and 22:6) for these lysophospholipids as well as 22:5 LPI. We also measured SM and diacylphospholipids, including PC, PE, PI, PG, and PS [26]. We monitored 17 diacyl chains (32:1, 32:2, 34:1, 34:2, 36:1, 36:2, 36:3, 36:4, 38:1, 38:2, 38:3, 38:4, 38:5, 38:6, 40:1, 40:2, and 40:7) for SM and 64 diacyl chains (28:0, 28:1, 28:2, 30:0, 30:1, 30:2, 32:0, 32:1, 32:2, 32:3, 32:4, 34:0, 34:1, 34:2, 34:3, 34:4, 34:5, 34:6, 36:0, 36:1, 36:2, 36:3, 36:4, 36:5, 36:6, 36:7, 38:0, 38:1, 38:2, 38:3, 38:4, 38:5, 38:6, 38:7, 38:8, 40:0, 40:1, 40:2, 40:3, 40:4, 40:5, 40:6, 40:7, 40:8, 40:9, 40:10, 42:0, 42:1, 42:2,42:3, 42:4, 42:5, 42:5, 42:6, 42:7, 42:8, 42:9, 42:10, 42:11, 44:0, 44:1, 44:2, 44:6, 44:7, and 44:12) for PC, PE, PI, PG, and PS. With the exceptions of SM and the diacylphospholipids, both the intra-day and inter-day coefficients of variation for the metabolites were below 20%, as validated in our previous papers [37, 38, 52]. The urinary levels of the measured lipids were adjusted to the urinary creatinine levels. To measure the urinary clinical markers, we used the reagents as described previously [35]. Renal tubular epithelial cells (RTE) were counted per high-power field of view (/HPF); urinary casts were classified into hyaline casts (HyaC), granular casts (GraC), epithelial casts (RTEC), and waxy casts (WaxC), and their numbers were counted per whole field (/WF). The RTE findings were classified as rank 0 (absent), rank 1 (< 1/HPF), rank 2 (1–4/HPF), rank 3 (5–9/HPF), or rank 4 (> 10/HPF), those of HyaC were classified as rank 0 (absent), rank 1 (< 4/WF), rank 2 (5–19/WF), rank 3 (20–49/WF), or rank 4 (> 50/WF), those of GraC and RTEC were classified as rank 0 (absent), rank 1 (< 4/WF), rank 2 (5–49/WF), or rank 3 (> 50/WF), and those of WaxC were classified as rank 0 (absent) or rank 1 (present). The data were analyzed using SPSS (Chicago, IL) or MetaboAnalyst (https://www.metaboanalyst.ca/). To examine differences in the time courses of urinary lipids among the control subjects and maximum severity groups 1, 2, and 3, we evaluated significant differences using the Kruskal–Wallis test, followed by the Steel–Dwass test as a post-hoc test. To examine differences in the urinary lipid levels longitudinally between specific time points in a specific maximum severity group, we used the paired Wilcoxon signed-rank test. To examine differences between the control subjects and the COVID-19 subjects, we performed non-parametric Volcano plot analyses. For the correlation studies, a Kendall rank correlation was performed to examine the correlations of lipids and clinical data with the maximum severity of COVID-19, using age, sex, and the presence of diabetes, hypertension, and current smoking as covariates of interest. To construct machine learning models, we used SPSS modeler ver. 18:2 (Chicago, IL) and performed CHAID analyses, SVM analyses, and neural network analyses. The Spearman rank correlation was performed to examine correlations between lipids and clinical data. The independent effects of the clinical properties and the results of urinary laboratory tests on urinary lipid levels were investigated with a stepwise multiple regression analysis, using urinary lipid levels as objective variables and clinical information, maximum severity, eGFR, CRP, d-Dimer, urinary chemical markers, urinary sediment findings, SG, pH, and urinary sodium levels as possible explanatory factors. To examine differences between the subjects treated with antiviral reagents and those without, we used the Mann–Whitney U test. The graphs shown in the figures were prepared using Graphpad Prism 9 (GraphPad Software, San Diego, CA) or MetaboAnalyst. P values of less than 0.05 were deemed as denoting statistical significance in all the analyses. The characteristics of all the subjects and the numbers of samples analyzed at each specific time point are described in Additional file 1: Tables S1 and S2, respectively. As shown in the tables, differences in patient age were seen among the maximum severity groups, while differences in the percentage of patients with hypertension were seen between the control subjects and the maximum severity groups. We also observed differences in sex between the control subjects and the maximum severity groups on day 19–24 and day 25–40. In the control group, no differences in the sphingolipid and total glycerophospholipid levels were seen between subjects with hypertension and those without hypertension. Therefore, the presence of hypertension might not have had a large effect on the results in the present analyses. Regarding sex, the urinary levels of several monitored lipids were higher in female subjects. The ratios of the lipid levels in female subjects relative to those in male subjects were 140.3% for the total SM levels, 191.2% for the S1P levels, 154.0% for the dhS1P levels, 186.6% for the Sph levels, 152.1% for the dhSph levels, 150.9% for the C18:1 Cer levels, 141.1% for the total LPG levels, 145.5% for the total PC levels, 144.2% for the total PE levels, 165.6% for the total PG levels, 151.5% for the total PI levels, and 125.1% for the total PS levels. Age was positively correlated with the total LPC levels (r = 0.214, p = 0.037) and the total PS levels (r = 0.218, p = 0.034). Therefore, we think that these correlations were thought to have had a minimal impact on the interpretation of the dynamic modulations of the monitored lipid levels, as described below. The time courses for the urinalysis results and other clinical parameters are shown in Additional file 1: Fig. S1. Overall, the modulations of these parameters seemed reasonable, while a remarkable decline in eGFR was not observed in the COVID-19 subjects. Figure 1 shows the time courses of the urinary sphingolipid levels in the COVID-19 subjects. C16:0 Cer and SM increased most rapidly from day 4–6. C18:1 Cer, C20:0 Cer, C22:0 Cer, C24:0 Cer, Sph, dhSph, and dhS1P significantly decreased or tended to decrease during the early phase (day 1–3) in maximum severity group 1 and then increased, especially in maximum severity group 3. Among the monitored sphingolipids, the C18:0 Cer levels rapidly decreased from day 1–3. The urinary levels of several sphingolipids seemed remarkably higher on day 25–40; however, several biases might be present, since samples were collected only from patients with severe COVID-19 who were still hospitalized on day 25–40. Regarding longitudinal comparisons, although we could compare the urinary lipid levels between only limited time points, the results showed the elevation of urinary sphingolipids during the time course of COVID-19, especially in day 19–40 in maximum severity group 3 (Additional file 1: Figs. S2A-D and S3). Figure 2 shows an overview of the total glycerophospholipid modulations. The total levels of all the monitored glycerophospholipids increased, especially in severe COVID-19. Regarding the PC-LPC-LPA axis, the urinary PC levels rapidly increased in maximum severity group 3; they tended to decrease in maximum severity groups 1 and 2. The urinary LPA and LPC levels increased, especially during the later phase. Regarding the PS-LPS axis, PS increased rapidly from the early phase (day 1–3) and LPS increased from day 7–9, especially in patients with severe COVID-19. Regarding the PE-LPE axis, LPE increased from the early phase (day 4–6) and reached a peak on day 10–12 in maximum severity group 2. In maximum severity group 3, LPE increased, especially during the later phase (day 19–24). The urinary total PE levels were modulated in an almost similar manner to those of LPE. Regarding the PG-LPG axis and the PI-LPI axis, LPG and LPI increased in maximum severity groups 2 and 3 from around the middle phase (day 7–15), while PG and PI increased only in maximum severity group 3. Regarding longitudinal comparisons, the paired statistical analyses showed the elevation of urinary glycerophospholipids during the time course of COVID-19, especially in day 19–40 in maximum severity group 3 (Additional file 1: Fig. S2E-H, S4). To investigate time-course-dependent lipid modulations in greater detail, we next created separate volcano plots for each sampling point, as shown in Additional file 1: Figs. S5–S8. To understand the lipid modulations that occur in patients with COVID-19 better, the lipids with the 20 lowest p values at each time period were selected; their log2(FC) and p values are shown in Fig. 3A. Among the sphingolipids, the C18:0 Cer level decreased markedly, especially during the early phase. Decreased levels of PC, LPA, and LPC species were clearly observed until day 16–18, while the levels of several species including 38:2 PC, 42:10 PC, and 44:2 PC increased. Regarding the PE-LPE axis, increases in PE and LPE species were clearly observed after day 7–9, especially on day 19–24 and day 25–40. The levels of several species including 28:0 PE, 30:0 PE, 34:3 PE, 38:8 PE, and 18:3 LPE decreased. Regarding the PS-LPS axis, specific PS species, such as 36:2 PS and 36:3 PS, increased, especially during the middle phase (day 16–18 and day 19–24), while 38:5 PS consistently increased almost throughout the time course. Decreases in 38:0 PS and 34:3 PS were observed on day 7–12 and day 4–6, respectively. 18:0 LPS increased on day 7–9. Regarding the PI-LPI axis, 14:0 LPI decreased on day 4–15 and 32:0 PI decreased on day 4–6, while 16:1 LPI, 18:2 LPI, and 18:3 LPI and several PI species containing 16:1, 18:1, and 18:3 acyl chains increased after day 10–12. Regarding the PG-LPG axis, the increase in 14:0 LPG after day 10–12 and the decrease in 34:5 PG throughout the time course seemed characteristic. The time courses of the representative lipids are shown in Fig. 3B–I and Additional file 1: Figs. S9, S10. Next, we performed correlation analyses with the maximum severity of COVID-19, using age, sex, the presence of diabetes and hypertension, and current smoking as covariates of interest. Figure 4A shows the correlation coefficients and the p values of the lipids and clinical parameters with the 20 lowest p values at any specific time points. Among sphingolipids, SM (except for 36:3 SM) and Sph were positively correlated with maximum severity on day 10–12. On day 19–40, C18:0 Cer, C22:0 Cer, and C24:0 Cer were positively correlated with maximum severity. Regarding LPA, the 18:1 LPA, 20:3 LPA, and 16:1 LPA levels on day 10–12 and day 13–15 were negatively correlated with maximum severity. The 14:0 LPC and 16:1 LPC levels on day 7–9 and the 20:4 LPC levels on day 19–40 were positively correlated with maximum severity. Many PC species had strong positive correlations with maximum severity, while the 32:3 PC, 40:2 PC, 42:0 PC, 44:0 PC, 44:1 PC, and 44:2 PC levels during the middle phase (day 10–18) had rather strong negative correlations with maximum severity. Many PE species were negatively correlated with maximum severity, while 34:4 PE, 34:5 PE, and 38:6 PE at some sampling points were positively correlated with maximum severity. Several PG and PI species, especially on day 10–15, were negatively correlated with maximum severity. Among LPG species, 14:0 LPG was positively correlated with maximum severity. Meanwhile, LPS and PS species were, in general, negatively correlated with maximum severity. Additional file 1: Fig. S11 shows the time courses of characteristic lipids. In addition to performing simple correlation studies, we investigated which lipids and clinical parameters were strongly correlated with the maximum severity of COVID-19 using machine learning techniques. Figure 4B and Additional file 1: Fig. S12A, B show the lipids or clinical parameters selected with high importance by CHAID analyses, SVM analyses, and neural network analyses, respectively. In the CHAID analyses, the PC-LPA-LPC axis throughout the time course, except day 16–18, had a high importance for determining maximum severity in the constructed models. The PE-LPE axis on day 7–15, PS on day 10–12, C16:0 Cer on day 1–6, SM on day 13–15, and 14:0 LPG on day 7–9 had some importance. In the SVM and neural network analyses, although the importance of each parameter was relatively low, the PC-LPA-LPC axis throughout the time course, sphingolipids during the early phase, and PS and the PE-LPE axis during the middle phase had importance for determining maximum severity in the constructed models. Next, we investigated the correlations of the monitored lipids with clinical parameters. Figure 5 and Additional file 1: Figs. S13, S14 shows the time courses of the correlations. As shown in Fig. 5A, the urinary levels of several lipids had positive correlations with serum CRP and d-Dimer levels. The lipids which had a negative correlation with the urinary SG levels and a positive correlation with sodium levels are deemed to increase in the pathogenesis the renal factors or decrease in the pathogenesis of prerenal kidney injuries. As shown in Fig. 5B, the urinary SM, C18:1 Cer, PC, and PG levels had negative correlations with the urinary SG levels and positive ones with the urinary sodium levels in the middle phase (day 13–18). The urinary TP levels were positively correlated with ceramides, Sph, LPC, PS, LPS, PE, LPE, and LPG in the early phase (day 1–6 and/or day 7–9) and in the late phase (day 16–18 and/or day 19–40). They consistently had positive correlations with the urinary C16:0 Cer and LPC levels. Regarding the urinary chemical biomarkers, generally, urinary sphingolipids except SM had positive correlations with urinary chemical biomarkers. Especially, the C16:0 and C18:1 ceramides had positive correlations almost throughout the monitored periods. The urinary glycerophospholipids, except PC, LPA, and LPI, generally had positive correlations with the urinary chemical markers (Fig. 5C). Interestingly, the eGFR levels were positively correlated with the urinary levels of several sphingolipids, while they were negatively correlated with the urinary LPC, LPE, and PE levels in the early to the middle phase. In the late phase (day 19–40), the eGFR levels were negatively correlated with the urinary sphingolipids and glycerophospholipids (Fig. 5D). Regarding the urinary sediment findings, the urinary SM and dhS1P levels were negatively correlated with RTE, while the urinary ceramides levels were positively correlated with RTE and GraC, except the negative correlations observed in day 16–18. Among glycerophospholipids, the urinary PC, LPA, PG, LPG, PI levels had negative correlations with RTE in day 7–15. The urinary LPC levels had positive correlations with the urinary sediment findings in many time points. The urinary PS, LPS, PE, and LPE also had positive correlations with the urinary sediment findings in the early phase (day 1–9) (Fig. 5E and Additional file 1: Fig. S14). We further investigated the independent effects of the systematic severity of COVID-19, represented by d-Dimer and CRP, and renal injuries, represented by the results of urinary laboratory tests on urinary lipid levels, were evaluated with a multiple regression analysis, using urinary lipid levels as subjective variables. As shown in Additional file 1: Figs. S15–S18, urinary chemical makers such as NGAL and L-FABP were selected as positive explanatory factors with high β values for ceramides, S1P, dhS1P, dhSph, LPC, LPS, LPE, LPG, LPA, PC, PE, PG, PI, and PS, whereas d-Dimer or maximum severity were selected as positive explanatory variables for SM, dhSph, LPI, when all the samples were analyzed. Although, when samples were analyzed separately according to the days after the onset of COVID-19, the results were not always consistent, these results suggested that both renal injuries and systematic severity would affect the dynamic modulations of urinary sphingolipids and glycerophospholipids in COVID-19. In addition, although, since this is a cross-sectional study, we could not conclude the possible influences of antiviral therapy on urinary lipid levels, we also observed some differences in urinary lipid levels between subjects treated with antiviral reagents such as remdesivir [63] and favipiravir [11] and those without (Additional file 1: Figs. S19, S20). Lastly, after we finished all the analyses, to validate the main results, we measured 46 additional urine samples collected from 31 independent subjects who had been diagnosed as having COVID-19 using an RT-PCR assay between April 2021 and August 2021. Additional file 1: Figs. S21 and S22 show the concentrations of lipids, overlayed on Figs. 1 or 2. As shown in these figures, the modulations of sphingolipids and glycerophospholipids were generally replicated. Moreover, when we investigate the accuracy of the predicting models for maximum severity described in Fig. 4B and Additional file 1: Fig. S12, using these independent samples, we obtained the accuracy of over 80% (Additional file 1: Table S3). Considering these results, we think that the modulations of these lipids could be replicated. The present study examined the dynamic modulations of urinary sphingolipids and glycerophospholipids in COVID-19 subjects. The modulations of the monitored lipids are summarized in Fig. 6. The modulations of each representative specie of lipids which were not shown in the previous figures are described in Additional file 1: Figs. S23–S25. The urinary SM levels increased only in maximum severity group 3 and were positively correlated with the maximum severity of COVID-19, suggesting that SM modulations were not specific to COVID-19-specific factors but were instead related to kidney injuries accompanying severe infection. Contrary to a previous paper reporting that the urinary SM levels were positively correlated with the urinary TP levels [67], the urinary levels of the SM species were not consistently correlated with the urinary TP level. Of note, the urinary total SM level was rather strongly negatively correlated with the urinary SG and positively with sodium levels (Fig. 5). The negative correlations of the SM with the RTE suggested the possibility that the urinary SM levels decrease in response to the prerenal factors, although a previous study reported that sphingomyelinase activities declined in the model of ischemic renal injury [70]. The modulations of urinary ceramides largely depended on the species. Overall, the levels of the monitored ceramides increased, especially in severe COVID-19, with the exception of C18:0 Cer (Fig. 1). Many ceramide species were downregulated during the early phase (day 1–3) in maximum severity group 1, suggesting that the downregulation of ceramides might originate from the direct influence of infection with SARS-CoV-2. A recent report demonstrated that the envelope of SARS-CoV-2 is rich in cholesterol and phospholipids and poor in sphingolipids [54], suggesting that these modulations are unlikely to be explained by the expenditure of sphingolipids during virus replication. During the later phase, the elevation of ceramides in severe COVID-19 might reflect the progression of kidney injuries, since ceramide production was induced in kidney injury models and ceramides are known to induce the apoptosis of renal mesangial cells and renal tubular epithelial cells [3, 18, 32]. Actually, the urinary ceramide levels were positively correlated with urinary chemical biomarkers and the urinary sediment findings in the COVID-19 subjects in the present study (Fig. 5). The urinary Sph, dhSph, and dhS1P levels increased, especially during the later phase in maximum severity group 3, while they tended to decrease during the early phase (day 1–3) in maximum severity group 1 (Fig. 1). These results suggested that reductions in Sph and dhSph might be direct effects of infection with SARS-CoV-2, while increases in these sphingolipids may occur as responses to kidney injuries associated with COVID-19, as observed for ceramides. Actually, their urinary levels had positive correlations with the urinary chemical markers (Fig. 5). Considering the agonistic properties of dhS1P for S1P receptors and the potential protective properties of S1P receptors against kidney injuries [1, 12, 24, 25], the elevation in urinary dhS1P levels in severe COVID-19 might reflect a compensatory mechanism in response to COVID-19-associated kidney injuries. Regarding the PC-LPC-LPA axis, the urinary total PC levels increased from the early phase (day 4–6) only in maximum severity group 3, while the total LPA levels increased during the later phase (Fig. 2). However, when the lipid modulations were investigated in detail, many PC, LPC, and LPA species decreased in the COVID-19 subjects (Fig. 3). These results suggested that SARS-CoV-2 infection generally downregulated the PC-LPA-LPA axis in a direct manner, while kidney injuries caused by critical COVID-19 disease resulted in upregulation. Urinary SG levels had rather negative correlations with PC and urinary sodium levels had positive ones with PC, while the urinary PC levels were rather negatively correlated with the RTE, suggesting that the urinary PC levels decrease in response to the prerenal factors. Regarding the pathophysiological significance, since LPA is involved in renal fibrosis as well as inflammation [20, 29, 53] and LPC has been shown to have a strong lipotoxicity in the field of nephrology [69], increases in LPA and LPC, especially during the later phase, might result in the translation of acute kidney injuries into chronic kidney injuries, which has been observed as a sequelae of COVID-19 [43]. Regarding the PS-LPS axis, the urinary PS and LPS levels increased rapidly, especially in patients with severe COVID-19 (Fig. 2). Although modulations of the urinary PS levels in AKI have not been reported, considering that PS is involved in apoptosis [39] and exosome formation [59], the elevation of PS in COVID-19-associated kidney injuries seems reasonable. The urinary PS and LPS levels were positively correlated with urinary chemical biomarkers and urinary sediment findings, suggesting that these levels reflect kidney injuries that have been mainly caused by renal factors. The roles of LPS remain to be elucidated in the fields of nephrology, while we recently demonstrated the elevation of PS-PLA1, a producing enzyme for LPS, in the serum of COVID-19 patients [58]. Since LPS and LPS receptors are involved in the regulation of the immune system through three kinds of specific receptors [17, 46], LPS might possess important roles in the pathogenesis of COVID-19-associated kidney injuries, in which inflammation might at least partly be involved [30]. The urinary levels of PE and LPE increased in COVID-19 beginning at an early phase. The upregulation of the PE-LPE axis might be characteristic of COVID-19, as shown in the volcano plots (Fig. 3). PE is abundant in the envelope of SARS-CoV-2 [54] and is reportedly involved in the replication of RNA viruses [66]. Previous studies suggested that LPE might possess anti-inflammatory properties on macrophages [48], which might activate natural killer T cell-dependent protective immunity [71]. Considering these potential biological properties of LPE and the negative correlation between LPE levels during the early phase and maximum severity, LPE might have protective biological properties against the pathogenesis of COVID-19, and a failure to increase LPE levels might be one mechanism resulting in the aggravation of COVID-19. Regarding the PG-LPG axis and the PI-LPI axis, the roles of PG in AKI are unknown, while PG reportedly suppresses toll-like receptor-mediated inflammation [8], suggesting that a decrease in PG might promote kidney injury. In contrast, the upregulation of LPG in patients with severe COVID-19 might contribute to the acceleration of inflammation, since LPG exerts agonist activities with proinflammatory GPR55 [22, 45]. LPI also acts on GPR55 [45], suggesting that the elevation of LPI during the later phase of severe COVID-19 might accelerate the pathogenesis of COVID-19-associated kidney injuries. Regarding the correlation with clinical phenotypes, the PG-LPG axis and the PI-LPI axis show somehow strange correlations with the urinary chemical markers and the urinary sediment findings (Fig. 5). Some unknown mechanisms are involved for the opposite results in the associations with lipids between the urinary chemical markers and the urinary sediment findings. To the best of our knowledge, the modulations of sphingolipids and glycerophospholipids in the urine of AKI have not been well studied, while urinary levels of ceramides, SM, LPA, LPC, and PC have been demonstrated to be higher in chronic kidney diseases, especially diabetic nephropathy, as described in the Introduction [38, 51, 55, 60, 67, 69]. Although the number of the subjects was limited, when we investigated the association between diabetes and urinary lipid levels in the control subjects used in the present study, we observed that the urinary levels of total LPG and S1P were also higher in the subjects with diabetes as well as LPC and ceramides. These results together with the previous reports suggested that the mechanisms similar to diabetic nephropathy, such as inflammation, oxidative stress, and fibrosis, might be somehow involved in the modulations of sphingolipids and glycerophospholipids in the present study. Anyway, since significant elevation of urinary levels of LPS, PS, LPE, PE, PG, LPI, and PI have not been observed or reported in the urine of chronic kidney diseases, several mechanisms specific to COVID-19 or AKI may be involved in the dynamic modulations of these lipids. Since this was an observational study, the main limitation is that the possible involvement of these lipid modulations in the pathogenesis of COVID-19-associated kidney injuries remains unknown. However, the accuracy of the models predicting maximum severity that were constructed using machine learning methods was generally high, especially during the early phase of COVID-19 (Fig. 4B and Additional file 1: Fig. S12), and several lipids were selected as important factors, in addition to important clinical parameters that are typically used to predict severity. These results suggest the potential usefulness of these lipids as biomarkers for predicting the maximum severity of COVID-19. Moreover, since the PC-LPA-LPC axis was important throughout the time course, sphingolipids were important during the early phase, and PS and the PE-LPE axis were important during the middle phase in all three machine learning models for predicting maximum severity, these lipids might have some important physiological properties in the pathogenesis of COVID-19 or its associated kidney injuries. Another limitation is that, although we evaluated possible confounding factors affecting the modulations of urinary sphingolipids and glycerophospholipids as described in the first section of the Results, we could not completely match the backgrounds of both the COVID-19 and control groups, since some characteristics such as age differed largely among the maximum severity groups. In addition, since the eGFR levels were not obviously modulated in the present study (Additional file 1: Fig. S1H), we were unable to investigate cases with severe AKI. At present, we could not conclude whether the urinary modulations of the lipids would recover when the patients are cured of COVID-19. Although the data is preliminary since the number of samples were limited, the urinary lipid levels generally recovered to the range of control subjects and no obvious differences among the maximum severity were observed except that the total PE levels were still higher in the subjects who had recovered from severe COVID-19 (Additional file 1: Fig. S26). Considering that serum modulation of lipids maintained for a long time [4, 65], further studies with post-COVID-19 subjects are necessary in the future to elucidate the mechanisms for long COVID-19. In summary, analyses of urine samples collected from COVID-19 subjects showed that decreases in the urinary levels of C18:0, C18:1, C22:0, and C24:0 ceramides, Sph, dhSph, PC, LPC, LPA, and PG and increases in those of PS, LPS, PE, and LPE, especially during the early phase, might be derived from the direct effects of SARS-CoV-2 infection, while increases in the urinary levels of SM, ceramides, Sph, dhSph, dhS1P, PC, LPA, PS, LPS, PE, LPE, PG, LPG, PI, and LPI, especially during the later phase, might result from kidney injuries accompanying severe COVID-19. We believe that these results may prompt researchers to perform further investigations to develop laboratory testing methods based on sphingolipid and glycerophospholipid modulations for predicting the maximum severity of COVID-19 and/or novel reagents to suppress the renal complications of COVID-19. Additional file 1. Supplementary tables and figures.

PMC9647969

Dengfang Guo,Qingling Wang,Jiancheng Huang,Zhanglin Hu,Chun Chen,Chun Zhang,Feng Lin

Downregulation of miR-451 in cholangiocarcinoma help the diagnsosi and promotes tumor progression

09-11-2022

miR-451,Cholangiocarcinoma,Early detection,Prognosis prediction,Biological function

Background Cholangiocarcinoma is a kind of invasive malignant tumor followed by hepatocellular carcinoma. miR-451 was suggested to function as regulator in various human tumors, but its role in mediating tumor progression and predicting the prognosis of cholangiocarcinoma remains unknown. The clinical significance and biological function of miR-451 in cholangiocarcinoma were assessed in this study. Results The tissue and serum expression of miR-451 was decreased in cholangiocarcinoma compared with corresponding normal samples. The downregulation of miR-451 was associated with the progressive TNM stage and positive lymph node metastasis of patients. miR-451 was identified to be an indicator of the diagnosis and prognosis of cholangiocarcinoma distinguishing cholangiocarcinoma patients from healthy volunteers and predicting the poor outcome of patients. miR-451 also served as a tumor suppressor negatively regulating the cellular processes of cholangiocarcinoma. Conclusions miR-451 played a vital role in the early detection and risk prediction of cholangiocarcinoma. miR-451 also suppressed the progression of cholangiocarcinoma, which provides a potential therapeutical target for cholangiocarcinoma treatment. Supplementary Information The online version contains supplementary material available at 10.1186/s12860-022-00445-2.

Downregulation of miR-451 in cholangiocarcinoma help the diagnsosi and promotes tumor progression Cholangiocarcinoma is a kind of invasive malignant tumor followed by hepatocellular carcinoma. miR-451 was suggested to function as regulator in various human tumors, but its role in mediating tumor progression and predicting the prognosis of cholangiocarcinoma remains unknown. The clinical significance and biological function of miR-451 in cholangiocarcinoma were assessed in this study. The tissue and serum expression of miR-451 was decreased in cholangiocarcinoma compared with corresponding normal samples. The downregulation of miR-451 was associated with the progressive TNM stage and positive lymph node metastasis of patients. miR-451 was identified to be an indicator of the diagnosis and prognosis of cholangiocarcinoma distinguishing cholangiocarcinoma patients from healthy volunteers and predicting the poor outcome of patients. miR-451 also served as a tumor suppressor negatively regulating the cellular processes of cholangiocarcinoma. miR-451 played a vital role in the early detection and risk prediction of cholangiocarcinoma. miR-451 also suppressed the progression of cholangiocarcinoma, which provides a potential therapeutical target for cholangiocarcinoma treatment. The online version contains supplementary material available at 10.1186/s12860-022-00445-2. Cholangiocarcinoma is a malignant tumor in the hepatic second to hepatocellular carcinoma. Cholangiocarcinoma is a group of epithelial cancers mentioning the intrahepatic, perihilar, and distal biliary tree [1]. Owing to the invasive characteristics of cholangiocarcinoma, the disease development is uncontrolled, and there was a lack of obvious clinical characteristics and risk factors, which makes patients always diagnosed at an advanced stage [2]. Although the diagnosis and therapy technology have been developed, the incidence and mortality of cholangiocarcinoma are still increasing [3]. Identifying effective biomarkers to diagnose cholangiocarcinoma at an early stage and predict disease development could ameliorate patients’ clinical outcomes and improve the cure rate of cholangiocarcinoma. microRNAs (miRNAs) have been demonstrated to serve as indicators in the diagnosis, prognosis, and progression of human cancers [4]. Binding with the 3’UTR of relevant mRNAs is the major characteristic of miRNA, by which miRNAs mediate the cycle progression, apoptosis, and growth of cancer cells, and therefore participate in the occurrence and development of tumors [5]. The dysregulation of different miRNAs always implies their functional role in human diseases. For example, increased miR-25 was correlated with malignant development and poor survival of cholangiocarcinoma patients [6]. miR-186 was disclosed to be downregulated and suppress the proliferation, migration, and invasion of cholangiocarcinoma cells [7]. In the previous identification of differently expressed miRNAs which were considered candidate biomarkers of cholangiocarcinoma progression, miR-451 was found to be downregulated [8]. It has been reported that miR-451 not only regulated the biological function of tumor cells, but also regulated the physiological and pathological processes of humans, and it was also considered a novel therapeutic target of human cancers [9, 10]. miR-451 also shows significant diagnostic value in ischemic stroke and papillary thyroid carcinoma [11, 12]. In colorectal cancer, miR-451 inhibited cell growth and metastasis via targeting MIF [13]. While the specific function of miR-451 remains unclear. This study aimed to validate the expression of miRR-451 in cholangiocarcinoma and disclose its potential in clinical diagnosis and risk prediction of cholangiocarcinoma. The expression of miR-451 was significantly lower in the serum of cholangiocarcinoma than that in the serum of healthy volunteers (P < 0.001, Fig. 1A). In the collected tissues, miR-451 was significantly downregulated in tumor tissues in comparison with the matched normal tissues (P < 0.001, Fig. 1B). Consistently in cholangiocarcinoma cell lines, the downregulation of miR-451 was also observed and showed a dramatic difference with normal cells (P < 0.001, Fig. 1C). Patients were partitioned into a high miR-451 group and a low miR-451 group based on the average expression level of miR-451 in serum and tissues of cholangiocarcinoma. The relatively low expression of miR-451 in tissues showed a significant association with the TNM stage (P = 0.014) and lymph node metastasis status (P = 0.015) of patients (Table 1). Consistently, a close association was also found between the serum miR-451 expression and the TNM stage (P = 0.022) and lymph node metastasis status (P = 0.042) of patients (Table 1). miR-451 could distinguish cholangiocarcinoma patients from healthy volunteers with the AUC value of 0.864 of the ROC curve (sensitivity = 0.859, specificity = 0.774, Fig. 2A). Additionally, in cholangiocarcinoma patients, the downregulation of mir-451 was associated with the worse survival of patients (log-rank P = 0.021, Fig. 2B). Moreover, Cox regression analysis further demonstrated the prognostic value of miR-451. miR-451 and the TNM stage served as independent prognostic indicators of patients with HR values of 2.651 and 2.277, respectively (Table 2). Due to the relatively high sensitivity of CCLP1 and HuCCT1 cells to the downregulation of miR-451, these two cells were selected for the following in vitro cell experiments. miR-451 was overexpressed by the transfection of miR-451 mimic and silenced by the transfection of miR-451 inhibitor in CCLP1 and HuCCT1 cell (P < 0.001, Fig. 3A). In transfected cells, miR-451 overexpression markedly suppressed cell proliferation, and miR-451 knockdown notably promoted CCLP1 and HuCCT1 cell proliferation (P < 0.05, P < 0.01, Fig. 3B). Additionally, the migration of CCLP1 and HuCCT1 cells was also inhibited by miR-451 overexpression and accelerated by the silencing of miR-451 (P < 0.001, Fig. 3C, Fig. S1). Similarly, the overexpression of miR-451 repressed the invasion of CCLP1 and HuCCT1 cells and the miR-451 knockdown showed a dramatically enhanced effect on cell invasion of cholangiocarcinoma (P < 0.001, Fig. 3D, Fig. S1). ATF2 was predicted to bind with miR-451 with several binding sites, and the luciferase of ATF2 was suppressed by the overexpression of miR-451 and enhanced by miR-451 knockdown (Fig. 4A). While the expression of ATF2 was also negatively regulated by miR-451 (Fig. 4B). Significant dysregulation of miRNAs in tumors always insinuates their potential functional roles in human diseases. miR-451 has been widely reported to possess abnormal expression and participate in the progression of human diseases. For example, miR-451 was identified as the most strongly downregulated miRNA in non-small cell lung cancer (NSCLC) and showed significant association with poor differentiation, advanced clinical stage, and positive lymph node metastasis of patients [14]. The abnormal expression of miR-451 was observed in colorectal cancer, gastric cancer, and bladder carcinoma [15–17]. miR-451 has been revealed to be downregulated in hepatocellular carcinoma (HCC) and was involved in the tumor progression and disease development of patients [18]. Both HCC and cholangiocarcinoma are derived from the substance of the hepatic parenchyma and are known as primary liver cancer [19]. In a previous study, miR-451 was demonstrated as a downregulated miRNA in cholangiocarcinoma [8]. miR-451 was speculated to be involved in the pathogenesis and development of cholangiocarcinoma, which lacked available data. The consistent downregulation of miR-451 in the cholangiocarcinoma was observed in the present study, and its significant association with TNM stage and lymph node metastasis status of patients, two major indicators of cholangiocarcinoma progression, was also dugout, suggesting its involvement in cholangiocarcinoma development. miR-451 was also demonstrated to participate in the development of many other cancers for its close relationship with the clinicopathological characteristics of patients. For instance, miR-451 was significantly correlated with the FIGO stage and lymph node metastasis of ovarian cancer patients, and it also predicted patients’ poor prognosis, indicating its significance in cancer progression and prognosis [20]. The significant association between miR-451 and lymph node metastasis was also observed in thyroid cancer and miR-451 was remarkably upregulated in lymph node metastasis tissues compared with tissues without lymph node metastasis [21]. The diagnostic value of miR-451 has been illustrated in various human solid tumors in previous studies, such as gastric cancer, breast cancer, and renal cell carcinoma [22–24]. miR-451 was also revealed to predict the recurrence of colorectal cancer and gastric cancer [25, 26]. Here, the downregulation of miR-451 could also differentiate cholangiocarcinoma patients from healthy volunteers, indicating that miR-451 could as serve as a diagnostic biomarker of cholangiocarcinoma. While the function of miR-451 in the prediction of cholangiocarcinoma recurrence needs further representative samples to estimate. Previously, miR-451 was disclosed to induce G0/G1 phase arrest and the apoptosis of glioblastoma, but the molecular mechanism was controversial [27, 28]. The inhibitory effect of miR-451 on cellular processes of osteosarcoma was revealed [29]. In vitro, the dysregulation of miR-451 affected the proliferation, migration, and invasion of cholangiocarcinoma cells. Specifically, the miR-451 overexpression inhibited cell growth, migration, and invasion, whereas the knockdown of miR-451 promoted the cellular processes of cholangiocarcinoma. These results leaked out that miR-451 functioned as a tumor suppressor during the progression of cholangiocarcinoma. Although the clinical significance and biological function of mir-451 has been revealed, the concrete mechanism underlying these functional roles is also an important part. Several molecules have been demonstrated as the direct targets of miR-451 during its biological function in many other tumors. For example, PGE2 has been reported to mediate the inhibition of osteosarcoma cellular processes by miR-451, and it was found to promote the development of cholangiocarcinoma [30, 31]. ATF2 was found to be negatively regulated by miR-451 through the results of luciferase reporter and expression validation, which is consistent with previous studies [32]. Therefore. ATF2 was speculated to mediate the suppressor role of miR-451 in cholangiocarcinoma. However, the identification of a single miRNA biomarker neglects the potential of other miRNAs with high scores. Recently, the establishment of miRNA signatures has become a research hot point in cancer research. Therefore, future studies would focus on the significance of miR-451 combining with other miRNAs to establish potential signatures. In conclusion, downregulated miR-451 in cholangiocarcinoma showed a close association with the disease development and clinical prognosis. Additionally, miR-451 could distinguish cholangiocarcinoma patients from healthy volunteers with high specificity and sensitivity and it also acted as a tumor suppressor that negatively regulated the proliferation, migration, and invasion of cholangiocarcinoma cells. This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Mindong Hospital Affiliated to Fujian Medical University. One hundred and fifty-nine patients diagnosed with cholangiocarcinoma and sixty-four healthy volunteers who received routine physical examinations at Mindong Hospital of Ningde City were included in this study during 2013–2015. The serum samples, tumor tissues, and matched normal tissues were collected after receiving informed consent from every participator. While only the serum samples were collected from healthy individuals. The cholangiocarcinoma patients were followed up for five years to obtain their survival status after surgery. It is a two-step process of miR-451 expression assessment. Total RNA was isolated and used to synthesize cDNA with the TaqMan Advanced miRNA cDNA Synthesis Kit (Thermo Fisher Scientific, USA). cDNA was diluted and mixed with the SYBR Green master reagent and primer mix. The PCR process was performed with an ABI 7500 system (Applied Biosystems, USA). The 2−ΔΔCt method was used to calculate the relative expression of miR-451 with GAPDH as the internal reference. Cholangiocarcinoma cell lines (CCLP1, HuCCT1, SNU1196, and KKU-100 cells, ATCC) and normal cholangiocyte H69 cells (ATCC) were cultured in a DMEM culture medium. Cell culture was conducted in a constant temperature incubator at 37°C with 5% CO2. Cells reached the logarithmic period were transfected with miR-451 mimic (5’-AAACCGUUACCAUUACUGAGUU-3’), miR-451 inhibitor (5’-AACUCAGUAAUGGUAACGGUUU-3’), or corresponding negative controls (mimic NC and inhibitor NC) with the help of Lipofectamine 2000 (Invitrogen, USA). Cells (1× 105 cells/well) were seeded into 96-well plates and incubated with DMEM culture medium for a specific period. Then, the CCK8 reagent was added to each well and incubated with the mixture for 1 h. OD450 of each well was detected with the employment of a microplate reader (Thermo Fisher Scientific, USA). The experiments were performed three times to obtain the mean values. A total of 2× 104 cells/well were seeded into the upper chamber of the 24-well transwell chambers with a pore size of 8 µm (Corning, USA). The upper chamber was supplied with a serum-free culture medium, while the FBS-containing medium was placed in the bottom chamber. The chambers were incubated at 37°C for 24 h, and the migrated and invaded cells on the lower surface were fixed and stained. The number of cells was counted with the help of a microscope (Olympus, Japan). The wild-type vector was established by cloning the binding sites between miR-451 and ATF2, while the mutant-type vector was constructed with the point mutations. The vectors were co-transfected with miR-451 mimic, inhibitor, or negative controls into the CCLP1 cell, and the relative luciferase activity of ATF2 was detected after 48 h of transfection using the Dual-luciferase repoter Assay System (Promega, USA). All data were represented as mean value ± standard deviation obtained from at least three independent experiments. The difference between groups was analyzed by the student’s t-test and one-way ANOVA. The difference in the expression of miR-451 between healthy volunteers and cholangiocarcinoma was used to evaluate the diagnostic value of miR-451 with the help of the ROC curve. While the prognostic value of miR-451 was assessed with the Kaplan-Meier and Cox regression analysis. P < 0.05 was considered to be statistically significant. Additional file 1: Figure S1. Representative images of Transwell assay inevaluating cell migration and invasion.

PMC9647996

Zhuoying Chen,Meixiu Huang,Jiaying You,Yanhua Lin,Qiaoyun Huang,Caiping He

Circular RNA hsa_circ_0023404 promotes the proliferation, migration and invasion in endometrial cancer cells through regulating miR-217/MAPK1 axis

09-11-2022

hsa_circ_0023404,miR-217,MARK1,Circular RNA,Endometrial cancer

Background Emerging studies indicated that circular RNA hsa_circ_ 0023404 and its target miR-217/MARK1 axis play a critical role in cancer progression such as non-small cell lung cancer and cervical cancer. However, the role of hsa_circ_0023404/miR-217/MARK1 involved in endometrial cancer (EC) was not investigated yet. The aim of this study is to investigate the functions of hsa_circ_0023404 in endometrial cancer (EC) and the potential molecular mechanism. Methods We used RT-qPCR and Western blot approach to detect the expressed levels of related genes in EC cell lines. Transfected siRNAs were applied to knockdown the level of related mRNA in cells. Cell proliferation by CCK-8 assay and colony formation assay were applied to detect cell proliferation. Transwell migration and invasion assay was for detecting the migration and invasion of the cells. Results RT-qPCR showed that the levels of hsa_circ_0023404 and MARK1 mRNA were upregulated, but mirR-217 was decreased in three endometrial cancer cell lines. Knockdown of hsa_circ_0023404 by siRNA markedly increased the level of miR-217 and reduced the proliferation of the Ishikawa cells. It also inhibited the cell migration and invasion. Anti-miR-217 can reverse the promoted proliferation, migrations and invasion of Ishikawa cells mediated by si-circ_0023404. si-MARK1 restored the inhibited cell proliferation, migration and invasion of the co-transfected Ishikawa cells with si- circ_0023404 and anti-miR-217. Conclusion hsa_circ_0023404 exerts a tumor-promoting role in endometrial cancer by regulating miR-217/MARK1 axis. hsa_circ_0023404 inhibit miR-217 as sponge which inhibit endometrial cancer cell growth and metastasis. MARK1 is downstream target of miR217 and upregulated by hsa_circ_ 0023404/miR-217 axis and involved in the endometrial cancer progression. Supplementary Information The online version contains supplementary material available at 10.1186/s40001-022-00866-x.

Circular RNA hsa_circ_0023404 promotes the proliferation, migration and invasion in endometrial cancer cells through regulating miR-217/MAPK1 axis Emerging studies indicated that circular RNA hsa_circ_ 0023404 and its target miR-217/MARK1 axis play a critical role in cancer progression such as non-small cell lung cancer and cervical cancer. However, the role of hsa_circ_0023404/miR-217/MARK1 involved in endometrial cancer (EC) was not investigated yet. The aim of this study is to investigate the functions of hsa_circ_0023404 in endometrial cancer (EC) and the potential molecular mechanism. We used RT-qPCR and Western blot approach to detect the expressed levels of related genes in EC cell lines. Transfected siRNAs were applied to knockdown the level of related mRNA in cells. Cell proliferation by CCK-8 assay and colony formation assay were applied to detect cell proliferation. Transwell migration and invasion assay was for detecting the migration and invasion of the cells. RT-qPCR showed that the levels of hsa_circ_0023404 and MARK1 mRNA were upregulated, but mirR-217 was decreased in three endometrial cancer cell lines. Knockdown of hsa_circ_0023404 by siRNA markedly increased the level of miR-217 and reduced the proliferation of the Ishikawa cells. It also inhibited the cell migration and invasion. Anti-miR-217 can reverse the promoted proliferation, migrations and invasion of Ishikawa cells mediated by si-circ_0023404. si-MARK1 restored the inhibited cell proliferation, migration and invasion of the co-transfected Ishikawa cells with si- circ_0023404 and anti-miR-217. hsa_circ_0023404 exerts a tumor-promoting role in endometrial cancer by regulating miR-217/MARK1 axis. hsa_circ_0023404 inhibit miR-217 as sponge which inhibit endometrial cancer cell growth and metastasis. MARK1 is downstream target of miR217 and upregulated by hsa_circ_ 0023404/miR-217 axis and involved in the endometrial cancer progression. The online version contains supplementary material available at 10.1186/s40001-022-00866-x. Endometrial cancer (EC) is one of the most common types of gynecological cancer and the fourth most common cancer among women. Morbidity and mortality rates among patients with EC remain high globally [1]. Each year, approximately 140,000 women worldwide develop endometrial cancer and an estimated 40,000 women die of this cancer. Most cases of EC are diagnosed after menopause and the highest incidence rate is around 70 years old. Survival is usually determined by the stage and histology of the disease, and the prognosis of endometrial cancer varies greatly in different stages and histological types. The most common lesions (type I) are typically hormone-sensitive and in low-stage with good prognosis, while type II tumors have a high grade and are prone to relapse even in the early stages [2]. Recent large-scale genomic studies have shown that a large number of non-coding RNAs (such as microRNAs and long non-coding RNAs) are associated with the occurrence of gynecological diseases [3, 4].Circular RNAs (circRNAs) belongs to a new class of non-coding RNAs and are formed by a peculiar pre-mRNA with a covalently closed continuous loop. Due to its structures, circRNA are resistant to degradation by exonuclease activity and more stable than linear RNAs. circRNAs have been implicated in microRNA (miRNA) sequestration, modulation of protein–protein interactions and regulation of mRNA transcription. Among them, the most striking function is acting as a miRNA sponge and regulate the expression of their downstream genes [5, 6]. MicroRNAs (miRNAs or miRs) are a class of non-coding RNA molecules that negatively regulate the translation of messenger (m) RNAs by interacting with complementary sites in the 3' untranslated region (UTR) [7]. Many miRNAs act as tumor regulator genes by directly targeting oncogenes or tumor suppressor genes [8]. CircRNAs were implicated not only involved in cellular physiological functions, but also in various human pathologies including cancer. It was found that circRNAs are aberrantly modulated in human cancer tissues. Furthermore, research is currently focusing on understanding the possible implications of circRNAs in diagnostics, prognosis prediction, and eventually therapeutic intervention in human cancer [3]. CircRNA hsa_circ_0023404 (chr11: 71668272–71671937) is derived from mRNA of ring finger protein 121 (RNF121, NM_018320). Increasing evidence supported that hsa_circ_0023404 play a critical role in cancer progression. For example, it showed that hsa_circ_0023404 can promote the proliferation, migration and invasion of non-small cell lung cancer (NSCLC) by regulating miR-217/ZEB1 axis [9]. Compared with the miR-con group, overexpression of miR-217 reduced the relative luciferase activity of the pGL3-circ_0023404-WT reporter vector in vitro and strongly validated that hsa_circ_0023404 interacted with and sponged miR-217. Other studies demonstrated that hsa_circ_0023404 was involved in cervical cancer by regulating miR-5047 and miR-136/TFCP2 /YAP pathway [10]. Recently, mounting evidence showed that miR‑217 can regulated tumor biology depending on the cell type [11, 12]. It was observed that the WNT, mitogen‑activated protein kinase (MAPK), and PI3K/AKT signaling pathways were important molecular targets of miR-217 in different cancers and contributed to cancer progression [13]. MAPK1 was identified as a novel miR‑217 target and was a key component of RAS/RAF/MAPK pathway which was found activated in about 30% of all human cancer tissues. Activated MAPK1 translocated to the nucleus and catalyzed the phosphorylation of numerous nuclear transcription factors such as ETS (erythroblast transformation specific), ELK-1 (ETS Like-1 protein), c-Fos and activated variety target genes such as ErbB, VEGF, etc., which contributes to the progression of tumors [14, 15]. Inhibition of MAPK1 was shown to block tumor growth and metastasis in prostate cancer [16]. It was found that miR‑217 can suppress tumorigenicity of colorectal cancer targeting MAPK1 [17]. These indicated that hsa_circ_0023404 and its target miR-217/MARK1 axis play a critical role in cancer progression such as non-small cell lung cancer and cervical cancer, but the role of hsa_circ_0023404/miR-217/MARK1 involved in endometrial cancer was not investigated yet. In this study, we investigated the role of hsa_circ_0023404 in promoting endometrial cancer cells associated with miR-217/MAPK1 axis. Human endometrial endothelial cell (HEEC) and human endometrial cancer cells (Ishikawa, RL95-2 and KLE) were purchased from the American Type Culture Collection (ATCC, USA) or National Infrastructure of Cell Line Resource (Beijing, China). Cells were incubated in DMEM (Gibco, USA) contained 10% fetal bovine serum (FBS; PAN biotech, Germany) and 1% penicillin/streptomycin (Solarbio, China) at 37 °C and 5% CO2. Total RNA was extracted using Neurozol reagent (Macherey–Nagel, Germany) and cDNA was generated using reverse transcription reagent kit (PROMEGA, USA). Real-time PCR was performed using SYBR Green PCR kit (TaKara, China). U6 and GAPDH are internal controls. The qPCR analysis was then performed on an ABI 7500 Real-time PCR System (Applied Biosystems, Thermo Fisher Scientific, USA) according to the instructions supplied by the manufacturer. The relative expression levels of the genes were calculated by comparing to U6 or GAPDH using 2 − ΔΔCT method. The primers were used as follows: miR-217 FORWARD: CGCGTACTGCATCAGGAACTG; miR-217 REVERSE: AGTGCAGGGTCCGAGGTATT; miR-217-5p RT (anti-miR-217) Primer: GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACTCCAAT; U6 FORWARD: CTCGCTTCGGCAGCACA; U6 REVERSE: AACGCTTCACGAATTTGCGT; circ_0023404 FORWARD: ACCGTGGCCATGAAGCTATG; circ_0023404REVERSE: GGTCACCATATTGTAGGAGCGT; GAPDH FORWARD: AGAAGGCTGGGGCTCATTTG; GAPDH REVERSE: AGGGGCCATCCACAGTCTTC; MAPK1 FORWARD: CAGTTCTTGACCCCTGGTCC; MAPK1 REVERSE: GTACATACTGCCGCAGGTCA. si-NC (negative control) sequence: UUCUCCGAACGUGUCACGUTT, si-circRNA (si-hsa_circ_0023404 #1–3; #3 sequence: GGUUCCUGCUAAUCUAUAATT, miR-217 or anti-miR-217 were synthesized by GenePharma (Shanghai, China). They were transfected in Ishikawa cells using Lipofectamine 3000 Reagent (Life Technologies, USA) and then culture in at 37 °C and 5% CO2 for 48–72 h. Ishikawa cells were plated at 2 × 10E3 cells/well in 96-well plates and grown in medium containing 10% FBS for 24 h. After transfection with siRNA, 10 μl of cell count kit-8 (CCK-8, CK04, Dojindo, Japan) was added into each well and cells were incubated for 2 h in a 5% CO2 incubator at 37 °C. The absorbance of each well at 450 nm was read in GloMax™ 96 MICROPLATE (Promega, USA). Ishikawa cells were transfected with siRNA for 48 h and trypsinized and dispensed into 6-well plates with a density of 800 cells/well. When the number of cells in a colony is more than 50, 10% formaldehyde was employed to fix colonies for 10 min and 0.5% crystal violet was adopted to stain colonies for 5 min. Images were photographed and the number of colonies was calculated by ImageJ. For migration assay, transfected Ishikawa cells (1 × 10E5 cells) were suspended in 200 ul serum-free medium and then seeded on the top chamber. Medium contained 10% FBS was added into the lower chamber. After 24 h of incubation, cells on the lower surface of the lower chamber were fixed with 4% PFA and stained with 0.1% crystal. Cells were counted from five randomly selected microscopic fields. For invasion assay, Transwell inserts (Fisher Scientific, USA) were coated with Matrigel (BD, USA). After 24 h incubation, cells on the upper surface of the Transwell membrane were gently removed, and cells on the lower surface of the Transwell membrane were fixed and stained with crystal violet, counted from five randomly selected microscopic fields. Cells were collected and lysed with RIPA buffer (Beyotime, China). Equal amount of protein was separated on SDS-PAGE and transferred to PVDF (Millipore, USA). Then, the membranes were incubated with the primary antibodies anti-MARK1 (Cat:125403, Novopro, China) and anti-actin (Sigma, USA). ECL substrates were used to visualize protein bands (Millipore, USA). All experiments were replicated thrice and all data were expressed as mean ± standard deviation (SD). The software GraphPad 8.0 were used to carry out all statistical analyzes. Student's t-test and one-way ANOVA followed by Bonferroni 's post hoc test were utilized to analyze 2 or multiple groups, respectively. * means p < 0.05; ** means p < 0.01; *** means p < 0.001. To examine the role of hsa_circ_0023404 and its target miR-217/MARK1 axis in endometrial cancer cell lines, The RT-qPCR was applied to determine the level of hsa_circ_0023404, miR-217 and MARK1 mRNA in human endometrial endothelial cell (HEEC) and three human endometrial cancer cells (RL95-2, KLE and Ishikawa). It was shown that hsa_circ_0023404 (Fig. 1A) and MARK1 (Fig. 1C) were upregulated in RL95-2, KLE and Ishikawa cell lines compared to HEEC. On the contrary, miR-217 (Fig. 1B) was downregulated in RL95-2, KLE and Ishikawa cell lines compared to HEEC. Among the three cell lines, the results in Ishikawa cell were most strikingly and we employed the Ishikawa cells in further study. Since the level of hsa_circ_0023404 is upregulated in endometrial cancer cells, we investigate its biological role in endometrial cancer by knockdown of hsa_circ_0023404 with si-circ_0023404 in Ishikawa cells. It showed all three siRNAs targeted to hsa_circ_0023404 significantly reduced mRNA expression of the hsa_circ_002340 in Ishikawa cells compared to control siRNA (si-NC) analyzed by RT-qPCR (Fig. 1D). Among all siRNA, the siRNA#3 had the highest efficiency and was employed for subsequent experiments. We next examined the effect of si-circ_0023404 on miR-217 expression in Ishikawa cells and it showed the si-circ_0023404 #3 reduced the level of circ_ 0023404(Fig. 1E) while the level of miR-217 was upregulated (Fig. 1F). This is consistent with that the circ_0023404 inhibited the miR-217 expression acting as a miRNA sponge. Our data further showed that downregulation of hsa_circ_0023404 markedly decreased the proliferation of the Ishikawa cells detected by CCK-8 assay (Fig. 1G). Down-regulation of hsa_circ_0023404 also markedly decreased the capacity of colony formation of in Ishikawa cells compared to control (Fig. 1H, I). Consistently, knockdown of hsa_circ_0023404 inhibited the cell migration and invasion in Ishikawa cells analyzed by Transwell migrations assay (Fig. 1J, K) and Transwell invasion assay (Fig. 1J, L). These data indicated that hsa_circ_0023404 promoted cell proliferation, migration and invasion in endometrial cancer cells. To investigate the role of miR-217 in endometrial cancer cells, Ishikawa cells were transfected with mimic NC and miR-217 mimic. The expression of transfected miR-217 mimic was confirmed by RT-qPCR (Fig. 2A). CCK-8 assay demonstrated that miR-217 mimic markedly decreased the proliferation of the Ishikawa cells (Fig. 2B). miR-217 mimic also markedly decreased the capacity of colony formation of in Ishikawa cells compared to mimic NC (Fig. 2C, D). In parallel, it showed that miR-217 reduced the cell migration and invasion in Ishikawa cells analyzed by Transwell migrations assay (Fig. 2E, F) and Transwell invasion assay (Fig. 2E, G). MARK1 is the target of miR-217 and WB analysis indicated that the miR-217 mimic transfection decreased the expression of MARK1 protein in Ishikawa cells (Fig. 2H, I). These data indicated that miR-217 played a critical role in inhibiting cell proliferation, migration and invasion in endometrial cancer cells and MARK1 protein is one downstream target of miR-217. Studies indicated that miR-217 was one sponge target of hsa_circ_0023404 and we examined their interaction by co-transfection with si-circ_0023404 and anti-miR-217 (miR-217 inhibitor). Co-transfection showed that si-circ_0023404 attenuated the expression level of hsa_circ_0023404 while anti-miR-217 increased hsa_circ_0023404 (Fig. 3A); si-circ_0023404 increased the expression level of miR-217 while anti-miR-217 blocked the increased miR-217 (Fig. 3B). CCK-8 assay showed that downregulation of hsa_circ_0023404 decreased the proliferation of the Ishikawa cells but anti-miR-217 reversed the decrease (Fig. 3C, D). Transwell migrations and invasion assay (Fig. 3E, F) also indicated that anti-miR-217 blocked the promoted migrations and invasion of Ishikawa cells mediated by si-circ_0023404. Summary, these data showed that anti-miR-217 can block the promoted proliferation, migrations and invasion of Ishikawa cells by si-circ_0023404, consistent with that hsa_circ_0023404 acts as sponge of miR-217. MARK1 is a potential target of miR-217 and our data showed that anti-miR-217 increased the MARK1 protein level which was blocked by co-transfection with si_circ_0023404 and anti-miR-217, supporting that MARK1 is downstream of the hsa_circ_0023404/mirR217 axis (Fig. 4A). Western blot showed that si-MARK1 can downregulate the induced MARK1 by co-transfection with si_circ_0023404 and anti-miR-217. si-MARK1 restored the inhibited cell proliferation of the co-transfected Ishikawa cells with si- circ_0023404 and anti-miR-217 analyzed by CCK8 assay (Fig. 4E) and colony formation assay (Fig. 4F, G). In parallel, it showed that si-MARK1 restored the inhibition in migration (Fig. 5A, B) and invasion (Fig. 5A, C) of the co-transfected Ishikawa cells with si-circ_0023404 and anti-miR-217 analyzed by Transwell migrations and invasion assay. These data supported that the MARK1 is downstream of hsa- circ_0023404/miR-217 axis and MARK1 knockdown by si-MARK1 can block the promotion of cancer biology mediated by si-circ_0023404/miR-217 axis. Plenty of studies supported that circular RNA hsa_circ_0023404 is associated with tumorigenesis. In this study, we found that hsa_circ_0023404 was upregulated with decreased miR-217 in endometrial cancer cell lines. Knockdown of hsa_circ_0023404 lead to increased miR-217 and inhibited the proliferation and metastasis of endometrial cancer cells. Anti-miR-217 can reverse the imbibition by si-circ_0023404. These data indicated that hsa_circ_0023404 promoted the proliferation, migration and invasion in endometrial cancer cells by sponging miR-217. In further study, knockdown of MARK1 blocked the promotion of cancer biology mediated by si-circ_0023404/miR-217 axis, supporting that MARK1 is the target of miR-217 and involved in circ_0023404/miR-217-mediated endometrial cancer biology. In human cancer, circRNAs were implicated in the control of oncogenic activities, such as tumor cell proliferation, epithelial–mesenchymal transition, invasion, metastasis and chemoresistance. The most widely described mechanism of action of circRNAs is their ability to act as competing endogenous RNAs (ceRNAs) for miRNAs, lncRNAs and mRNAs, thus impacting along their axis [2, 18, 19]. Several studies revealed that circRNA hsa_circ_0023404 play critical role in tumorigenesis. For example, it enhances cervical cancer metastasis and chemoresistance through VEGFA and autophagy signaling by sponging miR-5047 [10]. hsa_circ_0023404 is also involved in cervical cancer progression through/miR-136/TFCP2/YAP axis. hsa_circ_0023404 promoted TFCP2 expression via inhibiting miR-136, leading to activation of YAP signaling pathway [20]. hsa_circ_0023404 was shown to interact with miR-217/ZEB1 axis to contribute to the growth, migration and invasion of NSCLC cells [9]. This study provided the strong evidence that hsa_circ_0023404 promoted the proliferation, migration and invasion in endometrial cancer cells through regulating miR-217/MARK1 axis. Dysregulated miRNA expression was involved in malignancies and miRNAs may serve as tumor suppressor or oncogene to participate in human cancer progression. As a miRNA, miR‑217 is closely linked to tumor progression and poor prognosis [21, 22]. Previous studies have reported that miR‑217 bound to its target mRNA to inhibit the formation and progression of tumors, including gastric cancer [22]. Bioinformatics identified MARK1 protein is the target of miR‑217 in cancer cells. There are two binding sequences for miR‑217 in MAPK1 3'UTRs which was confirmed by the luciferase activity assay [23]. Consistently, previous study showed that downregulated MAPK1 by miR-217 facilitated the metastasis and EMT process of HCC cells, indicating that miR-217 suppressed HCC via negatively modulating MAPK1 expression[24]. Mutiple evidence demonstrated that miR217-MAPK axis was involved in tumorgenesis. For example, it was uncovered that the apoptosis-inducing potential of miR-217-5p can induced apoptosis via blocking multiple target genes PRKCI, BAG3, ITGAV and MAPK1 in colorectal cancer cells [25]. It also is reported that circMAN2B2 acted as an onco-miRNA in HCC by sponging miR-217 to promote MAPK1 expression [26]. The MAPK pathway is effectively involved in the regulation of cancer cell proliferation, invasion and survival by activating target genes such as transcriptional factor ELK1, C-Fos and the ErbB, VEGF, which contributes to the progression of tumors [14, 15]. Previous studies have confirmed that increased MAPK1 expression could function as tumor promoter in human hepatocellular carcinoma (HCC) [27, 28], ovarian cancer [29] and cervical cancer [30]. It also showed that lncRNA RHPN1-AS1 activated ERK/MAPK pathway and promoted cell proliferation, migration and invasion of endometrial cancer [31]. Another study demonstrated that activation of MAPK and AKT by Type II transmembrane serine proteases 4TMPRSS4 were associated with the progression of endometrial cancer [32]. These data provided the evidence that MAPK can be regulated by non-coding RNA (ncRNA) including lnRNA and cirRNA, etc. In this study, our data showed that MARK1 is downstream target of hsa_circ_0023404/miR-217 axis and involved in the endometrial cancer progression. With the advancement of RNA sequencing technology and the rapid development of bioinformatics, a large number of circRNAs were discovered widely involved in a variety of cancer-related pathogenesis and drug resistance and in the diagnostic and prognostic biomarker and the therapeutic target in human cancer [33]. The powerful functions and unique properties of circRNAs have made them the focus of scientific and clinical research. Due to the structure of covalently closed continuous loop, circRNAs are relatively stable and exist stably at high levels in body fluids, including plasma, serum, exosomes and urine, etc. Therefore, circRNA potentially service as the liquid biopsy-based novel biomarkers for monitoring the development and progression of cancer including lung cancer [34], endometrial Cancer [35], bladder cancer [36], prostate cancer [37], etc. Downregulated circBNC2 and higher circSETDB1 levels were identified in patients with ovarian cancer [38]. hsa_circ_ 0109046 and hsa_circ_0002577 were found increase in the serum of patients with endometrial cancer [39]. The unique cellular stability and capacity of circRNA to sponge miRNA and protein may place circRNA as a promising vehicle for the delivery of cancer therapeutics [40]. In the current study, we demonstrated the molecular mechanism of hsa_circ_0023404/miR-217/MAPK involved in the endometrial cancer progression. However, there are several limitation. First, it was limit to draw the conclusion completely only dependent in vitro experiments, therefore, we would further carry out in vivo experiments on hsa_circ_0023404/miR-217/MAPK axis involved in the endometrial cancer. Second, the application of hsa_circ_0023404 on liquid biopsy was not perform on patients with endometrial cancer. We will collect the patients to investigate the level of hsa_circ_0023404/miR-217/MAPK in their blood sample and examine their potential as novel biomarker for endometrial cancer. In this study, our data demonstrated that hsa_circ_0023404 exerts a tumor-promoting role in endometrial cancer by regulating miR-217/MARK1 axis. The hsa_circ_0023404 act as sponge for and inhibit miR-217 which inhibit endometrial cancer cell growth and metastasis. MARK1 is downstream target of miR217 and the induced MARK1 by hsa_circ_0023404 through miR217 inhibition contribute to the endometrial cancer progression (Fig. 5D). Targeting or knockdown of hsa_circ_0023404 by short hairpin RNA (shRNA) or CRISPR technique would be a potential therapeutic approach for endometrial cancer and will be investigated in the future. Additional file 1: Figure S1. Uncropped Western blot images.

PMC9648055

Qin Zhou,Shanshan Liu,Yuying Kou,Panpan Yang,Hongrui Liu,Tomoka Hasegawa,Rongjian Su,Guoxiong Zhu,Minqi Li

ATP Promotes Oral Squamous Cell Carcinoma Cell Invasion and Migration by Activating the PI3K/AKT Pathway via the P2Y2-Src-EGFR Axis

26-10-2022

Oral cancer is one of the most common malignancies of the head and neck, and approximately 90% of oral cancers are oral squamous cell carcinomas (OSCCs). The purinergic P2Y2 receptor is upregulated in breast cancer, pancreatic cancer, colorectal cancer, and liver cancer, but its role in OSCC is still unclear. Here, we examined the effects of P2Y2 on the invasion and migration of oral cancer cells (SCC15 and CAL27). The BALB/c mouse model was used to observe the involvement of P2Y2 with tumors in vivo. P2Y2, Src, and EGFR are highly expressed in OSCC tissues and cell lines. Stimulation with ATP significantly enhanced cell invasion and migration in oral cancer cells, and enhanced the activity of Src and EGFR protein kinases, which is mediated by the PI3K/AKT signaling pathway. P2Y2 knockdown attenuated the above ATP-driven events in vitro and in vivo. The PI3K/AKT signaling pathway was blocked by Src or EGFR inhibitor. Extracellular ATP activates the PI3K/AKT pathway through the P2Y2-Src-EGFR axis to promote OSCC invasion and migration, and thus, P2Y2 may be a potential novel target for antimetastasis therapy.

ATP Promotes Oral Squamous Cell Carcinoma Cell Invasion and Migration by Activating the PI3K/AKT Pathway via the P2Y2-Src-EGFR Axis Oral cancer is one of the most common malignancies of the head and neck, and approximately 90% of oral cancers are oral squamous cell carcinomas (OSCCs). The purinergic P2Y2 receptor is upregulated in breast cancer, pancreatic cancer, colorectal cancer, and liver cancer, but its role in OSCC is still unclear. Here, we examined the effects of P2Y2 on the invasion and migration of oral cancer cells (SCC15 and CAL27). The BALB/c mouse model was used to observe the involvement of P2Y2 with tumors in vivo. P2Y2, Src, and EGFR are highly expressed in OSCC tissues and cell lines. Stimulation with ATP significantly enhanced cell invasion and migration in oral cancer cells, and enhanced the activity of Src and EGFR protein kinases, which is mediated by the PI3K/AKT signaling pathway. P2Y2 knockdown attenuated the above ATP-driven events in vitro and in vivo. The PI3K/AKT signaling pathway was blocked by Src or EGFR inhibitor. Extracellular ATP activates the PI3K/AKT pathway through the P2Y2-Src-EGFR axis to promote OSCC invasion and migration, and thus, P2Y2 may be a potential novel target for antimetastasis therapy. Oral cancer is one of the most common malignancies of the head and neck. Approximately 90% of oral cancers are oral squamous cell carcinomas (OSCCs). According to the World Health Organization (WHO), more than 260,000 people are newly diagnosed with oral cancer every year, and the incidence in people aged over 65 years accounts for more than 50% of the total population. Despite recent advances in treatment, the rich blood supply and complex anatomical structure of the oral and maxillofacial region are conducive to recurrence and distant migration in approximately one-third of patients treated with conventional surgery or radiotherapy. Tumor invasion and metastasis are still the main causes of death in OSCC patients, and therefore, new antitumor methods are required for more effective clinical treatment of OSCC. Adenosine 5′-O-triphosphate (ATP) has long been considered as the body’s most direct source of energy. In 1972, Geoff Burnstock put forward the “purinergic hypothesis” of neurotransmission, and the concept of ATP as an extracellular signaling molecule was formalized as a scientific hypothesis. Studies have shown that ATP (P2 receptors) is an important transmitter of various biological effects mediated by purinergic receptors, including cell proliferation, differentiation, and death. ATP may be crucial in promoting or preventing malignant metastasis. Under normal conditions, the extracellular ATP concentration (mmol/L) is much lower than the intracellular concentration (3–5 mmol/L), and it remains balanced. In a tumor microenvironment, the ATP concentration (about 100 μmol/L) is higher than that in the normal extracellular environment. ATP invasive transfer activity was first reported in prostate cancer. However, the pathogenesis of OSCC is still unclear. Purinergic receptors are divided into P1 and P2 receptors; the natural ligand of the P1 receptor is adenosine. P2 receptors are divided further into the following two categories: P2X and P2Y receptors. The P2X receptor is a ligand-gated ion channel receptor, with seven currently known subtypes (P2X1-P2X7), activated by extracellular ATP to release cation flow. The P2Y receptor is a G-protein-coupled receptor (GPCR) that plays an important role in a variety of signaling pathways. Currently, eight functional mammalian P2Y receptors (P2Y1, P2Y2, P2Y4, P2Y6, P2Y11, P2Y12, P2Y13, and P2Y14) have been cloned and identified as GPCRs. P2Y2 is a functional receptor. Its first ligands are ATP and uridine triphosphate (UTP). P2Y2 can activate a variety of signaling pathways, with the typical path being a Gqα signaling path. Thus, when it is coupled with IP3 to promote the release of Ca2+ from the endoplasmic reticulum calcium store, it increases the intracellular Ca2+ concentration and activates protein kinase C(PKC). P2Y2 acts at the c-terminus of cells, and it activates the mitogen-activated protein kinase (MAPK) pathway by activating nonreceptor tyrosine protein kinase (Src). P2Y2 activates the matrix metalloproteinases ADAM10 and ADAM17, and the catalytic film binds to the growth factor to activate the epidermal growth factor receptor (EGFR). Although P2Y2 is upregulated in breast cancer, pancreatic cancer, colorectal cancer, and liver cancer, and is activated in cell proliferation, invasion, and migration, the role of P2Y2 in OSCC is still unclear and requires further research. In this study, we used CAL27 and SCC15 oral squamous cell lines to explore the reaction of P2Y2 to cellular and associated mechanisms of extracellular nucleotide induction. P2Y2 was purchased from Santa Cruz Biotechnology (Santa Cruz, CA). p-AKT, AKT, p-PI3K, PI3K, Src, p-Src, EGFR (D38B1), and phospho-EGFR Y1068 (D7A5) antibodies were purchased from Abcam (MA). Anti-GAPDH was purchased from Protein Tech (Wuhan, China). ATP was purchased from GLPBIO (Shanghai, China), and siRNA was purchased from RIBOBIO (Guangzhou, China). AG1478 and Dasatinib were purchased from MCE (Shanghai, China). OSCC tissues were collected from patients (n = 6) who underwent radical surgery between January 2019 and January 2020 at Shandong University (Jinan, China) with informed consent obtained concerning the use of surgically resected specimens for research purposes. All of the patients agreed and signed the informed consent. All human tissue and sample experiments were approved by the Ethics Committee of the School of Stomatology, Shandong University (ref Med. No. 20210802; 10 August 2021). The experiments conformed to the guidelines set by the Declaration of Helsinki. The patients did not receive any form of adjuvant therapy before surgery. Human OSCC cell lines (CAL27 and SCC15) were obtained from the Shanghai Cell Bank of the Chinese Academy of Sciences (Shanghai, China). The cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM-F12, Hyclone) containing 10% fetal bovine serum (Gibco, Grand Island, NY) and 1% penicillin-streptomycin, in a 37 °C, 95% humidified air and 5% CO2. CAL27 and SCC15 cells were seeded in a 96-well culture plate at a density of 1 × 103 cells/well. CAL27 and SCC15 cells were treated with ATP, and after 24, 48, and 72 h of incubation, cell viability was determined by the cell counting kit assay (CCK8, Soleibao, Beijing, China). Subsequently, an enzyme-labeling instrument (iMark, Bio-Rad Laboratories, Inc.) was used to detect the absorbance to determine cell viability. The sequence of a single small interfering RNA (siRNA)1 was CCCGTGCTCTACTTTGTCA; single siRNA2 was GTAGCGAGAACACTAAGGA, all siRNA single strands are synthesized in vitro, at Guangzhou Borui Co, Ltd. Division (Guangzhou, China). An appropriate number of cells were seeded onto six-well cell culture plates 24 h before transfection, allowing the cell density to reach 30–50%. A transfection complex solution was prepared in accordance with the manufacturer’s protocol. Subsequently, 50 nM siRNAs were mixed with the siRNA Transfection Reagent. CAL27 and SCC15 cells were then transfected for 48 h. Subsequently, quantitative real-time polymerase chain reaction (qPCR) and western blotting were used to confirm the transfection of cell lines, and the siP2Y2 with the highest transfection efficiency was selected. Transfected cells as the experimental group were treated with 100 μM ATP for 24 h, and the expression of related genes and proteins was detected. The migration ability of CAL27 and SCC15 cells was tested via scratch assay. The two types of cells were inoculated into six-well plates at a density of 5 × 105 cells/well, adhere to 80% density, and then replace the serum-free α-DMEM-F12 cell culture medium for culture. Subsequently, the bottom of the plate was scraped vertically with the tip of a 200 μL liquid pipette and washed with PBS. Then, the cells were treated with 100 μM ATP for 24 and 48 h in serum-free medium. Finally, take pictures under an inverted microscope (BX53; Olympus, Japan) at ×100 magnification at 0, 24, and 48 h. Image-Pro Plus 6.0 software (Media Controlnetics, Inc., Rockville, Maryland) was used to calculate the width of the healing area in the cell monolayer Learn analysis. The effect of P2Y2 on the invasion ability of CAL27 and SCC15 cells was evaluated by the transwell chamber test. A small chamber with 8 μM pore size was placed into a 24-well plate. The upper chamber was filled with 60–80 μl Matrigel (BD, Franklin Lakes, NJ), inoculated with 3.5 × 103 cells in 200 ml of culture medium in the upper chamber, experimental group cells were treated with 100 μM ATP, the lower chamber was filled with 750 mL of α-DMEM containing 10% serum after culturing the F12 medium at 37 °C for 24 h, and then the cells were removed. A cotton swab was used to gently remove the cells in the upper chamber. The cells were fixed with 4% methanol, stained with 0.1% crystal violet, photographed under an optical microscope, counted, and statistically analyzed. CAL27 and SCC15 cells were seeded in a six-well plate at a density of 400 cells per well. The cells were treated with 100 μM ATP cultured with α-DMEM-F12 for 14 days, and when they grew into a colony of 50 cells, the cells were washed with PBS and fixed with 4% methanol. Then, the cells were stained with 0.1% crystal violet and scanned. The number of colonies greater than 50 cells was counted, and statistical analysis was performed. CAL27 and SCC15 cell RNA was extracted from the cells using Trizol reagent (AG21102, Accurate Biotechnology Co., Ltd., China). cDNA was synthesized using the Evo M-MLV RT Reverse Transcription kit II (AG11711, Accurate Biotechnology). QPCR was performed using the SYBR Green Pro Taq HS premixed qPCR kit (AG11701, Accurate Biotechnology) in an RT fluorescence quantitative PCR system (Light Cycler 96 SW 1.1, Roche Ltd, Switzerland). The parameters required for denaturation, annealing, and extension were as follows: 95 °C for 30 s, 45 cycles at 95 °C for 5 s, and 60 °C for 20 s. The primer sequences are shown in Table 1. All data were normalized to GAPDH expression. Quantification of the qRT-PCR results was performed by the 2–ΔΔCT method. CAL27 and SCC15 cells were washed three times with precooled PBS, RIPA lysate was added to lyse the cells and then collected, and the protein concentration was detected with the BCA protein detection kit. The same amount of total protein (10 μg) was separated by 10% sodium salt-polyacrylamide gel electrophoresis (SDS-PAGE) and then transferred to a poly(vinylidene fluoride) (PVDF) membrane. After being blocked with 5% bovine serum albumin (BSA)/tris-buffered solution with Tween (TBST) for 1 h, the PVDF membrane was incubated with the P2Y2 antibody (concentration 1:2000), Src/p-Src antibody (concentration 1:2000), EGFR/p-EGFR antibody (concentration 1:2000), PI3K/p-PI3K antibody (concentration 1:500), AKT/p-AKT antibody (concentration 1:500), and GAPDH (concentration 1:10 000), at 4 °C overnight. Subsequently, the proteins were washed three times in TBST and then incubated in horseradish peroxidase-conjugated goat anti-rabbit IgG (1:2000) for 1 h. After washing in TBST, immune reaction zones were determined with the ECL detection system and then captured by the gel imaging system (Amersham Imager 600; General Electric Company). Athymic nude BALB/c female mice (aged: 4 weeks, n = 10) were purchased from Jinan Peng Yue Laboratory Animal Breeding Co. Ltd. They were housed in a specific pathogen-free environment under the condition of a 12-h light/12-h dark cycle as well as free access to food and water. The mice were randomly divided into two groups (n = 5), and 2 × 106 CAL27 cells were subcutaneously injected into the back of the right upper limb of each mouse. First, 2 × 106 CAL27 cells and siRNA CAL27 cells were subcutaneously injected into the back of the right upper limb of each mouse. Tumor size was detected every 3 days using a slide caliper, and the tumor volume was calculated using the following formula: A × B2/2, where A is the length of the tumor and B is the width. After 30 days, the mice were euthanized and the tumors were isolated, weighed, photographed, and fixed immediately with 4% paraformaldehyde for subsequent analysis. The animal experiments were approved by the Institutional Animal Care and Use Committee (IACUC) of Shandong University. Animal study and euthanasia were carried out following the recommendations of the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. The protocol was approved by the Committee on the Ethics of Animal Experiments of the Department of School and Hospital of Stomatology, Shandong University (ref Med. No. 20210803; 10 August 2021). GraphPad Prism 6.0 software (GraphPad Software, Inc.) was used for statistical analysis. Paired Student’s t-test was used for comparison between the two groups, and the significance level was adjusted according to the number of tests in multiple comparisons. The cells of the experimental group and control group were tested by independent sample t-test. The differences between three or more groups were tested by one-way analysis of variance (ANOVA). All experiments are repeated at least three times unless otherwise stated. All results are expressed as mean values ± standard deviation. p < 0.05 was considered statistically significant. To investigate the expression and role of P2Y2 in OSCC, we collected three surgical specimens of OSCC. Western blotting and qRT-PCR showed that P2Y2 expression was significantly higher in tumor tissue than in adjacent noncancerous tissues (Figure 1A–C). P2Y2 was significantly expressed in all OSCC cells (Figure 1E–G). Furthermore, Src and EGFR were also highly expressed in OSCC (Figure 1A,B,D). To further study the role of P2Y2 receptors in human cancer and explore their clinical significance, we used TCAG database analysis to display that Src and EGFR were highly expressed in head and neck squamous cell carcinoma (HNSCC) (Figure 1H) and that the expression of P2Y2 was highly correlated with that of Src and EGFR (Figure 1I). To determine whether OSCC cell lines express functional P2Y2, we used Cell Counting Kit-8 (CCK-8) to ascertain the optimal stimulating concentration of ATP. With increasing ATP concentration, cellular activity was also increased, but at a concentration of 200 μM, the cellular activity was significantly decreased (Figure 2A). Western blotting and qRT-PCR were used to verify the CCK8 results. According to these results in combination, the optimal ATP concentration for stimulation was determined to be 100 μM (Figure 2B–D). The Western blot analysis showed that ATP stimulated the phosphorylation of Src and EGFR in a dose- and time-dependent manner and reached peak activation at 100 μM ATP within 10–20 min (Figure 2E–H). The OSCC cell lines were treated with the extracellular nucleotide ATP. In cell colony analysis, CAL27 and SCC15 cells were treated with 100 μM ATP for 14 days. The number of cell colonies was increased in comparison to that in the control group (Figure 3A). The scratch test and transwell test showed that CAL27 and SCC15 cell migration and invasion were significantly promoted compared to those in the control group (Figure 3B–D). To investigate whether extracellular ATP can enhance the invasion ability of OSCC cells, we used qRT-PCR assay to analyze the gene expression. In CAL27 and SCC15 cells stimulated with 100 μM ATP for 24 h, the expression of Zinc finger protein SNAI1 (Snail), matrix metalloproteinase (MMP)2, and MMP9 genes increased. At the same time, expression of E-cadherin and Vimentin decreased, and these results suggested that ATP induced the invasion and migration of OSCC cells and that P2Y2 receptor activation may play a major role in mediating the expression of genes related to invasion and migration (Figure 3E,F). To investigate whether the PI3K/AKT signaling pathway is involved in the regulation of ATP-induced invasion and migration of OSCC cells, we treated CAL27 and SCC15 cells with 100 μM ATP for 24 h. Compared with the control group, the expression of p-PI3K and p-AKT was significantly increased after 24 h of ATP treatment (Figure 3G). To demonstrate the association between P2Y2 and the invasion and migration ability of OSCC cells, P2Y2 was knocked down by siRNA. CAL27 and SCC15 cells were transfected with 50 nM siRNA for 48 h, and the optimal siRNA was determined using real-time PCR and western blotting. The most effective siRNA of P2Y2-siRNA1 was selected and used in the experiment (Figure 4A,B). In the cell colony analysis, the cells were treated with ATP (100 μM) for 14 days, and after that, the number of cell colonies was lower compared with the P2Y2 knockdown group (Figure 4C). In the in vitro invasion test, P2Y2-siRNA cells were treated with 100 μM ATP for 24 h. The number of CAL27 and SCC15 cells in the P2Y2 knockdown group was significantly lower than that of the control group, indicating that P2Y2 may promote the invasion of CAL27 and SCC15 cells (Figure 4D). The wound-healing assay showed the same result in that after treatment with ATP (100 μM) for 24 h, the wound gap between CAL27 and SCC15 cells was significantly reduced compared with that in the control group, suggesting that P2Y2 is involved in ATP-promoted migration and invasion of OSCC cells (Figure 4E). To study the downstream effect of invasion driven by ATP-P2Y2, we first focused on the genes related to invasion and migration. After P2Y2 was transfected, OSCC cells were stimulated with 100 μM ATP for 24 h. PCR detection showed that the ATP-mediated expression of Snail, MMP2, and MMP9 genes was inhibited, and the expression of E-cadherin and Vimentin was activated (Figure 5A–E). Nude mice were subcutaneously implanted with CAL27 cells from the NC and siRNA groups to study the anticancer effect of P2Y2 in vivo. The average tumor volume in the siRNA group was significantly smaller than that in the control group. The average tumor weight in the P2Y2-siRNA group was also significantly smaller than that in the control group (Figure 5F,G). These data support the hypothesis that P2Y2 receptors play an important role in ATP-mediated invasion and migration in vivo and in vitro. To explore how P2Y2 regulates the Src and EGFR signaling pathways through extracellular ATP, we performed P2Y2 silencing. After P2Y2 silencing, CAL27 and SCC15 cells were stimulated with 100 μM ATP for 20 min. Western blotting showed that the phosphorylation of Src and EGFR was significantly reduced (Figure 6A,B). Following the application of Dasatinib and AG1478 inhibitors for 1 h, CAL27 and SCC15 cells were stimulated with 100 μM ATP for 20 min, and the phosphorylation of EGFR was found to be significantly reduced (Figure 6C,D). To further investigate whether P2Y2 activates the PI3K/AKT signaling pathway through the Src-EGFR axis, CAL27 and SCC15 cells that have knocked down P2Y2 were treated with ATP (100 μM) for 24 h. Compared with the control group, the expression of p-PI3K and p-AKT was significantly decreased (Figure 7A,B). The cells were further pretreated with Dasatinib and AG1478 for 1 h and then stimulated with ATP. The expression of p-PI3K and p-AKT was decreased (Figure 7C,D). In the cell colony analysis, the cells were treated with PI3K/AKT inhibitors for 14 days, after which the number of cell colonies was lower compared with the ATP (100 μM) group (Figure 7E). In the in vitro invasion test, the cells were treated with PI3K/AKT inhibitors for 24 h. The number of CAL27 and SCC15 cells in the PI3K/AKT inhibitors group was significantly lower than that of the control group, indicating that PI3K/AKT may promote the invasion of CAL27 and SCC15 cells (Figure 7F). The wound-healing assay showed the same result; after treatment with PI3K/AKT inhibitors for 24 and 48 h, the wound gap between CAL27 and SCC15 cells was significantly reduced compared with that in the control group, suggesting that PI3K/AKT is involved in ATP-promoted migration and invasion of OSCC cells (Figure 7G,H). The tumor microenvironment (TME) is a dynamic environment, and its biochemistry and cellular composition play a crucial role in the regulation of tumor cell metabolism, proliferation, and motility. The transdifferentiation of epithelial cells into motile mesenchymal cells, a process known since the 1980s as epithelial-mesenchymal transition (EMT), was first observed by Elizabeth Hay, who described epithelial to mesenchymal phenotype changes in the primitive streak of chick embryos. EMT is an indispensable part of the developmental process, and its underlying process is reactivated during wound healing, fibrosis, and cancer progression. During the development of cancer, the cytoplasmic damage caused by inflammation and hypoxia and the tissue destruction caused by tumor invasion result in an increased concentration of extracellular ATP, which plays a key role as an extracellular messenger. In 1980, P2Y2 suggested that specific plasma membrane receptors for extracellular ATP were expressed by inflammatory and cancer cells; P2Y2 is the first selected ligand of ATP. The role of ATP in the TME is multifaceted, as it regulates the permeability of cell connections by mobilizing intracellular Ca2+ storage, leading to tumor cell invasion and metastasis. In cancer, EMT is highly deregulated, and EMT-transcription factors exert important roles in all cancer stages, including initiation, primary tumor growth, invasion, dissemination, metastasis, colonization, and therapy resistance as well. Research findings in different models have demonstrated the participation of the P2Y2 receptor in inducing migration or the EMT process. In ovarian cancer, ATP induces EMT of ovarian cancer cells through the P2Y2 receptor-dependent activity of EGFR. In gastric cancer, purinergic P2Y2 and P2X4 receptors are involved in changes in the expression of EMT and related genes in gastric cancer cell lines. It is generally understood that ATP and other nucleotides, and their plasma membrane receptors play a central role in tumor cell proliferation and immune cell regulation. For this reason, an in-depth understanding of purinergic signals in the TME may provide new therapeutic prospects. Extracellular ATP acts on tumors via specific plasma membrane receptors. Almost all cancer and immune cells express P2 receptors and are sensitive to extracellular ATP. P2Y2, a member of the purine P2 receptor family, is a G-protein-coupled receptor (GPCR). P2Y2 was first cloned from mouse NG108-15 neuroblastoma, and it induces a variety of cancer cell responses through its unique structure, including cell proliferation, migration, and invasion. In pancreatic ductal cancer cells, P2Y2 activation induces cell proliferation dependent on the activation of platelet-derived growth factor receptor-β (PDGFR-β) and PI3K/AKT. P2Y2 is highly expressed in prostate cancer and promotes the invasion and migration of prostate cancer cells in vivo. The expression of P2Y2 in human hepatocellular carcinoma cells is higher than that in normal hepatocytes. ATP has been shown to promote the proliferation of gastric adenocarcinoma cells, which is blocked by specific purinergic antagonists, and ATP treatment can induce proliferation of different glioma cell lines after 24 and 48 h. ATP and UTP activation of P2Y2R can induce migration and proliferation of MDA-MB231 and MCF-7 breast cancer cells, and it is associated with inflammation cascade activation. ATP and UTP also support cancer cell growth in A-549 human lung cancer cells. However, the effect of P2Y2 on most other tumors is still unknown, and the association between P2Y2 and OSCC has been rarely studied. In our study, P2Y2 was found to be overexpressed in the OSCC cell line and to promote the growth, invasion, and migration of tumors. In clinical samples, the expression of P2Y2 in OSCC tissues was significantly higher than that in normal tissues. In addition, stimulation with the P2Y2 agonist ATP significantly increased P2Y2 expression in OSCC cells, increased the expression of Snail, MMP2, and MMP9 genes, and decreased the expression of E-cadherin and Vimentin; the invasion and migration ability of tumor cells was enhanced. After the knockdown of the P2Y2 receptor, the expression changes of genes related to intracellular invasion and migration regulated by ATP were also weakened, and the invasion and migration of tumor cells were weakened. These results are also consistent with other findings demonstrating that P2Y2 receptors have the potential for transformative use in both in vitro and in vivo. Extracellular ATP can activate many signaling pathways, and selectively modulate Src and EGFR. Based on analysis using the TCGA database data, it was shown that Src and EGFR are highly expressed in squamous cell carcinoma tissue and also that Src and EGRF are closely correlated with P2Y2. Src, a nonreceptor tyrosine kinase, can cause phosphorylation of tyrosine residues by substrate, serving as a signal transducer of the cell surface receptor. Studies have shown that Src is excessively expressed and highly activated in OSCC and is a cancer protein that drives OSCC progression. Furthermore, Src has been shown to have a close relationship with the progression, migration, and prognosis of solid tumors, The Src family protein kinase has been demonstrated to mediate epidermal growth factor receptor (EGFR)-dependent and nondependent passages, and can even act as an upstream activator of EGFR. EGFR is considered to be the main goal of a new treatment for OSCC, as it is overexpressed in the advanced stage of the disease and prognosis in OSCC patients. It has been confirmed that GPCR-induced cell migration requires the participation of EGFR. The mechanism may be that GPCR activates MMP to release heparin-binding epidermal growth factor (HB-EGF), which is originally bound to the cell surface or extracellular matrix, and subsequently interacts with EGFR. The P2Y receptor is activated by attracting nonreceptor tyrosine protein kinase Src phosphorylated EGFR. The phosphatidylinositol-3-kinase (PI3K)/AKT signaling pathway is involved in the regulation of various cell activities, and the activation of the PI3K/AKT signaling pathway can regulate the growth, proliferation, apoptosis, and energy metabolism of tumor cells. Many studies have confirmed that AKT can increase the glycolysis level of tumor cells and promote the production of ATP without affecting aerobic oxidation, thus providing sufficient substances for biosynthesis. Abnormal activation of PI3K/AKT signaling has been found in a variety of tumors. It was found that ATP promotes MCF-7 cell proliferation through the PI3K/AKT signaling pathway. The PI3K/AKT pathway also induces stem-cell-like properties in gastric cancer cells. The Src inhibitor PP2 has been shown to inhibit the PI3K activity of colonic cancer cells, and Src has been reported to play a role in the upstream modulation of PI3K. Therefore, investigations into blocking this signaling pathway as a potential therapeutic mechanism have been undertaken. The biological function of the PI3K/AKT signaling pathway in tumor progression has been well established, but the role of P2Y2 in its regulation of the PI3K/AKT pathway remains poorly understood. Our study found that Src and EGFR increase in a time-and dose-dependent manner with ATP. ATP upregulates Src and EGFR through P2Y2 expression and then activates the PI3K/AKT pathway. After silencing P2Y2, the expression of Src and EGFR was downregulated; the PI3K/AKT pathway was weakened; and tumor growth, invasion, and migration were significantly inhibited. After adding Src and EGFR inhibitors, the expression of EGFR and PI3K/AKT was significantly inhibited. By considering these results in the context of previous studies, we concluded that P2Y2 promotes the invasion and migration of OSCC by activating the PI3K/AKT signaling pathway through the Src-EGFR axis. P2Y2 is an active regulator in tumor progression. We found that ATP was involved in tumor metabolism through the P2Y2 receptor. Also, ATP promoted the invasion and migration of OSCC cells through the Src-EGFR axis activation of the PI3K/AKT signaling pathway. These findings provide important new insight into the occurrence and development of OSCC and deliver evidence that P2Y2 has the potential to be a new therapeutic target for OSCC.

PMC9648122

Joydeep Chakraborty,Anis Ahmad Chaudhary,Salah-Ud-Din Khan,Hassan Ahmad Rudayni,Sayed Modinur Rahaman,Hironmoy Sarkar

CRISPR/Cas-Based Biosensor As a New Age Detection Method for Pathogenic Bacteria

18-10-2022

Methods enabling rapid and on-site detection of pathogenic bacteria are a prerequisite for public health assurance, medical diagnostics, ensuring food safety and security, and research. Many current bacteria detection technologies are inconvenient and time-consuming, making them unsuitable for field detection. New technology based on the CRISPR/Cas system has the potential to fill the existing gaps in detection. The clustered regularly interspaced short palindromic repeats (CRISPR) system is a part of the bacterial adaptive immune system to protect them from intruding bacteriophages. The immunological memory is saved by the CRISPR array of bacteria in the form of short DNA sequences (spacers) from invading viruses and incorporated with the CRISPR DNA repeats. Cas proteins are responsible for triggering and initiating the adaptive immune function of CRISPR/Cas systems. In advanced biological research, the CRISPR/Cas system has emerged as a significant tool from genome editing to pathogen detection. By considering its sensitivity and specificity, this system can become one of the leading detection methods for targeting DNA/RNA. This technique is well applied in virus detection like Dengue, ZIKA, SARS-CoV-2, etc., but for bacterial detection, this CRISPR/Cas system is limited to only a few organisms to date. In this review, we have discussed the different techniques based on the CRISPR/Cas system that have been developed for the detection of various pathogenic bacteria like L. monocytogenes, M. tuberculosis, Methicillin-resistant S. aureus, Salmonella, E. coli, P. aeruginosa, and A. baumannii.

CRISPR/Cas-Based Biosensor As a New Age Detection Method for Pathogenic Bacteria Methods enabling rapid and on-site detection of pathogenic bacteria are a prerequisite for public health assurance, medical diagnostics, ensuring food safety and security, and research. Many current bacteria detection technologies are inconvenient and time-consuming, making them unsuitable for field detection. New technology based on the CRISPR/Cas system has the potential to fill the existing gaps in detection. The clustered regularly interspaced short palindromic repeats (CRISPR) system is a part of the bacterial adaptive immune system to protect them from intruding bacteriophages. The immunological memory is saved by the CRISPR array of bacteria in the form of short DNA sequences (spacers) from invading viruses and incorporated with the CRISPR DNA repeats. Cas proteins are responsible for triggering and initiating the adaptive immune function of CRISPR/Cas systems. In advanced biological research, the CRISPR/Cas system has emerged as a significant tool from genome editing to pathogen detection. By considering its sensitivity and specificity, this system can become one of the leading detection methods for targeting DNA/RNA. This technique is well applied in virus detection like Dengue, ZIKA, SARS-CoV-2, etc., but for bacterial detection, this CRISPR/Cas system is limited to only a few organisms to date. In this review, we have discussed the different techniques based on the CRISPR/Cas system that have been developed for the detection of various pathogenic bacteria like L. monocytogenes, M. tuberculosis, Methicillin-resistant S. aureus, Salmonella, E. coli, P. aeruginosa, and A. baumannii. Rapid, sensitive, specific, and on-site detection of pathogenic bacteria is critical in clinical diagnosis, treatment, surveillance of foodborne disease, and biological research. It helps to gather clinical information in order to provide appropriate treatment and prevent the spread of disease. According to the World Health Organization’s standard, an ideal pathogen diagnostic test should be assured: affordable, sensitive, specific, easy-to-use, rapid, without large equipment, and delivered to the user. In order to detect nucleic acid signatures of pathogens, a vast array of detection methods have emerged based on PCR/qPCR, isothermal amplification-based detection assays, and next-generation sequencing. To improve sensitivity, affordability, simplicity, and rapidity there are various advanced nucleic acid detection techniques developed so far. One of the latest and advanced methods is clustered regularly interspaced short palindromic repeats (CRISPR) associated systems (CRISPR/Cas), which have recently gained great importance and attention in nucleic acid analysis and detection. In order to achieve higher sensitivity, the CRISPR/Cas system is frequently associated with polymerase chain reaction (PCR) and with isothermal nucleic acid amplification techniques like NASBA, RCA, SDA, LAMP, RPA, and EXPAR. The combination of CRISPR/Cas with advanced isothermal amplification technologies is promoting the development of novel optical and electrochemical biosensing devices. CRISPR/Cas systems provide adaptive protection to bacteria and archaea against invading foreign nucleic acids. The CRISPR/Cas system in bacteria recognizes and degrades foreign genetic elements generally from viruses. These systems are primarily guided by an RNA called guide-RNA (gRNA) or CRISPR RNA (crRNA), which recognizes the target and directs Cas proteins to locate and cleave invading DNA sequences. This system works through three steps: adaptation or spacer-acquisition, crRNA processing, and interference. In the spacer acquisition step, when new foreign DNA/RNA is introduced in the bacterial cell, a short piece of the DNA/RNA segment, called protospacer in the immediate upstream vicinity of a protospacer adjacent motif (PAM) present in the foreign DNA/RNA, is excised out by the help of the Cas1–Cas2 complex. This protospacer was then inserted as a new spacer into the bacterial genomic region of the CRISPR array (Figure 1a) where all the acquired spacer resides. The second step is crRNA biogenesis, which occurs when pre-CRISPR RNA (pre-crRNA) is transcribed by RNA polymerase (RNAP) from the CRISPR array region, then cleaved by specific endoribonucleases into small mature crRNA (Figure 1a). Each crRNA contains one complementary sequence of a spacer. The final step is interference, which entails sequence-specific targeting and cleavage of foreign DNA/RNA having a protospacer that is complementary to the spacer sequence in crRNA. To commence crRNA-mediated DNA binding, a protospacer adjacent motif (PAM) must be present in the immediate vicinity of a protospacer sequence. crRNAs recognize and produce complementary base pairs unique to foreign RNA or DNA, resulting in the cleavage of the crRNA-foreign nucleic acid complex (Figure 1a). CRISPR/Cas systems are classified according to their utilization of specific Cas enzymes and methods of interference. In accordance with recent publications, CRISPR/Cas systems can be categorized into two classes: class 1 and 2, six types: types I–VI, and numerous subtypes. Class 1 comprises types I, III, and IV; and Class 2 includes types II, V, and VI (Figure 1b). Each type is distinguished by discrete effector module configurations that include various signature proteins. The most widely used toolbox for nucleic acid detection belongs to the class 2 system that contains Cas9, Cas12, Cas13, and Cas14. Cas9 (type II) and Cas12 (type V) target DNA, while Cas13 (type VI) targets RNA and Cas14 targets ssDNA. CRISPR/Cas systems, specifically Cas9 (type II), have become a popular tool for transcription regulation, genome editing, and in situ DNA/RNA detection in recent years. Cas12 and Cas13 effectors have a unique property called “collateral cleavage”. In the presence of a target or reporter DNA/RNA, these Cas effectors are activated and can do collateral (nonspecific) cleavage on any single-stranded DNA/RNA present in the near vicinity. The advantage of this collateral cleavage is that it can easily be detected by fluorescence reporters tagged in single-stranded DNA/RNA. This has recently displayed remarkable potential in developing novel biosensing technologies for nucleic acid detection. This technology is widely harnessed for the detection of viral diseases, such as specific high-sensitivity enzymatic reporter unlocking (SHERLOCK) to detect Zika and Dengue and DNA endonuclease-targeted CRISPR trans reporter (DETECTR) for rapid and specific detection of HPV and SARS-CoV-2 in humans. Though the futuristic developments of CRISPR/Cas-based viral detection techniques are expanding rapidly in biosensing, this technique is limited to very few bacterial pathogens to date. This limited use for bacterial pathogens may be because there is a well established gold-standard detection technique for pathogenic bacteria and establishing these emerging techniques in practice will be time-consuming. Also, the viral rate of mutation is relatively much higher than bacteria, which provides more priority to develop new methods for viral detection and lesser focus on bacterial detection. In this review, we have discussed the different CRISPR/Cas systems as biosensors used for the detection of bacterial pathogens like L. monocytogenes, M. tuberculosis, Methicillin-resistant S. aureus, Salmonella, E. coli, P. aeruginosa, and A. baumannii. Listeria monocytogenes is one of the most virulent foodborne pathogens and can be found in a variety of foods like milk, milk products, eggs, poultry, and meat. The FDA upholds a zero-tolerance policy for L. monocytogenes since it has a low infectious dose and high mortality rate. In healthy people, it can cause invasive listeriosis. In young, elderly, or immunocompromised people, it can cause septicemia, meningitis, and infections related to the central nervous system. Infections in pregnant women can be fatal and can result in spontaneous abortion or fetal death. The slow growth rate of L. monocytogenes is challenging for the conventional culture and plating-based detection methods, which can take up to 7 days to yield results. CRISPR/Cas9-triggered isothermal exponential amplification reaction (CAS-EXPAR) based detection against Listeria monocytogenes was developed by Huang et al. Here the hemolysin (hly) gene of L. monocytogenes was used as the target sequence. It utilizes the target-specific nicking activity of Cas9 and nicking endonuclease (NEase)-mediated amplification. From bacteria, RNA was isolated and cDNA was generated. cDNAs were cleaved by Cas9 with the help of specific sgRNA and PAMmers. These cleaved products are now subjected to EXPAR-mediated amplification by EXPAR templates and without exogenous primers. Finally, the amplified products were detected by fluorescence using SYBR green (Figure 2a). This method combines the benefits of Cas9/sgRNA site-specific cleavage and EXPAR fast amplification kinetics. This process is reported to be highly specific in discriminating single-nucleotide mismatch. Reprogrammable cleavage activity of Cas9/sgRNA is also beneficial for targeting various other pathogens. The merit of this method does not require exogenous primers for amplification. Therefore, the chances of nonspecific amplification followed by false positivity could be minimized. The sensitivity of this technique was reported to be 0.82 amol (Table 1) of synthetic ssDNA, but in bacteria, this technique was verified with 1.25 and 2.5 μg of total RNA. This method may have a problem to detect long targets, as EXPAR is not efficient for long DNA or RNA targets. Another method to detect L. monocytogenes was developed by Wang et al. based on CRISPR/Cas9 system integrated with lateral flow nuclic acid (CASLFA). Here also, the hly gene was used as a target. This technique is termed as the DNA unwinding-based hybridization assay with a lateral flow device for simple and easy detection by the naked eye. Genomic DNA from bacteria was subjected to amplification (by PCR or any isothermal reaction) with gene-specific biotinylated primers. Then biotinylated amplicons are incubated with target specific sgRNA and dCas9 to form a complex (Cas9/sgRNA-biotinylated amplicons) without cleaving the targets. This complex, when applied to a lateral flow device, bound with an AuNP-DNA probe (gold nanoparticle bound with complementary DNA sequence of target gene/sgRNA) and combined with immobilized streptavidin in the test line (T). Accumulation of AuNP-generated color band occurred on the test line (T). The excess unbound AuNP-DNA probe will form a control line (C) by binding with the precoated DNA probes with their control line hybridization region (Figure 2b). There are two kinds of DNA probes reported by the authors: DNA unwinding and sgRNA anchor-based. The DNA unwinding probe has a specific DNA sequence for individual target. Therefore, for every target, there is a need to generate a new probe. The sgRNA-based probe has a target sequence that has DNA sequences specific to the crRNA region. The advantage of the sgRNA-based probe is that it can detect multiple targets as this is specific to a portion of sgRNA but not the target DNA. This is a simple and rapid method for the detection of genetic targets by the naked eye. This detection limit was reported as low as 150 copies (Table 1) of bacterial targets. As reported, this method can detect the gene target with almost no background signal interference and can be completed based on a cheap and portable tool kit within 40 min in point of care. Tuberculosis (TB) is the leading cause of death among infectious diseases. Due to the difficulty in its diagnosis, it was anticipated that 40% of the cases failed to be identified and reported. Zhang et al. have developed a novel and sensitive detection method to detect M. tuberculosis using CRISPR/dCas9. The Mtb 16S rRNA gene was used as a target sequence. In this method, the luciferase gene was split into N- and C-terminal halves (NFluc and CFluc) and fused each with separate dCas9 termed as a paired-dCas9 (PC) reporter system. Two guide RNAs (sgRNA) were also employed which are complementary to the upstream and downstream proximal segments (∼44bp) of a target DNA. Upstream and downstream sgRNAs are separately mixed with NFluc-Cas9 and CFluc-Cas9, respectively, to achieve higher efficiency and specificity. The two halves can initiate heterodimerization to rebuild the intact enzyme when target DNA containing the two segments in proximity is detected and bound by the corresponding sgRNAs followed by a pair of dCas9 (Figure 2c). Luminescence is generated from the catalytic activity of luciferase. The PC reporter system was reported to be sensitive up to 5 × 10–5 nmol/mL (Table 1) but also observed that when the target concentration was beyond 6 × 10–3 nmol/mL detection signals decreased due to inefficient pairing between two halves of luciferase. This method requires two target sites, for separate sgRNAs, spanning approximately 44bp, which could limit the selection of target region for other pathogens. The other method to detect M. tuberculosis by CRISPR-MTB was developed by Ai et al. Here MTB-specific insertion sequence IS6110 of ∼1.5 kb in length was used as the target sequence. In this method, optimization of the extraction process was done based on a combination strategy of bead beating, chemical lysis, and heating to ensure the higher efficiency of DNA extraction. As the amplification technique is RPA, there was no special need for any thermal-cycler. This method is a combination of RPA reaction with a CRISPR/Cas12a system (Figure 2d). Fluorescence signal can be detected by target activated reporter cleavage of Cas12a trans cleavage activity. The sensitivity of this method was reported to be 2–5 copies/μL or 50 CFU/mL (Table 1). It was reported that the extraction efficiency was found to be high and the extraction time was reduced with fewer centrifugation steps over the column-based traditional method. This method also was tested on a variety of sample types such as sputum, BALF, CSF, and pus. The drawback of this detection will be that it will fail to detect MTB strains that lack IS6110 genomic segments. Methicillin-resistant Staphylococcus aureus (MRSA) is one of the most important multidrug-resistant human pathogens, causing severe life-threatening diseases. MRSA infections are four times more likely than methicillin-susceptible S. aureus (MSSA), and it causes serious morbidity and mortality worldwide. Traditional culture-based identification methods for MRSA are time-consuming, and conventional techniques like MALDI-TOF, RT-PCR,S. aureus protein A (spa) typing, multilocus sequence typing (MLST), and pulsed-field gel electrophoresis (PFGE) are labor intensive and require a high level of professional expertise. Therefore, MRSA detection requires simplified detection procedures that are faster, less labor-intensive, and highly specific. Kyeonghye Guk and colleagues recently introduced a CRISPR-mediated DNA-FISH. This CRISPR-mediated DNA-FISH was developed to detect methicillin-resistant Staphylococcus aureus (MRSA) by targeting the gene mecA. This technique involves dCas9 to specifically recognize the target gene without cleavage activity and SYBR Green as a fluorescent probe. The genomic DNA of the target organism was isolated and treated with dCas9/sgRNA for 15 min at room temperature to bind the dCas9 with the target. After hybridization, the dCas9/sgRNA complex was isolated using Ni-NTA magnet beads, and nontarget unbound DNA was removed by washing. SYBR green was added to detect the presence of bound DNA as a target (Figure 2e). In clinical isolates, this method can detect as low as 10 CFU/mL within 30 min (Table 1). This approach is both quick and sensitive. This method does not require any amplification which is an advantage that reduces the detection time and complexity. The combination of dCas9/sgRNA and SYBR green as a fluorescent probe makes for labeling a reasonably straightforward and inexpensive approach. This method of detection has great potential to be used easily in patient point of care. Suea-Ngam et al. have developed another amplification-free method for the detection of MRSA. Here also, the mecA gene was chosen as the target. In this method of detection, the silver metallization technology was combined with the CRISPR/Cas to create a novel silver-enhanced E-CRISPR biosensor (E-Si-CRISPR) for MRSA detection. In the presence of target DNA the Cas12a–gRNA complex cleaves the ssDNA at random sites, destroying the electrode’s ssDNA surface layer (Figure 2f). The trans-cleavage mechanism fails in the absence of the target. The degree of silver deposition during the succeeding silver metallization stage is proportional to the quantity of ssDNA left and thus proportional to the initial amount of target DNA. Square wave voltammetry was used to read the final electrochemical signal (Figure 2f). The detection and quantification limits were found to be 3.5 and 10 fM (Table 1). This new electrochemical CRISPR/Cas biosensor, based on silver metallization, was stated to be highly selective, sensitive, and without DNA amplification cycles. As reported, this amplification-free detection method could yield results within 1.5 h. This method is innovative in the aspect of its unique readout of results through electrochemical signals. One drawback can be perceived that, contrary to the other conventional methods, the positive signals are lower than the negative signals in this method, which could be inconvenient. Food poisoning by Salmonella species is the second most prevalent cause of food poising followed by severe gastroenteritis and bacteremia worldwide. To date, traditional biochemical culture, immunological testing, and molecular biological approaches (PCR/real-time PCR) have been used to detect Salmonella. These procedures are time-consuming, have low specificity, and require expensive laboratory equipment. Detection of Salmonella enteritidis using a unique allosteric probe (AP) with a combination of CRISPR/Cas13a (APC-Cas)is developed by Shen et al., where whole bacteria were used as a target. The allosteric probe (AP) comprises of three functional domains (Figure 3a): aptamer domain for target pathogen identification (purple), primer binding site domain (blue), and T7 promoter domain (yellow). A phosphate group was added to the 3′ end of AP to prevent self-extension and make the DNA molecule resistant to enzymatic hydrolysis. The aptamer domain of AP can specifically recognize and engage with the target pathogen that is Salmonella enteritidis. The hairpin structure of AP will unfold and flip to its active configuration, allowing primers to anneal to the exposed primer binding site domain. The AP then acts as a template for the production of double-stranded DNA (dsDNA) with the help of DNA polymerase, followed by the displacement of the target pathogen for the next catalytic cycle (primary amplification) because of the polymerase extension reaction. T7 RNA polymerase is then utilized to identify the T7 promoter sequence on the created dsDNA and perform amplification via transcription to generate a large number of single-stranded RNAs (ssRNAs) (secondary amplification). Finally, the crRNA is intended to contain two areas, a guide sequence that is complementary to the transcripted ssRNA, and the repeat sequence that is required for crRNA to attach the Cas13a enzyme. When the above ssRNAs hybridize with Cas13a/crRNA, Cas13a/collateral crRNA’s cleavage capacity is activated, allowing numerous RNA reporter probes to be cleaved (tertiary amplification), resulting in the amplification of fluorescence signals (Figure 3a). This procedure does not require bacterial isolation, nucleic acid extraction, and a washing step. It is cost-effective, very sensitive up to 1 CFU (Table 1), and can be done in a relatively short period. Designing an allosteric probe for different bacteria could be a challenging task for this method of detection. In order to detect Salmonella, Ma et al. have developed gold-nanoparticles (AuNPs)-based method termed as CRISPR/Cas12a-powered dual-mode biosensor. The target DNA was Invasion gene A (invA), a virulence gene of Salmonella. This method involves DNA extraction as well as PCR amplification of target sequence. The designed biosensor is built on the trans-cleavage activity of the CRISPR/Cas12a. The AuNPs probe is coated with DNA, and a linker ssDNA hybridizes with AuNPs-DNA probe pairs. In the absence of target amplicons, the linker ssDNA remains intact, and aggregated AuNPs are maintained with a purple color (Figure 3b). Upon recognition of the target amplicons with designed crRNA, the trans-cleavage of CRISPR/Cas12a is activated and the linker ssDNA is cut off, and the AuNPs are dispersed in solution. The dispersed AuNP solution exhibits a red color, and the change can be detected by the naked eye or colorimetrically or photothermally (Figure 3b). The detection limit for this technique was reported as 1 CFU/mL (Table 1). This method was just used to detect the bacteria in milk samples. There is a need to explore with other food samples. This technique was the first to explore the gold-based nanoparticles as a probe. Escherichia coli O157:H7 is one of the most common causes of hemorrhagic colitis.E. coli O157:H7 can be found in water as well as other food sources such as milk, juice, fruits, and vegetables. Infections that are severe enough can lead to hemorrhagic colitis, hemolytic uremic syndrome, and even death. A CRISPR/Cas9 triggered SDA–RCA method on the UiO66 platform was developed by Sun et al. to detect Escherichia coli O157:H7. The method employs the target sequence of gene hemolysin A (hlyA). Nanoparticle (UiO66) and Two amplification methods: strand displacement amplification (SDA) and rolling circle amplification (RCA) are used for this method. After isolation of DNA from the bacteria, the pair of CRISPR/Cas9 (by sgRNA1 and sgRNA2) recognized and cleaved the two proximal regions of the target DNA. Primary amplification by SDA synthesis and extending at the nicked position results in short–ssDNA indefinitely. This short–ssDNA was the template for secondary amplification by RCA, which generates long–ssDNA having repetitive sequences complementary to the fluorescence-labeled DNA probe (Figure 3c). This probe in bound form with long–ssDNA can be detected with 480 and 518 nm of excitation and emission wavelength. Unbound probes are absorbed in the UiO66 where fluorescence is quenched. In the presence of the target sequence, short (by SDA) followed by long ssDNA (by RCA) will be generated. The fluorescence probes will leave UiO66 and hybridize with the long–ssDNA, resulting in a fluorescence signal. As a result, the fluorescence intensity can be used to detect the target DNA quantitatively (Figure 3c). It is reported that this technique can detect low amounts of E. coli O157:H7 (40 CFU/mL) with high sensitivity and a wide detection range under mild response conditions (Table 1). Pseudomonas aeruginosa is a multidrug-resistant, highly infectious opportunistic human pathogenic bacteria with a large and complex genome. Its widespread distribution in nature indicates a high level of genetic and physiological flexibility in response to environmental changes. In 2017, the World Health Organization designated P. aeruginosa as a critical pathogen that poses a serious threat to human health, necessitating the development of new treatments. Mukama et al. have developed a method to detect P. aeruginosa(37) based on CRISPR/Cas and loop mediated Isothermal Amplification (CIA). The acyltransferase gene from P. aeruginosa was used as a target. Here the samples were directly used for loop-mediated isothermal amplification (LAMP) for the target gene. Products of LAMP were incubated with CRISPR/Cas12, to activate the collateral cleavage of the biotinylated ssDNA reporter, followed by a run on a strip for final results (Figure 3d). In the absence of a target, the gold nanoparticle-streptavidin (AuNP-SA) complex binds with the biotin of the ssDNA reporter. Then the whole complex (AuNP-SA-ssDNA) binds with complementary DNA to the ssDNA reporter immobilized in the test (T) line which results in a visible colored band (Figure 3d). But, in the presence of a target, the reporter DNA was cleaved by CRISPR/Cas12, so there will not be any formation of the AuNP-SA-ssDNA complex, hence no visible band on the test line. In the control line (C), only AuNP-SA is bound with immobilized antibodies to streptavidin. That means in the presence of any P. aeruginosa, there will be only one band in the control and no visible band in the test line (Figure 3d). Whereas, if the sample is negative for P. aeruginosa then the visible band will be there in the T as well as in the C line in the strip. This method was reported to be a fast, accurate, robust, and inexpensive technique with a detection limit of 1 CFU/mL (Table 1). The best feature of this approach is that it allows for naked-eye detection. That means this detection technique has the potential to apply in the patient point of care. On the other hand, the method of detection is unconventional too. In general, we are accustomed with a positive sample with a positive band in the test line, but here the positive sample is associated with a negative band in the test line, which may have some inconvenience in the hand of technicians. Additionally, we cannot eliminate the possibility of false positivity due to various reasons like degradation of ssDNA reporter, AuNP-SA, or complementary DNA in the test line. The rapid emergence of multidrug-resistant A. baumannii has posed a severe threat to worldwide public health. In humans, it can be an opportunistic pathogen that affects immunocompromised persons and is becoming more common as a hospital-borne (nosocomial) infection. Detection of A. baumannii based on the CRISPR/Cas system was developed by Wang et al. This method was integrated with multiplex PCR where simultaneously many genes of β-lactamase, responsible for antibiotic resistance, were detected. Extracted genomic DNA from bacteria was used for the multiplex PCR reaction. When the target gene is present, the system will amplify the target and then initiate Cas12a’s nonspecific ssDNA trans cleavage activity. The ssDNA reporter, conjugated with fluorophore and quencher, was cleaved after the Cas12a-crRNA-DNA assembly, resulting in an increase in fluorescence signals (Figure 3e). Different crRNAs were used to detect different genes. The detection limit was reported to be 50 CFU/mL (Table 1). Integration of multiplex PCR provided an added advantage where multiple targets can be detected at once, provided the individual target specific crRNA needs to be developed. All the methods discussed above based on CRISPR/Cas-based approaches for bacterial detection are distinct in their own way, employing various Cas enzymes and techniques. According to the ASSURED standards of WHO, all the above-described methods may not fully qualify all the standards, whereas some might be more affordable and others might be more sensitive or have less bulky equipment, or be easy to use in point of care. Likewise, amplification free methods, such as DNA-FISH and E-Si-CRISPR, need less time and fewer components, making them relatively inexpensive. Methods like CASLFA, APC-Cas, CIA, and CRISPR-MTB can also be conducted utilizing the cost-effective isothermal amplification technique. The CASLFA, CIA, and CRISPR/Cas12a-Powered Dual-Mode Biosensor have naked eye readout capabilities requiring no expensive equipment. The APC-Cas technique also does not need bacterial isolation or DNA extraction, which contributes to its low cost. The turnaround times for the methods described above ranged from 30 to 140 min. The CRISPR-mediated DNA-FISH for Methicillin-resistant Staphylococcus aureus (MRSA) detection has the quickest turnaround time of 30 min as it is an amplification free method. In terms of the analytical sensitivity of the CRISPR/Cas assays, the limit of detection (LoD) was reported in different units: copies/mL, CFU/mL, moles, and molarity. The majority of methods reported the LoD in CFU/mL, with the lowest reported LoD being 1 CFU/mL for both CRISPR-Cas12a-powered dual-mode biosensor and CIA (Table 1). APC-Cas and CASLFA was reported to be 1 CFU and 150 copies (Table 1). Between dCas9 and E-Si-CRISPR, where both the LoD were reported in molar concentration, the lowest being for E-Si-CRISPR that is up to 3.5 fM (Table 1). The LoD of CAS-EXPAR was estimated to 0.82 amol. Expressing the sensitivity with different units is misleading; therefore, it is hard to compare among methods. In the case of the bacterial detection method, it would have been helpful for the readers, if the sensitivity could have been reported to the standard unit that is CFU/mL. The CRISPR-based methods reduced the need for large equipment, which is a notable feature that has significant possibilities for field implementation, particularly for controlling epidemic outbreaks in resource-limited areas. CRISPR/Cas systems make it easier to create a wide range of readout signals from fluorescence to naked eye detection. Methods based on LFA such as CASLFA and CIA might have higher utility at the patient point of care. Though CRISPR/Cas-based pathogen detection technologies have exceptional sensitivity and specificity, there are yet many scopes for future advancements. Due to the concerning inherent off-target impact of CRISPR-based detection, improved specificity is the utmost requirement for practical detection approaches. For example, outside of the PAM-proximal 5–12 bp seed areas, Cas9-mediated cleavage is very tolerant to mismatches, and dCas9 off-target binding is random, which can weaken the analytical specificity and sensitivity. In the past few years, Cas9 variants with reduced off-target cleavage, such as SpCas9-HF1, eSpCas9 (1.1), and HypaCas9, have been developed, thereby providing potential future solutions for the off-target effect of Cas9. Therefore, for Cas enzymes, future research should be concentrated on high-fidelity nucleic acid detection to minimize off-targeting. A more user-friendly one-step diagnostic that comprises pathogen nucleic acid release, preamplification, CRISPR/Cas-induced reaction, and signal readout should be developed in the future. For example, several simple formats, such as paper-based biosensors with visual readout (NASBACC, SHERLOCK, DETECTR), pathogen detection without nucleic acid extraction (HUDSON), and a single-tube test combining isothermal amplification and Cas-mediated reaction, have been developed, which might be combined for more simplicity, affordability, and user-friendliness. More equipment-free approaches for signal readout, such as lateral flow assay or naked-eye view under light, should be introduced, as they might be easier at the point of care. Currently, the CRISPR-based detection system must be stored and delivered in a cold chain, which is an inconvenience in many remote areas. The NASBACC detection system with freeze-dried reagents and the SHERLOCK system with freeze-dried and paper spotting reagents showed long-term storage and transport. Therefore, more storage and transportation strategies for the CRISPR-based reaction kit should be developed. CRISPR/Cas-mediated detection system is a very powerful and advanced technique with high specificity and sensitivity. Therefore, this CRISPR/Cas technique could be of high potential for early diagnosis in the present emerging scenario of antibiotic resistance. In most of the techniques discussed above for pathogenic bacterial detection, there are different amplification techniques like PCR, LAMP, RCA, and SDA integrated along with the CRISPR/Cas system. Previously, only positive amplification was sufficient for detection, but due to the high rate of false positivity, reliance toward only amplification to detect pathogens accurately becomes untrustworthy. Therefore, target amplification followed by CRISPR/Cas as biosensor-mediated detection of pathogenic bacteria can make the process more robust, reliable, sensitive, and specific.

PMC9648152

Tomonori Unno,Masaki Ichitani

Epigallocatechin-3-Gallate Decreases Plasma and Urinary Levels of p-Cresol by Modulating Gut Microbiota in Mice

28-10-2022

p-Cresol (PC), a gut bacterial product of tyrosine catabolism, is recognized as a uremic toxin that has negative biological effects. Lowering the plasma PC level by manipulating the gut bacterial composition represents a promising therapeutic strategy in chronic kidney disease. This study was conducted to reveal whether epigallocatechin-3-gallate (EGCG) decreases plasma PC levels by limiting its bacterial production in a mouse model. The PC concentration in the samples was measured by high-performance liquid chromatography (HPLC) after treatments with sulfatase and β-glucuronidase. The results showed that the addition of EGCG to the diet decreased the plasma and urinary concentrations of PC in a dose-dependent manner, with a statistically significant difference between the control group and the 0.2% EGCG group. However, once EGCG was enzymatically hydrolyzed to epigallocatechin (EGC) and gallic acid, such effects were lost almost completely. The addition of 0.2% EGCG in the diet was accompanied by a decreased abundance of Firmicutes at the phylum level and Clostridiales at the order level, which constitute a large part of PC produced from tyrosine. In conclusion, EGCG, not EGC, reduced plasma and urinary concentrations of PC in mice by suppressing its bacterial production with accompanying alteration of the relative abundance of PC producers.

Epigallocatechin-3-Gallate Decreases Plasma and Urinary Levels of p-Cresol by Modulating Gut Microbiota in Mice p-Cresol (PC), a gut bacterial product of tyrosine catabolism, is recognized as a uremic toxin that has negative biological effects. Lowering the plasma PC level by manipulating the gut bacterial composition represents a promising therapeutic strategy in chronic kidney disease. This study was conducted to reveal whether epigallocatechin-3-gallate (EGCG) decreases plasma PC levels by limiting its bacterial production in a mouse model. The PC concentration in the samples was measured by high-performance liquid chromatography (HPLC) after treatments with sulfatase and β-glucuronidase. The results showed that the addition of EGCG to the diet decreased the plasma and urinary concentrations of PC in a dose-dependent manner, with a statistically significant difference between the control group and the 0.2% EGCG group. However, once EGCG was enzymatically hydrolyzed to epigallocatechin (EGC) and gallic acid, such effects were lost almost completely. The addition of 0.2% EGCG in the diet was accompanied by a decreased abundance of Firmicutes at the phylum level and Clostridiales at the order level, which constitute a large part of PC produced from tyrosine. In conclusion, EGCG, not EGC, reduced plasma and urinary concentrations of PC in mice by suppressing its bacterial production with accompanying alteration of the relative abundance of PC producers. Uremic toxins are substances that interact negatively with biological functions. The accumulation of uremic toxins is associated with systemic disorders such as chronic kidney disease (CKD), endothelial dysfunction, insulin resistance, and cognitive impairment, leading to higher mortality and lower quality of life. Phenol and p-cresol (PC) are typical uremic toxins generated from tyrosine by intestinal bacteria. They are taken into the blood circulation and then undergo conjugations with sulfation or glucuronidation in the liver. p-Cresyl sulfate (PCS) is considered to be a potential cause of excess cardiovascular disease and mortality in patients with CKD. Mechanistically, it can trigger inflammation and oxidative stress in endothelial cells, contributing to endothelial dysfunction and arterial stiffness. The excretion of PCS mainly depends on the versatile tubular transporter systems in the kidney, and limited renal clearance in patients with CKD leads to a progressive accumulation in the blood. PCS is bound with a high affinity to plasma proteins and therefore is poorly removed by dialysis. Not only preserving renal excretory function but also inhibiting bacterial production of PC would also seem to be a useful way to lessen the disproportionate burden in patients with CKD. Some approaches to controlling PC production have been evaluated; for instance, the use of probiotics, prebiotics, and their combinations, is a topic of keen interest in lowering plasma levels of PCS. Human intervention studies have provided evidence that certain types of dietary fiber bring about beneficial effects in CKD patients. In general, treatments with prebiotics and probiotics aim to modulate gut microbiota based on the concept of increasing bacterial saccharolytic activity and limiting proteolytic activity in the large intestine. Strategies for decreasing the bacterial production of PC show great promise as alternative treatments to mitigate the complications of CKD. Recent studies also provide evidence that a certain type of polyphenols can modulate gut bacterial composition by bilaterally acting as an antimicrobial and stimulating growth. Green tea is a dietary source of polyphenols, mainly epicatechin, epigallocatechin (EGC), epicatechin-3-gallate, and epigallocatechin-3-gallate (EGCG). EGCG is the most abundant polyphenol in green tea. Parts of orally ingested EGCG can reach the large intestine, which is inhabited by a wide variety of bacteria. Green tea, or some of its catechins, stimulates or hinders the growth of specific gut bacterial species, and as a consequence of altered microbial composition, changes in the types and amount of microbially produced metabolites may occur. Therefore, we propose the hypothesis that EGCG influences the bacterial production of PC in the intestine by altering PC-producing bacteria. To test this hypothesis, the present work used animal experiments to evaluate the dose-dependent response (the first experiment, Exp. 1) and structure–activity (the second experiment, Exp. 2) of EGCG by measuring urinary and plasma concentrations of PC. In both Exp. 1 and Exp. 2, there were no significant differences in food intake or dried feces weight in the 2-week feeding period (Table 1). There were also no significant differences in the final body weight, but the cecal digesta weight of the 0.2% EGCG-treated mice showed statistically significant increases. The conjugated forms of phenol and PC were converted into free forms by enzymatic hydrolysis with sulfatase and β-glucuronidase, and the urinary and plasma levels of phenol and PC were measured as the total concentrations of their free and sulfate/glucuronide conjugation forms. In the control mice, the mean amount of PC excreted in the urine was 4 times higher than that of phenol (Figure 1A). The dietary addition of EGCG decreased urinary excretion of both compounds in a dose-dependent manner, with the levels in the urine of the mice fed the 0.2% EGCG diet being nearly undetectable. Statistical analysis demonstrated significant decreases in urinary phenol and PC in the 0.2% EGCG group compared to the control group (p < 0.05 for phenol and p < 0.01 for PC). Total plasma concentrations of PC were also higher than those of phenol (Figure 1B). The mean plasma concentration of PC was 9.3 ± 2.2 μM for the control group and 0.2 ± 0.1 μM for the 0.2% EGCG group, a statistically significant difference (p < 0.05). Plasma phenol concentration exhibited a declining trend in line with the addition of EGCG, but it did not reach statistical significance. Phenol and PC in their free form were determined without an enzymatic hydrolysis reaction. The proportion of the free form to the total concentration of urinary phenol and PC constituted only a small percentage, with values of 1.7% for phenol and 3.7% for PC (Figure 1C). The amount of the glucuronide form was calculated by subtracting the free form from the data obtained after glucuronidase treatment. The amount of the sulfate form was also calculated by subtracting the free and glucuronide forms from the data obtained after glucuronidase plus sulfatase treatments. In the control mice, the sulfated form of phenol in the urine held a dominant share, accounting for 77% of the total concentration. Yet, at the same time, the sulfated form of PC accounted for 45%, showing a different trend a bit lower than the glucuronide form (51%). In mice fed the diets with EGCG at 0.05 and 0.1%, regardless of the additive amount of EGCG, the proportions of the sulfated or glucuronide forms to the total urinary concentration were almost consistent with the mice fed the control diet (Table 2). The proportions of the conjugated forms in the urine of mice fed the 0.2% diet did not follow such a pattern. In Exp. 2, the dietary addition of EGCG at 0.2% decreased the total urinary concentration of PC (p < 0.001), but this effect was completely lost with the hydrolysis of EGCG (Figure 2A). Between EGCG and its hydrolysate (an equimolar mixture of EGC and gallic acid (GA)), there was a statistically significant difference in the total urinary concentration of PC (p < 0.0001). The case was somewhat different for phenol; urinary phenol concentration did not show significant differences among the groups. Such outcomes appeared consistently in the plasma concentrations. Total PC concentration in the plasma of mice fed the control diet, the EGCG diet, and the EGC + GA diet was 10.8 ± 5.2, 0.2 ± 0.1, and 12.4 ± 8.0 μM, respectively (Figure 2B). The EGCG group had significantly lower levels than other groups (p < 0.05 against the control group and p < 0.01 against the EGC + GA group). There were no significant differences in plasma phenol concentration among the groups. The concentrations of phenol and PC in their free form per wet weight of cecal digesta are shown in Table 3. In both experiments, the cecal digesta of mice fed the 0.2% EGCG diet contained almost no PC, a statistically significant difference with the control group (p < 0.05 for Exp. 1 and p < 0.01 for Exp. 2). EGCG hydrolysate had little impact on the cecal level of PC. Figure 2C shows that the relationship between the cecal concentration of PC was closely correlated with its plasma concentration. The concentrations of PC both in the plasma and the cecal digesta in the EGCG group were at almost zero levels. In the case of phenol, a dose-dependent decrease was seen in Exp. 1, but dietary addition of EGCG did not lead to a statistically significant difference in Exp. 2. In Exp. 2, the four main phyla (Firmicutes, Bacteroidetes, Actinobacteria, and Proteobacteria) represented 98.2% of the sequences in the feces of control mice (Figure 3A). In every mouse in the control group, the phylum Firmicutes was the most abundant. The addition of 0.2% EGCG in the diet provided a significant reduction in the relative abundance of Firmicutes compared to the control group (p < 0.05), whereas the EGC + GA diet had no effect (Figure 3B). The phyla Bacteroidetes and Verrucomicrobia were inversely increased (p < 0.05 for both), but in the case of EGCG hydrolysate, there was no practical impact. Principal component analysis (PCA) partially explained much of the variation (PC1, 38.4%, and PC2, 23.4%); the phylum taxonomic profiles of mice fed the control diet and the EGC + GA diet located on the near side, whereas the EGCG diet produced to a different profile (Figure 3C). At the order level, bacteria of 12 orders were detected in the feces of the control group, of which 96.3% were represented by bacteria belonging to Clostridiales, Lactobacillales, Erysipelotrichales, Bacteroidales, Bifidobacteriales, Eggerthellales, and Enterobacterales (Figure 4A). Comparisons among the experimental groups for the top five orders and Verrucomicrobiales in the total sequence are shown in Figure 4B. EGCG treatment caused a downward shift in the relative abundance of the order Clostridiales, a member of phylum Firmicutes, but order Bacteroidales, a member of phylum Bacteroidetes, shifted upward, with significant differences from the control group (p < 0.01 for Clostridiales, p < 0.05 for Bacteroidales). Orders Lactobacillales and Erysipelotrichales, which also belong to the phylum Firmicutes, did not show significant differences. Interestingly, the relative abundance of Verrucomicrobiales rose precipitously in the EGCG group. In recent studies, polyphenol-rich dietary sources have received much attention for their impact on elevating or depressing the bacterial production of metabolites while also modifying the gut microbial composition. Once orally consumed, parts of polyphenols reach the large intestine, where they have direct contact with a vast variety of bacteria. As a consequence of the modification of the bacterial community by polyphenols, metabolite production could be up- or downregulated. Short-chain fatty acids are an example of bacterial metabolites. Green tea polyphenols suppress their production in the intestine, but black tea polyphenols conversely increase them. Our previous paper also reported that the diet addition of EGCG decreased the cecal PC level in rats, but whether EGCG has a significant impact on the plasma and urinary levels of phenol and PC via direct modulation of their producers in the gut remains unsettled. In addition, the impacts of tea polyphenols in a gallate form or a nongallate form on the relative changes of PC producers have not been systematically compared. Here, we evaluated the effects of EGCG and its hydrolysate (the mixture of EGC and GA) on urinary and plasma PC levels in healthy mice in relation to their modulation effect against PC producers. It is well recognized that phenol and PC in urine and plasma consist largely of conjugated forms. In this study, the conjugated forms of phenol and PC were hydrolyzed by sulfatase and β-glucuronidase to convert them back into their free form, and then, the compounds were purified by the solid-phase extraction (SPE) method. In Exp. 1, the concentrations of PC in urine and plasma were decreased in response to the amount of EGCG added to the diet. This result provides direct evidence that EGCG has a beneficial effect on the suppression of the bacterial production of PC. Especially given that the cecum is one of the major organs where gut bacteria produce this uremic toxin, the finding that the cecal concentration of PC significantly decreased in the 0.2% EGCG group offers conclusive evidence to support the theory. It is reasonable to say that EGCG decreases the plasma and urinary concentrations of PC by reducing their production in the intestine. Next, to evaluate the respective ratio of conjugation with glucuronide and sulfate, we calculated the urinary concentrations of p-cresyl glucuronide (PCG) by subtracting the free form from the data obtained after hydrolysis only with β-glucuronidase. The concentration of PCS was also calculated by subtracting the concentrations of the free form and PCG from the total concentrations. It has been reported that PCG and PCS are found almost equally in rodents. The same was true in this Exp. 1 of this study, which showed that the urine collected from the control mice had almost an equal percentage of glucuronate and sulfated forms. Since the urine of mice fed the 0.1% EGCG diet also maintained a balanced proportion similar to that in the control mice, it is reasonable to suggest that 0.1% EGCG exerted little influence on the conjugating reaction of PC in the liver. Green tea polyphenols are divided into two main classes: one is catechins having a galloyl moiety and the other is catechins not having a galloyl moiety. To reveal the structure–activity relationship between the galloyl type and the nongalloyl type, we next conducted another animal study (Exp. 2). Mice consumed either a diet containing EGCG at the 0.2% concentration or a diet containing the equimolar preparation of EGC and GA (prepared by the enzymatic hydrolysis of EGCG). The results demonstrated that the mice fed the 0.2% EGCG diet had markedly lower concentrations of PC in urine and plasma compared to the control diet, but the mice fed the EGC + GA diet excreted a significant amount of PC in the urine. This observation clearly shows that EGC was ineffective in reducing the bacterial production of PC, implying that the attachment of galloyl moiety to the structure of flavan-3-ol plays an important role. On the one hand, it is known that a part of EGCG entering the large intestine is hydrolyzed to EGC and GA by intestinal bacteria. With the hydrolysis of EGCG, it may become progressively less effective, but the rest of EGCG can play a role in exerting the intended effect. Tyrosine is microbially metabolized to 4-hydroxyphenylacetate and then converted into PC by 4-hydroxyphenylacetate decarboxylase (4-Hpd). In a recent study by Saito et al.,Blautia hydrogenotrophica, Clostridium difficile, Romboutsia lituseburensis, which are members of the order Clostridiales (heterotypic synonym of Eubacteriales, according to the NCBI Taxonomy Database), and Olsenella uli, a member of the order Coriobacteriales, were identified as major PC producers. These four PC-producing bacteria harbor a homolog of 4-Hpd. Amaretti et al. also found that the families Lachnospiraceae and Ruminococcaceae, which are members of the order Clostridiales, had relevance to the production of PC. In light of this knowledge, it seems reasonable to predict that bacteria belonging to the order Clostridiales have a major role in producing PC in the gut. To find out whether the ingestion of EGCG induces an effect on PC-producing bacteria, we also determined the bacterial compositional change based on the taxonomic category. The results showed that the addition of 0.2% EGCG in the diet brought about a significant decrease in the relative abundance of the phylum Firmicutes. Of the major members constituting the phylum Firmicutes, only the order Clostridiales showed a statistically significant decrease as a result of EGCG. The relative abundance of the orders Lactobacillales and Erysipelotrichales could not be influenced by EGCG. A potential explanation for this is that some of the ingested EGCG reached the large intestine and reduced the abundance of the order Clostridiales exclusively, consequently suppressing PC production. However, whether EGCG interferes with enzyme reactions via direct inhibition of bacterial 4-Hpd is not known yet. A vast variety of bacteria with phenol-producing ability seem to reside in the intestine commonly. Saito et al. also identified some types of bacteria belonging to the orders Clostridiales, Fusobacteriales, and Enterobacterales that are capable of effectively producing phenol from tyrosine. These are phylogenetically classified in the phylum Firmicutes, Fusobacteria, and Proteobacteria, respectively. As explained above, EGCG was able to reduce the relative abundance of the phylum Firmicutes, but it showed a reverse trend for Proteobacteria. EGCG might function more to increase the abundance of Proteobacteria than to decrease it. Such a diversified range of phenol producers led us to suppose that EGCG could not by itself meaningfully reduce urinary and plasma levels of phenol. There is a need for more detailed studies to investigate the role of EGCG against phenol-producing bacteria. Dietary supplementations with polyphenol-rich plant extracts may offer an opportunity to control the production of certain uremic toxins. For example, the consumption of a mixture of red wine and grape juice extracts for 4 days brought about a clinical advantage in reducing colonic protein fermentation or changing microbial amino acid metabolism, particularly a reduction of urinary PC. In another study, supplementation with cranberry dry extract (daily dose of 1000 mg) for 2 months did not reduce the plasma levels of PCS in non-dialysis CKD patients. The present study demonstrated a possible beneficial effect of green tea polyphenols on reducing bacterial production of PC in a mouse model. The dietary addition of EGCG had a strong reducing effect on urinary and plasma PC levels with a decreased abundance of PC producers in fecal microbiota. Based on the amount of food intake throughout the experimental period, the daily consumption of EGCG in the 0.2% EGCG group was calculated to be 305 mg/kg body weight of mice. This could be converted to the human equivalent dose at 24.7 mg/kg. If efficient ways were devised to help a greater amount of EGCG reach the large intestine, it should be possible to reduce the dosage of EGCG to some extent. Given that the microbial production of PC has been linked to a significant risk of cardiovascular mortality in CKD patients, EGCG may be a candidate agent for the treatment of the disease. In fact, EGCG has been studied for potential use in the management and prevention of various kidney diseases, with the major mechanisms of action associated with the reduction of oxidative stress and inflammation. The present study looked at the beneficial health effect from a different perspective, focusing on uremic toxin control. Further investigation is warranted to elucidate the clinical benefit to which an EGCG-microbiota interaction is attributed. This study demonstrated that dietary addition of EGCG reduced the plasma level and urinary excretion of PC in mice. The addition of 0.2% EGCG in the diet was accompanied by decreased abundance of PC-producing bacteria in the feces. However, once EGCG was hydrolyzed to EGC and GA, such effects were lost almost completely. Thus, the intervention of EGCG, not EGC, is a promising strategy for the prevention of disorders derived from this uremic toxin. Standards of phenol, PC, and p-chlorophenol were purchased from Fujifilm Wako Pure Chemical Co. (Osaka, Japan). Both β-glucuronidase from Helix pomatia and sulfatase from abalone entrails were purchased from Sigma-Aldrich Japan K.K. (Tokyo, Japan). A commercial EGCG product (>94% of purity) was obtained from DSM Nutrition Japan, K.K. (Tokyo, Japan). EGCG hydrolysate (a mixture of EGC and GA) was prepared by enzymatic hydrolysis of EGCG according to a previous procedure with a slight modification. One gram of enzyme preparation (tannase-KTFHR, Kikkoman Co., Chiba, Japan), which consisted of 0.9% (w/w) tannase from Aspergillus oryzae, 99.0% glucose, and 0.1% inositol, was added to an aqueous solution of EGCG (10 g/L) and incubated at 37 °C for 60 min. The reaction mixture was evaporated and freeze-dried. The disappearance of EGCG after tannase treatment was confirmed by high-performance liquid chromatography (HPLC) (Figure S1). Except for EGC and GA, newly generated peaks were undetectable. In Exp. 1, a total of 12 male ICR mice (4 weeks old) were purchased from Tokyo Laboratory Animals Science Co., Ltd. (Tokyo, Japan) and acclimated for 3 days in stainless steel metabolic cages at 22 °C in a room with an automatically controlled 12 h lighting cycle. During the acclimation period, the mice were fed the AIN93G formulation diet. They were then divided into four groups (n = 3 per group) according to their body weight and fed respective diets: a control diet, a 0.05% (w/w) EGCG diet, a 0.1% EGCG diet, or a 0.2% EGCG diet (Table S1). They were given free access to their experimental diets and tap water for 2 weeks. Feces were collected throughout the experimental period, and urine was collected during the last 2 days of the experiment. The feces were freeze-dried and stored at −40 °C. The mice were humanely killed by inhalation of high levels of carbon dioxide, and blood was immediately collected from the abdominal vein. Plasma was obtained after centrifuging at 2000g for 10 min and stored at −40 °C in a plastic microtube. The cecum was excised, and the cecal digesta was also collected and stored at −40 °C until use. In Exp. 2, we again purchased a total of 12 male ICR mice from the same breeder. After an acclimation period of 3 days, the mice were divided into three groups (n = 4 per group) and fed respective diets, a control diet, an EGCG diet, or an EGC + GA diet (Table S1) for 2 weeks. Feces and urine were collected as in Exp. 1. Blood and cecal digesta were collected on the final day of the experiment. All experiments were approved by the Committee for the Use and Care of Experimental Animals of Tokyo Kasei Gakuin University (approval number 2-11). Thawed urine was diluted 50-fold with distilled water. For the measurement of the total amounts of phenol and PC (the sum of free, sulfate, and glucuronide forms), 50 μL of the diluted urine sample was first reacted with 20 units of sulfatase solution in 0.1 mL of 0.1 M acetate buffer (pH 5.0) at 37 °C for 2 h and second with 100 units of β-glucuronidase solution in 0.9 mL of 0.1 M phosphate buffer (pH 6.8) containing 5 mM sodium chloride at 37 °C for 15 min. For the glucuronide forms, the diluted urine sample (50 μL) was reacted only with 100 units of β-glucuronidase solution in 0.9 mL of the same phosphate buffer at 37 °C for 15 min. For the free form, the diluted urine sample proceeded without enzyme treatments. After adding 10 μL of 0.5 mM p-chlorophenol solution as an internal standard, the reaction mixture was directly applied to a polymer-based SPE cartridge (Strata-X, particle size of 33 μm, Phenomenex, Inc., CA), which had been preconditioned with 1 mL of water, 1 mL of methanol, and 1 mL of water again. The cartridge was washed with 1 mL of water and 1 mL of 20% (v/v) aqueous methanol and then successively eluted phenol and PC with 1 mL of methanol. After passing through a 0.45 μm filtration membrane, 10 μL of the resulting filtrate was injected into an HPLC system (Shimadzu Co., Kyoto, Japan) that consisted of dual pumps (model LC-20 AD), an autosampler (model SIL-10A), a column oven (model CTO-20A), a fluorescence detector (model RF-20A), and a system controller (model SCL-10A). An analytical column (Unison UK-3C18, 100 mm × 4.6 mm i.d., Imtakt, Co., Kyoto, Japan) was used for separation. The fluorescence detector was set at wavelengths of 270 nm for emission and 305 nm for excitation. Gradient elution was performed by varying the proportion of solvent A (methanol–water, 25:75 v/v) to solvent B (methanol), at a flow rate of 1 mL/min. The mobile phase composition started at 100% solvent A (0% solvent B), after which the ratio of solvent B was linearly increased to 50% over 15 min, followed by a further increase of solvent B to 70% over 2 min. The composition was then brought back to the initial conditions over 2 min for the next run. The measured values were normalized to the urinary creatinine concentration. Urinary creatinine concentrations were measured by the Jaffé assay. Fifty microliters of thawed plasma was sequentially treated with 20 units of sulfatase in 0.1 mL of 0.1 M acetate buffer (pH 5.0) at 37 °C for 2 h and then with 100 units of β-glucuronidase in 0.1 M phosphate buffer (pH 6.8) containing 5 mM sodium chloride at 37 °C for 15 min. The subsequent procedure for SPE and HPLC measurements was the same as for urine. To prepare the homogenate, an aliquot of thawed cecal digesta was added to four volumes of distilled water. The homogenate (0.1 mL) was mixed with 0.4 mL of methanol and then centrifuged at 2000g and 4 °C for 10 min. The supernatant (0.4 mL) was diluted with 3.6 mL of water, and the SPE and HPLC analysis proceeded according to the method described above. DNA was extracted from feces and PCR was performed according to the paper by Takahashi et al. The V3–V4 regions of the 16S rRNA gene were amplified by PCR with universal primers 341F (5′-CCTACGGGAGGCAGCAG-3′) and 805R (5′-GGACTACCAGGGTATCTAAT-3′). Sequencing was conducted using a paired-end modified to a 600 bp cycle run on a MiSeq sequencing system with a MiSeq Reagent Kit version 3 (Illumina, Inc., San Diego, CA). The paired-end reads for each sample were joined using Fastq-join and then processed with quality filtering with the FASTX-Toolkit. The quality of the sequences was checked, and the passed sequences were clustered into operational taxonomic units (OTUs) with 97% pairwise identity. Taxonomic annotation of the representative sequence was performed using the Microbial Identification Database NGS-DB-BA 16.0 (TechnoSuruga Laboratory, Shizuoka, Japan). p values less than 0.05 were considered significant. In Exp. 1, the statistical analysis was performed by Dunnett’s test to compare the difference with the control group. If the data were not normalized, Dunn’s test was adopted. In Exp. 2, statistics were calculated using one-way ANOVA, followed by the Tukey test. For the comparison of microbiota abundance, a nonparametric approach with Dunn’s test involving pairwise comparisons was employed. All statistical analyses were conducted using GraphPad Prism version 9.03 (GraphPad Software, San Diego, CA).

PMC9648433

Biçem Demir,Elmas Beyazyüz,Murat Beyazyüz,Aliye Çelikkol,Yakup Albayrak

Long-lasting cognitive effects of COVID-19: is there a role of BDNF?

10-11-2022

BDNF,Cognitive,COVID-19,Impairment

Coronavirus disease 2019 (COVID-19) affects numerous systems of the body during the illness, and there have been long-lasting effects. BDNF plays an important role in synaptic plasticity and synaptic communication. According to the inclusion and exclusion criteria, 54 patients who had COVID-19 infection participated in this study. Thirty-six age-, sex-, body mass index (BMI)-, education level- and smoking status-matched healthy controls were included in the present study. All participants were individually administered the Stroop test and Visual Aural Digit Span Test Form B (VADS-B). Serum BDNF levels were measured by ELISA. Stroop test word reading spontaneous correction number and reading time, word color saying wrong number, spontaneous correction number and reading time, box color speaking spontaneous correction number and reading time, Stroop interference and speed factor duration were significantly higher in the COVID-19 group than in the control group. All scores of the VADS-B test were found to be significantly lower in the COVID-19 group. The mean serum BDNF levels were found to be 10.9 ± 6.9 ng/ml in the COVID-19 group and 12.8 ± 6.4 ng/ml in the healthy control group. Two-way ANOVA showed that the serum mean BDNF level was significantly lower in the COVID-19 group than in the control group. Gender had a significant effect on BDNF levels (F = 12.21; p = 0.008). The present study is the first to demonstrate the association between the role of serum BDNF and cognitive decline in patients with COVID-19 infection. Additionally, there is a significant role of male gender in terms of lower BDNF level and cognitive decline.

Long-lasting cognitive effects of COVID-19: is there a role of BDNF? Coronavirus disease 2019 (COVID-19) affects numerous systems of the body during the illness, and there have been long-lasting effects. BDNF plays an important role in synaptic plasticity and synaptic communication. According to the inclusion and exclusion criteria, 54 patients who had COVID-19 infection participated in this study. Thirty-six age-, sex-, body mass index (BMI)-, education level- and smoking status-matched healthy controls were included in the present study. All participants were individually administered the Stroop test and Visual Aural Digit Span Test Form B (VADS-B). Serum BDNF levels were measured by ELISA. Stroop test word reading spontaneous correction number and reading time, word color saying wrong number, spontaneous correction number and reading time, box color speaking spontaneous correction number and reading time, Stroop interference and speed factor duration were significantly higher in the COVID-19 group than in the control group. All scores of the VADS-B test were found to be significantly lower in the COVID-19 group. The mean serum BDNF levels were found to be 10.9 ± 6.9 ng/ml in the COVID-19 group and 12.8 ± 6.4 ng/ml in the healthy control group. Two-way ANOVA showed that the serum mean BDNF level was significantly lower in the COVID-19 group than in the control group. Gender had a significant effect on BDNF levels (F = 12.21; p = 0.008). The present study is the first to demonstrate the association between the role of serum BDNF and cognitive decline in patients with COVID-19 infection. Additionally, there is a significant role of male gender in terms of lower BDNF level and cognitive decline. Severe acute respiratory syndrome (SARS-CoV-2) first appeared in Wuhan, China, in December 2019 and has been defined as coronavirus disease 2019 (COVID-19). COVID-19 was declared a pandemic by the World Health Organization (WHO) on March 11, 2020 [1]. COVID-19 affects numerous systems of the body during the course of the illness, and there have been long-lasting effects of the disease after recovery. It has been well established that cognitive deficits can be seen during the illness, and moreover, cognitive problems have also been reported to be seen after recovery [2]. The most common neurological symptoms in COVID-19 are headache, dizziness, anosmia, fatigue, myalgia, anorexia and ageusia. Severe neurological manifestations include confusion, seizures, cerebrovascular diseases, meningoencephalitis, acute necrotizing encephalopathy, posterior hemorrhagic encephalopathy syndrome, myopathy, radiculopathy, cerebellar ataxia, myoclonus and Guillain–Barre syndrome [3, 4]. The most common psychiatric symptoms of COVID-19 are as follows: depression, anxiety, sleep disorders, chronic fatigue syndrome and posttraumatic stress disorder symptoms [5]. Additionally, cognitive symptoms have recently been addressed [6]. SARS COV-2 binds to the ACE-2 receptor and enters epithelial cells in the lung. The S protein is cleaved by proteases such as TMPRSS2, cathepsin G, trypsin or disintegrin, and metalloprotease 17 (ADAM17) to facilitate viral entry. As a result, ACE-2 receptors are blocked. When ACE-2 activity is lost, the levels of angiotensin 1–7 and angiotensin 1–9 decrease. Based on these decreases, MAS/G protein-dependent receptors cannot be activated, vasodilation cannot occur, and cell protective mechanisms cannot be activated. All these mechanisms result in vasoconstriction, fibrosis, proliferation and atherogenesis, which are significantly associated with thrombophilia, microthrombosis, alveolar epithelial damage and respiratory failure [7]. BDNF is a protein member of the neurotrophin family, which includes neurotrophin 3 and neurotrophin 4. BDNF plays an important role in synaptic plasticity and synaptic communication [8]. The neurotrophic functions of BDNF are associated with memory, learning, sleep, appetite and neuronal survival. It is also well established that BDNF plays a critical role in hippocampal long-term potentiation (LTP), which is a long-term result of synaptic activity [9]. BDNF participates in many neurophysiological processes [10]. Angiotensin 1–7, which is produced by ACE-2, increases BDNF levels through the MAS receptor/PI3K/Akt/BDNF pathway. Given the decrease in the activity of ACE-2 receptors in the brain in COVID-19 patients, the level of BDNF may decrease, which causes neurodegeneration [11]. In the present study, we aimed to investigate whether there might be an association between cognitive impairment, which has been observed after mild COVID-19 infection, and serum BDNF levels. The present study was conducted at Tekirdağ Namık Kemal University Hospital, Department of Psychiatry, between July 1, 2021, and January 1, 2022. The inclusion criteria were as follows: (1) a positive COVID-19 PCR test during the disease period and two negative tests postdisease, (2) having had mild disease according to the WHO's COVID-19 disease severity classification, (3) being between the ages of 18 and 50, (4) having a minimum education of 12 years, (5) having a BMI ≥ 18 and < 30 and (6) volunteering to participate in the study. The exclusion criteria included: (1) having a score above 7 on the Hamilton Depression Scale (HAM-D), (2) having a score of 6 or above on the Hamilton Anxiety Rating Scale (HAM-A), (3) having a psychiatric illness or a previous psychiatric illness and treatment, (4) having an alcohol or substance use disorder or a history of alcohol or substance use, (5) having current neurological disease or a history of neurological disease, (6) being treated with antidepressant, antipsychotic, mood stabilizer, antiepileptic, benzodiazepine and other drugs that may affect neurocognitive test evaluation, (7) presence of a known chronic inflammatory disease, cancer or autoimmune disease, (8) having acute or chronic infectious disease, (9) having a history of head trauma, (10) having a disease that increases intracranial pressure, (11) having a physical disease that affects the main organs of the body or that prevented neurocognitive testing, (12) presence of a defect in visual function that could not be corrected with lenses, (13) diagnosis of color blindness, (14) presence of a known allergy. According to the inclusion and exclusion criteria, 54 patients who had COVID-19 infection participated in the study. Thirty-six age-, sex-, BMI-, education level- and smoking status-matched healthy controls were included in the study. The inclusion criteria for healthy controls were as follows: (1) having no history of COVID-19 infection, (2) being between the ages of 18–50, (4) having a minimum education of 12 years, (5) having a BMI ≥ 18 and < 30 and (6) volunteering to participate in the study. The exclusion criteria for healthy controls were the same as those for the COVID-19 group. All participants were vaccinated with the BNT162b2 mRNA COVID-19 vaccine. This form was designed based on the literature. The form consisting of a total of 19 questions prepared in order to collect demographic information about the participants in the COVID-19 and healthy control groups and was completed by the researcher for all participants. The Hamilton Depression Rating Scale (HDRS) was established in 1960. It uses the 5-level rating method of 0 to 4 points. The total score is 0–78, and the depression level can be divided as follows: < 8 means no depression, 8–17 means possible depression, 18–24 means mild to moderate depression and > 24 means severe depression [12]. The HAMA-14 is one of the most commonly used clinician-rated measurements of anxiety in studies of depression. The HAMA-14 is rated from 0 to 4 with general guidelines provided for distinguishing stagewise anxiety severity. It is a reliable and valid measure of the severity of anxiety in depressed patients and has become the standard in this field. A score higher than 7 indicates the presence of anxiety symptoms [13]. All participants in our study were individually administered the Stroop test and Visual Aural Digit Span Test Form B (VADS-B) by a supervised test practitioner to evaluate cognitive function. The Stroop test was first developed by Stroop in 1935 as a neuropsychological test that measures focused attention, selective attention, response inhibition, resistance to interference and information processing speed in order to assess frontal lobe functions [14]. The reliability and validity study of the Turkish version of the Stroop test was performed by Karakaş et al. in 1999 [15]. The Stroop test consists of 5 cards, which are used as follows: In the 1st part, the subjects are asked to read the names of colors printed in black ink on the 1st card; in the 2nd part, they are asked to read the names of colors printed in colors different from the cards themselves as presented on the 2nd card; in the 3rd part, they are asked to say which color the colored circles are as presented on the 3rd card; in the 4th part, they are asked to say some neutral words printed in different colors; and finally, in the 5th part, they are asked to name the colors of the mismatching words printed in colors different from themselves. In each part, the total time for a subject to read words or say the colors, the number of correct answers, the number of errors and the number of spontaneous corrections are calculated. The Stroop interference score is calculated as the difference of 3 points, which is obtained by subtracting the duration of the 3rd part from that of the 5th part. The reading time of the 1st card with the color names printed in black, that is, the duration of the first part, shows the basic level of reading speed and is calculated as the speed factor [14]. The Visual Aural Digit Span Test Form B (VADS-B) is a neuropsychological test developed by Karakaş et al. based on the Visual Aural Digit Span test developed by Koppitz for use in children in 1977 to measure the attention and short-term memory function of the hippocampus and prefrontal cortex regions of the brain. One of the validity and reliability studies of the VADS-B was conducted by Karakaş et al. in 1995 [16]. The VADS-B is a test in which visual and aural stimuli are given and responses are received both orally and in writing. The VADS-B consists of consecutive number sequences, with the shortest sequence consisting of 2 numbers and the longest sequence consisting of 9 numbers. When the number sequences are repeated incorrectly, the subject is given a second try. This test consists of four subtests: aural oral (AO), visual oral (VO), aural written (AW) and visual written (VW). The VADS-B has a total of 11 points. Four of these scores consist of the basic scores obtained from each subtest, namely, AO, VO, AW and VW, and 6 of them are related to the combined scores of the aural input score (AO + AW), visual input score (VO + VW), oral expression score (AO + VO), written expression score (AW + VW), intrasensory integration score (AO + VW) and intersensory integration score (VO + AW). The total score is calculated as follows: AO + VO + AW + VW. A maximum of 9 points can be obtained for each subtest, a maximum of 18 points for each combined test and a maximum of 36 points in total [14]. Peripheral blood samples (5–8 ml) were collected in a red-capped gel tube between 08:00 and 10:00 in the morning after 8 h of fasting. All peripheral blood samples were centrifuged at 1000 rpm for 15 min to obtain serum, and the obtained serum samples were stored in a deep freezer (− 80 °C). Serum BDNF levels were measured by ELISA. A commercial ELISA kit (Catalog No: E1302Hu) from Bioassay Technology Laboratory (Shanghai Korain Biotech Co., Ltd. Shanghai, China) was used for this measurement. The mass was measured using the sandwich ELISA principle. Power analysis was used to determine the sufficiency of the sample size for the study. For the comparison of patient and control groups, the Mann‒Whitney U test was performed for two independent samples. Additionally, the normal distribution assumptions were checked by using the Shapiro‒Wilks normality test. In correlation analysis, Spearman's coefficient of correlation was used for non-normally distributed data or ranked data. Otherwise, Pearson's coefficient of correlation can be used for normally distributed data. Statistical analyses were performed using SPSS version 23.0 (SPSS Inc., Chicago, IL, USA). Two-way ANOVA was used to compare serum BDNF levels between groups. Specifically, sex and group were selected as fixed factors, and the serum BDNF value was selected as the dependent variable. A post hoc Tukey test was used for comparisons. To calculate the power of the study, the Mann‒Whitney U test results were used. The effect size was derived by G*Power statistical software. The sample size of 72 achieved 91.6% power to detect an effect size of 0.83 using a Mann‒Whitney U test with a significance level (alpha) of 0.05. A sample size of 90 was considered, and the power was approximately 96% at the alpha level. There was no significant difference between the two groups in terms of sociodemographic characteristics and HAM-D and HAM-A scores. The data are shown in Table 1. The COVID-19 clinical characteristics are presented in Table 2. The duration of recovery was found to be as follows: 35 (64.8%) patients had COVID-19 6–12 months before participating in the study (Table 2). Stroop test word reading spontaneous correction number and reading time, word color saying wrong number, spontaneous correction number and reading time, box color speaking spontaneous correction number and reading time, Stroop interference and speed factor duration were significantly higher in the COVID-19 group than in the control group (p < 0.05). All scores of the VADS-B test were found to be significantly lower in the COVID-19 group than in the control group (p < 0.05). Stroop test and VADS-B test data are shown in Table 3. The mean serum BDNF level was selected as an independent factor, and sex and group were administered as fixed factors. BDNF levels were found to be 10.92 ± 6.91 ng/ml in the COVID-19 group and 12.83 ± 6.41 ng/ml in the healthy control group. Gender had a significant effect on BDNF levels (F = 12.21; p = 0.008). Two-way ANOVA showed that the serum mean BDNF level was significantly higher in the COVID-19 group than in the control group (F = 12.22; p = 0.044). A comparison of the serum BDNF levels of the two groups is shown in Table 4 (Table 4). There were no significant correlations between neurocognitive tests and serum BDNF levels in female participants in case group. In male participants, there were significant negative correlations between Stroop Word Reading (number of correct word and reading time), Stroop Saying The Word’s Color (number of correct word), Stroop Saying The Box’s Color (number of correct word and reading time) and speed factor duration and serum BDNF level (Table 5). There were not any correlations between both female and male groups’ neurocognitive tests and serum BDNF level in control group (Table 6). There were not any significant correlations between the scores of HDRS, HAMA-14, times passed after COVID-19 and serum BDNF levels (respectively, r = 0.076, p = 0.637; r = 0.126, p = 0.744; r = 0.214, p = 0.432). In the present study, the main findings were as followings: Stroop test word reading spontaneous correction number and reading time, word color saying wrong number, spontaneous correction number and reading time, box color speaking spontaneous correction number and reading time, Stroop interference and speed factor duration were significantly higher in the COVID-19 group than in the control group. All scores of the VADS-B test were found to be significantly lower in the COVID-19 group. The mean serum BDNF levels were found to be 10.9 ± 6.9 ng/ml in the COVID-19 group and 12.8 ± 6.4 ng/ml in the healthy control group. Two-way ANOVA showed that the serum mean BDNF level was significantly lower in the COVID-19 group than in the control group. Gender had a significant effect on BDNF levels (F = 12.21; p = 0.008). Several studies have investigated the effects of COVID-19 infection on cognitive function after recovery. In a previous study, 18 men and 11 women who had experienced COVID-19 were assessed, and it was found that cognitive functions were impaired in the field of selective attention three weeks after the disease [17]. In another study in which 97 patients were included, cognitive functions were screened 8 months after COVID-19. It was found that 33% of the patients reported impaired attention, and 27% of them reported a decrease in memory [18]. In a study that evaluated the cognitive function of patients who did not need to be hospitalized due to COVID-19, it was shown that there were decreases in attention and short-term memory function compared to healthy controls [19]. The results of the present study are in line with the literature and indicate a decline in cognitive function, especially in attention and short-term memory. The patients who had mild COVID-19 were evaluated 6 months later in terms of cognitive function, and it was found that cognitive function decreased in these individuals compared to the preepidemic situation [20]. Another study showed that memory, attention, executive functions and language were lower in people who had COVID-19 than in those who had not, and it has been shown that decreased cognitive function was not associated with the severity of the disease [21]. In another study, the cognitive function of people who had COVID-19 was examined 3 months after the infection, and it was reported that one-third of these people had deterioration in cognitive function. However, it was also found that the severity of the disease did not correlate with the deterioration in cognitive function [22]. In a meta-analysis that included 43 studies, it was reported that approximately 20% of people showed cognitive dysfunction for 3 or more months after COVID-19; however, there was no significant association between deterioration of cognitive function and severity of illness [23]. A recent study demonstrated that cognitive dysfunction was more common in people who had severe illness and who needed to stay in the intensive care unit for a longer period of time [24]. Although there are conflicting results about the relationship between the degree of cognitive function and the severity of the disease, our study indicated that cognitive decline can be observed even in young people with mild illness and in people who have had the disease for more than 6 months. There have been few studies that have investigated the role of serum BDNF and cognitive decline in patients with COVID-19 infection. Azoulay et al., showed that lower serum BDNF levels were found in patients with severe disease, and serum BDNF levels returned to normal over time. They also reported that the serum BDNF levels in males were lower than those of females, and thus, it was interpreted that the serum BDNF level could be a prognostic indicator, especially in male patients [25]. Studies have reported that the more severe course of COVID-19 in men may be related to the higher expression of ACE-2 in men [26–28]. In our study, we found that serum BDNF levels were significantly lower in the COVID-19 group, when a two-way ANCOVA model was applied. Sex was shown to have a significant effect on serum BDNF levels. Higher expression levels of ACE-2 in males may be associated with lower levels of serum BDNF in male patients with COVID-19 infection. Although we calculated the sample size for the present study, the small sample size can be considered a limitation. The inclusion of only patients who recovered from mild COVID-19 and the exclusion of patients who recovered from severe to moderate COVID-19 might have resulted in false-negative findings, and this issue is another limitation of the present study. Pro-BDNF is the precursor of mature BDNF and has been reported to have different effects on the etiology of major depressive disorder [29]. We could not measure serum pro-BDNF levels, which is another limitation of the present research. The present study is the first to demonstrate the association between the role of serum BDNF and cognitive decline in patients with COVID-19 infection. Additionally, there is a significant role of male gender in terms of lower BDNF level and cognitive decline. Our results indicated that cognitive decline occurred after recovery and that this decline persisted. Further studies are needed to demonstrate the effects of COVID-19 infection on long-lasting cognitive dysfunction.

PMC9648873

36357651

Qingni Wu,Longxue Li,Yao Jia,Tielong Xu,Xu Zhou

Advances in studies of circulating microRNAs: origination, transportation, and distal target regulation

10-11-2022

Circulating microRNA,Plant microRNA,Distal regulation,Gastrointestinal absorption,Post-translational regulation

In the past few years, numerous advances emerged in terms of circulating microRNA(miRNA) regulating gene expression by circulating blood to the distal tissues and cells. This article reviewed and summarized the process of circulating miRNAs entering the circulating system to exert gene regulation, especially exogenous miRNAs (such as plant miRNAs), from the perspective of the circulating miRNAs source (cell secretion or gastrointestinal absorption), the transport form and pharmacokinetics in circulating blood, and the evidence of distal regulation to gene expression, thereby providing a basis for their in-depth research and even application prospects. Graphical Abstract

Advances in studies of circulating microRNAs: origination, transportation, and distal target regulation In the past few years, numerous advances emerged in terms of circulating microRNA(miRNA) regulating gene expression by circulating blood to the distal tissues and cells. This article reviewed and summarized the process of circulating miRNAs entering the circulating system to exert gene regulation, especially exogenous miRNAs (such as plant miRNAs), from the perspective of the circulating miRNAs source (cell secretion or gastrointestinal absorption), the transport form and pharmacokinetics in circulating blood, and the evidence of distal regulation to gene expression, thereby providing a basis for their in-depth research and even application prospects. MicroRNAs (miRNAs) are a class of small, single-stranded endogenous non‐coding RNA comprising ~ 22 nucleotides (nt) that processed from a stem-loop structured precursor transcript (Kim 2005). MiRNAs exert their affects via base complementary pairing with target message RNA (mRNA), degrading corresponding mRNA or suppressing mRNA translation, as well as act as fine-tuners of the expression of mRNAs (Bartel and Chen 2004). In 2008, Chen et al. found that there were a large number of miRNAs stably existing in human plasma and serum that could circulate to recipient cells and may play a role in regulating gene expression, termed as circulating miRNAs (Chen et al. 2008). Moreover, the circulating miRNA has been demonstrated to own unusually high stability is able to remain stable under various extreme conditions, such as boiling, a very low or high pH, repeated freeze and thaw, and storage at room temperature for a long time (Chen et al. 2008). This feature assures the miRNAs enter and stably exist in the circulatory system of the body and then reach the receptor cells to play a distal regulatory role. In addition, miRNAs were also found in other body fluids such as saliva (Park et al. 2009), urine (Hanke et al. 2010), breast milk (Kosaka et al. 2010), but these miRNAs are outside the scope of this paper. Since researchers first discovered some dietary-derived plant miRNAs stably in human blood in 2011 (Zhang et al. 2012), the academic community has carried out a series of studies and made significant progress on the biological process that how a circulating miRNA originates from cells or plant food enters the circulatory system, circulate to receptor cells, and play a distal regulatory role in the gene expression. The present article performed a review study by dividing the process into the circulating miRNAs source (cell secretion or gastrointestinal absorption), the transport form and pharmacokinetics in circulating blood, and the evidence to function as distal regulators for gene expression, aiming to demonstrate the feasibility of exogenous miRNA as a new active substance entering the receptor cells of the body to play a regulatory role, and to provide scientific basis for subsequent development, research and application prospects. Typically, miRNA is synthesized by transcription of cell genes. The process of miRNA biogenesis in animal cells involves the following steps. First of all, genes are transcribed as primary transcripts (pri-miRNAs) containing stem-loop structures in the nucleus. Next, the stem-loop structure in pri-miRNA will be cut by the endonuclease Drosha, resulting in a length of about 70nt precursor miRNA (pre-miRNAs). The pre-miRNA is then transported from the nucleus by Exportin-5 into the cytoplasm and the “loop” structure is further cleaved by Dicer/TRBP (TAR RNA-binding protein) to generate mature miRNA/miRNA* duplex. The mature miRNA is then loaded onto Argonautes (Ago) to form the core effector complexes, known as miRNA-induced silencing complexes (miRISCs), whereas the miRNA* strand in the duplex undergoes unwinding, shedding, and degradation (Iwakawa and Tomari 2022). In plants, the biological process of miRNA is different from that in animals. In plants, the cleavage of pri-miRNA is performed by Dicer-like protein (DCL) due to the lack of Drosha homologous proteins, generating pre-miRNA with hundreds of nt in length (Liang et al. 2014). Coincidentally, the cleavage of pre-miRNA is also performed by Dicer-like protein, and both the cleavage of pri-miRNA and pre-miRNA are occurred in the nucleus. After transport to the cytoplasm, the 3’ terminal riboses of plant miRNA undergoes methylation by Hua enhancer 1 (HEN1), resulting in the stability of plant miRNAs against strong acid, strong alkali, and high temperature, which lays a molecular biological foundation for its entry into the organism through the digestive tract (Yu et al. 2005). In animal or human cells, miRNAs bind target mRNA sequences mainly through the canonical base pairing between the seed sequence, which includes nucleotides 2–8 from the 5′-end, and the complementary sequence found in the 3′ untranslated region (3′UTR) of its target mRNA, leading to transcriptional inhibition (Fabian et al. 2011), cleavage (Yekta et al. 2004) or degradation (Wu et al. 2006) of target mRNA. In-depth study of miRNA has illustrated that it also targeted bind to the 5’UTR (Jopling et al. 2008; Orom et al. 2008) and open reading frame (ORF) (Bartel 2004), to play the role of gene regulation. In common with animal miRNAs, plant miRNAs exert their affects via base complementary pairing with target gene mRNA after entering animal or human cells (Cavalieri et al. 2016; Chin et al. 2016; Hou et al. 2018). MiRNAs have been estimated to be involved in about 1/3 of human gene expression regulation, covering many aspects of cellular behavior such as cell growth, division, differentiation, proliferation, apoptosis, and metabolism, and are an important class of gene regulatory molecules (Lewis et al. 2005; Chen et al. 2006). Part of the miRNA synthesized in cells can be actively secreted into the blood circulation, which increases the content of the corresponding circulating miRNAs and reaches the receptor cells to perform distal regulatory functions. This phenomenon is most prominent during special physiological and pathological periods, like pregnancy, intestinal flora changes, pathogenic microorganism infection, and tumor occurrence. There is evidence that the expressions of miR-516-5p, miR-518b, miR-520 h, miR-525, and miR-526a in the plasma of pregnant women are up-regulated and increase with the progress of pregnancy, and eventually return to basal levels after delivery (Gilad et al. 2008; Gunel et al. 2011; Kotlabova et al. 2011). The above-mentioned circulating miRNAs can pass the placental barrier and affect fetal development (Li et al., 2015). Host miRNAs or food miRNAs may enter the gut bacteria and affect the growth and reproduction of gut flora (Liu et al. 2016, 2019; Teng et al. 2018). On the contrary, the disturbance of intestinal flora may lead to the change of miRNA expression profile in the host circulation (Peck et al. 2017; Moloney et al. 2018; Virtue et al. 2019; Zhu et al. 2020a, b), exerting a regulatory role in the physiological function of the body. Generally, virus infection often induces the differential expression of host cells and secretes signature miRNA (Xu et al. 2021), which is an important source of circulating miRNA in the infected state and contributes to the repertoire of virus-host interactions (Gonda et al. 2019; Zhu et al. 2020a, b). For example, Epstein Barr virus (EBV) can specifically express viral miRNA in B cells, such as miR-BART15, which is secreted by cells through exosomes and transferred to uninfected cells, inhibiting the expression of its binding nucleotide binding oligomerization domain like receptor protein 3 (NLRP3), thereby increasing the susceptibility of B cells to the virus (Pegtel et al. 2010). During tumorigenesis, tumor cells, stromal cells and endothelial cells exposed to the tumor microenvironment can secrete miRNAs through micro-vesicles (Yin et al. 2014) and exosomes (Zhang et al. 2015; Liang et al. 2016; Sun et al. 2018), thereby significantly changing the expression profile of circulating miRNAs. Tumor cells secrete exosomes at least 10-fold more than normal cells (Sun et al. 2018), and the miRNA carried by tumor cell-derived exosomes can enter the circulatory system to play a distal regulatory role as a carcinogen or tumor suppressor (Selth et al., 2012; Chin et al. 2016). For example, miR-222 is highly expressed in exosomes derived from tumors (Di Leva and Croce, 2010; Mao et al. 2018), the expression of miR-222 in the plasma of breast cancer patients is significantly higher than that of normal people, furthermore, the expression of miR-222 in breast cancer patients with lymphatic metastasis is higher than that in non-metastatic group (Ding et al. 2018). Other pathological tissue cells may also lead to significant changes of circulating miRNA (Zhang et al. 2010; Kimura et al. 2018). The expression levels of these characteristic circulating miRNAs have clinical implications for disease diagnosis, prognosis, and even treatment. MiRNAs are mainly secreted into the circulatory system by cells in the form of encapsulated exosomes or micro-vesicles. The secretion of miRNA by cells in the body has been proved to be selective (Squadrito et al. 2014; Garcia-Martin et al. 2022). RNA binding protein (RBP) holds an irreplaceable position in the selective secretion of miRNA, which can regulate the loading of specific miRNA into extracellular exosomes or micro-vesicles through various mechanisms, and finally achieve the purpose of regulating the secretion of miRNA (Groot and Lee 2020), as shown in Table 1. Currently, the underlying mechanism of many RBPs regulating miRNA remains elusive. In addition, studies have shown that the selective secretion of miRNAs by cells is affected by the expression ratio of intracellular miRNAs to target mRNAs (miRNA/mRNA). Specifically, a small miRNA/mRNA ratio favors miRNA residency in cells, and vice versa favors miRNA exocytosis, thereby ensuring moderate regulation of target mRNAs (Squadrito et al. 2014). In addition to active secretion, apoptotic bodies formed during normal cell metabolism can also carry miRNAs into the circulatory system. For example, vascular endothelial cells can release miR-126 into the blood through apoptotic bodies once the body develops atherosclerosis (Zernecke et al. 2009). In the case of tissue cell damage and necrosis, its miRNA can also be passively released into the circulatory system. For example, a large number of cardiomyocytes miR-208 and miR-499 are passively released into the circulatory system during acute myocardial infarction, increasing their plasma concentrations by 1600- and 100-fold, respectively (Corsten et al., 2012). Another important source of circulating miRNAs is the absorption of exogenous miRNAs (such as plant miRNAs) into the blood through the digestive tract. Zhang et al. (2012) first found that about 5% of miRNAs in human and animal serum have anti-sodium periodate properties in 2011, and thus speculated that their possible sources were plant miRNAs in daily diet, and further proposed the view that plant miRNAs in food could be absorbed into the body’s blood circulation through digestive tract. The research team subsequently confirmed through experiments that mice miR-168a was absorbed into the blood circulation through the digestive tract and reached the liver, simultaneously targeted the liver low-density lipoprotein receptor adaptor protein 1 (LDLRAP1) and inhibited its expression, ultimately promoted the metabolism of low-density lipoprotein (LDL) (Zhang et al. 2012). Since then, the view that exogenous plant miRNAs can enter the blood through the digestive tract and reach distant tissues and organs in the body has been continuously confirmed by other studies. Liang et al. detected cabbage miRNA in the gastrointestinal, blood, spleen, liver, kidney, feces, and other samples of mice after feeding mice with cabbage (Liang et al. 2014). Liang et al. recruited volunteers to eat watermelon juice or mixed fruits and detected a variety of corresponding fruit miRNAs in their plasma (Liang et al. 2015). Luo et al. detected 16 maize miRNAs in the serum, pancreas, and longissimus dorsi of pigs after feeding fresh maize for 7 days (Luo et al. 2017). In addition, Tarallo et al. found that there were significant differences in the expression profiles of fecal miRNA among vegans, vegetarians, and omnivores, indirectly indicating the influence of food miRNA on human body (Tarallo et al. 2021). Based on the above argument and evidence, the researchers further studied the absorption of herbal miRNA in the body, successfully found that herbal miRNA could still exist stably after being decocted at high temperature and was able to be absorbed into the blood through the digestive tract, as well as played a regulatory role in the distal tissues of the body (Li et al., 2015; Zhou et al. 2015; Zhou et al. 2020; Kalarikkal and Sundaram, 2021; Teng et al. 2021). Besides plant miRNAs, miRNAs in milk exosomes could also be absorbed into the blood through the digestive tract (Manca et al. 2018). Moreover, our body might obtain circulating miRNA by in vitro injection as well, making sense for its applicable usage (Teng et al. 2021). Some plant or herbal miRNAs have been confirmed by existing studies to have strong heat, strong acid, and other stability, and can withstand high-temperature cooking, torment, and digestion and degradation. The reasons can be attributed to the following aspects: (1) Methylation at the 3’ end of plant miRNAs (Zhang et al. 2012) and/or its special sequence composition (such as rich in CG) (Zhou et al. 2015); (2) Plant cell exosomes have strong thermal and acid stability (Lasser et al. 2011; Mu et al. 2014), which can effectively protect their encapsulated miRNA. The differences in the expression profiles of plant miRNAs in food and plasma indicate that the body also selectively absorbs plant miRNAs (Zhang et al. 2010). Based on the synthesis and action process of miRNA, it is speculated that the existing forms of miRNA in food include pri-miRNA, pre-miRNA, miRNA:miRNA*duplex, free miRNA, AGO2-bound miRNA, miRISC. Studies have shown that mature free miRNA (Zhang et al. 2012) and miRNA:miRNA*duplex (Chin et al. 2016; Hou et al. 2018) can be absorbed into the blood and become circulating miRNAs. Two mechanisms may be involved in the entry of exogenous plant miRNAs into the body’s circulation. First, plant miRNAs are absorbed by gastrointestinal epithelial cells, and then actively secreted into the blood by gastrointestinal epithelial cells (Jia et al. 2021). Chen et al. demonstrated through in vitro and in vivo experiments that dietary plant miRNAs can be absorbed into the blood by SID-1 transmembrane family member 1 (SIDT1) in the plasma membrane of gastric epithelial mucous cells (Chen et al. 2021). Second, plant miRNAs are encapsulated in plant exosomes and absorbed by gastrointestinal epithelial cells through endocytosis (Kusuma et al. 2016; Manca et al. 2018); and then actively secreted into the blood by gastrointestinal epithelial cells. Circulating miRNAs are characterized by high stability, and one of the important reasons for this characteristic is that they are usually encapsulated and transported by exosomes, micro-vesicles, apoptotic bodies, and other vesicles (A et al., 2009; Blanc and Vidal 2010), thus reducing or avoiding the degradation of various nucleic acid metabolizing enzymes. There is evidence that up to 83–99% of circulating miRNAs are stored in exosomes (Gallo et al. 2012); miRNAs in exosomes include AGO2-bound miRNAs, free miRNA, mature miRISCs (Mao et al. 2015). Another important reason is that circulating miRNAs inside and outside vesicles are often bound to proteins, which can also improve their stability. Studies have shown that 90% of circulating miRNAs are bound to RBPs (Chang et al. 2004; Arroyo et al. 2011), such as AGO2 protein (Arroyo et al. 2011), high density lipoprotein (HDL) (Vickers et al. 2011; Tabet et al. 2014) and LDL (Wagner et al. 2013), and most circulating miRNAs are transported as AGO2-bound miRNAs (Arroyo et al. 2011). The metabolism of circulating miRNAs in blood is likewise a significant parameter. In theoretical terms, plant miRNA in food is absorbed through the gastrointestinal tract, and its content level first increases in the gastrointestinal tissue, and then enters the blood circulation to become circulating miRNA, which is selectively absorbed by distant tissue cells. Zhang et al. (2012) showed that the content level of MIR-168a in plasma and liver tissue increased significantly at 6-hour after mice ate fresh rice. Further research found that the content level of MIR-168a in plasma and liver tissue reached the peak 3-hour after mice were intervened by total RNA extract of rice (Zhang et al. 2012). Hou et al. found that serum MIR-156a peaked within 1 to 3 h in most of the five healthy volunteers eating lettuce (Hou et al. 2018). Additional studies have shown that the plasma MIR-2911 of animals reached the peak after continuous feeding of honeysuckle for 3 days and returned to the baseline level 2-day after stopping feeding (Yang et al. 2015a, b). After oral administration of honeysuckle decoction, the content of MIR-2911 in plasma and lung tissue of mice increased and reached the peak at 6 h, followed by a gradual decline, eventually the lung MIR-2911 recovered to the baseline level at 12 h (Zhou et al. 2015). Furthermore, it is reported that MIR-2911 could be detected in the serum of male ICR mice 5-minute after tail vein injection of an equal mixture of MIR-2911, MIR-168a, MIR-156a and MIR-161, and all miRNAs were cleared after 3 h (Yang et al. 2015a, b). All the data above indicates that circulating miRNAs can exist in the body for hours or days (Ruegger and Grosshans 2012), and their levels can be kept stable through continuous feeding. It is worth noting that intravenous injection can rapidly increase circulating miRNA content (Yang et al. 2015a, b). Recent research demonstrates that circulating miRNAs, especially exogenous plant miRNAs that are absorbed into the blood, can enter receptor cells through endocytosis, membrane fusion or ligand-receptor binding (Cavalieri et al. 2016; Chin et al. 2016; Hou et al. 2018), and play a distal role to regulate gene expression. Table 2 lists the research reports about researchers intervening in cells or organisms by molecular biology techniques in recent years so that exogenous miRNAs can enter recipient cells and play a regulatory role in cells or organisms. These identified studies amply illustrate that circulating miRNAs, especially exogenous plant miRNAs, can overcome multiple barriers to enter the body, reach recipient cells, and perform transboundary gene regulation functions. Taken together, endogenous miRNA and exogenous plant miRNA can enter the body’s blood circulation through cell excretion or gastrointestinal absorption, correspondingly causing changes of circulating miRNA expression profile, as well as enter the distal tissue receptor cells to play a role in regulating gene expression. The above-mentioned findings provide significance for the application prospects of miRNAs, especially plant miRNAs. Currently, circulating miRNA has been used as a biomarker for early screening, diagnosis, and prognosis of diseases (Takahashi et al. 2019) because of its stable existence in body fluids such as plasma and serum, state-specific differences in expression profiles in different physiological and pathological states, convenient sampling, and sensitive detection. Moreover, the long-range regulatory properties of miRNA make it potential for gene-targeted therapy. For the moment, miRNA drugs for targeted therapy of leukemia have entered phase I and phase II clinical trials (Takahashi et al. 2019). How to deliver exogenous miRNA drugs to target tissue cells for targeted therapy is a key technology to improve the effect of miRNA targeted therapy on cancer (Sabit et al. 2021) and also a difficulty in the development and utilization of miRNA drugs. In recent years, viruses are generally considered to be the first-choice for miRNA delivery due to their advantages of strong specificity, high delivery efficiency, low off-target effects, and long expression duration (Havlik et al., 2020). Some plants are rich in miRNAs with specific functions (Cavalieri et al. 2016; Zhu et al. 2017; Hou et al. 2018; Teng et al. 2021), and can introduce miRNAs with therapeutic effects into the body using natural vegetables or herbs as carriers. This type of miRNA delivery method is low-toxic and economical, which is considered to be a promising research and development direction. Although existing studies have proved that circulating miRNAs, especially exogenous plant miRNAs, can overcome many barriers to reach the receptor cells and exert the function of remote gene regulation (Cavalieri et al. 2016; Zhu et al. 2017; Hou et al. 2018; Teng et al. 2021), the molecular biological processes of plant miRNAs, including selective uptake and secretion, entry into recipient cells, and formation of mature miRISCs, are still poorly understood. As outlined previously, during the assembly of intracellular miRNAs into miRISCs, mature miRNAs are loaded onto AGO proteins in the form of miRNA:miRNA*duplex to form miRISC precursors, and mature miRISCs are formed after miRNA* unwinds and falls off. However, in the current functional studies on the introduction of exogenous miRNAs into the body or cells, the initial miRNA interveners used involve many forms, including mature free miRNA monomer (Zhang et al. 2012), miRNA: miRNA* dimer (Chin et al. 2016), exosomes (Shen et al. 2021) or natural plants (Hou et al. 2018) containing target miRNA, pre-miRNA (Almanza et al. 2018). Here are some key issues to be further interpreted. What is the difference between the above-mentioned exogenous miRNA interveners and intracellular miRNAs during their assemblies of miRISC? Which exogenous miRNA interveners have better distal regulatory effect? And how to artificially synthesize exogenous miRNA interveners with good stability and high absorption rate for subsequent application? All these are important issues to be solved in the process of miRNA drug development. In conclusion, circulating miRNAs, originated from cell excretion or gastrointestinal absorption, can overcome barriers to reach the receptor cells and exert the function of remote gene regulation. While the detail mechanisms are still waiting to be interpreted.

PMC9649601

36225129

Debasish Kumar Ghosh,Shruti Pande,Jeevan Kumar,Dhanya Yesodharan,Sheela Nampoothiri,Periyasamy Radhakrishnan,Chilakala Gangi Reddy,Akash Ranjan,Katta M. Girisha

The E262K mutation in Lamin A links nuclear proteostasis imbalance to laminopathy‐associated premature aging

12-10-2022

lamin A,laminopathy‐associated progeroid disorder,loss of DNA damage repair,nuclear proteostasis imbalance,protein aggregation,protein instability

Abstract Deleterious, mostly de novo, mutations in the lamin A (LMNA) gene cause spatio‐functional nuclear abnormalities that result in several laminopathy‐associated progeroid conditions. In this study, exome sequencing in a sixteen‐year‐old male with manifestations of premature aging led to the identification of a mutation, c.784G>A, in LMNA, resulting in a missense protein variant, p.Glu262Lys (E262K), that aggregates in nucleoplasm. While bioinformatic analyses reveal the instability and pathogenicity of LMNAE262K, local unfolding of the mutation‐harboring helical region drives the structural collapse of LMNAE262K into aggregates. The E262K mutation also disrupts SUMOylation of lysine residues by preventing UBE2I binding to LMNAE262K, thereby reducing LMNAE262K degradation, aggregated LMNAE262K sequesters nuclear chaperones, proteasomal proteins, and DNA repair proteins. Consequently, aggregates of LMNAE262K disrupt nuclear proteostasis and DNA repair response. Thus, we report a structure–function association of mutant LMNAE262K with toxicity, which is consistent with the concept that loss of nuclear proteostasis causes early aging in laminopathies.

The E262K mutation in Lamin A links nuclear proteostasis imbalance to laminopathy‐associated premature aging Deleterious, mostly de novo, mutations in the lamin A (LMNA) gene cause spatio‐functional nuclear abnormalities that result in several laminopathy‐associated progeroid conditions. In this study, exome sequencing in a sixteen‐year‐old male with manifestations of premature aging led to the identification of a mutation, c.784G>A, in LMNA, resulting in a missense protein variant, p.Glu262Lys (E262K), that aggregates in nucleoplasm. While bioinformatic analyses reveal the instability and pathogenicity of LMNAE262K, local unfolding of the mutation‐harboring helical region drives the structural collapse of LMNAE262K into aggregates. The E262K mutation also disrupts SUMOylation of lysine residues by preventing UBE2I binding to LMNAE262K, thereby reducing LMNAE262K degradation, aggregated LMNAE262K sequesters nuclear chaperones, proteasomal proteins, and DNA repair proteins. Consequently, aggregates of LMNAE262K disrupt nuclear proteostasis and DNA repair response. Thus, we report a structure–function association of mutant LMNAE262K with toxicity, which is consistent with the concept that loss of nuclear proteostasis causes early aging in laminopathies. Abbreviations DNA deoxyribonucleic acid HGPS Hutchinson‐Gilford progeria syndrome HSPA1A heat shock protein family A (Hsp70) member 1A LMNA lamin A MRE11 MRE11 homolog, double strand break repair nuclease mRNA messenger ribonucleic acid NAT10 N‐acetyltransferase 10 nM nanomolar nm nanometer PCR polymerase chain reaction PSMD8 proteasome 26S subunit, non‐ATPase 8 ORF open reading frame RanBP2 RAN binding protein 2 RMSD root mean square deviation RMSF root mean square fluctuation SASA solvent accessible surface area S.D. standard deviation SUMO2 small ubiquitin like modifier 2 UBE2I ubiquitin conjugating enzyme E2 I UV ultraviolet The imbalance of proteome in the nucleus of eukaryotic cells is orchestrated by the accumulation of misfolded proteins or suboptimal protein quality control systems, leading to a systemic failure of nuclear homeostasis (Enam et al., 2018). Mutant proteins could evade the surveillance mechanisms of quality control systems, and their overload in the nucleus in the form of aggregates could potentially lead to intrinsic proteotoxicity (Morimoto, 2008). Usually, nuclear protein aggregates form specific structures such as inclusion bodies, promyelocytic leukemia bodies, and speckles (Matera et al., 2009). In this context, not only nucleoplasmic proteins but also mutants of nuclear envelope proteins, such as lamins, could also cause nuclear pathogenicity in various rare diseases such as laminopathies. (Rankin & Ellard, 2006). Laminopathies are a class of rare genetic disorders that are characterized by de novo heterozygous mutations in the lamin A (LMNA) gene (Kudlow et al., 2007). Clinically, many of the individuals with laminopathic disorders show several physiological symptoms of early aging, such as progeroid facial features, short stature with lower body weight, ectodermal tissue, and connective tissue defects etc. (Hennekam, 2006). Based on the mutation in LMNA, laminopathies are classified into typical and atypical forms (Hennekam, 2006). For example, Hutchinson–Gilford progeria syndrome (HGPS) is a typical laminopathy‐associated premature aging disorder. On the other hand, Malouf syndrome, mandibuloacral dysplasia and congenital muscular dystrophy are laminopathic disorders that show atypical features of early aging. Although the characteristics are similar, early‐onset laminopathies have more severe phenotypes than late‐onset laminopathies. In severe cases of laminopathies, lipoatrophy, cardiovascular problems (coronary occlusions), cerebrovascular occlusions, and stenosis are also observed (Hennekam, 2006). Lamins (LMNA, LMNB, and LMNC) are a class of nuclear proteins that form the core of the meshwork of the lamina of nuclear envelope (Gruenbaum & Foisner, 2015). Structurally, lamin proteins form a filamentous network in the lamina that defines the proper shape and morphology of the nucleus (Gruenbaum & Foisner, 2015). In addition, they also function in maintaining the elasticity of the matrix, proper positioning of nuclear receptors, and mechanotransduction (Dubik & Mai, 2020). The structure of LMNA protein comprises two N‐terminal helical rod domains, typically forming a coiled‐coil region, and a C‐terminal globular beta‐sheet domain flanked by a long‐disordered region (Ahn et al., 2019). LMNA is synthesized as prelamin A, which is C‐terminally isoprenylated and farnesylated. A proteolytic cleavage of C‐terminal eighteen amino acids of post‐translationally modified prelamin A forms the mature LMNA (Simon & Wilson, 2013). The rod domains are amphipathic helices, while the C‐terminal region resembles a low‐complexity region enriched with serine, histidine, and glycine residues. The N195 residue is reported to be required for nuclear translocation of LMNA (Dechat et al., 2010). Besides its function of mechanically supporting the nuclear lamina, LMNA interacts with various proteins to regulate transcription during fibroblast proliferation and myoblast differentiation (Burke & Stewart, 2013). Typical laminopathic cells show a deformed nuclear morphology with thick and lobular nuclear envelopes (Eriksson et al., 2003). Several of the mutations in LMNA result in this cellular phenotype. For example, a mutation in the 608th codon of LMNA creates a cryptic splice site that leads to a C‐terminally fifty amino acid truncated version of LMNA, called progerin (Eriksson et al., 2003). While progerin itself causes nuclear envelope disruption, its farnesylation exacerbates the condition (Yang et al., 2006). Some other truncation mutations of LMNA, such as Q432X (Yang et al., 2013), also lead to similar pathological features as progerin. A number of other studies have confirmed that the loss of binding of mutants of LMNA to nuclear envelope causes structural collapse of the nuclear lamina (Piekarowicz et al., 2017). Interestingly, most of these mutations are clustered in the C‐terminal globular domain of LMNA (Krimm et al., 2002), making this domain a mutational hotspot for laminopathic diseases. Although it is not known how the C‐terminal mutations render LMNA unable to tether to the nuclear envelope, an interplay of nesprin‐2 (Yang et al., 2013) and NAT10 (Larrieu et al., 2014) with LMNA may be a regulatory mechanism in this process. While many of the mutants of LMNA remain profusely distributed in the nucleoplasm (West et al., 2016), mutations in LMNA also lead to the protein's nucleoplasmic aggregation (Boudreau et al., 2012). Interestingly, mutations in both rod domains of LMNA can transform the protein into an aggregation‐prone entity, suggesting a common mechanism of aggregation of rod‐domain mutants of LMNA. Strikingly, a large number of disease‐associated mutations in LMNA involve substitutions of charged amino acids, suggesting that imbalance of charge distribution and hydrophobicity may lead to instability of LMNA under physiological conditions. However, a detailed mechanistic understanding and correlation between the mutations and the phenotypes of laminopathies are lacking. In this study, we report a p.Glu262Lys (E262K) mutation in the second rod domain of LMNA in an individual with atypical progeroid manifestations. Mutation of the conserved glutamic acid to lysine in LMNA leads to destabilization of the protein by inducing a helix‐to‐disorder structural transition of the mutation region, thus forming a hydrophobic patch that promotes aggregation of the protein in aqueous environments. The E262K mutation also prevents SUMOylation of LMNAE262K by preventing the binding of UBE2I to the mutant LMNA. Nucleoplasmic aggregates of LMNAE262K not only resist degradation but also sequester nuclear chaperones, proteasomal proteins, and DNA repair proteins to deregulate nuclear proteostasis and DNA repair pathways. The proband is a second born male child of non‐consanguineous parents (Figure 1a). He had an uneventful antenatal period and born at term via normal vaginal delivery with a birth weight of 2.5 kg (−1.7 SD). His early growth and development were normal. Short stature, sparse hair, eyebrows and eyelashes, shallow orbits, narrow nasal bridge with broad nasal tip, craniofacial disproportion with micro‐retrognathia, and dental crowding were noted around sixteen years of age (Figure 1b). At this age, his weight was 21 kg (−2.6 SD), height was 141.5 cm (0.3 SD), and head circumference was 49 cm (−2.9 SD). While his fingernails were dark colored with longitudinal ridges, his hand radiographs were age appropriate without features of acroosteolysis (Figure 1b). The proband has high pitched voice, and he did not develop secondary sexual characters. Proband's echocardiography was unremarkable. Exome sequencing was performed from the genomic DNA of the blood cells of the proband. A heterozygous substitution at g.156134949G>A (corresponding to c.784G>A in mature mRNA) was identified in exon 4 of the LMNA gene of proband (Figure 1c and Tables S1, S2, Figure S1). Sanger sequencing in his parents did not show this variation (Figure 1c), confirming the de novo status of the variant in the proband. Interestingly, the c.784G>A base substitution in the LMNA gene, resulting in a non‐conservative E262K missense mutation in the LMNA protein (Figure 1d). The E262K mutation had also been previously reported in an individual with atypical progeroid syndrome and lipodystrophy (Yukina et al., 2021). The in silico algorithms predicted this mutation as damaging and pathogenic (Figure S2). The E262K mutation in LMNA occurs at the conserved sites (Figures 1e and S3). Therefore, this mutations would likely to generate unfavorable consequences on the structural and functional landscape of LMNA. Multiple sequence alignment of LMNA proteins of different organisms shows that the 262nd position of LMNA prefers acidic amino acids (Figure 1e). Substitution of this negatively charged amino acid with a basic amino acid, such as a lysine in LMNAE262K, would, as expected, affect the local stability of the region near the substitution. Several of the laminopathy‐associated sequence variations in LMNA are known to induce alternative splicing by introducing cryptic splice sites in LMNA pre‐mRNA, resulting in the formation of different isoforms of LMNA protein (Rodriguez et al., 2009). PCR of the reverse transcribed product of the open reading frame (ORF) of LMNA mRNA from fibroblasts showed no difference in the LMNA‐ORF length of the proband compared to the control (Figure 1f). This observation showed that the c.784G>A base substitution did not generate a cryptic splice site in the mutant LMNA mRNA. Thus, LMNAE262K protein predictably had the same length as the wild‐type LMNA protein. In addition, quantification of the mature mRNA of LMNA by qPCR showed no significant difference in the expression of the LMNA gene in the proband compared with control (Figure 1g). Therefore, the effect of the c.784G>A mutation in the LMNA gene on laminopathy‐associated progeroid manifestation is not related to the formation of alternative isoforms of LMNA nor to the differences in the expression of LMNAE262K and LMNA. Alternatively, the early aging in the proband could be due to an aberrant structure–function association of LMNAE262K. We examined the expression and localization of LMNA and LMNAE262K in the control and proband fibroblasts. Strikingly, mutant LMNA formed nuclear aggregates in a significant number of fibroblasts of proband and showed a loss of its localization in the nuclear envelope (Figure 2a,b). In addition, LMNAE262K also showed a higher accumulation in cell (Figure 2c,d). LMNAE262K showed SDS insoluble aggregates which were observed as the higher molecular weight complexes in the immunoblot. Since the transcript level of LMNAE262K in the proband was not significantly different from the LMNA transcript in the control, the higher level of LMNAE262K in the proband compared to LMNA in the control may be due to decreased degradation of the former protein in the proband. LMNA contains two N‐terminal helical rod domains and a C‐terminal globular domain of beta‐sheets. Although the globular domain is a hotspot for laminopathy‐associated mutations, structural destabilization of LMNA is also driven by mutations in the rod domains of the protein. There is strong evidence that multiple mutations in the rod domains cause aggregation of LMNA (Boudreau et al., 2012; Piekarowicz et al., 2017), although the mechanism of this phenomenon is not clear. Having established that LMNAE262K aggregates in proband fibroblasts, we speculated that the E262K mutation contributes critically to the aggregation of LMNA, probably by modulating the stability of the second rod domain of the protein. To test this hypothesis, we purified bacterially expressed recombinant LMNA, LMNAE262K, and their individual second rod domains—LMNA‐rod2 and LMNAE262K‐rod2 (residues 226–387), and tested their ability to aggregate. Both LMNAE262K and LMNAE262K‐rod2 formed aggregates in a concentration‐dependent manner, whereas LMNA and LMNA‐rod2 did not form aggregates even at higher concentrations (Figure 2e–h). Aggregates of LMNAE262K and LMNAE262K‐rod2 formed in solution were probed by dynamic light scattering. At a higher concentration (10 μM), LMNAE262K and LMNAE262K‐rod2 formed soluble aggregates with the most abundant particulate diameters in the range of 192–342 nm and 458–1106 nm, respectively (Figure 2e,f). The formation of soluble aggregates of LMNAE262K and LMNAE262K‐rod2 was insensitive to salt concentrations, suggesting that aggregation of the rod2 domain of LMNAE262K is driven by hydrophobic interactions. Ex situ measurements of protein particles' height distribution by atomic force microscopy also revealed large aggregates of LMNAE262K and LMNAE262K‐rod2 at higher concentrations (Figure 2g,h). The aggregates of LMNAE262K and LMNAE262K‐rod2 were not fibrillar but appeared as liquid droplets, suggesting that the aggregates were probably formed by phase separation in a mechanism similar to that of other aggregation‐prone proteins such as MAPT (also known as tau; Kanaan et al., 2020). On the other hand, LMNA did not form large particulates in solution. At higher concentration (10 μM), LMNA formed most abundant particulates with a diameter of 7.5–92 nm, while LMNA‐rod2 showed two distinct particle populations with diameters ranging from 6.5–15 to 68–164 nm (Figure 2e,f). In atomic force microscopy, LMNA and LMNA‐rod2 did not show large droplets but smaller oligomeric organization (Figure 2g,h). At lower concentration (100 nM), LMNA and LMNA‐rod2 showed particles with diameter of <10 nm. These results indicate that the second rod domain of LMNA is intrinsically prone to oligomerization, possibly due to its coiled‐coil structural form. However, the E262K mutation transforms the rod2 domain into a structure which is more prone to aggregation. To understand whether the E262K mutation alters the structural properties of LMNAE262K compared to LMNA, we analyzed the secondary structure components of LMNA and LMNAE262K. Slow thermal denaturation during circular dichroism spectroscopy at 222 nm light wavelength revealed faster melting of LMNAE262K than wild‐type LMNA (Figure 2i), suggesting that LMNAE262K contains a lower proportion of secondary structures than wild‐type LMNA. Furthermore, scanning at far UV wavelengths in circular dichroism spectroscopy clearly confirmed a reduction in helical structures and an increase in the disordered region of LMNAE262K compared with wild‐type LMNA (Figure 2j). Thus, aggregation of LMNAE262K not only required the second rod domain but is also coupled with unfolding of one or more regions of the protein. To understand how unfolding relates to the aggregation of LMNAE262K‐rod2 in a real‐time setting, we performed unconstrained molecular dynamics simulations of the LMNA‐rod2 and LMNAE262K‐rod2 structures. The simulation data showed that the LMNAE262K‐rod2 is more unstable than the LMNA‐rod2. The higher root mean square deviation (RMSD) values of the backbone of LMNAE262K‐rod2 indicated greater instability of the protein compared to LMNA‐rod2 (Figure 3a). The higher root mean square fluctuation (RMSF) values of K262 and the neighboring residues in LMNAE262K‐rod2 compared with E262 and the neighboring residues of LMNA‐rod2 (Figure 3b) indicated specific instability of the N‐terminal region of LMNAE262K‐rod2. Accordingly, the overall structure of LMNAE262K‐rod2 became more rigid than the structure of LMNA over time, as evidenced by a smaller radius of gyration (Rg) of LMNAE262K‐rod2 than LMNA‐rod2 (Figure 3c). The K262 and its upstream helical regions (residues 242–268) in LMNAE262K‐rod2 were rapidly unfolded into an unstructured region (Figures 3d and S4), and the nascent unfolded region reorganized such that several of the hydrophobic residues (A242, A244, L245, L248, A250, V256, and L263) in this region were brought closer together to form a topology of two continuous hydrophobic patches (Figure 3d). In contrast, E262 and the upstream region remained as an intact helix (Figure 3d), and the hydrophobic residues in the 242–268 amino acid region of LMNA‐rod2 did not undergo proximal positioning (Figure 3d). Moreover, the solvent‐accessible surface area (SASA) of many hydrophobic residues in the two patches of LMNAE262K‐rod2 was higher than that SASA of the corresponding hydrophobic residues of LMNA‐rod2 (Figure 3e). Since the exposure of the hydrophobic segments to the aqueous environment is thermodynamically unfavorable, the two solvent‐exposed hydrophobic patches of LMNAE262K‐rod2 exposed to the solvent also contributed to the decrease in the energetic stability of the protein. Analysis of the equilibrium data revealed the aggregation index of the different regions of LMNA‐rod2 and LMNAE262K‐rod2 over the simulation period. While both structures contained an aggregation‐prone region in the C‐terminal side of the rod domain (Figure 3f), the temporal unfolding‐coupled clustering of the nonpolar residues in the two hydrophobic patches near the mutation of LMNAE262K‐rod2 also resulted in an aggregation propensity in these residues (Figure 3f). The extended hydrophobicity of the patches appeared to correlate with the aggregation properties of the mutation region of LMNAE262K‐rod2. In contrast, the hydrophobic residues near E262 of LMNA‐rod2 did not induce aggregation properties (Figure 3f). Because the hydrophobic residues near E262 of LMNA‐rod2 were scattered and surrounded by polar and charged residues, they were more in equilibrium with water and did not have much aggregation potential. Taken together, these results indicate that local unfolding of helical structures near the E262K mutation of LMNAE262K facilitates juxta‐positioning of hydrophobic residues that act as aggregation‐prone patches in aqueous environments. Having established the mechanism of aggregation of LMNAE262K, we sought to determine whether there were differences in clearance of LMNAE262K in proband fibroblasts compared to LMNA in control fibroblasts and whether modulation of posttranslational modifications of LMNAE262K affected its differential degradation. Immunoblotting against LMNA from nuclear lysate of proband and control fibroblasts clearly showed higher accumulation of LMNAE262K in proband fibroblasts (Figure 4a,b). Because transcriptional expression of the LMNA gene was not significantly different in proband compared with control, the data suggest that LMNAE262K protein is more resistant to degradation than LMNA. To further validate the lower intracellular degradation and higher half‐life of LMNAE262K compared to LMNA, we checked the level of wild‐type and mutant LMNA in control and proband fibroblasts that were treated cycloheximide (translation inhibitor). While the level of LMNA temporally decreased in the cycloheximide‐treated control fibroblasts (Figure 4a,b), the level of LMNAE262K did not reduce significantly over time in cycloheximide‐treated proband fibroblasts (Figure 4a,b), indicating that LMNAE262K is more resistant to degradation in cellular system. SUMOylation regulates the LMNA degradation potential under various physiological and stressed conditions (Zhang & Sarge, 2008). SUMOylation of nuclear LMNA during DNA damage and replication stress facilitates nucleophagy (Li et al., 2019), whereas cardiomyopathy‐related mutants of LMNA, such as K201R, E203G, and E203K, exhibit loss of SUMOylation (Zhang & Sarge, 2008). Several lysine residues of LMNA are targets of SUMOylation by SUMO2 (Hendriks et al., 2017), and we tested whether the E262K mutation can abolish SUMOylation of the lysine residues of LMNAE262K. Denaturing immunoprecipitation of LMNA and LMNAE262K from the nuclear lysates of fibroblasts from Proband and control, followed by immunoblotting against SUMO2 and LMNA, showed lower SUMOylation of LMNAE262K of proband compared with LMNA of control (Figure 4c). Similarly, LMNAE262K of proband did not show as strong colocalization with SUMO2 in the nucleus as colocalization of SUMO2 with LMNA of control (Figure 4d,e). The lack of SUMOylation of aggregated LMNA mutant has been reported previously (Zhang & Sarge, 2008), and our finding of the loss of SUMOylated LMNAE262K in laminopathy‐associated progeroid condition supports the idea that aggregated nuclear LMNA restricts its SUMOylation by one or more mechanisms. UBE2I is a ubiquitous E2 ligase known to SUMOylate LMNA (Li et al., 2019). To determine the possible reason for the lack of SUMOylation of LMNAE262K, we focused on the effects of the E262K mutation on the binding of UBE2I to LMNAE262K. Sequence analysis revealed a consensus UBE2I binding site (ΨKxE, Ψ is a large hydrophobic amino acid, (Bernier‐Villamor et al., 2002)) at 259YKKE262 of LMNA (Figure 4f), which is also the region mutated in LMNAE262K. Because the E262K mutation disrupts the consensus binding site of UBE2I in LMNAE262K (the sequence is 259YKKK262 in LMNAE262K), we suspected that UBE2I would not bind to LMNAE262K. Indeed, UBE2I showed significantly less colocalization with LMNAE262K aggregates in proband fibroblasts compared with its colocalization with LMNA in control fibroblasts (Figure 4g,h). Since UBE2I remains in a multisubunit complex of RanBP2/RanGAP1‐ SUMO/UBE2I (Werner et al., 2012), we tested whether LMNA interacts directly with UBE2I or via other subunits of the E3 ligase complex. Isothermal titration calorimetry showed a high binding affinity of recombinant UBE2I to LMNA but not to LMNAE262K (Figure 4i), indicating a direct association of UBE2I with the 259YKKE262 region of LMNA and an inability to interact with the 259YKKK262 of LMNAE262K. Based on the above observation, we checked whether the absence of SUMOylation of LMNAE262K mediated by UBE2I was responsible for its decreased degradation in proband. Overexpression of UBE2I in control fibroblasts decreased the nuclear concentration of LMNA, but the same effect was not observed in UBE2I‐overexpressed proband fibroblasts (Figure 4j), proving that the increased accumulation of LMNAE262K and its aggregates in proband fibroblasts is due to the lack of SUMOylation of LMNAE262K due to the nonbinding of UBE2I to LMNAE262K. To understand the mechanisms linking aggregation of LMNAE262K to proteotoxicity, we investigated whether accumulation of LMNAE262K aggregates globally disrupt nuclear proteostasis. Because phase‐separated aggregates nonspecifically sequester other proteins (Yang & Hu, 2016), LMNAE262K aggregates could attract and sequester essential proteostasis‐maintaining proteins. Based on co‐immunostaining of LMNA and HSPA1A (chaperone protein HSP70, member 1A) and LMNA and PSMD8 (proteasomal protein), followed by fluorescence analysis in proband fibroblasts, significant colocalization of HSPA1A and PSMD8 with nuclear aggregates of LMNAE262K was evident (Figure 5a–d), implying sequestration of chaperones and proteasomal proteins by LMNAE262K aggregates. We found that sequestration of chaperones such as HSPA1A by LMNAE262K aggregates promoted the formation of nuclear aggregates, as evidenced by positive staining of these aggregates with Proteostat dye in the nucleus of proband fibroblasts (Figure 5e,f), whereas the nucleus of control fibroblasts did not show a significant amount of protein aggregates (Figure 5e,f). Interestingly, Proteostat not only stained LMNAE262K aggregates, but there was also an abundance of non‐LMNAE262K aggregates in the nucleus (Figure 5g). These data suggest that sequestration of HSPA1A (and probably other nuclear chaperones) by LMNAE262K aggregates reduced the pool of active chaperones in the nucleoplasm, a phenomenon that correlated with a global failure of nuclear proteostasis, leading to the formation of aggregates of various proteins in the nucleus. Using ubiquitin staining, we found that LMNAE262K‐induced proteotoxicity not only formed nuclear protein aggregates, but that these aggregates were progressively ubiquitinated (Figure 5h,i). However, because of possible inactivation of proteasomes sequestered by LMNAE262K, the ubiquitinated proteins were not optimally degraded, and their successive accumulation led to reorganization of the ubiquitinated protein aggregates in the form of large spheres (Figure 5j,k). A combination of the above data is consistent with the phenotypes of nuclear proteotoxicity and suggests an active role of LMNAE262K in triggering nuclear stress through the formation of heterogeneous protein aggregates. Aggregation of proteins in the nucleus has emerged as one of the major DNA‐damaging stressors in nucleopathies (Gruenbaum & Foisner, 2015). Immunocytochemistry against the DNA damage marker phosphor‐serine‐139‐H2A.X (pS15‐H2A.X, also known as γ‐H2A.X) showed a higher number of γ‐H2A.X‐positive foci in a significant number of proband fibroblasts compared with control fibroblasts (Figure 6a,b), representing a higher level of DNA damage in the proband cells. However, the damaged LMNAE262K aggregates did not exhibit coaccumulation of damaged DNA foci, as indicated by the very low colocalization of LMNAE262K with γ‐H2A.X (Figure 6c,d). The lack of physical association of LMNAE262K with damaged DNA sites suggests a passive mechanism that blocks DNA repair by LMNAE262K aggregates. Previous studies have shown that proteomic stress perturbs DNA repair pathways and associated signaling mechanisms (McAdam et al., 2016; Squier, 2001). As with HSPA1A and PSMD8, LMNAE262K aggregates were observed to sequester many of the essential DNA damage repair proteins such as MRE11 and XRCC5 (also known as KU80; Figure 6e,f). While these DNA repair proteins were diffusely distributed in the nucleus of control fibroblasts, they were highly enriched in LMNAE262K aggregates (Figure 6e,f). Therefore, it was evident that proteotoxic stress in nucleus modulated the decreased DNA repair process in proband fibroblasts. Progeroid cells and DNA damage are known to trigger cellular senescence (d'Adda di Fagagna, 2008; Wheaton et al., 2017). Loss of nuclear lamins is also observed in aging cells undergoing senescence (Matias et al., 2022). Therefore, we further investigated the extent to which loss of LMNAE262K from the nuclear envelope and DNA damage induce senescence in the proband fibroblasts. Because increased expression of p16INK4a and p21WAF1/Cip1 are hallmarks of senescent cells (Kohli et al., 2021; Matias et al., 2022), we checked the levels of these proteins in control and proband fibroblasts. Compared with the control fibroblasts, the proband fibroblasts showed significantly increased expression of p16INK4a and p21WAF1/Cip1 (Figure 6g–j), indicating that the proband fibroblasts were undergoing senescence. From these observations, we concluded that sequestration of DNA damage repair proteins by LMNAE262K aggregates induces critical genotoxicity, effectively leading to deregulation of DNA damage repair pathways and cellular senescence in proband cells. Premature aging is a class of developmental disorders that are characterized by genetic mutations and hallmarked by proteomic imbalance in the cell (Morimoto & Cuervo, 2014). The shift in proteostasis during normal aging overloads the cellular protein quality control system with nonfunctional and toxic protein forms, such as misfolded and aggregated proteins, resulting in proteotoxicity (David, 2012). While in normal aging, the effectiveness of chaperones and proteolytic mechanisms is gradually reduced by various molecular mechanisms, in premature aging diseases, particularly in various types of progeria and neurodevelopmental disorders, the components of the protein quality control system are overwhelmed by the accumulation of one or more mutant protein aggregates, thereby modulating organellar homeostasis and cell survival signaling pathways (Dreesen, 2020). However, whereas normal age‐related proteotoxicity causes endoplasmic reticulum and cytosolic stress, certain types of progeria, such as HGPS, exhibit nuclear proteotoxicity (Kubben & Misteli, 2017). There are at least eleven phenotypically distinct single gene disorders, including both autosomal recessive and autosomal dominant disorders, due to genomic alterations in LMNA. To date, most progeroid‐associated LMNA mutations have been reported to be found primarily in the C‐terminal globular domain. A few scattered mutations in the rod‐1 and rod‐2 domains also result in a similar phenotype. The C‐terminal mutations of LMNA, such as C‐terminally truncated LMNA and G608S, do not lead to mislocalization of the protein (Kubben & Misteli, 2017). Instead, binding of these mutant LMNA proteins to the envelope results in irregularly shaped nuclei (Kubben & Misteli, 2017). Farnesylation of the C‐terminal residues of mutant LMNA may contribute to this process. Mutations such as A57P, L140R, etc. in the rod‐1 domain preclude dimerization of LMNA monomers, resulting in diffuse nucleoplasmic localization of LMNA (Casasola et al., 2016). However, some of the mutations such as S143P, E161K, etc. in the rod‐1 domain generate aggregation‐prone LMNA structures (West et al., 2016). On the other hand, the functions of the second rod domain of LMNA are unclear, and the effects of mutations in this region in respect to laminopathic disorders are not well characterized, although such mutations, such as D300G (Kane et al., 2013), have more severe effects on progeria. Our data show that an E262K mutation in the rod‐2 domain collapses the helical structure in the mutation region. Because the conserved E262 residue is involved in a large number of inter‐residue interactions, this residue could be considered as an important node in the protein structure network of wild‐type LMNA. The E262K mutation in LMNAE262K is destabilizing, possibly due to repulsive interactions of K262 with the similarly charged neighboring lysine residues (K260, K261, and K265). The repulsive and steric effects of the E262K mutation lead to a loss of interaction of K262 with the neighboring residues, allowing the region to undergo a transition from helix to disorder. Interestingly, the unfolded region reorganizes temporally such that several of the hydrophobic residues near the mutation are proximal to each other, forming two contiguous hydrophobic patches. The energetically unfavorable solvent‐exposed hydrophobic patches near the mutation of LMNAE262K possibly undergo hydrophobic patch collapse and form aggregates. However, in wild‐type LMNA, these hydrophobic residues are dispersedly distributed by intermittent charged and polar residues. Given the energetic constraints, E262 of the wild‐type LMNA can only be replaced by an aspartic acid to maintain the essential salt bridges with K260, K261, and K265. Global level analysis revealed that substitution of this residue with another amino acid would essentially destabilize this region. Disordered regions in multiple proteins, such as in TDP‐43, amyloid beta peptides, etc., have been reported to cause aggregation of the respective proteins (Uemura et al., 2018). While hydrophobic residues play a crucial role in this process (Fink, 1998), charged residues can also trigger aggregation through electrostatic interactions in some proteins, such as in FUS (Shelkovnikova et al., 2014). Aggregation of LMNAE262K follows the former model of disorder and hydrophobicity for aggregation. This model of aggregation can be extrapolated to other LMNA mutants. For example, disruption of the helix by introduction of the helix‐breaking proline and glycine residues in certain mutations, such as in A57P, R60G, S143P, and D300G, could potentially cause destabilization and aggregation of LMNA in a manner similar to E262K. In contrast, mutations in the globular beta‐sheets or in the C‐terminal ‘SHG‐rich’ region have not been shown to cause aggregation. Although the liquid–liquid phase separation of LMNAE262K in the nucleoplasm is evident, further studies on its amyloidogenic properties and nucleation steps could shed light on the generalized aggregation mechanisms of the rod domain‐associated mutations of LMNA. Mutations in the rod domains of LMNA are known to induce aggregation properties of the protein. However, different mutations transform LMNA into different types of aggregation‐prone entities. For example, mutation of a basic or acidic amino acid to uncharged amino acids, such as D192G, H222P, R249W, and D446V, forms mild and smaller aggregates of the mutant LMNA. In contrast, mutation of residues to charged amino acids, such as L85R, E161K, E262K, and R386K, results in large nucleoplasmic aggregates of mutant LMNA. Moreover, some aggregate‐forming LMNA mutants remain in the nuclear lamina, whereas other aggregation‐prone LMNA mutants are completely mislocalized to the nucleoplasm. Interestingly, the aggregates of the different LMNA mutants are morphologically different. While some of the mutants form smaller, punctate aggregates, others form filamentous and globular aggregates. It is likely that specific mutations in LMNA trigger aggregation of the protein by different mechanisms. Although not much is known about the nucleation process of LMNA mutants, the aggregation of the phosphorylation‐deficient mutant (S143P) and the charged‐to‐nonpolar mutants demonstrate the importance of specific charged residues in maintaining the stability of LMNA. Loss of these charged residues could lead to a local change in hydrophobicity, resulting in aggregation of LMNA mutants in the aqueous nucleoplasm. Long‐distance electrostatic interactions may also play an enhancing role in the aggregation of the mutant LMNA proteins. Our study shows that the formation of disorder region and hydrophobic patches near the E262K mutation causes phase separation and aggregation of LMNAE262K. Although not deciphered, phase‐separated aggregates of LMNAL85R and LMNAR386K may form in the nucleoplasm by a mechanism similar to that of LMNAE262K. However, the formation of intermediate filaments of LMNAE161K and nuclear speckles of LMNAH222P could occur by different mechanisms. Nevertheless, aggregate‐forming LMNA mutants, with the exception of LMNAE262K, are involved in the development of dilated cardiomyopathy and Emery–Dreifuss muscular dystrophy 2. A previous study and we show that LMNAE262K causes atypical progeria. The aggregation characteristics and clinical manifestations of LMNA mutant aggregates are summarized in Table 1. Posttranslational modifications of various residues of LMNA occur in response to cell stage or stress. LMNA is phosphorylated at several serine and threonine residues during mitotic division (Olsen et al., 2010), whereas several of the lysine residues are SUMOylated upon DNA damage and replication stress (Hendriks et al., 2015). SUMOylation of nuclear proteins, including LMNA, is redundantly mediated by the E2 SUMO ligase UBE2I. The E262K mutation in LMNA abolishes the consensus binding site of UBE2I (259YKKE262 to 259YKKK262). Surprisingly, this mutation completely destroys the binding potential of UBE2I to LMNAE262K, although there is another UBE2I binding site at 200MKEE203. Surprisingly, the 200MKEE203 region in rod‐1 is located exactly opposite to the 259YKKE262 region of rod‐2. Therefore, the UBE2I in the heterotrimeric complex of RanBP2/RanGAP1‐ SUMO/UBE2I could bind alternatively to both regions without being released from LMNA. Unfolding of the E262K mutation region potentially affects binding of the UBE2I complex to LMNAE262K in a manner that also diminishes the binding potential of UBE2I to the 200MKEE203 region, resulting in possible loss of SUMOylation of lysine residues such as K201 and K260. The very low level of SUMOylation of LMNAE262K may be due to the activity of an uncharacterized SUMO ligase of LMNA. The function of SUMOylation of LMNA is elusive. It is possible that SUMOylation complements ubiquitination in terms of modulating the stability of LMNA. Our results indicate that loss of SUMOylation of LMNAE262K prevents its degradation, which may also contribute to aggregation of the protein. A previous report suggests that SUMOylation of LMNA drives its degradation during nucleophagy (Li et al., 2019). Loss of SUMOylation at K201 also leads to the accumulation of LMNAK201R in nucleoplasmic aggregates (Zhang & Sarge, 2008), suggesting that SUMOylation is not only required for the localization of LMNA in the nuclear envelope, but that loss of this modification also regulates the unusual accumulation of mutant LMNA under pathological conditions. SUMOylation of the LMNA tail is also impaired in partial lipodystrophy‐causing mutations (Simon et al., 2013). Consistent with these results, our finding highlights the need for proper SUMOylation of LMNA with respect to the localization specificity and degradation capacity of this protein. Additionally, the interplay of ubiquitination and SUMOylation may be an interesting aspect for understanding the spatiotemporal clearance of LMNA in normal and diseased conditions. Mutation of LMNA as a cause of several premature aging disorders has long been known, although the molecular mechanisms underlining the link between mutant LMNA and progeria are not clear. Because many reports cite an imbalance in cellular proteostasis as a driver of aging in the organism, it was interesting to understand whether deregulated nuclear proteostasis due to mutant LMNA is a cause of early aging. Indeed, we observed a fundamental link between the laminopathy‐associated E262K mutation of LMNA and the induction of nuclear proteotoxicity. Aggregates of LMNAE262K included essential chaperones, proteasomal proteins, etc., a phenomenon that not only disrupted protein folding but also thwarted the elimination of misfolded proteins and eventually generated further aggregates of nuclear proteins. Although nuclear protein aggregates were ubiquitinated, they were not efficiently degraded because of proteasome inactivation. Previously, aggregates of LMNAQ432X were shown to sequester the transcription factor SREBP1 (Yang et al., 2013). In addition, a high‐throughput screening of interactors of different LMNA mutants revealed enrichment of several transcription factors such as zinc‐finger transcription factors (e.g., ZNF69, ZNF569, ZNF440, etc.) and CREB (Dittmer et al., 2014), indicating a definitive role of LMNA mutants in triggering transcriptional deregulation. While aberrant transcription in laminopathies would result in qualitative and quantitative alteration of the proteome, we note that nuclear proteotoxicity represents an additional stress that could act at the levels of protein folding and degradation. The lethal effects of LMNAE262K extend beyond proteotoxicity to impairment of DNA damage repair pathways. Like chaperones and proteasomal proteins, aggregates of LMNAE262K sequester DNA damage repair proteins. Sequestration of DNA damage repair proteins by such aggregates would gradually reduce repair of spontaneously occurring DNA damage, leading to accumulation of extensive damaged DNA foci over time. Based on the observation that LMNAE262K aggregates do not colocalize with DNA damage sites, we rule out the possibility that aggregated LMNAE262K binds directly to DNA to cause the damage. Similar to LMNAE262K, condensed chromatin was previously observed in cells expressing LMNAS143P, although LMNAS143P aggregates also did not bind to chromatin (West et al., 2016). Thus, the modulation of chromatin structure and impairment of the DNA repair process would be a passive effect due to the proteotoxicity of the aggregates of mutant LMNA. In summary, we identified that E262K mutation in the rod‐2 domain of LMNA leads to early aging and that structural unfolding‐induced aggregation of the mutant LMNA causes severe proteotoxicity and failure of DNA damage repair, thus elucidating the mechanism of early aging due to an imbalance in nuclear proteostasis. Details of all methods and materials are provided in the Supporting Information. DKG: Conceptualization, Methodology, Resource acquisition, Investigation, Formal analysis, Validation, Data curation, Writing original draft. SP: Methodology, Investigation, Formal analysis, Writing original draft. JK: Methodology, Formal analysis. DY: Clinical evaluation. PR: Methodology. SN: Clinical evaluation. CGR: Methodology, Software, Validation, Writing original draft. AR: Resource acquisition, Formal analysis, Validation. KMG: Conceptualization, Resource acquisition, Clinical evaluation, Formal analysis, Validation, Project administration, Funding acquisition and overall supervision. KMG is founder and director of Suma Genomics Private Limited, interested in rare disease diagnosis. Other authors report no conflict of interest. This work was supported by the DBT/Wellcome Trust India Alliance grant [Grant number: IA/CRC/20/1/600002] awarded to KMG. Shruti Pande is supported by the Nurturing Clinical Scientist fellowship (Grant number: HRD/Head NCS‐2019‐03) from Indian Council for Medical Research, New Delhi. The patient and his parents provided written informed consent to the study. Click here for additional data file.

PMC9649622

Hoda T. Amer,Reda A. Eissa,Hend M. El Tayebi

A cutting-edge immunomodulatory interlinkage between HOTAIR and MALAT1 in tumor-associated macrophages in breast cancer: A personalized immunotherapeutic approach 10.3389/fmolb.2022.1032517

28-10-2022

breast cancer,immunotherapy,epigenetics,tumor-associated macrophages (TAMs),MALAT1,HOTAIR,CD80,MSLN

Breast cancer (BC) is one of the most common cancers, accounting for 2.3 million cases worldwide. BC can be molecularly subclassified into luminal A, luminal B HER2-, luminal B HER2+, HER2+, and triple-negative breast cancer (TNBC). These molecular subtypes differ in their prognosis and treatment strategies; thus, understanding the tumor microenvironment (TME) of BC could lead to new potential treatment strategies. The TME hosts a population of cells that act as antitumorigenic such as tumor-associated eosinophils or pro-tumorigenic such as cancer-associated fibroblasts (CAFs), tumor-associated neutrophils (TANs), monocytic-derived populations such as MDSCs, or most importantly “tumor-associated macrophages (TAMs),” which are derived from CD14+ monocytes. TAMs are reported to have the pro-inflammatory phenotype M1, which is found only in the very early stages of tumor and is not correlated with progression; however, the M2 phenotype is anti-inflammatory that is correlated with tumor progression and metastasis. The current study focused on controlling the anti-inflammatory activity in TAMs of hormonal, HER2+, and TNBC by epigenetic fine-tuning of two immunomodulatory proteins, namely, CD80 and mesothelin (MSLN), which are known to be overexpressed in BC with pro-tumorigenic activity. Long non-coding RNAs are crucial key players in tumor progression whether acting as oncogenic or tumor suppressors. We focused on the regulatory role of MALAT1 and HOTAIR lncRNAs and their role in controlling the tumorigenic activity of TAMs. This study observed the impact of manipulation of MALAT1 and HOTAIR on the expression of both CD80 and MSLN in TAMs of BC. Moreover, we analyzed the interlinkage between HOTAIR and MALAT1 as regulators to one another in TAMs of BC. The current study reported an upstream regulatory effect of HOTAIR on MALAT1. Moreover, our results showed a promising use of MALAT1 and HOTAIR in regulating oncogenic immune-modulatory proteins MSLN and CD80 in TAMs of HER2+ and TNBC. The downregulation of MALAT1 and HOTAIR resulted in the upregulation of CD80 and MSLN, which indicates that they might have a cell-specific activity in TAMs. These data shed light on novel key players affecting the anti-inflammatory activity of TAMs as a possible therapeutic target in HER2+ and TNBC.

A cutting-edge immunomodulatory interlinkage between HOTAIR and MALAT1 in tumor-associated macrophages in breast cancer: A personalized immunotherapeutic approach 10.3389/fmolb.2022.1032517 Breast cancer (BC) is one of the most common cancers, accounting for 2.3 million cases worldwide. BC can be molecularly subclassified into luminal A, luminal B HER2-, luminal B HER2+, HER2+, and triple-negative breast cancer (TNBC). These molecular subtypes differ in their prognosis and treatment strategies; thus, understanding the tumor microenvironment (TME) of BC could lead to new potential treatment strategies. The TME hosts a population of cells that act as antitumorigenic such as tumor-associated eosinophils or pro-tumorigenic such as cancer-associated fibroblasts (CAFs), tumor-associated neutrophils (TANs), monocytic-derived populations such as MDSCs, or most importantly “tumor-associated macrophages (TAMs),” which are derived from CD14+ monocytes. TAMs are reported to have the pro-inflammatory phenotype M1, which is found only in the very early stages of tumor and is not correlated with progression; however, the M2 phenotype is anti-inflammatory that is correlated with tumor progression and metastasis. The current study focused on controlling the anti-inflammatory activity in TAMs of hormonal, HER2+, and TNBC by epigenetic fine-tuning of two immunomodulatory proteins, namely, CD80 and mesothelin (MSLN), which are known to be overexpressed in BC with pro-tumorigenic activity. Long non-coding RNAs are crucial key players in tumor progression whether acting as oncogenic or tumor suppressors. We focused on the regulatory role of MALAT1 and HOTAIR lncRNAs and their role in controlling the tumorigenic activity of TAMs. This study observed the impact of manipulation of MALAT1 and HOTAIR on the expression of both CD80 and MSLN in TAMs of BC. Moreover, we analyzed the interlinkage between HOTAIR and MALAT1 as regulators to one another in TAMs of BC. The current study reported an upstream regulatory effect of HOTAIR on MALAT1. Moreover, our results showed a promising use of MALAT1 and HOTAIR in regulating oncogenic immune-modulatory proteins MSLN and CD80 in TAMs of HER2+ and TNBC. The downregulation of MALAT1 and HOTAIR resulted in the upregulation of CD80 and MSLN, which indicates that they might have a cell-specific activity in TAMs. These data shed light on novel key players affecting the anti-inflammatory activity of TAMs as a possible therapeutic target in HER2+ and TNBC. Breast cancer (BC) is one of the most commonly diagnosed cancers in women. It has now exceeded lung cancer as the leading cause of overall cancer incidence in 2020, with 2.3 million new cases; 685,000 deaths occur due to BC, making it the fifth leading cause of cancer mortality worldwide (Sung et al., 2021). Due to the molecular heterogeneity, the subtypes of BC are divided according to the expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2), in addition to the percentage of the proliferating index (Ki67). Accordingly, they are classified into five different subtypes: luminal A, luminal B HER2-, luminal B HER2+, HER2+, and TNBC (basal-like) (Kondov et al., 2018). The luminal A subtype is considered the most abundant subtype with 50–60% prevalence among BC patients and is characterized by the expression of ER and/or PR with no expression of HER2 and with Ki67 < 14% (Blows et al., 2010), while luminal B abundance is 10–20% among BC patients with a more aggressive diagnostic profile than luminal A and is further classified into luminal B HER2- and luminal B HER2+. The luminal B HER2- subtype is characterized by the expression of ER and/or PR with no expression of HER2 and with Ki67 ≥ 14% (Blows et al., 2010), while the luminal B HER2+ subtype expresses ER and/or PR+ in addition to HER2+ with the expression of any Ki67 percentage (Blows et al., 2010). The HER2-enriched subtype is hormonal negative, expressing only HER2 receptors with any Ki67 percentage. The HER2-enriched subtype suffers from a worse prognosis than luminals in spite of the availability of its targeted therapy, anti-HER2. Finally, TNBC, ER, PR, and HER2- have the worst prognosis of all subgroups (Blows et al., 2010). Generally, the immune system plays a major role in cancers. Immunity can be subcategorized into innate immunity, which is fast, immediate, and non-specific, and adaptive immunity, which is specific and long lasting (Wills-Karp, 2007). Within innate immunity, myeloid cells are the most abundant hematopoietic cells (Gabrilovich et al., 2012). Tumor-infiltrating myeloid cells include granulocytes (such as basophils, eosinophils, and neutrophils), monocytes, dendritic cells, tumor-associated macrophages (TAMs), immature myeloid cells (IMCs), and MDSCs (6). Recently, the tumor-infiltrating myeloid cells have been reported to have an important role in angiogenesis, invasion, and metastasis, indicating their possible immunosuppressive role (Gabrilovich et al., 2012). Tumors have the ability to recruit stromal cells (e.g., fibroblasts), immune cells, and vascular cells through the secretion of growth factors, cytokines, and chemokines building a tumor microenvironment (TME) by releasing growth-promoting signals and remodeling tissue structure affecting initiation, progression, metastasis, vascularization, and therapy responses (Garofalo et al., 2006). Many treatments focus only on the cancer cell while special attention to the TME is needed since it has the key players of BC development and progression. Tumor not only tries to escape from the host immune system but also benefits from the infiltrating cells by modifying their functions to create the microenvironment that is favorable to its progression (Pollard, 2004). Monocytes play a very critical role in the TME either by themselves or on reprogramming to myeloid-derived suppressor cells (MDSCs) or tumor-associated macrophages (TAMs) (Allaoui et al., 2016). Monocytes are divided into three subsets based on the expression of CD16 and CD14 surface markers (Coillard and Segura, 2019). The CD16 (FcgRIII) molecule was only known initially to be expressed on mature macrophages; however, it is recognized recently as a surface marker for monocytes (Feng et al., 2011). The three subsets of monocytes are “classical” CD14+CD16− monocytes, which make up around 85% of monocytes, “intermediate” CD14+CD16+ monocytes, which account for 5–10% of total monocytes, and finally, “non-classical” CD14−CD16+, which also accounts for 5–10% (Coillard and Segura, 2019). Within the TME, monocytes can differentiate to M1 macrophage, which expresses the CD163 receptor marker at low levels (CD163neg/low macrophages), mediating defensive immune response (Ding et al., 2016). M1 macrophages usually receive stimulation from GM-CSF, LPS, and IFN- γ to produce IL-23 and IL-12 and promote Th1 responses (Rey-Giraud et al., 2012), thus having a pro-inflammatory response. In addition, M1 can secrete IL-6, ROS, and TNF- α (Sousa et al., 2015). Moreover, monocytes can infiltrate the tumor and differentiate into the M2 subtype, which is CD163 high (Feng et al., 2011). M2 can reduce tissue damage caused by inflammatory processes and stimulate their repair, thus having anti-inflammatory responses. M2 is usually activated by M-CSF, IL-4, IL-10, and IL-13 and can produce anti-inflammatory IL-10 and TGF-β. In the presence of tumors, macrophages are plastic and can be reprogrammed to polarize into either M1-like macrophages or M2-like macrophages, according to the cytokines present in the TME (Benner et al., 2019). Interestingly, TAMs contribute 5–40% of tumor mass in solid tumors. In the beginning, TAMs are said to have a pro-inflammatory phenotype (M1-like) and inhibit the tumorigenesis by ROS and TNF- α or even phagocytosis (Zhou et al., 2020). Nevertheless, once the cancer starts progressing, TAMs tend to secrete IL-10, TGF-β, and IL-12, suppressing cytotoxic T lymphocyte (CTL) and NK cells with the upregulation of programmed death ligand-1 (PDL-1), thus having anti-inflammatory action (Zhou et al., 2020). Moreover, TAMs can induce Tregs activity by IL-10, TGF- β, and PDGF-2, thus suppressing T cells. TAMs also promote angiogenesis by releasing VEGF, PDGF, and IL-8. Furthermore, they contribute to invasion and metastasis, particularly in malignant solid tumors, reducing the survival in BC patients and worsening their clinical outcomes (Campbell et al., 2011). TAMs contribute to the extracellular tissue remodeling, thus metastasis via MMPs. It was observed that COX-2 in breast TAMs promotes the metastatic potential of breast cancer cells. COX-2 in TAMs induces MMP-9 expression and enhances epithelial–mesenchymal transition (EMT) in breast cancer cells (Gan et al., 2016). Not only this but also TAMs express VEGF-C/D at the tumor site, which is shown to be related to lymph node metastasis (Schoppmann et al., 2006). TAMs not only play a role in BC but also in other cancers, namely, colorectal cancer. A study was carried out to show the contribution of TAMs in the metastasis of colorectal cancer (CRC). Briefly, the study showed that CRC-conditioned macrophages regulated EMT to enhance migration and invasion by the secretion of IL-6. TAMs-derived IL-6 was shown to activate the JAK2/STAT3 pathway. STAT3 transcriptionally inhibited the tumor suppressor miR-506-3p in CRC cells (Wei et al., 2019). It was also shown in another study that IL-6 secreted by TAMs promote the invasion of the prostate cancer cells and express low levels of the epigenetic factor (SFMBT2) (Gwak et al., 2020). In general, immune cells are known to express a number of immunomodulatory proteins, including CD80 and mesothelin (MSLN) proteins. CD80 (cluster of differentiation 80), a type-1 transmembrane glycoprotein is first identified in Epstein–Barr virus-activated B-cell blasts, B lymphoblastoid cell lines, and Burkitt’s lymphomas (Mir, 2015). It is a costimulatory molecule known to activate T cell and regulate the activity of normal and malignant B cells (Orabona et al., 2004). CD80 is expressed on activated B cells, macrophages, DCs, and cancer cells and binds to CD28 on T cells. It was shown that the loss of CD80 is enough to allow tumor to escape the immune system, thus imparting apoptosis caused by tumor-infiltrating T cells (Lázár-Molnár et al., 2010). Consequently, even if the tumor cell expresses MHC-I molecule while the co-stimulation is absent, the recognition of antigens by CTL cells will not cause any response. Conversely, on transfecting tumor cells with CD80, the tumor cell is found to be more susceptible to the lysis by T cells ex vivo. Moreover, it was evident that CD80 may enhance the memory responses by CTLs (Lázár-Molnár et al., 2010). However, CD80 has also been reported to be overexpressed in a number of cancers including BC (Li et al., 2020). This may be explained by the fact that CD80 is a ligand not only for CD28 but also for CTLA4 (cytotoxic T lymphocyte antigen-4 or CD152). CTLA4 displays important sequence and structure homology with CD28. Conversely to CD28, CTLA4, a negative regulator of T-cell activation, was found to have an anti-inflammatory action and facilitated the escape of tumor immunity (Brahmer et al., 2015). In BC, it was reported that CTLA4 has a higher binding affinity to CD80 than CD28 (Walker and Sansom, 2011). Consequently, blocking CTLA4 is a target for immunotherapy (Walker and Sansom, 2011). As a matter of fact, CD80 is highly expressed on APCs including macrophages, thus it is expected to be highly expressed on TAMs. Upon isolation of TAMs from human renal cell carcinomas, TAMs were shown to induce the CTLA4 expression on T lymphocytes (Daurkin et al., 2011). Mesothelin (MSLN) is another immunomodulatory protein that is present on normal mesothelial cells of the pleura, peritoneum, and pericardium (Hassan et al., 2004). The MSLN gene is shown to be overexpressed in many cancers including ovarian cancer, adenocarcinoma, pancreatic cancer, mesothelioma, lung adenocarcinoma, acute myeloid leukemia (Pastan et al., 1992), endometrial adenocarcinomas, and squamous cell carcinomas of the cervix, lung, head, and neck (Ordóñez, 2003). MSLN is reported to be overexpressed in 67% of TNBC cases and less than 5% of hormonal BC. Not only this but also the presence of MSLN in BC cells is associated with tumor infiltration of the lymph node. MSLN is shown to have a very limited expression in normal tissues, thus making it a very attractive candidate for cancer therapy (Macdonald et al., 2016). Several immunomodulatory proteins are known to be controlled epigenetically. Basically, the epigenetic machinery is composed of three components, namely, DNA methylation, histone modification, and non-coding RNAs. Non-coding RNAs (ncRNAs) are classified into two major classes based on the transcript size, in which 200 nucleotides is the threshold: small ncRNAs (sncRNAs) and long non-coding RNAs (lncRNAs) (Carninci et al., 2005). Metastasis associated in lung adenocarcinoma transcript 1 (MALAT1) is considered as one of the most studied lncRNAs in cancer and is also known as nuclear enrichment autosomal transcript 2 (NEAT2). MALAT1 was observed to be highly expressed in cancer tissues and it was initially observed in the metastatic non-small lung cancer tissues (Guan et al., 2020). Its function is still controversial that whether it acts as oncogenic or tumor suppressor lncRNA, thus understanding MALAT1 better would be important in epigenetics studies. One mechanism of how MALAT1 functions as an oncogene is by interacting with the polycomb repressive complex 2 (PRC2). On interaction, the RNA–protein complex with EZH2 and SUZ12 is formed. EZH2 and SUZ12 are two components of the PRC2 complex, thus facilitating the histone H3K27 trimethylation at the promoters of some tumor suppressor genes such as E-cadherin and N-myc downregulated gene-1 (NDRG1). Consequently, b-catenin and c-myc expression are increased (Chen et al., 2020). On the other hand, MALAT1 can function as a tumor suppressor since its expression was found to be positively correlated with the expression of the tumor suppressor PTEN, and their decreased levels were associated with mortality and poor patient survival in both colorectal cancer and BC patients (Guan et al., 2020). MALAT1 is shown in the literature to have an oncogenic activity enhancing both the progression and metastasis of breast tumors. A research group studied this oncogenic potential of MALAT1 by using MTT and transwell assay to detect proliferation, migration, and invasion. Furthermore, the drug resistance test was performed to assess the sensitivity of BC cells to doxorubicin. The study has shown that silencing of MALAT1 could significantly suppress the proliferation, migration, and invasion of BC cells. Moreover, downregulation of MALAT1 sensitized BC cells to doxorubicin (Yue et al., 2021). Not only this but also another group used the MMTV (mouse mammary tumor virus)-PyMT mouse mammary carcinoma model to assess the proliferative and metastatic potential of MALAT1. The results showed slower tumor growth accompanied by significant differentiation into cystic tumors and a reduction in metastasis upon loss of MALAT1 (Arun et al., 2016). In addition, HOTAIR (HOX transcript antisense RNA) is the first lncRNA found to promote tumor progression. HOTAIR is highly expressed in metastatic BC. It is transcribed from the antisense strand of the HOXC genes and partly overlaps with HOXC11 (Rinn et al., 2007). HOTAIR can function as an oncogene using several mechanisms, especially by the negative regulation of a number of miRNAs. For example, it competes with miR-34a, leading to the upregulation of SOX2, thus cell proliferation. It can also promote cell growth, mobility, and invasiveness by suppressing miR-20a-5p and consequently upregulate HMGA2 (Li et al., 2016). HOTAIR is known in the literature to have a proliferative and metastatic potential. In a research study, CCK-8 and colony formation assays showed that HOTAIR overexpression promoted the proliferation of MCF-7 cells. Furthermore, transwell invasion and migration assays showed that HOTAIR overexpression increased the migration and invasion of BC cells. These results indicate that HOTAIR facilitates the growth and metastasis of BC cells in vitro (He et al., 2022). Another research study confirmed these findings by using qRT-PCR to determine the expression of HOTAIR. CCK-8 and transwell assays were also used to detect the proliferation, migration, and invasion of cells. In addition, animal experiments were conducted to validate the effect of HOTAIR on BC tumor growth in vivo. The results showed that HOTAIR was upregulated in BC tissues and cells, and its knockdown suppressed the proliferation, migration, invasion, and the activity of the AKT signaling pathway of BC cells. Additionally, interference of HOTAIR had impacted BC tumor growth in vivo (Wang et al., 2020). Even though TAMs have a crucial role in tumorigenesis, the epigenetic players controlling and regulating this anti-inflammatory activity have never been studied before through their direct manipulation in TAMs. The aim of this study is to investigate the interlinkage between MALAT1 and HOTAIR and their regulatory activity on immunomodulation by examining the expression profile of CD80 and MSLN in tumor-associated macrophages in the hormonal, HER2+, and TNBC subtypes. In total, 43 blood samples were collected in EDTA tubes from breast cancer (BC) patients, after taking their consents. All patients were females ranging between the age of 34 and 78 years. Clinical features for each patient are represented in Table 1. Gender-matched healthy controls were used. Within 3 or 4 h of sample collection, the Ficoll separation technique was optimized and used to isolate the PBMCs from the whole blood (Jaatinen and Laine, 2007). The PBMCs of each sample were cryopreserved and stored in the −80 C freezer for later use. Detailed clinical features for each patient are mentioned in Supplementary Table S1. Cryopreserved PBMCs are left to melt at room temperature. The melted PBMCs are transferred into a falcon tube containing a 6 ml wash mix and was allowed to shake for 5 min followed by centrifugation for 5 min at 1,500 rpm. A pellet of PBMCs is formed, and the supernatant is discarded. The cells are suspended in an appropriate volume of complete media and counted. Isolation of CD14+ monocytes by negative depletion was performed using MojoSort™ Human CD14+ Monocytes Isolation Kit protocol (Cat. No. 480047), MojoSort™ Buffer (5X) (Cat. No. 480017), and MojoSort™ Magnet (Cat. No. 480019/480020). Isolation of CD8+ T cells by negative depletion was carried out using MojoSort™ Human CD8+ T cell Isolation Kit protocol (Cat. No. 480012), MojoSort™ Buffer (5X) (Cat. No. 480017), and MojoSort™ Magnet (Cat. No. 480019/480020). Freshly isolated CD14+ monocytes were subjected to centrifugation at 1,500 rpm, buffer supernatant was discarded, and CD14+ monocytes were plated in a 48-well plate (10,000 cells per well). Monocytes were cultured in 1:1 ratio of 10% cell culture medium and TCM with the addition ofIL-4 (1 μg/ml) (Schenendoah (United States), ID:100-09), IL-10 (1 μg/ml) (Schenendoah (United States), ID:100-83), and M-CSF (1 μg/ml) (Schenendoah (United States) 100-03). The medium was refreshed every other day. TAMs were harvested on day 7. After incubation of TAMs for 7 days to ensure differentiation (3 × 104 cells per well in a 48-well plate), the supernatant was discarded and refreshed by the transfection media. The appropriate amount of siRNAs (2 ul) was diluted in 60 μL of free culture medium without serum. The appropriate amount of HiPerFect transfection reagent (Qiagen) was added (1 ul) to the diluted siRNAs (MALAT1 siRNA, Qiagen ID: SI04342233) (HOTAIR siRNA, Qiagen ID: SI04446036) and then mixed by vortexing. The siRNAs-HPTR mixture was incubated for 5–10 min at room temperature (15–25°C) to allow the formation of transfection complexes. The complex was then added dropwise onto the cells. The plates were then rotated gently to ensure uniform distribution of the transfection complexes. After 6 h, 140 ul complete culture media of RPMI containing serum and antibiotics were added to the PBMCs and incubated for 48 h for RNA extraction and subsequent analysis of gene silencing or induction. Total RNA was isolated from TAMs, which were previously differentiated from CD14+ monocytes and treated against diluted silencers. Total RNA was extracted using Thermo Scientific GeneJET RNA Purification kit (Catalog no: K0732), according to the manufacturer’s protocol. In brief, lysis buffer and absolute ethanol were added to each sample eppendorf and centrifuged for 1 min at 12,000 × g. The flow-through solution in the collection tube was then discarded. Each sample was washed twice with wash buffer 1 and wash buffer 2. Finally, the collection tube containing the flow-through solution was discarded, and the GeneJet RNA purification column was transferred to a sterile 1.5-ml RNAse-free microcentrifuge tube. Nuclease-free water was finally added, and the centrifugation was repeated. Purified RNA was stored in a −80°C freezer. Total RNA extracted was reverse transcribed into the single-stranded cDNA using the high-capacity cDNA reverse transcription kit (Thermo Fisher, Cat No: K1652), according to the manufacturer’s protocol. Each component of the reverse transcription kit and the extracted RNA of each sample were thawed on ice and mixed by vortexing to ensure appropriate resuspension. Each reaction’s total tube volume was 20 ul with 1:1 ratio (reaction mix: total RNA). Finally, the reaction tubes were placed in a thermo cycler with a heated lid whose thermal profile was adjusted, according to the manufacturer’s protocol. All the cDNA samples were stored in the −20°C freezer until qRT-PCR analysis was performed. The expressions of MALAT1, HOTAIR, CD80, and MSLN mRNA levels were quantified using RT-PCR. Reagents used were the TaqMan, MALAT1, HOTAIR, CD80, and MSLN expression assays (Themo Fisher (United States)-TaqMan MALAT1 assay (Hs00273907), TaqMan CD80 assay (Hs01045161_m1), TaqMan HOTAIR assay (Hs05502358_s1), and TaqMan MSLN assay (ID: Hs00245879) along with B-actin as an endogenous control housekeeping gene to normalize the expression values. Probes used for MALAT1, HOTAIR, CD80, and MSLN were labeled with the FAM reporter dye. B-actin was reported with the VIC reporter dye. Each reaction tube’s total volume was 20 ul with 1:4 ratio (total cDNA:reaction mix). Each reaction mix was composed of nuclease-free water, Premix Ex TaqTM (Probe qPCR), TaqMan target gene assay expression assay (x20), and B-actin (VIC). Upon preparing the reaction tubes, they were placed into the StepOne® real-time PCR instrument and the run was performed in the standard mode, consisting of two stages. A first stage where the Taq-polymerase enzyme is activated followed by the second stage of 40 amplification cycles (each cycle comprises a 15 second denaturation step followed by 60 s of annealing and extension). The StepOne® real-time PCR yields a cycle threshold value (Ct) for each sample, which represents the fractional cycle number at which the fluorescence produced exceeds a threshold line. Each cycle threshold (Ct) obtained was subsequently used for quantification of the amplified target compared to its endogenous control housekeeping gene (B-actin for target genes), yielding a ΔCt value. After transfecting TAMs with silencers against MALAT1 and HOTAIR, VEGF-A protein was quantitatively examined using the VEGF-A Human ELISA Kit (Invitrogen, BMS277-2). The kit is a sandwich ELISA, which is designed to measure the amount of the target bound between a matched antibody pair. Briefly, 400 μL wash buffer per well was used to wash pre-coated microwells from the microplate. After washing, different concentrations of standards were added to the wells, according to the manufacturer’s protocol. The plates were incubated for 2 h at room temperature. After washing, 100 μL of biotin-conjugated antihuman VEGF-A polyclonal antibody (1:100) was then added to the wells and incubated for 1 h at room temperature. After washing, 100 μL of horseradish peroxidase-labeled streptavidin was added to the wells and incubated for 1 h at room temperature. After washing, 100 µL of TMB substrate solution was added to all wells. Color development on the plate is monitored for 30 min at room temperature, then the stop solution was added to terminate the reaction, and the absorbance was analyzed for each microwell for both standards and samples at 450 nm wavelength. The results are calculated by constructing a standard curve plotting the mean OD and concentration for each standard. MDA-MB-231 cell line was purchased from the tissue culture unit, Egyptian Company for Vaccines and Sera, after proper authentication and testing for mycoplasma contamination. MDA-MB-231 cells are ER, PR, and HER2-. MDA-MB-231 cells were cultured using our previous protocol (Hamed et al., 2021). The cell line was cultured in a suitable culture media [high-glucose DMEM supplemented with 10% FBS and 1% penicillin/streptomycin in 10 cm Petri dishes (Gibco, United States)]. Culture media were changed every 2–3 days until the cells reached 80–90% confluency. The cells were washed with PBS, trypsinized, and split into two clean Petri dishes. After the second splitting, the cells were harvested and prepared for further experimentation. The cells were kept in a 37°C, 5% CO2 incubator. Prior to seeding and experimentation, the cells were counted and checked for viability using trypan blue. In a 96-well plate, CD8+ T cells were cultured (2-3 x 104 cells per well) in 200 μL culture media (100 μL of TAMs supernatant +100 μL of full RPMI media) for 24 h. The supernatant of TAMs culture media was isolated after TAM culturing for 7 days under four conditions (untransfected TAMS, siMALAT1 TAMs, siHOTAIR TAMs, and siMALAT1/HOTAIR TAMs). After culturing CD8+ T cells in TAM-conditioned media, LDH toxicity assay was performed to evaluate the cytotoxicity potential of CD8+ T cells upon co-culturing with MDA-MB-231 cell line with and without the addition of PDL-1 inhibitor drug. LDH assay is used as rapid determination of cytotoxicity based on lactate dehydrogenase released into the cell culture medium using the Canvax Biotech protocol (Cat No: CA0020). In the LDH cytotoxicity assay, the lysis control wells are first prepared with the addition of lysis solution and incubating the plate in a 37°C, 5% CO2 incubator for 45 min. Then, 50 μL of culture supernatant from each well is transferred to a new 96-well flat-bottom plate and the reaction mixture is then added (50 μL on each well). After incubation for 30 min, the reaction is terminated by the addition of stop solution (50 μL on each well). Absorbance for all controls and experimental samples is measured at 450 nm wavelength. Data are analyzed by measuring the % relative cytotoxicity. To evaluate the CD8+ T-cell isolation efficiency using flow cytometry [CytoFLEX, Beckman Coulter Life Sciences (United States)], CD8+ was measured using flow cytometry anti-CD8 PE (Biolegend, Cat No: 344706). In brief, the cells were first dissociated, and then, single-cell suspensions were prepared (240,000 cell/tube). The cells were then washed twice with 2 mls (PBS 1% FBS) and centrifuged at 350 × g for 5 min. After washing, 1.2 µg anti-CD8 PE was added (5 µg per one million cells), and the cells were incubated at 4° for 30 min. All data were expressed in relative quantitation (RQ) for RT-qPCR. For the purpose of comparison between two different studied groups, Student’s unpaired t-test was used. One-way Anova followed by Dunette’s test of multiple comparison was used for the comparison between more than two different studied groups. Data were expressed as mean ± standard error of the mean (SEM). A p-value less than 0.05 were considered statistically significant **** = p < 0.0001, *** = p < 0.001, ** = p < 0.01, and * = p < 0.05. Analysis was performed using GraphPad Prism 7.02 software. The expression of MALAT1 was found to be upregulated in TAMs of the hormonal, HER2+, and TNBC compared to healthy donors (p = 0.0002, 0.0001, and 0.0001, respectively). In a similar pattern, the expression of HOTAIR was found to be upregulated in TAMs of the three subgroups (hormonal, HER2+, and TNBC) compared to healthy donors (p=<0.0001, <0.0001, and <0.0001, respectively), as shown in Figure 1. The expression of CD80 was found to have a non-significant difference in TAMs of the hormonal subtype compared to healthy donors. However, it was significantly upregulated in TAMs of both HER2+ and TNBC subtypes (p = 0.0008 and 0.0016 respectively) compared to TAMs of healthy donors. Meanwhile, the expression of MSLN was found to have significant downregulation in TAMs of the hormonal subtype (p = 0.0002) compared to healthy donors. However, it was significantly upregulated in TAMs of both HER2+ and TNBC subtypes (p = 0.0023 and <0.0001, respectively) compared to TAMs of healthy donors, as shown in Figure 2. For transfection efficiency purposes, HOTAIR and MALAT1 expressions were analyzed after transfection with their silencers (siRNAs). The transfection of TAMs with silencers against HOTAIR resulted in downregulation, thus silencing the expression of HOTAIR in hormonal, HER2+, and TNBC (p = 0.0010, <0.0001, and 0.0059, respectively) in comparison to untransfected controls (mocks). Moreover, the transfection of the TAMs with silencers against MALAT1 resulted in downregulation, thus silencing of the expression of MALAT1 in hormonal, HER2+, and TNBC (p = 0.0009, 0.0008, and <0.0001 respectively), as shown in Figure 3. Silencing of HOTAIR in TAMs of different BC subtypes resulted in a decrease in MALAT1 expression in the hormonal, HER2+, and TNBC subgroups (p = 0.0009, <0.0001, and <0.0001, respectively) in comparison to untransfected controls (mocks). Unexpectedly, in the three subtypes, MALAT1 showed a more significant downregulation upon silencing with HOTAIR than upon silencing with MALAT1 itself in hormonal, HER2+, and TNBC (p = 0.0003, 0.0002, and <0.0001, respectively) in comparison to untransfected TAMs (mocks), as shown in Figure 4. Silencing of MALAT1 in TAMs of different subtypes resulted in an increase in the expression of HOTAIR in hormonal, HER2+, and TNBC TAMs (p = 0.0008, <0.0001, and 0.0009, respectively) in comparison to untransfected controls (mocks). HOTAIR mRNA levels were quantified using qRT-PCR and normalized to β-actin as an endogenous control, as shown in Figure 5. CD80 was found to be inversely correlated with MALAT1 in HER2+ and TNBC TAMs, its expression was upregulated by siMALAT1 (p = 0.0478 and 0.0012 respectively), while the upregulation was more remarkable by siHOTAIR in HER2+ and TNBC TAMs (p = 0.0013 and <0.0001, respectively) in comparison to untransfected controls (mocks). Conversely, in hormonal BC TAMs, CD80 was downregulated by siMALAT1 (p = 0.0007) in comparison to untransfected controls (mocks). MALAT1/HOTAIR co-silencing was able to upregulate CD80 in TAMs of hormonal, HER2+ and TNBC (p= <0.0001) in comparison to untransfected controls (mocks), as shown in Figure 6. MSLN was found to have a similar pattern of expression as CD80, where it was inversely correlated with MALAT1 and HOTAIR in HER2+ and TNBC TAMs. Its expression was upregulated by siMALAT1 (p = 0.0367 and 0.0015), while the upregulation was much remarkable by siHOTAIR in HER2+ and TNBC TAMs (p = 0.0066 and 0.0003, respectively) in comparison to untransfected controls (mocks). However, MSLN expression showed no significant change in the expression in TAMs of the hormonal subtype upon silencing of HOTAIR or MALAT1 in comparison to untransfected controls (mocks). Meanwhile the most significant upregulation for MSLN was found upon MALAT1/HOTAIR co-silencing in hormonal, HER2+, and TNBC (p= <0.0001, 0.0002, and <0.0001, respectively) in comparison to untransfected controls (mocks). As a functional analysis, the ELISA technique was used to measure the VEGF-A in the TAMs of the three subgroups (hormonal, HER2+, and TNBC) on silencing MALAT1 or HOTAIR or co-silencing both lncRNAs together in comparison to untransfected TAMs (mocks) corresponding to each subtype. It was shown that VEGF-A was downregulated upon silencing MALAT1 and HOTAIR and co-silencing MALAT1 and HOTAIR in the hormonal BC subtype (p = 0.0155, 0.0330, and 0.0199) compared to mock cells. The same downregulation expression profile was observed in the HER2+ subtype upon silencing MALAT1 and HOTAIR and co-silencing MALAT1 and HOTAIR (p = 0.0006, 0.0015, and 0.0002) compared to mock cells. Finally, TAMs of TNBC also showed a significant downregulation upon silencing MALAT1 and HOTAIR and co-silencing both of them together (p = 0.0009, 0.0025, and 0.0007) compared to untransfected controls (mocks). LDH toxicity assay was performed to evaluate the cytotoxicity potential of CD8+ T cells upon co-culturing with MDA-MB-231 cell line with and without the addition of PDL-1 inhibitor drug. CD8+ T cells were cultured under three conditions of treated TAMs-conditioned media (siMALAT1, siHOTAIR, and siMALAT1+siHOTAIR). The results showed that the cytotoxicity percentage of CD8+ cells upon culturing in siMALAT1, siHOTAIR, and siMALAT1/siHOTAIR TAMs had an average of approximately 76.6% cytotoxicity; however, upon the addition of the PDL-1 inhibitor on CD8+ T cells that is previously cultured in siMALAT1, siHOTAIR, and siMALAT1/siHOTAIR TAMs-conditioned media, the cytotoxicity percentages had an average of approximately 61.3% and upon addition of the PDL-1 inhibitor alone on CD8+/MDA-MB-231 co-culture, the cytotoxicity was 79%. The morphological change was assessed to confirm the differentiation efficiency of CD14+ monocytes to TAMs. CD14+ monocytes were isolated from total PBMCs by negative selection using magnetic nanobeads against antibodies of all cells other than CD14+ monocytes. Freshly isolated CD14+ monocytes were observed under the microscope. Examination showed small, spherical-shaped cells with a smooth surface. However, upon culturing CD14+ monocytes for 7 days in culture media supplemented with anti-inflammatory ILs (IL-10, IL-4, and M-CSF) with the addition of TCM, morphological change was observed where the cells became larger with a non-smooth edgy surface. To assess the isolation purity of CD8+ T cells, the flow cytometry technique was conducted using anti-CD8+ PE after isolating the CD8+ T cells from PBMCS using magnetic nanobeads. The results showed that the isolation purity reached 70.2%. Breast cancer (BC) is one of the most commonly diagnosed cancers in women worldwide, accounting for 11.7% of all cancer cases in 2020. It is classified molecularly according to the expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) with the percentage of the proliferating index (Ki67). Accordingly, BC is classified into five major subclasses, which are luminal A, luminal B HER2-, luminal B HER2+, HER2 enriched, and TNBC. Luminal A is the most abundant subtype having a better prognosis than luminal B. Furthermore, HER2+, which is a hormonal negative subtype, has a worse prognosis than the hormonal subtypes despite the presence of anti-HER2-targeted therapy “for example, trastuzumab.” Finally, TNBC (basal-like) has the worst and most aggressive prognostic profile of all the subtypes (Kondov et al., 2018). The most common BC treatment strategies are hormonal therapy, anti-HER2-targeted therapy, and chemotherapy. Both hormonal and anti-HER2-targeted therapies are limited to specific subtypes of BC. Moreover, chemotherapy has the ability to target all BC subtypes but causes profoundly aggressive side effects (Yerushalmi et al., 2009). In this context, targeted immunotherapy can be a new therapy alternative to overcome the limitations of other therapeutic strategies. In general, tumors have the ability to recruit stromal cells (e.g., fibroblasts), immune myeloid cells, and vascular cells by the secretion of cytokines building up a tumor microenvironment (TME) by releasing growth-promoting signals and remodeling the tissue structure affecting initiation, progression, metastasis, vascularization, and therapy responses. During the last decades, cancer treatment strategies focus only on the cancer cell ignoring the TME, which is a key player in the tumor progression (Allaoui et al., 2016). Within the TME, CD14+ monocytes can infiltrate and differentiate into tumor-associated macrophages (TAMs) that can reach 40% of the tumor’s volume. TAMs are known to have pro-inflammatory phenotype (M1) in the early stages of cancer while having anti-inflammatory (M2) phenotype once cancer starts progressing in suppressing many immune cells. The differentiation to M2 takes place in the presence of some anti-inflammatory cytokines in the TME, namely, IL-10, IL-4, IL-13, and M-CSF (Zhou et al., 2020). In addition to TAMs correlation with tumor progression, there are some immunomodulatory proteins that have been reported in the last years to be overexpressed in various types of cancers, especially CD80 and mesothelin (MSLN). Furthermore, in the past few years, the role of non-coding RNAs in the pathogenesis of BC has been of significant importance and their correlation with immunomodulatory proteins, as shown in our previous study (Hussein et al., 2022). Interestingly, HOTAIR and MALAT1 are known to be the master regulators of tumor progression (Aiello et al., 2016). In our previous work, MALAT1 was shown to be upregulated in BC tissues, especially TNBC epigenetically upregulating MSLN; Barsoum et al. (2020) proposed that there may be a possible interlinkage between MALAT1 and immunomodulatory proteins. The aim of the current study is to investigate if MALAT1 will give the same pattern of expression in TAMs of the hormonal, HER2+, and TNBC, highlighting that its role in cancer is still controversial whether it is an oncogenic or a tumor suppressor lncRNA. Additionally, HOTAIR is another lncRNA that is known to function as an oncogenic lncRNA as previously mentioned. Therefore, the aim of this work is to study the interlinkage between MALAT1 and HOTAIR and their effect on the expression of oncogenic immunomodulatory proteins in TAMs of the hormonal, HER2+, and TNBC subtypes. To fulfill this aim, MALAT1 and HOTAIR expressions were first screened. MALAT1 was found to be over expressed in hormonal, HER2+, and TNBC compared to controls, as shown in Figure 1A. This finding aligns with our previous studies confirming that MALAT1 is overexpressed in BC tissues more than healthy tissues (Barsoum et al., 2020). Nevertheless, another research study observed the overexpression of MALAT1 in many BC cell lines (MCF-7, SK-BR-3, MDA-MB-468, MDA-MB-231, T-47d, and MDA-MB-453) compared to normal breast epithelial cell line (MCF-10A) (Yue et al., 2020). Moreover, MALAT1 was shown to be overexpressed in other cancers such as multiple myeloma (Liu et al., 2020) and hepatocellular carcinoma (HCC) (Lai et al., 2012). The screening of HOTAIR expression in TAMs of hormonal, HER2+, and TNBC also showed a significant upregulation in BC samples compared to healthy samples, as shown in Figure 1B. This finding is supported by another study that was conducted on MCF-7 and MDA-MB-231, showing significant upregulation of lncRNA HOTAIR when compared to MCF-10A (Wang et al., 2020). Moreover, HOTAIR is shown to be not only overexpressed in BC but also in ovarian cancer (Qiu et al., 2014); thus, suggesting that HOTAIR and MALAT1 are oncogenic lncRNAs. Therefore, it was tempting to study the impact of lowering the expression of MALAT1 and HOTAIR in TAMs. Our research group has previously studied the role of these lncRNAs along with their candidate downstream targets, immunomodulatory proteins, CD80, and MSLN in TNBC tissues and MDA-MB-231 (Barsoum et al., 2020). However, here, this study is concerned with their expression pattern in TAMs. CD80 and MSLN were found to be overexpressed in HER2+ and TNBC while showing a non-significant change of expression on examining the hormonal subtype, as shown in Figure, suggesting that there is another key player regulating the expression of CD80 in the hormonal subtype and preventing its overexpression. This key player may be estrogen that is known in the literature to downregulate CD80 levels. It was previously observed that estrogen has an inverse correlation with CD80, as shown in a study conducted on RAW264.7 cell line (Yang et al., 2016). Highlighting the fact that RAW264.7 is a murine macrophages cell line, similarity in the expression is expected. Moreover, another research group showed that estrogen promotes the B cell activity in vitro by downregulating CD80 expression (Fu et al., 2011). Thus, concluding that estrogen may be the factor inhibiting CD80 to upregulate. Upon screening of MSLN in TAMs of the three subgroups, it was found that MSLN is also overexpressed in HER2+ and TNBC, while it was significantly downregulated in the hormonal subtype, as shown in Figure 2B, suggesting that estrogen plays a role in this downregulation. This finding is partially supported by a study that screened 99 primary BC samples by immunohistochemistry analysis using formalin-fixed paraffin-embedded archival tumor tissues confirming that MSLN was only overexpressed in TNBC (67%) in contrast to its rare expression in the hormonal and the HER2+ subtypes (Tchou et al., 2012). These data partially support our findings that MSLN is not upregulated in the hormonal subtype (possibly due to the presence of estrogen), however, contradicting with our data that suggest MSLN to be upregulated in HER2+. In another study, MSLN expression was detected in 77 cases out of 482 patients (16.0%) and was the highest in TNBC (31/75; 41.3%), followed by the HER2+ subtype (6/33, 18.2%), and then the luminal subtype (36/374; 9.6%) (Suzuki et al., 2020). Moreover and more interestingly, MSLN was shown in our previous work to be upregulated in BC subtypes, especially TNBC (Barsoum et al., 2020). To observe the impact of knocking down of MALAT1 and HOTAIR in TAMs of the three BC subtypes (hormonal, HER2+, and TNBC), the correlation between MALAT1 and HOTAIR was first observed showing that on the silencing of HOTAIR, MALAT1 was downregulated in all subtypes of BC, as shown in Figure 4, suggesting that HOTAIR may be an upstream regulator for MALAT1. Moreover, upon silencing MALAT1, HOTAIR expression was upregulated, and this was observed in the three subtypes, as shown in Figure 5, suggesting that HOTAIR expression may have increased as a compensatory mechanism for the loss of MALAT1 expression. These findings suggest that MALAT1 expression is directly correlated with HOTAIR expression; however, it was reported that HOTAIR and MALAT1 have opposite expression profiles in estrogen-mediated transcriptional regulation in prostate cancer cells (Rinn et al., 2007). These differences may be due to cancer specificity or tissue specificity as this study observed cancer cell lines in prostate cancer; however, our study is on TAMs in BC. Moreover, the opposite expression profile may be due to the fact that estrogen has an opposite correlation with MALAT1 expression in prostate cancer, as shown in the mentioned study, thus upon downregulation of MALAT1, HOTAIR is overexpressed as a compensatory mechanism, thus having an inverse correlation. Knowing that in the same study upon treatment of the BC cell line with estrogen, no change in the expression of MALAT1 was observed (Aiello et al., 2016), thus no correlation between estrogen and MALAT1 expression in BC supporting our identical finding that MALAT1 and HOTAIR show same overexpression patterns in the three subgroups regardless the hormonal expression. Furthermore, in another study that aligns with our finding states that MALAT1 and HOTAIR have a positive correlation with each other after conducting a correlation analysis between their serum levels in BC (El-Fattah et al., 2021). Unexpectedly, upon comparing MALAT1 expression after silencing its gene vs. silencing HOTAIR, it was shown that the HOTAIR knockdown effect was more significant in downregulation of MALAT1 compared to knocking down of MALAT1 itself, as shown in Figure 4. As a consequence of the CD80 overexpression in HER2+ and TNBC TAMs, thus highlighting the possibility of being an oncogenic immunomodulatory protein (Li et al., 2020), it was worth observing the effect of MALAT1 as an important lncRNA in the regulation of CD80. Unexpectedly, our data showed that upon silencing of MALAT1, CD80 expression was upregulated, thus having an inverse correlation in HER2+ and TNBC, as shown in Figures 6B,C, respectively. This finding aligns with another study showing MALAT1 and CD80 to be inversely correlated in A549 cells (neonatal respiratory distress syndrome) (Juan et al., 2018). MALAT1 and CD80 inverse correlation was also supported in another research study that suppressed MALAT1 in the dendritic cells (Wu et al., 2018). Observing this inverse correlation in dendritic cells validate our data since that macrophage (TAMs) and dendritic cells are both derived from the same progenitor (monocytes), thus having similarities in the expression can be expected. CD80 is a ligand for two receptors on T cells, namely, CD28 and CTLA4. T cells are activated upon binding of CD80 and CD28, thus having a pro-inflammatory activity. However, on binding with CTLA4, T cells were found to be suppressed causing energy and anti-inflammatory activity. Surprisingly, it was found that CTLA4 has a higher affinity to bind with CD80 than CD28 (Lai et al., 2012). Taking this into account, this study proposes that the overexpression pattern of CD80 in TAMs of the three subgroups, as shown in Figure 2, occurs due to the immune-suppressive effect of CD80 due to its binding to CTLA4 on T cells. However, CD80 shifts its binding toward CD28 upon silencing MALAT1, thus its overexpression has immunostimulatory activity, and this may happen due to the downregulation of CTLA4 as a result of downregulation of MALAT1. The relationship between MALAT1 and CTLA4 is not previously studied in cancer; however, its data in asthma showed that MALAT1 sponges miR-155 upregulating CTLA4 (Liang and Tang, 2020). In other words, our study proposes that the overexpression of MALAT1 in TAMs might have upregulated CTLA4, thus upregulating CD80 to enhance its immunosuppressive activity and build up an environment favorable for its tumorigenic role. Furthermore, it was expected that CD80 to be downregulated upon the silencing of MALAT1 but unexpectedly an inverse correlation was observed. In this context, we propose that this might have happened after silencing MALAT1, thus consequently and considerably impacting the oncogenic potential of TAMs making the CD80 regain its potential (in the absence of MALAT1) to function as a pro-inflammatory protein, thus upregulation was observed. Focusing onto the hormonal subtype, CD80 expression was observed to be significantly downregulated upon silencing MALAT1, as shown in Figure 6A. This could be due to the dominancy of the effect of estrogen in downregulating CD80. Estrogen might have the potential to abolish the upregulation effect caused by MALAT1 silencing. This finding supports our previous conclusion that estrogen and CD80 are inversely correlated to each other, and upon analyzing the effect of silencing of HOTAIR on the expression profile of CD80, the same expression pattern was observed, in which CD80 was upregulated in HER2+ and TNBC, as shown in Figures 6B,C, respectively. The correlation between CD80 and HOTAIR is not studied before, thus in this context, this is considered the first research study to be conducted focusing on HOTAIR and CD80 correlation. Focusing on to the hormonal subtype, CD80 expression is shown to have a non-significant change in the expression upon HOTAIR silencing compared to mock, as shown in Figure 6A. MSLN is known to be an oncogenic immunomodulatory protein in many types of cancers, for example ovarian, adenocarcinoma, and most importantly BC (Wang et al., 2020). Our previous work confirmed that MSLN is overexpressed in BC tissues and cell lines, thus functioning as an oncogenic protein (Barsoum et al., 2020). So, it was a good candidate to examine its correlation with MALAT1 and HOTAIR. Upon silencing MALAT1, MSLN expression had the same pattern as CD80 in both HER2+ and TNBC. Unexpectedly, upon silencing MALAT1, MSLN expression was upregulated in both non-hormonal subtypes: HER2+ and TNBC, as shown in Figures 7B,C, respectively. The relationship between MALAT1 and MSLN is not studied except in our previous work on TNBC tissues that showed downregulation of MSLN upon MALAT1 silencing (Barsoum et al., 2020). The contradiction might be due to the difference in the cell type studied as our previous work was on the cancer cell; however, this work focused on TAMs (immune cell). It is important to highlight that MSLN overexpression correlates to the increased levels of soluble MSLN that by its turn binds to CD206 (mannose receptor) via GPI anchor facilitating macrophages polarization to TAMs (Dangaj et al., 2011). In this context and as mentioned before it was unexpected to observe an upregulation in MSLN expression after MALAT1 silencing. MALAT1 silencing consequently and considerably impacted the oncogenic potential of TAMs. The unexpected upregulation of MSLN upon MALAT1 silencing might have happened as a compensatory mechanism performed by TAMs for the loss of MALAT1. Upon upregulation of MSLN, soluble MSLN levels would also increase, thus increasing the binding potential to CD206. As a consequence, the polarization of macrophages to TAMs would be enhanced. Focusing on to the hormonal subtype, MSLN gives a close expression pattern to CD80, its change in expression is non-significant compared to controls upon MALAT1 silencing, as shown in Figure 7A, proposing again that estrogen is a factor playing an important role in downregulating MSLN, thus balancing the upregulation effect caused by MALAT1 silencing in the hormonal subtype leading to a non-significant change in expression of MSLN compared to mocks. Upon silencing HOTAIR, MSLN gave the same pattern of expression on silencing with MALAT1 in HER2+ and TNBC. MSLN was shown to be upregulated in both HER2+ and TNBC, as shown in Figures 7B,C, respectively. Interestingly, the correlation between HOTAIR and MSLN is not been previously studied on any cell type, thus studying this correlation in TAMs highlights the importance of investigating this relationship in other cell types either cancer cells or immune cells. On comparing the MSLN or CD80 expression upon silencing with HOTAIR and upon silencing with MALAT1 in both HER2+ and TNBC, immunomodulatory proteins are found to be upregulated more significantly upon silencing with HOTAIR, as shown in Figures 6B,C and Figures 7B,C. This is may again be due to that HOTAIR is the upstream regulator for MALAT1 as previously mentioned having a greater effect on the downregulation of MALAT1 than knockdown of MALAT1 itself. Thus, upon silencing HOTAIR, MALAT1 is abolished, making the downregulation of MALAT1 significantly remarkable and consequently more significant upregulation for immunomodulatory proteins was observed. Co-transfection of silencers against MALAT1 with silencers against HOTAIR was expected to affect the expression of CD80 and MSLN more significantly than silencing each lncRNA on its own. Thus, upon silencing both lncRNAs, CD80 and MSLN were upregulated more significantly compared to MALAT1 or HOTAIR separately in HER2+ and TNBC, as shown in Figures 6B,C and Figures 7B,C. As for the hormonal subtype, a significant upregulation of CD80 and MSLN was observed upon co-transfection of silencers against MALAT1 and HOTAIR (Figure 6A and Figure 7A). These results were expected due to the dual effect of the simultaneous silencing of HOTAIR and MALAT1 and their success to dominate the effect of downregulation caused by estrogen, confirming that, in general, MALAT1 and HOTAIR are inversely correlated with CD80 and MSLN in the three subtypes, but estrogen can dominate in the hormonal subtype causing the effect of silencing HOTAIR and MALAT1 to be masked. As previously mentioned, there are various studies that tackled the role of MALAT1 and HOTAIR functionally as oncogenic lncRNAs in breast cancer tissues. However, TAMs were still questionable and not confirmed whether they were functioning as oncogenic or tumor suppressor lncRNAs. For this reason, one of the functional analyses for oncogenesis and metastasis was observed to be a confirmatory tool for the role of these two lncRNAs. Vascular endothelial growth factor A (VEGF-A) was chosen as a suitable candidate for the functional analysis for two major reasons: first, to confirm that TAMs have a role in metastasis through VEGF-A, highlighting that the role of TAMs in metastasis is functionally analyzed for the first time, and the second reason was that VEGF-A is considered a reflection for the metastasis thus tumorigenesis, and this will confirm whether MALAT1 and HOTAIR are oncogenic or tumor suppressor. The VEGF-A protein level was shown to be downregulated upon silencing lncRNAs whether separately or simultaneously in the hormonal, HER2+, and TNBC, as shown in Figure 8, confirming the idea that MALAT1 and HOTAIR are oncogenic lncRNAs in TAMs. The regulatory role of TAMs on CD8+ T cells was for the first time assessed in the presence of MDA-MB-231 cell line. The purity of CD8+ T cells isolation was assessed using flow cytometry as shown in Figure 9. CD8+ T cells (previously cultured in transfected TAMs-conditioned media) were co-cultured with MDA-MB-231 cell line. As previously mentioned, TAMs have an inhibitory role on CD8+ T cells, thus our work aims to observe if the silencing of MALAT1 and HOTAIR will impact this immune-suppressive activity. The immune-suppressive activity was assessed by observing the cytotoxic activity of CD8+ T cells on MDA-MB-231 cell line. LDH assay was conducted showing that upon culturing of CD8+ T cells in TAMs-conditioned media previously treated with silencers against MALAT1 and HOTAIR, the cytotoxicity activity of CD8+ T cells is increased, as shown in Figure 10, supporting the fact that MALAT1 and HOTAIR are oncogenic lncRNAs, and upon their knocking down, the T cells restored its immunostimulant cytotoxic activity. Programmed cell death protein 1 (PDL-1) is known to be a negative regulator expressed on the surface of T cells. PDL-1 binds to its ligand programmed death ligand-1 (PD-1) on the tumor cell suppressing the activity of CD8+ T cell (53). Consequently, upon culturing CD8+ T cells with MDA-MB-231 cell line, the cytotoxic activity of T cells is expected to be suppressed, as a result, the PDL-1 inhibitor drug was added to the co-culture media (CD8+ T cells cultured in treated TAM-conditioned media) to observe the impact of adding the PDL-1 inhibitor with oncogenic lncRNAs silencing, and whether this addition will cause a synergetic activity increasing the cytotoxicity of CD8+. Unexpectedly, the cytotoxicity of CD8+ T cells was not increased upon the addition of the PDL-1 inhibitor in comparison to CD8+ cytotoxicity upon only silencing of the oncogenic lncRNAs without the addition of PDL-1, as shown in Figure 10. Our study suggests that this might have happened either due to the PDL-1 inhibitor, which does not have any additional role in increasing the cytotoxicity of CD8+ T cells or that the dose of PDL-1 inhibitor has to be adjusted knowing that the dose used was 200 nM as indicated in the previous literature using same immune cell (CD8+) and same cell line (MDA-MB-231) (Passariello et al., 2019). In all experiments conducted, The efficiency of monocytes differentiation into TAMs was confirmed using microscopic examination as shown in Figure 11. In conclusion, as shown in Figure 12, HOTAIR is suggested to be an upstream regulator for MALAT1 because upon downregulation of HOTAIR, MALAT1 was also downregulated. Supporting this, downregulation of MALAT1 led to upregulation of HOTAIR as a possible compensatory mechanism explaining that both have the same function. Upon downregulation of MALAT1 and HOTAIR in HER2+ and TNBC, unexpectedly, upregulation of CD80 and MSLN was observed with the fact that silencing HOTAIR was more significant, and upon co-silencing HOTAIR and MALAT1, the expressions of both were upregulated more significantly than silencing each lncRNA, separately. Moreover, on silencing MALAT1 and HOTAIR in the hormonal subtype, estrogen played an important role due to its effect in downregulation of CD80 and MSLN expressions, thus masking the effect of MALAT1 silencing in upregulating CD80 or MSLN. Certainly, upon co-transfection, the expressions of CD80 and MSLN were upregulated due to the dual action of silencing both lncRNAs, thus dominating the downregulatory activity of estrogen. It has been concluded that MALAT1 and HOTAIR might be oncogenic lncRNAs in TAMs. This finding was confirmed by the upregulation of VEGF-A protein on silencing MALAT1 and HOTAIR in TAMs of BC and also on assessing the cytotoxicity activity of CD8+ T cells on knocking down of these two lncRNAs where the cytotoxicity activity was increased. Future recommendations involve conducting the dose–response curve for the PDL-1 inhibitor, measuring MSLN and CD80 on the protein level, and examining the interlinkage between MALAT1 and CTLA4 in TAMs of BC.

PMC9649651

Mohamed S. Hasanin,Mahmoud E. Abd El-Aziz,Islam El-Nagar,Youssef R. Hassan,Ahmed M. Youssef

Green enhancement of wood plastic composite based on agriculture wastes compatibility via fungal enzymes

10-11-2022

Biotechnology,Materials science

This study deals with the production of natural fiber plastic composites (NFPCs) to reduce environmental pollution with agricultural and plastic waste. Where the NFPCs were prepared from waste/pure polyethylene (WPE) (pure polyethylene (50%)/recycled polyethylene (50%)) and modified sunflower waste via an eco-friendly and economic biological process. The sunflower fibers (SF) were treated via whole selective fungal isolate, namely, Rhizopus oryzae (acc no. OM912662) using two different incubation conditions; submerged (Sub), and solid-state fermentation (SSF) to enhance the fibers' compatibility with WPE. The treated and untreated fibers were added to WPE with various concentrations (10 and 20 wt%). The morphology and structure of fibers were characterised by a scanning electron microscope (SEM) and attenuated total reflection-Fourier transform infrared (ATR-FTIR). Furthermore, the mechanical properties, morphology, biodegradation and water vapour transmission rate (WVTR) for the prepared NFPCs were investigated. The results showed that compatibility, mechanical properties and biodegradation of NFPCs were improved by the addition of sunflower fibers treated by SSF conditions.

Green enhancement of wood plastic composite based on agriculture wastes compatibility via fungal enzymes This study deals with the production of natural fiber plastic composites (NFPCs) to reduce environmental pollution with agricultural and plastic waste. Where the NFPCs were prepared from waste/pure polyethylene (WPE) (pure polyethylene (50%)/recycled polyethylene (50%)) and modified sunflower waste via an eco-friendly and economic biological process. The sunflower fibers (SF) were treated via whole selective fungal isolate, namely, Rhizopus oryzae (acc no. OM912662) using two different incubation conditions; submerged (Sub), and solid-state fermentation (SSF) to enhance the fibers' compatibility with WPE. The treated and untreated fibers were added to WPE with various concentrations (10 and 20 wt%). The morphology and structure of fibers were characterised by a scanning electron microscope (SEM) and attenuated total reflection-Fourier transform infrared (ATR-FTIR). Furthermore, the mechanical properties, morphology, biodegradation and water vapour transmission rate (WVTR) for the prepared NFPCs were investigated. The results showed that compatibility, mechanical properties and biodegradation of NFPCs were improved by the addition of sunflower fibers treated by SSF conditions. In the past few years, timber has been the main material for many industries such as the manufacturing of paper, furniture, and packaging boxes, but the increasing need for timbers has become a major contributor to the evanescence of forests. It is known that forests play important role in contributing to climate change mitigation, where they consume approximately 2.6 billion tons of carbon dioxide (CO2), which is one-third of the CO2 released from burning fossil fuels. Also, agricultural waste is considered a cumbersome by-product, where its disposal by landfilling or incineration is seen as a non-valid solution to the environment. However, agricultural residues are rich in cellulose materials that can be used in various applications as an alternative to forest wood. Moreover, the accumulation of plastic waste (e.g. plastic bottles and bags) in the environment adversely affects wildlife and aquatic life as well as humans. It is known that plastics are cheap and durable, which makes them suitable for different uses, but, due to their chemical structure being resistant to many natural processes of degradation, they are non-degradable. Wood-plastic composites (WPCs) are considered a subset of a huge class of matter called natural fiber plastic composites (NFPCs). Wood-plastic composites are produced from the fiber or the flour of wood and thermoplastic, while NFPCs are composites of thermoplastic polymers that contain pulp fibers, straw, peanut hulls, coffee husk, and bamboo as a filler. The former could be manufactured from agriculture and plastic waste, which is considered an environmentally friendly approach to using agricultural waste and recycled plastic material. Adding cellulosic materials obtained from agricultural waste to plastic materials to prepare WPC enhances their mechanical properties, durability and biodegradability. In addition, WPCs have beneficial characteristics such as low density and cost, durability, high strength, as well as excellent sound-absorbing capacity. So it could be utilized in various applications such as railway coaches, packaging and building. Unfortunately, the poor compatibility between fibers (hydrophilic) and polymer (hydrophobic) plays an important role in the properties of produced WPC. Indeed, the compatibility between cellulose and hydrophilic plastics is the main problem facing researchers. They, therefore, tend to apply various strategies to overcome this obstacle, which include specific modifications for cellulosic fiber surfaces to minimize their hydrophilicity either by physical or chemical modifications. The volume of sunflower production is about 11 million tons, and the volume of waste generated from its cultivation is about one million tons. Sunflower seeds are used in the production of oil, while the waste is used in many industries, such as livestock feed, compost (alternative soil), water treatment, biodiesel, etc.. Sunflower waste was riched by lignocellulosic fibers with high content of lignin about 25%, and so it is difficult to be degradable. Some microorganisms are cabaple to production of enzymes that is able to attack the lignin. Mateusz et al. showed that the addition of sunflower husk to epoxy had a huge effect on the deterioration of flexural strength and tensile of the prepared composite. Physical treatment of natural fibers includes grinding, thermal and radiation. Grinding is usually used to cut fibers into very small and homogeneous lengths. The thermal treatment is used to change the fibers chemical composition as well as extract of the active components of fibers as a dual-role treatment method. While the radiation treatment includes ultrasonic, plasma, and irradiation treatments. The chemical treatment removes non-desirable components or adds a functional group to improve the compatibility between the fibers and polymer by decreasing the hydrophilicity of the fibers. The chemical modification can also be carried out using benzene diazonium salt, sodium hydroxide and dodecane bromide, or esterification using different fatty acids. In addition, the biological treatment could be used as a tool to reactivate the lignocellulosic fibers surface via purified or crude enzymes as well as other in-suite fermentation conditions using microbial costive agents directly on the substrate with Sub or SSF. The in-suite treatment offers an economic and eco-friendly process in comparison with other tools with aspects of biological treatment. In the current work, Sub and SSF conditions were used to activate the surface of the sunflower fibers and modified to become suitable for contact and compactable with a hydrophobic plastic surface. The prepared natural fiber plastic composites (NFPCs) could be suitable for many applications such as alternative wood, household equipment, and packaging. All chemicals (Sodium nitrate, dipotassium phosphate, magnesium sulfate, potassium chloride, cetyltrimethylammonium bromide (CTAB) and ferrous sulfate) and media (potato dextrose agar (PDA) medium, potato dextrose broth (PDB) medium and mineral salts medium (MSM)), as well as reagents (3,5-dinitro salicylic acid, pyrogallol, glucose and xylose), were purchased from Sigma-Aldrich in analytical grade without any purification before useing. Pure low density polyethylene (LDPE) was food grade (density 0.93 g/cm3, softening point 87.4 °C, and melt flow index (190 °C, 2.16 kg) 6.0 g/10 min), and was obtained from ExxonMobil Chemical (Kingdom of Saudi Arabia) as pallets with particles size ranged 2–5 mm. The polyethylene waste was obtained as pallets with particles size ranged 4–8 mm from Bekia, Egypt. Sunflowers trimmings, as a source of lignocellulosic fibers, were collected from a farm located in El-Menofia Governorate, Egypt, which has considerable amounts of agricultural waste. They were dried in the oven at 70 °C to attain a constant weight and then ground mechanically and sieved with a 200 mesh sieve. The collection of plant material complies with the guidelines of the Ethics Committee in the National Research Centre. Samples for isolation were compiled from the soil of the sunflower farm in Giza, Egypt. Fungal isolation was performed according to our previous work. The isolated fungal was subjected to the cultivation on the replaced Carbone source Czapic broth medium by the fine powder of sunflowers as the sole Carbone source. The selected isolated fungal (Rhizopus oryzae) was observed with the highest biomass growth. The selected ligninolytic fungus was identified according to its morphological characteristics and 18s ribosomal DNA (18S rDNA) sequence according to our previous work. The morphological characteristics were examined using a light microscope (Olympus cx41) after 4 days of growth on PDA medium plates via a light microscope at a magnification of 40×. For molecular identification, fungal mycelium from a 4-day-old culture in PDB medium was harvested using Whatman No. 1 filter paper. The total genomic DNA was extracted using the CTAB protocol. The identification was achieved by comparing the contiguous 18S rDNA sequence with data from the reference and type strains available in public databases GenBank using the BLAST programme (National Center for Biotechnology Information). The obtained sequences were aligned using the Jukes Cantor model and the isolate was registered in GenBank. The sunflower fibers were treated using isolated fungi according to two different incubation conditions. The Sub and SSF conditions were used with the same condition except for the ratio of nutritional medium and fibers. The fungal isolate was cultivated using modified Czapek broth media. The carbon source of the previous media was changed with the lignocellulosic fibers. The submerged fermentation conditions were carried out using 1:25 fibers to medium. The solid state fermentation ratio is 1:5 fibers to medium. The incubation conditions were used for both types of fermentation as follows; temperature 25 °C, initial pH 5.5, and flask volume (1:5) in static condition for 10 days. The liquid filtrate from different incubation times (7 days) old fungal cultures cultivated on PDB medium or liquid MSM amended with waste as the sole carbon source were collected and applied directly as the crude enzyme in enzymes assay experiments. The activity of cellulase and xylanase enzyme was carried out via reducing sugars estimations using 3,5-dinitro salicylic acid (DNS) assay with glucose for cellulase and xylose for xylanase. Lignin peroxidase activity was determined spectrophotometrically at 420 nm. All appeared values are the average of triplicate experiments. The fibers were dried at 60 °C for 12 h to remove the moisture content. Also, the recycled PE was washed with distilled water and dried at 60 °C overnight. The NFPCs were synthesized from pure LDPE and recycled PE (WPE; 50:50 wt%) which was loaded with untreated fibers, and treated fiber either by Sub and SSF condition at concentration 10 wt% to obtain NFPC10%SF, NFPC10%Sub, and NFPC10%SSF, respectively, and at concentration 20 wt% to obtain NFPC20%SF, NFPC20%Sub, and NFPC20%SSF, respectively. The composition of the prepared samples is shown in Table 1. The PE was melted in a twin-screw extruder (Haake RheomexTW100, USA) at 140 °C and a rotation speed of 60 rpm. After the PE was melted, the sunflower fibers were added and mixing was continued for an additional 10 min. The mixture was collected and left for cooling then cut into small pieces suitable for feeding into a stainless-steel pressure mould to make plates with dimensions 8 × 10 cm and 0.5 mm thick. The moulding was done using pressure (5 MPa) at 150 °C and for 5 min. after that; the plates were left to cool in the piston to room temperature under pressure. Three replicates were performed for each blend. The Attenuated total reflection Fourier-transform infrared spectroscopy (ATR-FTIR) of samples was measured on the Shimadzu 8400S FT-IR Spectrophotometer in the range of 500–4000 cm−1. The samples were measured as films. In addition, a scanning electron microscope (SEM; JSM 6360LV, JEOL/Noran) was used to study the surface morphology of the prepared samples. The mechanical characterization was carried out for three replicates of each prepared NFPCs to gauge the tensile strength, elongation, Young’s modulus of bending, and modulus of rupture according to the ASTM D638-91 utilizing a testing machine LK10k (Hants, UK) fitted with a 1kN load cell and operated at a rate of 5 mm/min. Biodegradation of NFPCs in soil was done a corroding to Dalev et al.. The soil was taken from the surface layer, and then all inert materials were removed to obtain a homogeneous mass. About 100 g of soil was poured into a plastic pot up to a thickness of about 3 cm. The prepared samples were accurately weighed and dried for 24 h at 50 °C, then were buried in the pots to a depth of 1 cm. Water was sprayed once a day to sustain the moisture. The samples were weighed weekly for 4 weeks after being washed with distilled water and dried at 50 °C for 24 h. The biodegradation was carried out for three replicates for each sample. WVTR was carried out using (GBI W303 (B), Water Vapor Permeability Analyzer; China) via the cup method for three replicates of each prepared NFPCs. The WVTR was designed by way of the quantities of water vapor transferred through a unit area of the NFPCs film in a unit time inferior to particular conditions of temperature (38 °C) and humidity (4%) as specified through the following standards (ASTM E96). The isolated fungal was identified via molecular techniques and morphological characterization. Figure 1a showed that the colonies of isolated fungal contained on the surface of the PDA medium a yellow-green spore on the upper surface and reddish-gold on the lower surface. The genomic DNA of the isolated fungal was extracted and identified via molecular techniques where 18s rRNA amplification was applied. The obtained sequences were compared with related sequences on the National Centre for Biotechnology Information (NCBI), Egypt database. It was found that it was closely associated with Rhizopus oryzae (acc no. OM912662) which confirms their morphological identification. Figure 1b illustrated the phylogenetic tree with a high similarity of about 91.37%. The fungal isolate was subject to two different cultivated conditions due to the ratio between fibers and liquid medium. The quantitative activity of lignocellulolytic enzyme clusters was assayed and shown in Fig. 2. All assayed enzymes in the SSF condition were confirmed as high value in comparison with the other ones produced in the submerged conditions. These results are referred to the harsh conditions which made the isolated fungi produce all enzymes possible to provide the carbon requirements needed to grow. On contrary, in the submerged condition, the fungal isolate grows in normal conditions with a high water content which has many soluble simple carbons dissolved from the fibers. These phenomena induced heavy enzyme production in solid-state fermentation conditions as well as feedback on the production of the enzymes in case of submerged incubation conditions. Moreover, the high productivity of the lignocellulolytic enzymes lead to fiber surface modifications and made it more compatible to interact with polymer plastic where these enzymes increase the hydrophobicity of the fibers. The lignin oxidative enzymes, including lignin peroxidase, polyphenol oxidase and laccase act as natural surface activators where the oxidative effect of these enzymes act as a surface modification process as well as increase surface compatibility to plastic polymer. Whereas, the enzymes eliminated the fine terminal of the fiber molecular structure which made fibers smoother as well as reduced the free active hydroxyl groups and so more hydrophobic. The ATR-FTIR spectra of sunflower fibers and treated ones were shown in Fig. 3. In general, all the untreated and treated fibers showed the broad band corresponding to hydroxyl group (–OH) that stretched at 3400 cm−1, C–H stretched at 2900 cm−1, C–O–C stretched at 1125 cm−1 and C=C stretched alkene at 1610 cm−1. While the band of C–OH that stretched at 1435 cm−1 was present in untreated fibers and disappeared completely in SSF. The disappearance of these bands could have been caused by the partial removal of hemicellulose and lignin from fibers during the fungal treatment. The fungal treatment reduces the hydroxyl groups and so high water resistance through a reaction with enzymes. This led to a decrease in hydrogen bond that increased the intensity of the peak between 3300 and 3500 cm−1 bands in treated fibers compared to untreated fibers. Werchefani et al. observed that fibers treated by enzymes have fibers lower in diameter and length as well as high water resistance as a result of elimination of the hydrophilic components (lignin, pectin and hemicellulose). The sunflower fibers consist of many components each of them having a unique performance in SEM imaging. Figure 4 illustrated the effect of the fungal different fermentation conditions on the fibers' surface morphology in comparison with the blank one. The blank and treated fibers observed a significant difference in the surface morphology level which may affect the compatibility of fibers and plastic behaviour. The blank fibers (Fig. 4a) are clear with a typical lignocellulosic fibers performance as smooth surface fibers loaded with some impurities observed as aggregated crystals. Otherwise, the SSF fibers surface in Fig. 4b observed with many pours located overall fibers surface as well as no smooth appearance enough in comparison with a blank one. In addition, the Sub condition fibers are shown as blank fibers with low smoothness, and the diameter of the fibers was clearly reduced. These observations may be related to the effect of SSF conditions in which the productivity of the fungal enzyme is higher than in the Sub condition according to harsh conditions. Moreover, in the SSF condition, the fungal hypha has penetrated the fibers to achieve the nutrients in harsh conditions (Fig. 4c). These results affirmed that the enzyme productivity, as well as fungal hypha biomass, are increased in the harsh conditions in which the fungal strain combat to gain the required nutrients and survive via degradation of the fibers. In contrast, the high humidity condition in the Sub case made fungal strains grow at a normal rate. Overall, the revised conclusion was concise that the treated fibers may affect the compatibility with plastic polymer and made the fibers attached strongly with plastic polymer in comparison with the blank fibers. Table 2 represents the effect of untreated and treated sunflower fibers content in the prepared wood plastic composites on the tensile, elongation, Young’s modulus of bending, and modulus of rupture. In general, the addition of sunflower fibers to NFPCs decreased the tensile and elongation of the prepared wood plastic composites in comparison with pure NFPCs. However, the tensile and the elongation of NFPCs containing sunflower fibers modified by the SSF condition were higher than that modified by Sub condition or unmodified fibers. This may be due to the high compatibility between modified fibers (SSF) and the polymer, which was higher than modified fibers (Sub) than unmodified sunflower fibers and the polymer. It was found that the content of untreated and treated sunflower fibers increases Young’s modulus of bending NFPCs compared to blank PE related to the increase of the elasticity. These results affirmed the elongation results that emphasised that the addition of fibers was lead to decreases in the elasticity where the relation between elongations, Young’s modulus is inverse. In addition, the treated fiber via SSF conditions is more effective on Young’s modulus (1032.1 MPa) of NFPC that contains 20% SSF. Also, the NFPC contains fiber treated by SSF conditions with a content of 20 wt% improves the modulus of rupture to be 18.8 MPa compared to other fibers, which are near to blank PE 19.7 MPa. This may be due to the compatibility between treated sunflower fibers and the polymer higher than that between untreated sunflower fibers and the polymer. In the same way, Sobczak et al. reported that as the degree of surface roughness increases in the fiber, the area available for interactions with the matrix will be increased. This leads to improving the mechanical performances of the prepared composites. The surface morphology of the prepared NFPCs has illustrated in Fig. 5. The surface morphology of blank PE films was smooth and showed high homogeneity between their polymeric chains (Fig. 5a). This homogeneity was decreased in WPE (50% pure PE/50% recycled PE) as shown in Fig. 5b due to the difference in the polymeric chain's length between pure PE and recycled one, in other words, due to the difference in the molecular weight between pure PE and recycled one. Also, the presence of different additives, impurities, or traces of other polymers in the polymer waste significantly affected on the melt flow index and the homogeneity between virgin and recycled polymer as reported by Patrizio et al.. The loading of WPE by untreated and treated sunflower fibers displayed a rough structure which was decreased in the case of treated fibers, especially with that treated by SSF condition (Fig. 5g). This indicates the happening of compatibility between polymer matrix and treated sunflower fibers. Additionally, the content of fiber in the matrix has a great effect on the surface morphology and so their internal construction. Indeed, the surface of untreated fibers is enveloped by lignin and some impurities of terminal groups of cellulose and hemicellulose which is the reason for decreasing the adhesion and so the compatibility between the fibers and WPE (Fig. 5c,d). Whilst, the treated fibers either submerged (Fig. 5e,f) or solid-state fermentation (Fig. 5g,h) showed higher compatibility with polymer. These results may be due to a decrease in the proportion of lignin via oxidization of its phenolic groups via peroxidases enzymes as well as the elimination of terminal side group of cellulose and hemicellulose and their decrease the hydrophilicity via acting the eradication of free hydroxyl groups and these observasions are in a nice agremment with other studies, especially those treated by the SSF method as shown in Figs. 3 and 4. Corradini et al. showed that the compatibility between recycled poly(ethylene terephthalate) and sugarcane bagasse fiber was increased by the addition of ethylene/n-butyl acrylate/glycidyl methacrylate copolymer as compatibilizing agent that worked to raise the interaction between the components. Also, Chen et al. illustrated that the alkali treatment of sugarcane bagasse increases their compatibility with high-density polyethylene than the untreated one. In the last decay, biodegradability is an important feature for any new component especially the synthetic polymer that has taken a century to start degradation. The addition of natural fibers to synthetic polymers made it more acceptable to degrade in the environment. The biodegradation of the WPE and the prepared NFPCs was illustrated in Fig. 6. The obtained results investigated that the WPE is not degraded in the soil after a complete 4 weeks. The biodegradability properties of polymers are a key factor to consider this polymer is environmentally friendly and not accumulated over. PE is not a biodegradable polymer that is not biodegraded in the environment naturally while taken to complete degradation many years. Otherwise, natural fibers are biodegradable and environmentally friendly. In this context, cellulose has a degradation half-life (t1/2) in the soil at 10–20 °C between 30 and 42 days. Moreover, after 2 months cellulose was decomposed into CO2 and water. The fast degradation rate of natural fibers is attributed to the breakdown of cellulose bonds randomly as the effect of the microorganism cleavage. In this work, the effect of the addition of the sunflower fibers on the biodegradability rate of PE in soil was studied and the results are presented in Fig. 6. The obtained results were observed that the sunflower fibers significantly affect the biodegradation of the polymer in soil. Whereas, LDPE offered resistance to biodegradation with lost about 0.18% of its weight after being buried in soil for 4 weeks. Additionally, the sunflower fibers enhanced the composite biodegradability where NFPCs containing 10 and 20 wt% of untreated sunflower fibers (SF) lost about 5.2 and 6.9%, respectively, after the same period buried in the soil. On the other hand, the fungal enzymes treated fibers (Sub) observed a slight increase in biodegradability rate in comparison with blank ones. However, NFPCs containing 10 and 20 wt% of treated sunflower fibers (SSF) lost about 6.4 and 11.4%, respectively, after the same period buried in the soil. These increases in the biodegradability rate of treated fibers composite in soil may be related to the removal of undesirable sunflower fibers constituent that made the fiber easy to microbial attack. These results are in agreement with previous findings on the biodegradability of non-degradable polymer reinforced with sunflower fibers where enhanced tensile strength properties as well as biodegradability. Additionally, the addition of PE did not prevent the biodegradability of sunflower fibers as well as the biodegradation of lignocellulosic material with time which may be made the degradation of PE easy in comparison with the pure one. Food degradation is meaningfully affected by the WVTR of packaging materials. It designates together the solubility of water molecules as well as the transfer of water molecules into packaging materials. Hence, the materials' high permeability to water vapor has a straightforward impact on their usage in packaging applications attributable to their capability to alter the block humidity between products and their neighboring air. Several issues contain chemical structure (high solubility for selecting polymer), size, molecular weight, etc. require an influence on moisture content as well as molecule mobility in the packaging films. Table 3 represents the values of the WVTR of the pure LDPE, WPE, NFPC10%SF, NFPC20%SF, NFPC10%Sub, NFPC20%Sup, NFPC10%SSF, and NFPC20%SSF were shown in (Table 3). The achieved data shows that the WVTR rises in the fabricated NFPCs films with the addition of different ratios of treated fibers (SF, SSF, and Sub fiber) from 10 to 20%. Where the WVTR was increased from 2.73 g/(m2 day) for pure LDPE to 4.99 g/(m2 day) in the case of using (50:50) percentage of pure LDPE to recycled PE (WPE). Moreover, the WVTR was increased from 4.99 g/(m2 day) to 26.37 g/(m2 day) in case of using 10% of (SF) fibers. Also, the WVTR increased from 4.99 g/(m2 day) to 34.64 g/(m2 day) in the case of using 20% of (SF) fibers. By using from 10 to 20% of treated fibers (Sub) for the fabrication of the NFPC film, the WVTR increased from 16.60 g/(m2 day) to 29.67 g/(m2 day). Furthermore, by usage from 10 to 20% of treated fibers (SSF) for fabrication of the NFPC film, the WVTR increased from 12.58 g/(m2 day) to 15.37/(m2 day). The principal causes for the rise of WVTR with the addition of cellulose fibers are the hydrogen bonds formed between (OH groups) of fibers and the polymer matrix. The increase in WVTR at higher SF concentration is related to the pore network and structure of the NFPCs films. It was realized that, generally, the WVTR improved with increasing treated fibers (Sub and SSF) ratios in the NFPCs samples. Moreover, the enhancement in the WVTR at higher humidity levels is associated with the improved passage of moisture. This phenomenon is convinced by the transmission of water molecules in the microscopic pores of the fiber material which are filled with water because of capillary condensation. For ingredients that display hysteresis in their sorption isotherm, it has been described previously that their WVTR is dependent on moisture content. We detected that there is a small increase in water vapor transmission rate as the percentage of different ratios of treated fibers (Sub and SSF) from 10 to 20% increases. This is as the percentage composition of modified cellulose increases, the hydrophilicity of the NFPCs films increases. This phenomenon could be associated with the significant hydrogen bonding interaction with water. The current manuscript established an inexpensive as well as sustainable approach for fabricating wood plastic composites from recycled PE waste as binding matrix and agricultural waste such as modified sunflower waste as filled materials through a biological unusual procedure designated as a green eco-friendly, and economic method. The sunflower fibers were treated via whole selective fungal isolate, Rhizopus oryzae (acc no. OM912662), to modify the fibers' surface and to improve their compatibility with polymer plastic by increasing the hydrophobicity of the fibers. The mechanical properties were improved by the addition of both forms of modified sunflower (Sub), and (SSF). Moreover, the untreated and treated sunflower fibers increase Young’s modulus of bending NFPCs compared to blank PE. Furthermore, the treated fiber via SSF conditions is more effective on Young’s modulus (1032.1 MPa) of NFPCs that contains 20% SSF. As well, the fiber treated by SSF conditions with a content of 20%/wt develops the modulus of rupture to be 18.8 MPa compared to other fibers, which are near to blank PE 19.7 MPa. The addition of modified sunflower waste fiber enhanced the composite biodegradability where NFPCs containing 10 and 20 wt% of untreated sunflower fibers (SF) lost about 5.2 and 6.9%, respectively, while the treated fiber by Sub condition showed a slight increase in biodegradability. However, NFPCs containing 10 and 20 wt% of treated sunflower fibers SSF condition lost about 6.4 and 11.4%, respectively, after the same period buried in the soil. Moreover, WVTR increases in the prepared NFPCs films with the addition of different ratios of various fibers from 10 to 20%. Thus, the fabricated wood plastic composites might be appropriate for many applications, e.g. alternative wood, household equipment, as well as packaging.

PMC9649745

Dalia Pakalniškytė,Tanja Schönberger,Benjamin Strobel,Birgit Stierstorfer,Thorsten Lamla,Michael Schuler,Martin Lenter

Rosa26-LSL-dCas9-VPR: a versatile mouse model for tissue specific and simultaneous activation of multiple genes for drug discovery

10-11-2022

Transgenic organisms,Gene expression

Transgenic animals with increased or abrogated target gene expression are powerful tools for drug discovery research. Here, we developed a CRISPR-based Rosa26-LSL-dCas9-VPR mouse model for targeted induction of endogenous gene expression using different Adeno-associated virus (AAV) capsid variants for tissue-specific gRNAs delivery. To show applicability of the model, we targeted low-density lipoprotein receptor (LDLR) and proprotein convertase subtilisin/kexin type 9 (PCSK9), either individually or together. We induced up to ninefold higher expression of hepatocellular proteins. In consequence of LDLR upregulation, plasma LDL levels almost abolished, whereas upregulation of PCSK9 led to increased plasma LDL and cholesterol levels. Strikingly, simultaneous upregulation of both LDLR and PCSK9 resulted in almost unaltered LDL levels. Additionally, we used our model to achieve expression of all α1-Antitrypsin (AAT) gene paralogues simultaneously. These results show the potential of our model as a versatile tool for optimized targeted gene expression, alone or in combination.

Rosa26-LSL-dCas9-VPR: a versatile mouse model for tissue specific and simultaneous activation of multiple genes for drug discovery Transgenic animals with increased or abrogated target gene expression are powerful tools for drug discovery research. Here, we developed a CRISPR-based Rosa26-LSL-dCas9-VPR mouse model for targeted induction of endogenous gene expression using different Adeno-associated virus (AAV) capsid variants for tissue-specific gRNAs delivery. To show applicability of the model, we targeted low-density lipoprotein receptor (LDLR) and proprotein convertase subtilisin/kexin type 9 (PCSK9), either individually or together. We induced up to ninefold higher expression of hepatocellular proteins. In consequence of LDLR upregulation, plasma LDL levels almost abolished, whereas upregulation of PCSK9 led to increased plasma LDL and cholesterol levels. Strikingly, simultaneous upregulation of both LDLR and PCSK9 resulted in almost unaltered LDL levels. Additionally, we used our model to achieve expression of all α1-Antitrypsin (AAT) gene paralogues simultaneously. These results show the potential of our model as a versatile tool for optimized targeted gene expression, alone or in combination. Genetically modified mice, as in vivo models to study human disease mechanisms, have a long history that started by the end of the twentieth century. Since that time, considerable technical advances and new technologies have revolutionized our ability to manipulate the mouse genome and enhance the potential of these models to support preclinical drug discovery. In particular, the application of endonucleases has greatly enhanced the feasibility for researchers to manipulate the mouse genome as desired. Amongst them, the most widely used is the RNA-guided endonuclease CRISPR associated protein 9 (Cas9) system, which induces DNA double strand breaks with high specificity. The specificity is provided by a short 20 base pair spacer sequence of a guide RNA (gRNA) that recognizes the target DNA region of interest and directs the nuclease for editing. Over the last few years, the CRISPR-Cas9 system was adopted, and several Cas9 variants have been generated that lack endonuclease activity, while retaining specificity for target DNA, for applications beyond classical endonuclease activity. One of them is the CRISPR activation (CRISPRa) system, where endonuclease dead Cas9 (dCas9) is fused with four tandem copies of Herpes Simplex Viral Protein 16 (VP64), human NF-kB p65 activation domain (p65), and Epstein-Barr Virus-derived R transactivator (Rta) domains to obtain a programmable transcription factor, termed VPR. This hybrid dCas9-VPR was demonstrated to have a highly efficient potential for activating gene transcription of almost any gene of interest in various species and cell types and led to the development of corresponding dCas9-VPR expressing mouse models. Here, the targeted transcriptional regulation of genes is obtained by delivering appropriate gRNAs complementary to the promoter region of the gene of interest. Amongst various delivery methods, recombinant AAV (rAAV) is one of the most investigated and preferred tools, due to its relative safety, low immunogenicity, and ability to transduce a broad range of cells. In addition, rAAV is replication defective, does not integrate into the host genome, and persists in transduced cells in an episomal fashion, thereby providing long-term transgene expression. Moreover, due to the broad range of natural and capsid-engineered rAAV variants that differ in their transduction efficiency and tissue tropism, transgene delivery to specific cell or tissue types can be achieved. One of the most efficient AAV serotypes for liver transduction in mice is AAV8, which was shown to transduce up to 90–95% of hepatocytes subsequent to intraportal vein or intravenous injection. The liver is one of the most important organs in the body, which is directly or indirectly involved in many essential physiological processes, where reduction or loss of liver function can be life threatening. Hence, liver associated enzymes, circulating proteins and cell receptors are popular targets in the focus of ongoing drug discovery approaches. In this context, hepatocyte expressed LDLR plays an important role in plasma cholesterol homeostasis, where dysregulation leads to a higher risk for the development of cardiovascular diseases LDLR is located at the cell surface of hepatocytes, where it interacts with plasma derived LDL. After binding, the LDLR-LDL complex is internalized and transported into endosomes. Once LDL has been released, LDLR can recirculate to the cell surface or is degraded in the lysosomes. The degradation rate of LDLR can be regulated by modifying another enzyme (PCSK9), which is mainly liver expressed. PCSK9 interacts with LDLR on the cell surface and targets LDLR to lysosomes for its degradation. It has been shown that high levels of circulating PCSK9 increase concentrations of plasma LDL, increasing the risk of atherosclerosis development. Another important protein expressed by hepatocytes is AAT, which is encoded by the SERPINA1 gene. Liver secreted AAT circulates in the blood and its main function is to control activity of various serine proteinases. The primary target of AAT is neutrophil elastase. The number of SERPINA1 genes vary among different mammalian species. Primates, including humans, and some mouse strains contain only a single gene copy, while other mouse lines contain multiple paralogues originating from the same ancestral gene. For instance, C57Bl/6 J mice contain five Serpina1 paralogues, namely Serpina1a, Serpina1b, Serpina1c, Serpina1d and Serpina1e. Here, we describe the development and use of a new Cre recombinase-dependent dCas9-VPR mouse model, with the potential of long-lasting transcriptional activation in vivo. This mouse model is applicable for gene induction by gRNAs to target different genes of interest, individually or in combination. Particularly, by using liver tropic AAV8 and specific gRNAs, we demonstrate tissue specific upregulation of LDLR, PCSK9 and AAT in hepatocytes. For AAT, all five Serpina1 gene variants could be simultaneously upregulated in our mouse model using a mix of five AAV8 preparations, each containing specific gRNAs against one of the five Serpina1 gene variants. Conditions of hyper- or hypocholesterolemia were successfully induced in these mice by activating the expression of either hepatic PCSK9 and/or LDLR. Taken together, these studies demonstrate the potential of the new Rosa26-LSL-dCas9-VPR mouse model for targeted transcriptional gene activation, thereby enabling rapid characterization and validation of gene function in basic biological research or drug discovery. We generated the Cre-dependent CRISPR activation mouse line, termed Rosa26-LSL-dCas9-VPR, using the recombinase mediated cassette exchange technology to integrate the dCas9-VPR gene containing cassette into the ROSA26 locus (Fig. 1A). The cloned targeting vector contained a NeoR cassette, the human CAG promoter, and a translation interrupting LSL cassette linked with the dCas9-VPR gene fused to a self-cleaving P2A sequence and Egfp gene (Fig. 1A). To investigate the effectiveness of Cre recombinase-mediated LSL cassette excision, and consequently dCas9-VPR expression activation, in a tissue specific manner, Rosa26-LSL-dCas9-VPR mice were either injected with a preferentially liver transducing AAV8 containing the Cre gene under the control of a liver specific LP1 promoter (AAV8-Cre) or AAV8 carrying U6 promoter driven guide RNAs targeting Pcsk9 (gPcsk9) (AAV8-gPcsk9), where each gRNA is driven by separate U6 promoter (Fig. 1B). Recombination (i.e., excision of the LSL cassette) was only observed in liver tissue of AAV8-Cre administered Rosa26-LSL-dCas9-VPR mice, but not in solely AAV8-gPcsk9 transduced mice (Fig. 1C). Moreover, in AAV8-Cre treated mice, dCas9-VPR expression was restricted to liver, with absent or only minor but not statistically significant increases in all other investigated tissues, including heart, lung, kidney, and spleen (Fig. 1D). These results show that Rosa26-LSL-dCas9-VPR mice in combination with AAV8 and LP1 promoter driven Cre expression allow efficient and liver specific induction of dCas9-VPR expression. To determine whether the Rosa26 knock-in construct provided functional levels of dCas9-VPR expression, we next investigated parallel transduction of CRISPRa mice with AAV8-Cre and a set of five AAV8s containing gRNAs targeting the five Serpina1 paralogues a-e (AAV8-gSerpina1a, AAV8-gSerpina1b, AAV8-gSerpina1c, AAV8-gSerpina1d, AAV8-gSerpina1e) (Supplementary Fig. S1). Each paralogue is targeted in parallel by 6 different gRNAs, where each gRNA is driven by an individual U6 promoter (Fig. 2A). We injected two groups of animals and collected blood samples 10- and 21-days post transduction. While the animals of the first group received only AAV8-Cre, the second group received a combination of AAV8-Cre and all five AAV8-gSerpina1, each targeting one of the five Serpina1 gene variants a-e (AAV8-gSerpina1(a1-6-e1-6)), with the aim to upregulate all 5 liver Serpina1 paralogues simultaneously in each animal (Fig. 2B). 21 days post transduction, Rosa26-LSL-dCas9-VPR mice were investigated for AAT expression in the liver and blood. mRNA analysis of all Serpina1 variants at day 21 showed an increased expression in the liver of mice that received both Cre and Serpina1 gRNAs, demonstrating that dCas9-VPR expression was successfully induced and capable to form a ribonucleoprotein complex (RNP) with the provided gRNAs to facilitate on-target gene over-expression (Fig. 2C). Furthermore, we performed protein analysis of liver samples and found a fivefold overexpression of the SERPINA1A paralogue as compared to animals receiving only AAV8-Cre (Fig. 2D). To confirm this observation, we additionally quantified AAT levels in plasma (Fig. 2E). In line with the results described above, injection of mice with AAV8-Cre and AAV8-gSerpina(n) led to significantly increased AAT plasma levels detected on day 10 post-transduction, which further increased on day 21 post injection, while control levels remained almost unchanged (Fig. 2E). Taken together, these data demonstrate the successful transcriptional induction of all five Serpina1 paralogues, thereby providing evidence for the use of the Rosa26-LSL-dCas9-VPR mice in combination with AAV8 encoded gRNAs for efficient upregulation of Serpina1 transcription resulting in increased protein levels in liver and plasma. A key advantage of CRISPR-based models is their potential for targeting of two or more genes at the same time by combining different gRNAs. To study the possibility of multiple gene upregulation in our Cre-dependent dCas9-VPR mice, we selected the well characterized PCSK9-LDLR-LDL regulatory loop for the next experiment. PCSK9 plays an important role in cholesterol homeostasis by forming a complex with LDLR on the cell surface, thereby inducing LDLR’s internalization and subsequent lysosomal degradation. To induce PCSK9 and LDLR expression, we selected a set of 6 different gRNAs, each targeting either Ldlr or Pcsk9 (Fig. 3A). To study the function of both genes on cholesterol homeostasis, we analyzed liver and blood 21 days post AAV injection. Animals of the first group were injected with AAV8-Cre alone, as a control, whereas the second and third groups additionally received either AAV8-gPcsk9 or AAV8-gLdlr, respectively (Supplementary Fig. S1). The fourth group was injected with a combination of three different viruses: AAV8-Cre, AAV8-gLdlr and AAV8-gPcsk9 (Fig. 3B). As expected, treatment with AAV8-Cre either in combination with AAV8-gLdlr or AAV8-gPcsk9 led to a significant, approximately threefold transcriptional upregulation of either Ldlr or Pcsk9, compared to the control group (Fig. 3C,D). In group four (AAV8-Cre + AAV8-gLdlr + AAV8-gPcsk9), the transcriptional upregulation of both Ldlr and Pcsk9 was comparable to the individual induction of Pcsk9 expression in group two (AAV8-Cre + AAV8-gPcsk9) or Ldlr in group three (AAV8-Cre + AAV8-gLdlr) (Fig. 3C,D). The increase in mRNA translated into an even more pronounced upregulation on protein levels, with a ninefold or fourfold overexpression for LDLR or PCSK9, respectively (Fig. 3E,F). Specificity of PCSK9 detection via Wes™ was confirmed using recombinant PCSK9 protein. Virtual blot-like images are shown in Supplementary Fig. S2. Upregulation of PCSK9 reduced LDLR protein in liver tissue by tenfold (Fig. 3E, AAV8-Cre + AAV8-gPcsk9), whereas upregulation of LDLR protein did not affect the PCSK9 protein amount in liver tissue (Fig. 3F, AAV8-Cre + AAV8-gLdlr), but reduced circulating PCSK9 levels in plasma by 13-fold (Fig. 3G, AAV8-Cre + AAV8-gLdlr). Simultaneous upregulation of PCSK9 and LDLR proteins led to an almost twofold increase in LDLR protein amount in liver (Fig. 3E, AAV8-Cre + AAV8-gLdlr + AAV8-gPcsk9), whereas the detected PCSK9 amounts were increased almost threefold compared to the control group (Fig. 3F, AAV8-Cre + AAV8-gLdlr + AAV8-gPcsk9). However, despite these higher PCSK9 protein amounts observed in the liver tissue lysates, no significant PCSK9 increase was detected in the corresponding plasma samples (Fig. 3G, AAV8-Cre + AAV8-gLdlr + AAV8-gPcsk9). To confirm the increase of LDLR on the surface of the hepatocytes from the dCas9-VPR expressing mice, we additionally performed immunohistochemistry analyses on liver sections stained with an anti-LDLR antibody. The antibody dilution was titrated to see a moderate staining in the control group showing faint cytoplasmic staining and, in a fraction of hepatocytes, distinct membrane staining. As expected, upregulation of PCSK9 in the experimental group two led to decreased LDLR staining compared to the control group (Fig. 3H, AAV8-Cre + AAV8-gPcsk9), whereas upregulation of the LDL-receptor resulted in a strong increase in membrane staining and, to a lesser extent, in cytoplasmic staining (AAV8-Cre + AAV8-gLdlr). After simultaneous upregulation of both PCSK9 and LDLR, the staining for LDLR levels is comparable to the control group (Fig. 3H, AAV8-Cre + AAV8-gLdlr + AAV8-gPcsk9). To evaluate the effect of hepatic LDLR overexpression, or its reduction by the enhanced PCSK9 expression, on plasma cholesterol levels, we subjected plasma of the AAV8-transduced dCas9-VPR mice to lipoprotein analysis. As expected, treatment with AAV8-Cre in combination with AAV8-gLdlr led to a significant decrease of LDL, HDL as well as cholesterol in plasma compared to the samples from the control group (Fig. 4A–C, AAV8-Cre and AAV8-Cre + AAV8-gLdlr). In detail, the HDL and total cholesterol levels dropped threefold, whereas plasma LDL dropped to an undetectable amount. In line with these data, AAV-mediated overexpression of PCSK9 increased LDL, HDL and total cholesterol concentrations in plasma compared to the control group samples (Fig. 4A–C, AAV8-Cre and AAV8-Cre + AAV8-gPcsk9). In this context, upregulation of PCSK9 resulted in fourfold higher plasma LDL levels and a twofold increase for total cholesterol when compared to the control group (AAV8-Cre), whereas HDL levels were almost unchanged (1.2-fold increase) (Fig. 4B). Notably, simultaneous upregulation of LDLR in parallel to PCSK9 (Fig. 4A–C, AAV8-Cre + AAV8-gLdlr + AAV8-gPcsk9) still resulted in a reduction of plasma LDL, HDL and cholesterol levels, but less pronounced than upon upregulation of LDLR alone. This finding is in concordance with a residual increase of LDLR levels of almost twofold compared to control, despite PCSK9 induction (Fig. 3E, AAV8-Cre + AAV8-gLdlr + AAV8-gPcsk9 and AAV8-Cre). In our study, we generated a novel, conditional (Cre-dependent) dCas9-VPR expressing mouse line and demonstrated its utility for tissue specific gene upregulation using AAV-mediated gRNA expression. Our mouse model offers a versatile basis for diverse research applications that require fine-tuning of targeted expression of any gene of interest using either a mixture or a single AAV with varying tissue tropism, thereby providing the opportunity to simultaneously activate multiple genes in vivo. Additionally, our model offers the advantage to restrict dCas9-VPR expression to a desired tissue by placing Cre-recombinase under a tissue-specific promoter, if needed. Alternatively, the mice could also be cross-bred with Cre driver lines to induce tissue- or cell-specific dCas9-VPR expression. The primary goal of our study was to establish and improve a CRISPR activation model to study pathways and molecular interactions in conjunction with tailored disease models to reproduce human pathological conditions for basic research and drug development. Because of their phylogenetic relatedness and physiological similarity to humans, the use of mice as tools in biomedical research is well established. Unfortunately, none of these models precisely mimic the human phenotype exactly enough, leading to variations in efficacy and toxicity of drug candidates compared to humans in the past. Even though the genetic pathways regulating normal and physiological conditions are quite conserved, the intrinsic genetic differences sometimes complicate the direct comparison between the species. One example is the generation of authentic AAT mouse-models, where the establishment of comparable disease models are hampered by the complexity of the murine Serpina1-genes. The need for mouse models to upregulate the expression of all Serpina1 paralogues simultaneously is of particular importance since there is evidence to suggest that overexpression of AAT is most probably involved in cancer related processes. It has been already shown that higher AAT expression promotes invasion and metastasis as well as correlates with poor prognosis in patients with lung, colon, skin, and gastric cancer. The mouse model presented here will therefore be highly relevant to further investigate not only these findings but can be easily adapted to similar genetic conditions. The most important features to reproduce human diseases are the precision of etiology as well as the ability to reproduce the features of the pathological process. The value of our mouse model to induce and study the regulation of complex pathological conditions is, therefore, demonstrated by the successful modulation of cholesterol metabolism by the hepatic overexpression of two system relevant key players, namely LDLR and PCSK9, alone and in combination. In accordance with recent studies, overexpression of circulating PCSK9 led to reduced LDLR, which was accompanied by increased LDL and cholesterol concentrations in plasma and vice versa. In addition to this, we also observed sex dependent differences in serum/plasma levels of alpa-1-antitrypsin (male > female), LDL (female > male) and PCSK9 in control mice in accordance with the literature. This distinction was still visible in AAT plasma levels despite a general 1.6-fold increase after overexpression. In contrast to this, we could not detect animal sex related significant differences in liver dCas9-VPR mRNA expression. As demonstrated with these data, our model is well suited to study PCSK9 function in the liver via LDLR depletion, but moreover, also possible effects on other organs can be easier addressed, e.g., by the investigation of compensatory effects mediated via additional tissue specific LDLR upregulation. Several CRISPRa mouse models have already been generated, which mainly differ in the choice of the transcriptional activator, chosen locus for dCas9 gene knock-in, the target vector design, and whether dCas9 is conditionally or constitutively expressed. We specifically decided to use dCas9 fused to VPR under the control of a CAG promoter within the Rosa26 locus and downstream of an LSL cassette. To allow gene upregulation in any tissue of interest, we followed the strategy to use the "safe harbor" locus Rosa26 as the preferred site for ubiquitous expression of our transgene. By doing so, we made sure to reach similar expression of dCas9-VPR across various tissues, without affecting endogenous gene expression as observed in mouse models in the past. This was also done by Hunt et al., who published a CRISPRa model using the Rosa26 locus. However, in this configuration the dCas9-synergistic activation mediator (SAM) was placed exclusively under the transcriptional control of the Rosa26 promoter, which might limit the transgene expression levels due to its moderate strength. To overcome this limitation, our (and other) CRISPRa mouse model was generated by additionally inserting a strong exogenous CAG-promoter into the Rosa26 locus upstream of the dCas9 transgene. The decision to use dCas9 fused to the VPR activator was mainly fostered by a comprehensive study by Chavez et.al., where they compared different Cas9 activator systems in several human, mouse and fly cell lines. Even though AAV based gRNA delivery can often be sufficient for tissue-specific target gene expression, we aimed to further increase tissue specificity in our model by conditional dCas9-VPR expression. In addition to that, transgene expression needs to be supervised to prevent unwanted side effects, as several publications pointed out a possible dCas9-VPR toxicity. Moreover, by limiting dCas9 expression to certain tissues, also the risk of gRNA off-targeting is reduced. Narrowing down dCas9-VPR expression to defined cell types and tissues in our mouse model can be achieved by combining tissue tropism provided by AAV serotypes with tissue specific promoter driven Cre. This strategy is of special interest since the field of AAV capsid engineering is thriving and a number of additional AAV serotypes have been isolated and new capsid variants have been generated in recent years. Despite these attractive features, the CRISPRa/AAV-guide system still possesses some limitations, such as the limited availability of truly tissue or cell specific promoters for controlled Cre expression, inefficient in vivo transduction of some tissues (e.g., bone marrow, immune cells, kidney) with AAVs, and specificity of gRNAs. Nevertheless, in light of the large body of information gained from studies in mice, where different AAV serotype vectors have been shown to exhibit distinct tropism for various tissues, ongoing efforts to identify novel promoter/enhancer elements for a variety of tissues, and continuous improvements of CRSIPR technology and guide design, our model holds the potential to target genes in hardly accessible tissues or cell types in the future. Finally, we have decided to use 6 gRNA sequences to target one gene, as it was demonstrated that most efficient gene upregulation is reached when more than 3 gRNAs are used in parallel. Although the selection of these gRNAs was based on a prediction algorithm that aims to select target-specific sequences, off-target effects cannot be fully excluded and therefore need to be carefully addressed in any future study using appropriate methods, e.g., ChIP-seq and prior in vitro evaluation to minimize off-target effects that might otherwise falsify data interpretation. Our Rosa26-LSL-dCas9-VPR model can be a used to study and validate pathways/molecular interactions by selected or combined overexpression of genes. It also offers the possibility for concerted overexpression of multiple gene variants in order to study their biological function jointly and/or individually. Additionally, it has the potential to generate mouse disease models by overexpression of endogenous genes based on the sustained and potentially long-lasting expression of AAV8 constructs in mouse liver and by combining multiple genes in order to achieve the expected disease phenotypes. The fast generation, the precise gene targeting, and the versatile combination of multiple genes makes this model highly attractive, not only for academia but also for industry, to support and accelerate drug discovery by providing detailed insights in target pathway biology and to set up new disease animal models with a better match to human pathology. Rosa26-LSL-dCas9-VPR mouse line was generated by recombinase mediated cassette exchange (RMCE) technology (Taconic Bioscience). RMCE vector containing F3 site, neomycin resistance (NeoR) gene, PGK polyadenylation signal, cytomegalovirus (CMV) immediate enhancer/β-actin (CAG) promoter, loxP-STOP-loxP (LSL) cassette, dCas9-VPR gene, P2A sequence, enhanced green fluorescent protein (EGFP) gene, hGH polyadenylation signal, PGK polyadenylation signal and FRT site was cloned, and transfected into a C57BL/6NTac embryonic stem cell (ESC) line containing an RMCE docking site in the Rosa26 locus (Taconic Bioscience). The targeted ESC clone was injected into BALB/c blastocysts. Spermatozoa from high-percentage chimeric male mice was used for in vitro fertilization of superovulated C57BL/6NTac female mice (Taconic Bioscience) to obtain a first colony of transgenic C57BL/6NTac-Gt(ROSA)26Sortm6458 (CAG-LSL-dCas9VPR-EGFP)Tac (termed Rosa26-LSL-dCas9-VPR or Rosa26LSL-dCas9-VPR/+) mice. Mice were housed in groups of 3–5 in individually ventilated cages at 22–25 °C, a humidity of 45–65%, and a 12 h day/night cycle with free access to water and food. Animal experiments were performed in accordance with the German Animal Welfare Act, and the guidelines of directive 2010/63/EU of the European Parliament and the Council 2010 on the protection of animals used for scientific purposes. Animal experiments performed in this study were reviewed and approved by the local authorities (Regierungspräsidium Tübingen, TVV-17-020). We hereby confirm that all methods in the study were carried out in compliance with the ARRIVE (Animal Research: Reporting of In Vivo Experiments) guidelines and regulations. AAV8 particles were resuspended in 0.9% NaCl saline (04671613, Deltamedica). 125 µl were injected into Rosa26LSL-dCas9-VPR/+ mice via tail vein injection at a dose of 1 × 1011 VG/mouse for each AAV variant. For all experiments, male and female mice at the age of 20–26 weeks were used and genders were distributed equally between the experimental groups. gRNA and LP1-Cre sequences were cloned into separate plasmids harboring AAV2-inverted-terminal repeats (ITRs) with one ITR harboring a mutated terminal resolution site, therefore resulting in a self-complementary genome. Each gRNA expression vector contained six consecutives human U6 promoters, each followed by a gRNA coding sequence. The native Streptococcus pyogenes-derived gRNA scaffold structure was optimized based on previous publications. gRNA sequences were taken from a data base, published by Horlbeck et al., and are listed in Supplementary Table S1. The Cre expression vector was composed of an LP1 promoter, Cre gene and an SV40 polyA sequence. Complete plasmid sequences and annotations can be found in Supplementary Table S2. AAVs were produced using frozen high-density HEK293 cell stocks and CELLdiscs (678116, Greiner Bio-one) as previously described. Briefly, AAVs were produced by calcium phosphate transfection in a minimum of three 16-layer CELLdiscs, resulting in total yield of 2.39E+12–6.84E+12 VG, depending on the construct. Freshly thawed high-density HEK293 cell stocks were seeded with a density of 6E+07 cells per disc, followed by incubation at 37 °C for 72 h. For one disc, 1800 µg of plasmid DNA was mixed with 69 ml 300 mM calcium chloride (C7902, Sigma-Aldrich), added dropwise to 69 mL 2× HBS buffer, pH 7.0 (15450257, Thermo Fisher Scientific) and added to the cells after 2 min of incubation. Plasmid DNA contained equimolar amounts of a rep2-cap8 plasmid, pHelper (Applied Biosystems), and either Cre or one of the gRNA expression plasmids. Six hours after transfection, the medium was changed, and cells were further incubated for 72 h before harvesting. Cells were then lysed in lysis buffer, containing 1300 IU (i.e., 0.325 IU/cm2) salt active nuclease (70910-150, Scientifix) and HALT protease inhibitor (78439, Thermo Fisher Scientific). After spinning the cell debris, supernatants were collected and further processed by PEG-precipitation, iodixanol gradient ultracentrifugation and ultrafiltration, as described in detail before. Droplet digital PCR for absolute quantification of viral genomes for gRNA-expressing virus was performed using an U6 promoter specific Custom TaqMan™ Gene Expression Assay (Applied Biosystems), containing U6-Fwd, U6-Rev and U6-probe (Supplementary Table S1). Absolute quantification for Cre-expressing AAVs was performed using primers LP1-Fwd, LP1-Rev and LP1-probe (Sigma-Aldrich) (Supplementary Table S1). For protein analysis, dissected tissues were immediately snap frozen, while for RNA analysis, organs were first submerged in RNAlater™ Stabilization Solution (AM7021, ThermoFisher) for 24 h at 4 °C. Tissues were transferred into Precellys® tubes (Bertin Instruments) together with 50 µl/10 mg RLT buffer (79,216, QIAGEN) containing 1% β-mercaptoethanol (M3148, Sigma-Aldrich) for RNAlater stabilized tissues or 100 µl/10 mg RIPA buffer (R0278, Sigma-Aldrich) containing 1X HALT Protease inhibitor Cocktail (1861279, ThermoFisher) for snap frozen tissues. Tissues were homogenized at 5500 rpm for 20 s using a Precellys® 24 homogenizer (Bertin Instruments). After disruption, protein lysates were incubated for 30 min at 4 °C. Lysates were centrifuged for 20 min at 15,294g to pellet cell debris and supernatant was collected. Protein concentration was measured using Pierce™ BCA Protein Assay Kit (23225, ThermoFisher) according to the manufacturer’s instructions. For total RNA isolation, 650 µl of prepared tissue lysate was transferred along with 325 μl Phenol–chloroform–isoamyl alcohol mixture (77617, Sigma-Aldrich) into 5PRIME Phase Lock Gel Heavy tubes (2302830, Quantabio), followed by vigorous shaking for 15 s and centrifugation at 16,000g for 5 min. Next, 325 µl of Chloroform–isoamyl alcohol mixture (25666, Sigma-Aldrich) was added and the tubes were shaken again for 15 s, followed by 3 min incubation and centrifugation at 16,000g for 5 min. 350 µl of aqueous phase was collected and used to extract total RNA and using AllPrep DNA/RNA 96 Kit (80311, Qiagen). Isolation was performed according to the manufacturer’s instructions with slight modification to remove DNA contamination from the RNA fraction. For this, the RNA fraction was loaded in the RNeasy® 96 Plate and washed with 400 µl RW1 buffer. 80 µl of DNase I (79254, Qiagen) was added to each well and the RNeasy® 96 Plate was incubated for 15 min at room temperature followed by standard protocol starting with washing the 96-well plate with RW1 buffer. Either 500 ng (dCas9-VPR, Ldlr and Pcsk9) or 1 µg (Serpina1(a-e)) of total RNA was reverse-transcribed into copy DNA (cDNA) using High-Capacity cDNA Reverse Transcription Kit (4368813, Applied Biosystems). Quantitative real-time PCR was performed with a QuantiFast Probe PCR Kit (204256, Qiagen) (for Serpina1(a-e)) or TaqMan™ Gene Expression Master Mix (4370074, Applied Biosystems) (for Ldlr, Pcsk9 and dCas9-VPR expression) using the following TaqMan™ Gene Expression Assays (Applied Biosystems): Mm01177349_m1 for Ldlr, Mm02748447_g1 for Serpina1a, Mm04207706_gH for Serpina1b and Serpina1d, Mm00833655_m1 for Serpina1e, Mm00842094_mH for Serpina1d and Mm04207703_mH for Serpina1a, Serpina1b, Serpina1c, Mm01263610_m1 for Pcsk9, Mm00839502_m1 for Polr2A. dCas9-VPR expression was analyzed using a Custom TaqMan™ Gene Expression Assay (Applied Biosystems), containing dCas9-Fwd, dCas9-Rev and dCas9-probe (Supplementary Table S1). Relative Serpina1(a-e), Pcsk9 and Ldlr expressions were calculated using 2−ΔΔCt method in relation to Polr2a. LSL cassette recombination PCR was performed on liver cDNA using Quick-Load® Taq Master Mix (M0271S, NEB) with primers p1, p3, p2 (Supplementary Table S1). The PCR products with 492 bp for floxed LSL-dCas9-VPR and 393 bp for recombined dCas9-VPR products were separated on a 2% E-Gel™ EX Agarose-Gel (G401002, ThermoFisher). For genotyping, the Rosa26 locus was amplified with the PCR primers GenFw1, GenFw2 and GenRev1 (Supplementary Table S1) by using the Taq polymerase High Fidelity (11304011, ThermoFisher). The expected PCR products were 299 bp for wild-type allele and 744 bp long for knock-in allele. Tissue lysates were analyzed using automated Simple Wes system (Protein Simple) with 12–230 kDa Wes Separation Module capillary cartridges (SM-W004, Protein Simple). Anti- mouse (DM-002, Protein Simple), anti-rabbit (DM-001, Protein Simple), anti-goat (DM-006, Protein Simple) detection modules or F(ab’)2 anti-Rat IgG (H + L)-HRPO (1:20, 112-036-062, Jackson Immuno Research) were used, depending on host species of the primary antibodies. The following primary antibodies were used: LDLR (1:50, PAB8804, Abnova), SERPINA1A (1:20, MAB7690, R&D Systems), PCSK9 (1:10, AF3985, R&D Systems) and β-actin (1:20, NB600-501, Novus Biologicals). Specificity of PCSK9 detection was confirmed using recombinant mouse PCSK9 protein (9258-SE-022, R&D Systems). Compass software version 6.0.0 (Protein Simple) was used to analyze the data. Area under the peak of the protein of interest was measured and normalized with respect to the β-actin area under the peak. Proteins were determined in plasma using Mouse Proprotein Convertase 9/PCSK9 Quantikine ELISA Kit (MPC900, R&D Systems) and Mouse A1AT ELISA Kit (E-90A1T, Immunology Consultants Laboratory) according to the manufacturer’s instructions detecting all 5 paralogues of Serpina1. HDL (high-density lipoprotein), LDL and total cholesterol plasma levels were determined using a COBAS INTEGRA® 400 Plus chemistry analyzer (Roche Diagnostics, Germany), according to the manufacturer's instructions. Mouse tissues were dissected and immediately transferred to 10% neutral buffered formalin (HT501128, Sigma-Aldrich). Tissues were fixed for at least 24 h before samples were processed with an automated tissue processor (Tissue-Tek® VIP® 6, Sakura), embedded in paraffin and cut into 3 µm sections. Immunohistochemical (IHC) staining for LDLR was carried out on the automated Leica Bond RX™ platform (Leica Biosystems, Melbourne, Australia) using a monoclonal rabbit anti-LDL receptor antibody (1:1200, clone EP1553Y, ab271846, abcam) after heat-induced epitope retrieval with Bond™ Epitope Retrieval Solution 1 (ER1, pH6, Leica Biosystems, Newcastle, United Kingdom). Antibody dilution was titrated to have a moderate staining signal in livers of AAV8-Cre-only treated mice. Bound antibodies were visualized using the Bond™ Polymer Refine Detection System (Leica Biosystems, Newcastle, United Kingdom). Anti-LDLR stained sections were scanned with the Axio Scan.Z1 (20 × objective, Carl Zeiss Microscopy GmbH, Jena, Germany). Statistical analyses were performed using GraphPad Prism 9 (GraphPad). Significance was determined according to the p values as *p < 0.05, **p < 0.01, ***p < 0.001 and ****p < 0.0001. Results are shown as mean values ± s.d. Comparison between experimental groups was made using a nonparametric Mann–Whitney test. Supplementary Information.