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==== Front
9215515
Neuroimage
Neuroimage
NeuroImage
1053-8119
1095-9572
36108798
ems177461
10.1016/j.neuroimage.2022.119624
Article
Psychedelics and schizophrenia: Distinct alterations to Bayesian inference
Rajpal Hardik abf*
Mediano Pedro A.M. pmediano@imperial.ac.uk
cde
Rosas Fernando E. aghp
Timmermann Christopher B. g
Brugger Stefan k
Muthukumaraswamy Suresh m
Seth Anil K. no
Bor Daniel de
Carhart-Harris Robin L. gi
Jensen Henrik J. abj
a Centre for Complexity Science, Imperial College London, South Kensington, London, United Kingdom
b Department of Mathematics, Imperial College London, South Kensington, London, United Kingdom
c Department of Computing, Imperial College London, South Kensington, London, United Kingdom
d Department of Psychology, University of Cambridge, Cambridge, United Kingdom
e Department of Psychology, Queen Mary University of London, London, United Kingdom
f Public Policy Program, The Alan Turing Institute, London, United Kingdom
g Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London, United Kingdom
h Data Science Institute, Imperial College London, London, United Kingdom
i Psychedelics Division, Neuroscape, Department of Neurology, University of California San Francisco, US
j Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Japan
k Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, United Kingdom
l Centre for Academic Mental Health, Bristol Medical School, University of Bristol, United Kingdom
m School of Pharmacy, The University of Auckland, New Zealand
n School of Engineering and Informatics, University of Sussex, United Kingdom
o CIFAR Program on Brain, Mind, and Consciousness, Toronto, Canada
p Department of Informatics, University of Sussex, Brighton, United Kingdom
* Corresponding author. h.rajpal15@imperial.ac.uk (H. Rajpal).
01 11 2022
13 9 2022
04 7 2023
18 7 2023
263 119624119624
https://creativecommons.org/licenses/by/4.0/ This work is licensed under a CC BY 4.0 International license.
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Schizophrenia and states induced by certain psychotomimetic drugs may share some physiological and phe-nomenological properties, but they differ in fundamental ways: one is a crippling chronic mental disease, while the others are temporary, pharmacologically-induced states presently being explored as treatments for mental illnesses. Building towards a deeper understanding of these different alterations of normal consciousness, here we compare the changes in neural dynamics induced by LSD and ketamine (in healthy volunteers) against those associated with schizophrenia, as observed in resting-state M/EEG recordings. While both conditions exhibit in-creased neural signal diversity, our findings reveal that this is accompanied by an increased transfer entropy from the front to the back of the brain in schizophrenia, versus an overall reduction under the two drugs. Furthermore, we show that these effects can be reproduced via different alterations of standard Bayesian inference applied on a computational model based on the predictive processing framework. In particular, the effects observed under the drugs are modelled as a reduction of the precision of the priors, while the effects of schizophrenia correspond to an increased precision of sensory information. These findings shed new light on the similarities and differences between schizophrenia and two psychotomimetic drug states, and have potential implications for the study of consciousness and future mental health treatments.
Psychedelics
Schizophrenia
Information theory
Predictive processing
==== Body
pmc1 Introduction
Classic serotonergic psychedelic drugs have seen a blooming resurgence among the public and the scientific community in recent years, largely driven by promising clinical research into their therapeutic potential Carhart-Harris et al. (2021, 2017). At the same time, and somewhat paradoxically, psychedelics are known to elicit effects that mimic some symptoms of psychosis – earning them the label of ‘psychotomimetic drugs’ Carhart-Harris et al. (2016a). In this context, our aims with this study are twofold: First, to explore the limits of psychotomimetic models of psychosis at a neurophysiological level, thus helping us refine these models. Second, to further our understanding of extended and acute alterations to normal consciousness, which may help the design better mental health therapies.
To contrast these conditions in an empirical manner, we compare neuroimaging data from patients suffering from schizophrenia and healthy subjects under the effects of two psychoactive substances: the classical psychedelic lysergic acid diethylamide (LSD) Carhart-Harris et al. (2016b) and the dissociative drug ketamine (KET) Frohlich and Van Horn (2014).
Using standardised assessments, it has been claimed that KET reproduces both positive and negative symptoms of schizophrenia in humans Beck et al. (2020), and its mechanism of action – NMDA receptor antagonism – is thought to reproduce a key element of the molecular pathophysiology of schizophrenia Friston et al. (2016); McCutcheon et al. (2020) LSD – in common with all classical psychedelics – is a potent agonist of a number of serotonin receptors, but its characteristic effects depend primarily on 5-HT2A Nichols (2004). These neurotransmitter systems have been linked to symptoms of early acute schizophrenic stages, such as “ego-disorders, affective changes, loosened associations and perceptual alterations” Vollenweider et al. (1998) (see Ref. Carhart-Harris et al. (2013) for a quantitative analysis of these associations).
Both psychotomimetic drug states and schizophrenia are also associated with marked changes in large-scale neural dynamics. For both LSD and KET, previous studies have found increased signal diversity in subjects’ neural dynamics Mediano et al. (2020); Schartner et al. (2017) and reduced information transfer between brain regions Barnett et al. (2020). However, in the case of KET, evidence from intracranial recordings in cats suggests a much more complicated picture than that of LSD, with very high variability across individuals, brain regions, and dose levels Pascovich et al. (2021). In a separate line of enquiry, work on EEG data from patients with schizophrenia has also found increased signal diversity Fernández et al. (2011); Li et al. (2008), akin to the effect found under these drugs. Nonetheless, a parsimonious account explaining the similarities and differences between the two states is still lacking.
A promising approach to gain insights into the mechanisms driving the core similarities and differences between psychotomimetic drug states and schizophrenia is to leverage principles from the predictive processing (PP) framework of brain function Clark (2015); Rao and Ballard (1999). A key postulate of the PP framework is that the dynamics of neural populations can be viewed as engaged in processes of inference involving top-down and bottom-up signals. Under this framework, brain activity can be viewed as resulting from a continuous modelling process in which a prior distribution interacts with new observations via incoming sensory information. In accordance with principles of Bayesian inference, discrepancies between the prior distribution and incoming signals (called ‘prediction errors’) carried by the bottom-up signals drive revisions to the top-down activity, so as to minimize future surprise.
The PP framework has been used to explain perceptual alterations observed in both psychotomimetic drug states Corlett et al. (2009); Leptourgos et al. (2020) as well as in psychiatric illnesses Adams et al. (2016) with a focus on schizophrenia Adams et al. (2013); Brugger and Broome (2018); Fletcher and Frith (2009); Speechley et al. (2010). Most of these accounts of PP are task-based studies, which manipulate stimuli in order to modulate prediction errors. In contrast, here we extend this approach to the resting state, focusing on spontaneous “prediction errors” that arise from naturally occurring neural activity. PP has also been used to understand the action of psychedelics, most notably through the “relaxed beliefs under psychedelics” (or REBUS) model Carhart-Harris and Friston (2019) which posits that psychedelics reduce the precision of prior beliefs encoded in spontaneous brain’s activity. REBUS has also been used to inform thinking on the therapeutic mechanisms of psychedelics, where symptomatology can be viewed as pathologically over-weighted beliefs or assumptions encoded in the precision weighting of brain activity encoding them.
To deepen our understanding of the similarities and differences between these conditions, in this paper we replicate and extend findings on neural diversity and information transfer under the two psychotomimetic drugs (LSD and KET) and in schizophrenia using EEG and MEG recordings, and we reproduce these experimental findings as perturbations to a single PP model. Our modelling results reveal that the effects observed under the drugs are indeed reproduced by decreasing the precision-weighting of the priors, while the effects observed under schizophrenia are reproduced by increased precision-weighting of the bottom-up sensory information. Overall, this study puts forward a more nuanced understanding of the relationship between two different psychotomimetic drug states and schizophrenia, and offers a new model-based perspective on how these conditions alter conscious experience.
2 Materials and methods
2.1 Data acquisition and preprocessing
Data from 29 patients diagnosed with schizophrenia and 38 age-matched healthy control subjects were obtained from the Bipolar-Schizophrenia Network on Intermediate Phenotypes (BSNIP) database Tamminga et al. (2013). The subjects were selected within an age range of 20–40 years to match the psychedelic datasets described below. Data included 64-channel EEG recordings sampled at 1000Hz of each subject in eyes-closed resting state, along with metadata about demographics (age and gender), patients’ medications and their PANSS symptom scores Kay et al. (1987). The strength of the medication was estimated using the number of antipsychotics taken by each patient (mean: 2.7, range: 0–8), as the dosage of each medication was not available.
Data from healthy subjects under the effects of both drugs was obtained from previous studies with LSD Carhart-Harris et al. (2016b) (N = 17) and ketamine Muthukumaraswamy et al. (2015) (N = 19). Data included MEG recordings from a CTF 275-channel axial gradiometer system with a sampling frequency of 600Hz. Each subject underwent two scanning sessions in eyes-closed resting state: one after drug administration and another after a placebo (PLA).
Preprocessing steps for all datasets were kept as consistent as possible, and were performed using the Fieldtrip Oostenveld et al. (2011) and EEGLAB Delorme and Makeig (2004) libraries. First, the data was segmented into epochs of 2 seconds, and epochs with strong artefacts were removed via visual inspection. Next, muscle and eye movement artefacts were removed using ICA Winkler et al. (2011). Then, a LCMV beam-former Van Veen et al. (1997) was used to reconstruct activity of sources located at the centroids of regions in the Automated Anatomical Labelling (AAL) brain atlas Tzourio-Mazoyer et al. (2002). Finally, source-level data was bandpass-filtered between 1–100Hz, and downsampled with phase correction to 250Hz (EEG) and 300Hz (MEG), and AAL areas were grouped into 5 major Regions of Interest (ROIs): frontal, parietal, occipital, temporal and sensorimotor (see Fig. 1 and Table D.2 in the Appendix). In the rest of the paper we refer to these 5 areas as “ROIs” and to the AAL regions as “sources.”
2.2 Analysis metrics
Our analyses are focused on two complementary metrics of neural activity: Lempel-Ziv complexity (LZ) and transfer entropy (TE). Both metrics are based on the same mathematical framework of information theory, and provide characterisations of different but complementary aspects of neural dynamics: LZ captures aspects of the temporal dynamics of single regions, while TE quantifies how different regions influence each other. Both metrics have a long history, and have been used and robustly validated across a wide range of states of consciousness, including psychedelic states Barnett et al. (2020); Bossomaier et al. (2016); Mediano et al. (2020); Schartner et al. (2017).
Lempel-Ziv complexity (LZ) is a measure of the diversity of patterns observed in a discrete – typically binary – sequence. When applied to neuroimaging data, lower LZ (with respect to wakeful rest) has been associated with unconscious states such as sleep Andrillon et al. (2016) or anaesthesia Zhang et al. (2001), and higher LZ with states of richer phenomenal content under psychedelics, ketamine Mediano et al. (2020);Schartner et al. (2017) and states of flow during musical improvisation Dolan et al. (2018).
To calculate LZ, first one needs to transform a given signal of length T into a binary sequence. For a given epoch of univariate M/EEG data, we do this by calculating the mean value and transforming each data point above the mean to 1 and each point below to 0. Then, the resulting binary sequence is scanned sequentially using the LZ76 algorithm presented by Kaspar and Schuster Kaspar and Schuster (1987), which counts the number of distinct “patterns” in the signal. Finally, following results by Ziv Ziv (1978), the number of patterns is divided by log2(T)/T to yield an estimate of the signal’s entropy rate Cover and Thomas (2006), which we refer to generically as LZ. This process is applied separately to each source time series (i.e. to each AAL region), and the resulting values are averaged according to the grouping in Table D.2 to yield an average LZ value per ROI.
In addition to LZ, our analyses also consider transfer entropy (TE) Bossomaier et al. (2016) — an information-theoretic version of Granger causality Barnett et al. (2009) — to assess the dynamical in-terdependencies between ROIs. The TE from a source region to a target region quantifies how much better one can predict the activity of the target after the activity of the source is known. This provides a notion of directed functional connectivity, which can be used to analyse the structure of large-scale brain activity Barnett et al. (2020); Deco et al. (2021).
Mathematically, TE is defined as follows. Denote the activity of two given ROIs at time t by the vectors Xt and Yt, and the activity of the rest of the brain by Zt. Note that Xt, Yt, and Zt have one component for each AAL source in the corresponding ROI(s). TE is computed in terms of Shannon’s mutual information, I, as the information about the future state of the target, Yt+1, provided by Xt over and above the information in Yt and Zt : (1) TEY→X|Z=I(Xt;Yt−1−|Xt−1−,Zt−1−),
where Xt− refers to the (possibly infinite) past of Xt, up to and including time t (and analogously for Yt and Zt). This quantity can be accurately estimated using state-space models with Gaussian innovations Barnett and Seth (2015) implemented using the MVGC tool-box Barnett and Seth (2014) Note that, when calculating the TE between ROIs, we consider each ROI as a vector — without averaging the multiple AAL sources into a single number. The result is a directed 5×5 network of conditional TE values between pairs of ROIs, which can be tested for statistical differences across groups.
2.3 Statistical analysis
For both LSD and KET datasets, since the same subjects were monitored under both drug and placebo conditions, average subject-level differences (either in LZ or TE) were calculated for each subject, and one-sample t-tests were used on those differences to estimate the effect of the drug.
For the data of patients and controls in the schizophrenia dataset, group-level differences were estimated via linear models. These models used either LZ or TE as target variable, and condition (schizophrenia or healthy), age, gender, and number of antipsychotics (set to zero for healthy controls) as predictors. Motivated by previous work suggesting a quadratic relationship between complexity and age Gauvrit et al. (2017), each model was built with either a linear or quadratic dependence on age, and the quadratic model was selected if it was preferred over a linear model by a log-likelihood ratio test (with a critical level of 0.05).
Finally, multiple comparisons when comparing TE values across all pairs of ROIs were addressed by using the Network-Based Statistic (NBS) Zalesky et al. (2010) method, which identifies ‘clusters’ of differences – i.e. connected components where a particular null hypothesis is consistently rejected while controlling for family-wise error rate. Our analysis used an in-house adapted version of NBS that works on directed networks, such as the ones provided by TE analyses.
2.4 Computational modelling
A computational model was developed in order to interpret the LZ and TE findings observed on the neuroimaging data. Building on predictive processing principles Rao and Ballard (1999), we constructed a Bayesian state-space model that provides an idealised common ground to contrast the three studied conditions – the psy-chotomimetic drug states, schizophrenia, and baseline (i.e. healthy controls). Our modelling is based on the postulate that the activity of neuronal populations across the brain can be interpreted as carrying out inference on the causes of their afferent signals. Following this view, the proposed model considers the following elements:the internal state of a low-level region (i.e. near the sensory periphery), denoted by st ;
the internal state of neural activity taking place functionally one level above, denoted by ht ;
the signal generated at the high-level region in the form of a prediction of the low-level activity, denoted by s^t;
the signal generated at the low-level region in the form of a prediction error ξt ; and
the precision of the prior λp and precision of sensory/afferent information λs.
This model represents neural activity within a larger hierarchical processing structure, as illustrated in Fig. 2. The key principle motivating this model is that minimisation of prediction error signals throughout the hierarchy, by updating top-down predictions, implements a tractable approximation to Bayesian inference.2
Within this model, we represent the schizophrenia and psychedelic conditions as different types of disruption to Bayesian inference. To describe the psychedelic state, we build on the REBUS hypothesis Carhart-Harris and Friston (2019), which posits a reduced precision-weighting of prior beliefs, leading to increased bottom-up influence.
Conversely, to describe schizophrenia we build on the canonical predictive processing account of psychosis in schizophrenia Sterzer et al. (2018), which postulates an increased precision of sensory input, along with decreased precision of prior beliefs Adams et al. (2016); Fletcher and Frith (2009). Therefore, both conditions are similar in that there is a relative strengthening of bottom-up influence, although instantiated in different ways – which, as shown in Section 3.3, bears important consequences for the behaviour of the model.
It is important to note that predictive processing accounts of schizophrenia remain hotly debated, with other works proposing an increase of prior precision (instead of decrease) as a model of auditory and visual hallucinations Corlett et al. (2019); Teufel et al. (2015). Recent reviews Sterzer et al. (2018) have attempted to reconcile both views by suggesting that sensory hallucinations may be caused by stronger priors, while hallucinations related to self-generated phenomena (like inner speech or self-attention Schneider et al. (2008)) may stem from weaker priors. Here, we base our modelling of SCZ on the weak prior hypothesis, as described above – we return to this issue in this discussion.
To simulate the aberrant dynamics of the inference process, as described above, we consider a given afferent signal (st) and construct the corresponding activity of a higher area (ht), prediction (s^t), and prediction error (λt), building on the rich literature of state-space models in neuroscience Dayan and Jyu (2003); Dayan et al. (2000); Ratcliff and Rouder (1998). Specifically, we use the linear stochastic process: (2a) ht=aht−1+εt
(2b) st=bht−1+vt
where a, b are weights, and εt, vt are zero-mean Gaussian terms with precision (i.e. inverse variance) λp and λs, respectively. Note that this formulation is equivalent to (3a) ht|ht−1~N(aht−1,λp−1)
(3b) st|ht~N(bht,λs−1).
As we show in the following, λp corresponds to the precision of the prior and λs to the precision of sensory/afferent information.
The dynamics of this system can be described as a recurrent update between predictions and prediction errors as follows. Eq. (3b) implies that the internal state ht, generates a prediction about the low-level activity given by s^t=bht. At the same time, the dynamics of the high-level region can be seen as a Bayesian update of ht, given st and ht+1. Under some simplifying assumptions, the mean of the posterior distribution of ht+1 (denoted by h^t+1) is equal to (see Appendix A) (4) h^t+1=ah^t+βξt
which effectively combines a prior ah^t (which is the optimal prediction of ht+1 given only h^t, as seen from Eq. (3a)) and a likelihood given by the prediction error ξt=st−bh^t that is precision-weighted via ß, a parameter known as the Kalman gain Durbin and Koopman (2012).
In our simulations, the model is first calibrated using as afferent signals (i.e. st) data from the primary visual cortex, corresponding to epochs randomly sampled from the placebo conditions in the LSD and KET datasets. This calibration results in the estimation of the model parameters acon, bcon, λpcon,λscon for the control condition, which is done us- p s ing the well-known expectation-maximisation algorithm Moon (1996). With these, the schizophrenia condition is then modelled by setting (5) λpscz=λpconandλsscz=ηλscon,
where η > 1 is referred to as a noise factor, This increase of λs induces a strengthening of bottom-up prediction errors, and makes the posterior of ht, excessively precise. Conversely, the drug condition is modelled by setting (6) λppsy=λpηconandλspsy=λscon,
Reducing λp also increases the influence of prediction errors, but reduces the precision of the posterior of ht,. Subsequently, for both conditions the parameters a, b are retrained with another pass of the expectation-maximisation algorithm on the placebo trials.
Finally, to compare the model with the empirical M/EEG data, the LZ of the neural activity elicited in the low-level area (i.e. the prediction errors, ξt) and the top-down transfer entropy (from the high-level activity s^t towards the low-level activity ξt) are calculated for each of these three models (control, schizophrenia, and drug).
3 Results
3.1 LSD, KET and schizophrenia all show increased LZ
We begin the analysis by comparing changes in signal diversity, as measured by LZ, across the LSD, ketamine (KET), and schizophrenia (SCZ) datasets.
Our results show strong and significant increases in LZ in all three datasets (Fig. 3), in line with previous work Fernández et al. (2011); Li et al. (2008); Mediano et al. (2020); Schartner et al. (2017). In all three cases the LZ increases are widespread throughout the brain, with the effects in schizophrenia patients being more pronounced in frontal and parietal regions. While the t-scores are higher in LSD and KET than schizophrenia, this could be due to the within-subjects design of both drug experiments – which are more statistically powerful than the between-subjects analysis used on the schizophrenia dataset.
Interestingly, we found that controlling for the medication status of each schizophrenia patient was crucial to obtain results that match prior work Fernández et al. (2011). A direct comparison of LZ values between patients and controls yielded no significant differences (t = −0.38, p = .70); however, when using a linear model correcting for age, gender, and number of antipsychotics, the antipsychotics coefficient of the model reveals a negative effect on LZ (ß = −0. 016, t = −2. 3, p = .021). Additionally, a two-sample t-test calculated between the corrected LZ values of patients and controls yields a substantial difference (t = 3.4, p = .001). Nonetheless, the sensitivity of this result to these preprocessing steps, as well as the lack of detailed dosage data for each medication, mean it should be considered preliminary and could only be properly interpreted after further investigation in future research (see the corresponding discussion in Section 4.3).
3.2 Opposite effect of psychotomimetic drugs and schizophrenia on information transfer
We next report the effects of LSD, KET, and schizophrenia on large-scale information flow in the brain, as measured via transfer entropy (TE). The TE between each pair of ROIs (conditioned on all other ROIs) is calculated for each subject, and used to build directed TE networks. The resulting networks were tested for differences between the drug states and placebo conditions (for LSD and KET), and between patients and controls (for SCZ), correcting for multiple comparisons via cluster permutation testing (see Section 2.3).
We found a ubiquitous decrease in the TE between most pairs of ROIs under LSD and KET (Fig. 4), which is consistent with previous findings Barnett et al. (2020). In contrast, SCZ patients exhibit marked localised increases in TE – and no decreases – with respect to the control subjects. Notably, most increases in TE originated in the frontal ROI, and are strongest between the frontal and occipital ROIs. The increase of information transfer seen in schizophrenia patients therefore takes place “front to back” – aligned with the pathways thought to carry top-down information in the brain from highly cognitive, decision-making regions to unimodal regions closer to the sensory periphery.
As was the case for LZ, controlling for antipsychotic use was key to revealing differences between the healthy controls and schizophrenia patients. In addition, we found a small negative correlation between antipsychotic use and TE between certain ROI pairs – but, unlike for LZ, this effect did not survive correction for multiple comparisons. Although we find significant increase in both LZ and TE between certain regions among the schizophrenia patients when compared to healthy controls, these are not correlated with the symptom scores within the schizophrenia cohort. (see the corresponding discussion in Section 4.3).
3.3 Computational model reproduces experimental results
So far, we have seen that subjects under the effects of two different psychotomimetic drugs display increased signal diversity and reduced information flow in their neural dynamics. In comparison, schizophrenia patients display increased complexity but also increased information flow with respect to healthy controls. We now show how complementary perturbations to the precision terms of the predictive processing model introduced in Section 2.4 reproduce these findings.
We compared the basline model against the drug and schizophrenia variants by systematically increasing the noise factor η, which results in reduced prior precision in the drug model, and increased sensory precision in the schizophrenia model. We then computed the corresponding LZ and TE based on the model-generated time series ξt, s^t as per Section 2.4 (Fig. 5).
Results show that the proposed model successfully reproduced the experimental findings of both LZ and TE under the two different psychotomimetic drugs and schizophrenia (Fig. 5).
Interestingly, the model also shows (Fig. 5b) that a relative strengthening of sensory information (via either increased sensory precision, or decreased prior precision) can trigger either an increase or a decrease (respectively) of top-down transfer entropy. This suggests that transfer entropy changes cannot be directly interpreted as revealing the changes in any underlying predictive processing mechanisms (see Discussion).
Finally, as a control, we repeated the analysis on the model but exploring the variation of the precision terms in the two unexplored di-rections — either reducing λp or increasing is (see Section 2.4). Neither of these changes reproduced the experimental findings (Supp. Fig. B.6), which highlights the specificity of the modelling choices.
4 Discussion
In this paper we have analysed MEG data from healthy subjects under the effects of the psychotomimetic drugs LSD and ketamine, as well as EEG data from a cohort of schizophrenia patients and healthy control subjects. We focused on signal diversity and information transfer, both widely utilised metrics which provide a complementary account of neural dynamics. We found that all datasets show increases in signal diversity, but diverging changes in information transfer, which was higher in schizophrenia patients but lower for subjects under the effects of either drug. In addition to replicating previous results reporting signal diversity and information transfer under the effects of both drugs Barnett et al. (2020); Mediano et al. (2020); Schartner et al. (2017), we described new findings applying these metrics to schizophrenia.
Using a computational model inspired by predictive processing principles Keller and Mrsic-Flogel (2018) Rao and Ballard (1999), we showed that this combination of effects can be reproduced via specific alterations to prediction updating, which can be interpreted as specific forms of disruption to Bayesian inference. Critically, the effects of both psychotomimetic drugs and schizophrenia, on both signal diversity and information transfer, are explained by a relative strengthening of sensory information over prior beliefs, although triggered by different mechanisms – a decrease in the precision of priors in the case of psychotomimetic drugs (consistent with Ref. Carhart-Harris and Friston (2019)), and an increase in the precision of sensory information for schizophrenia.
4.1 Increased sensory precision in schizophrenia
The idea that the symptoms of schizophrenia can be understood as alterations to processes of Bayesian inference has been particularly fertile in the field of computational psychiatry Adams et al. (2016). In particular, various studies based on PP have related psychosis to decreased precision of prior beliefs and increased precision of the sensory inputs Corlett et al. (2009); Fletcher and Frith (2009); Friston et al. (2014); Notredame et al. (2014); Sterzer et al. (2016). These computational models have been supported by a growing number of related experimental findings, including an enhanced confirmation bias Balzan et al. (2013), impaired reversal learning Leeson et al. (2009); Waltz and Gold (2007), and a greater resistance to visual illusions Silverstein and Keane (2011). For instance, schizophrenia patients are less susceptible to the Ebbinghaus illusion, which arises primarily from misleading prior expectations, suggesting that patients do not integrate this prior context with sensory evidence and thus achieve more accurate judgements Horton and Silverstein (2011).
Most of the above mentioned studies are task-based, focusing on differentiating perceptual learning behaviours between healthy controls and schizophrenia patients. Though these studies provide a range of experimental markers, the corresponding methodologies cannot be applied to resting-state or task-free conditions, under which it is known that certain behavioural alterations (e.g. delusions, anhedonia, and paranoia) persist Northoff and Duncan (2016); Northoff and Qin (2011).
The findings presented in this paper provide a step towards bridging this important knowledge gap by providing empirical and theoretical insights into resting-state neural activity under schizophrenia. Although we build on and replicate results related to signal diversity, we are not aware of previous studies of information transfer on schizophrenia in resting state.
4.2 Beyond unidimensional accounts of top-down vs bottom-up processing
The findings presented here link spontaneous brain activity to the PP framework using empirical metrics of signal diversity and information transfer. In the psychotomimetic drug condition, the former increases while the latter decreases; in schizophrenia, both increase – in both cases as compared to baseline placebo or control. The explanation for this pattern of results, articulated by our computational model, is based on the idea that a bias favouring bottom-up over top-down processing can be triggered by changing different precision parameters, which can give rise to opposite effects in specific aspects of the neural dynamics. This observation, we argue, opens the door to more nuanced analyses for future studies.
The increased transfer entropy from frontal to posterior brain areas observed under schizophrenia could be naively interpreted as supporting increased top-down regulation; however, neither the empirical analysis nor the computational model warrant this conclusion. Transfer entropy simply indicates information flow and is agnostic about functional role. Our model-based analyses illustrate how aberrant Bayesian inference in which bottom-up influences become stronger can trigger either an increase or a decrease in transfer entropy from frontal to posterior regions, depending on which precision terms are involved. An interesting possible explanation for this divergence between mechanisms and TE is provided by recent results that show that TE is an aggregate of qualitatively different information modes Mediano et al. (2021b). Future work may explore if resolving TE into its finer constituents might provide a more informative mapping from observed patterns to underlying mechanisms, as well as how these quantities may be related to other consciousness-related electrophysiology metrics Nilsen et al. (2020) ; Sitt et al. (2014).
Taken together, these findings suggest that conceiving the bottom-up vs top-down dichotomy as a single-dimensional trade-off might be too simplistic, and that multi-dimensional approaches could shed more light on this issue. In particular, our results show how such a simplistic view fails to account for the rich interplay of similarities and differences between schizophrenia and psychosis.
4.3 Limitations and future work
While our empirical and modelling results agree with the canonical PP account of psychosis Sterzer et al. (2018), some reports have suggested a stronger influence of priors over sensory signals – especially in some cases of hallucinations Alderson-Day et al. (2017); Cassidy et al. (2018); Powers et al. (2017). It is important to remark that the ‘strengthened prior’ interpretation put forward by these task-based studies cannot be accounted for by the simple computational modelling developed here. At the same time, the resting-state model presented here relates spontaneous activity, and our results cannot be directly generalised to task-based settings. Future work may investigate whether a richer hierarchical model is able to reproduce both rest and task data, bridging between these results and prior work.
Regarding the empirical analyses, it is important to note that our analyses are subject to a few limitations due to the nature of the data used. First, the analyses used only 60 AAL sources across 5 ROIs (due to the spatial resolution limitations of EEG), and therefore may neglect potential PP effects that may exist at smaller spatial scales. In addition, the studies on both drugs and schizophrenia used different imaging methods (MEG vs EEG), sampling rate, and experiment designs (within vs between subjects), complicating direct comparisons. Finally, future work should examine how power spectra across the different conditions relate to the findings presented, in terms of both their effect on LZ Mediano et al. (2021c), and their relationship with top-down and bottom-up signalling, for example using band-limited Granger causality Bastos et al. (2015), as well as how directed functional connectivity measures relate to undirected measures such as mutual information and coherence Barnett et al. (2020).
Similarly, while the measures discussed here capture significant differences between schizophrenia patients and healthy controls, more work needs to be done to further characterise the differences within the schizophrenia spectrum, which features a heterogenous array of symptoms and states, e.g. at different phases of the so-called ‘psychotic process’ Brouwer and Carhart-Harris (2021). A crucial part of this research will be to analyse the clinical symptom scores of the patients and their relationship to both medication and neural dynamics, which was not possible here due to the lack of appropriate metadata on the dosage of antipsychotics. In our preliminary analysis we use the number of antipsychotics as a proxy to the missing dosage data. This proxy measure was found to be negatively correlated with the positive symptom scores of the PANSS scale (see Appendix C) among the schizophrenia patients, suggesting that the symptom scores are confounded by antipsychotic use - but without dosage data it is difficult to disentangle this effect from potential confounds. An interesting possibility is that the neural underpinnings of positive and negative symptoms could be different Fletcher and Frith (2009), and investigating these differences may yield further insight into schizophrenia itself and its relationship with the psychotomimetic drug states. Moreover, both schizophrenia and drug-induced states can be conceived of as dynamic states of consciousness, comprised of several sub-states and/or episodes with hallucinations, delusions and negative symptoms varying widely between and within individuals. Future studies could explore these finer fluctuations in conscious state Mediano et al. (2021a), as well as what features or episodes overlap in the neural and psychological levels between psychotomimetic drug states and schizophrenia.
Finally, recall that (as described in Section 2.1) we used the number of antipsychotic medications being used by each patient as a proxy measure for their medication load. This is a significant oversimplification, as it ignores the specifics of all drugs and their dose-response effects, and future work with richer datasets should explore in more detail the effects of each particular medication – which would potentially bring more nuance to these analyses. Also, the models used for statistical analysis (as per Section 2.3) are linear and may not capture possible non-linear dependencies between antipsychotic use and its effect on neural dynamics (in our case, LZ or TE). Bearing this caveat in mind, our tentative results in the schizophrenia group suggest that antipsychotic use may bring the patients’ neural dynamics closer to the range of healthy controls. This finding should be replicated with more detailed analyses involving dosage information and clinical symptom scores, and, if robust, could potentially be used to investigate the mechanism of action of current antipsychotic drugs.
4.4 Final remarks
In this paper we have contrasted changes in brain activity in individuals with schizophrenia (compared to healthy controls) with changes induced by a classic 5-HT2A receptor agonist psychedelic, LSD, and an NMDA antagonist dissociative, ketamine (compared to placebo). Empirical analyses revealed that both schizophrenia and drug states show an increase in neural signal diversity, but they have divergent transfer entropy profiles. Furthermore, we proposed a simple computational model based on the predictive processing framework Rao and Ballard (1999) that recapitulates the empirical findings through distinct alterations to optimal Bayesian inference. In doing so, we argued that both schizophrenia and psychotomimetic drugs can be described as inducing a stronger “bottom-up” influence of sensory information, but in qualitatively different ways, thus painting a more nuanced picture of the functional dynamics of predictive processing systems. Crucially, the proposed model differs from others in the literature in that it is a model of resting-state (as opposed to task-based) brain activity, bringing this methodology closer to other approaches to neuroimaging data analysis based on complexity science Turkheimer et al. (2021).
Overall, this study illustrates the benefits of combining information-theoretic analyses of experimental data and computational modelling, as well as of integrating datasets from patients with those from healthy subjects. We hope our findings will inspire further work deepening our understanding about the relationship between neural dynamics and high- level brain functions, which in turn may accelerate the development of novel, mechanism-based treatments to foster and promote mental health.
Supplementary Material
Supplementary Material
Acknowledgements
H.R. is supported by the Imperial College President’s PhD Scholarship. P.M. and D.B. are funded by the Wellcome Trust (grant no. 210920/Z/18/Z). F.R. is supported by the Ad Astra Chandaria foundation. C.T. is funded by the Psychedelic Research Group, Imperial College London. R.C.-H. is the Ralph Metzner Chair of the Psychedelic Division, Neuroscape at University of California San Francisco. A.K.S. is supported by the European Research Council (Advanced Investigator Grant 101019254.) This work was supported in part by grant MR/N0137941/1 for the GW4 BIOMED MRC DTP, awarded to the Universities of Bath, Bristol, Cardiff and Exeter from the Medical Research Council (MRC)/UKRI (S.B.). The LSD research described here was supported by the Beckley Foundation and Walacea Crowd Funding campaign. We thank Imperial College London’s Research Computing Service for the computing facilities. Parts of this work were carried out using the computational facilities of the Advanced Computing Research Centre, University of Bristol.
Data and code availability statement
Raw MEG data for the LSD and KET conditions (and their respective placebo controls) is available in the Harvard Dataverse repository. EEG data from schizophrenia patients and healthy controls was obtained from the BSNIP study, accessed via the NIMH Data Archive. Open-source implementations are available online for all tools used in the study, including LZ (link), TE (link), and NBS (link).
Fig. 1 Regions of interest (ROIs) represented on the MNI-152 standard template.
Each ROI is comprised of several regions of the AAL atlas, as per Table D.2.
Fig. 2 Graphical illustration of the predictive processing model.
The activity of a high-level neural population is represented as a prediction s^t and the activity of a low-level population as a prediction error ξt. The internal states of the high- and low-level regions are captured by st and ht, respectively.
Fig. 3 Increased signal diversity in subjects under the effects of psychotomimetic drugs and in schizophrenia patients.
LZ changes are widespread across all ROIs in the three datasets. For the schizophrenia dataset, LZ values shown are corrected for age, gender, and the number of antipsychotic medications taken by each patient using a linear model.
Fig. 4 Lower information transfer under LSD and ketamine but higher information transfer for schizophrenia patients.
Transfer entropy (TE) shows a strong widespread decrease in subjects under the effect of LSD or ketamine (KET), compared to a placebo. Conversely, schizophrenia (SCZ) patients show an increase in TE with respect to controls (CTRL), especially from the frontal region to the rest of the brain (controlling for age, gender and antipsychotic use). Links shown are significant after multiple comparisons correction.
Fig. 5 A computational model based on predictive processing principles reproduces experimental findings in the LSD, ketamine and schizophrenia datasets
(a) By increasing the sensory precision (for schizophrenia; blue), or reducing the prior’s precision (for LSD and KET; orange) by a given ‘noise’ factor η, the model can reproduce the experimental findings of (b) increased in LZ in both conditions, and (c) opposite changes in TE in both conditions, compared to a baseline (grey). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
2 Note that in the simple scenario of Eqs. (3), with a single level and Gaussian distributions, inference is in fact exact. In larger or more complicated models inference is often carried out only approximately Gershman (2019).
Ethics statement
The LSD study Carhart-Harris et al. (2016b) was approved by the National Research Ethics Service committee London-West London and was conducted in accordance with the revised declaration of Helsinki (2000), the International Committee on Harmonization Good Clinical Practice guidelines, and National Health Service Research Governance Framework. Imperial College London sponsored the research, which was conducted under a Home Office license for research with Schedule 1 drugs. The KET study Muthukumaraswamy et al. (2015) was was approved by a UK National Health Service research ethics committee. The schizophrenia data was collected by the B-SNIP consortium Tamminga et al. (2013).
Credit authorship contribution statement
Hardik Rajpal: Conceptualization, Methodology, Software, Formal analysis, Writing – original draft, Writing – review & editing. Pedro A.M. Mediano: Conceptualization, Methodology, Software, Formal analysis, Writing – original draft, Writing – review & editing. Fernando E. Rosas: Conceptualization, Writing – original draft, Writing – review & editing. Christopher B. Timmermann: Writing – review & editing. Stefan Brugger: Data curation, Writing – review & editing. Suresh Muthukumaraswamy: Data curation, Writing – review & editing. Anil K. Seth: Writing – review & editing. Daniel Bor: Writing – review & editing. Robin L. Carhart-Harris: Data curation, Writing – review & editing. Henrik J. Jensen: Supervision, Writing – review & editing.
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PMC009xxxxxx/PMC9384215.txt |
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ACS Agric Sci Technol
ACS Agric Sci Technol
as
aastgj
ACS Agricultural Science & Technology
2692-1952
American Chemical Society
10.1021/acsagscitech.2c00084
Article
Highly Efficient and Reproducible Genetic Transformation in Pea for Targeted Trait Improvement
Kaur Rajvinder ‡
Donoso Thomas †
Scheske Chelsea ‡
Lefsrud Mark ‡
https://orcid.org/0000-0002-1139-9251
Singh Jaswinder *†
† Department of Plant Science, McGill University, 21111 Rue Lakeshore, Sainte-Anne-de-Bellevue, Quebec, Montreal H9X 3V9, Canada
‡ Department of Bioresource Engineering, McGill University, 21111 Rue Lakeshore, Sainte-Anne-de-Bellevue, Quebec, Montreal H9X 3V9, Canada
* Email: jaswinder.singh@mcgill.ca.
19 07 2022
15 08 2022
19 07 2023
2 4 780787
© 2022 The Authors. Published by American Chemical Society
2022
The Authors
https://creativecommons.org/licenses/by-nc-nd/4.0/ Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/).
A reproducible tissue culture protocol is required to establish an efficient genetic transformation system in highly recalcitrant pea genotypes. High-quality callus with superior regeneration ability was induced and regenerated on optimized media enriched with copper sulfate and cytokinins, 6-benzylaminopurine and indole-3-acetic acid. This successful regeneration effort led to the development of a highly efficient transformation system for five pea genotypes using immature and mature seeds. The new transformation protocol included the addition of elevated glucose and sucrose concentrations for cocultivation and inoculation media to improve callus induction and regeneration, thus resulting in consistent transformation frequencies. Using the Agrobacterium strain AGL1, a transformation frequency of up to 47% was obtained for the pea genotype Greenfeast, using either of two different selection marker genes, PAT or NPT, sourced from two different vectors. Sixty-two transgenic pea events were able to survive kanamycin and phosphinothricin selection. A total of 30 transgenic events for Greenfeast, 15 for CN 43016, 9 for snap pea, and 5 for CN 31237 are reported herein. Two additional transgenic events were recovered from particle gun bombardment experiments. Quantitative RT-PCR analysis confirmed the transgenic status of pea plants, indicating elevated expression of relevant genes cloned into the transformation constructs.
pea
transformation
tissue culture
agrobacterium
biolistic
transgene
Natural Sciences and Engineering Research Council of Canada 10.13039/501100000038 CRDPJ 477365-14 Lefsrud Seed and Processors Ltd. NA NA Harbin Seed Farm Ltd. NA NA Agrocentre Belcan Inc. NA NA document-id-old-9as2c00084
document-id-new-14as2c00084
ccc-price
==== Body
pmcIntroduction
Plant-based diets have risen in popularity in recent years, and evidence suggests that swapping meat servings with protein-rich crops could shape the future of food systems and the environment worldwide.1 Field pea (Pisum sativum) belongs to the Leguminosae family and it is an important cool season grain legume of the Galegoid clade, which also includes lentils (Lens culinaris), chickpeas (Cicer arietinum), and faba beans (Vicia faba). These legumes are good sources of dietary protein and rich in other nutrients such as isoflavonoids, iron, and dietary fibers2,3 that are considered beneficial to human health through anticancer and other health-promoting activities.4 Legumes provide major benefits to cropping systems and the environment because of their ability to perform symbiotic nitrogen fixation in their root nodules, which enhances both soil fertility and water-use efficiency.4−6 Given that pulses can be utilized as human food and animal feed while improving soil and environmental health, they are considered sustainable crops. Those with high protein content are currently being exploited as alternatives to meat worldwide.3,4
Canada is the largest producer and exporter of dry pea, with an annual production of 4.9 million tonnes.7 However, field pea production is constrained by diseases and pests, resulting in significant economic losses.8 Improvement by conventional breeding is as old as agriculture itself, but, until recently, it has been limited to agronomic traits to achieve the desired results. Crop breeding faces a number of challenges due to undesirable genetic linkages and limited knowledge exists for the genetic basis of disease resistance. Genetic transformation has additionally been used to increase crop productivity with the introduction of foreign genes,4 yet gene transfer to higher plants requires robust tissue culture and transformation systems for the generation of viable and fertile transgenic plants. Efficient genetic transformation systems play a key role in molecular breeding and understanding the genetic control of physiological and developmental traits. Nevertheless, field pea is a known recalcitrant plant species for genetic manipulation.9
Pea tissue culture, including callus induction and plant regeneration, has been successful with certain explants. However, genetic transformation of pea genotypes remains limited, likely due to a lack of effective protocols and appropriate plant materials. Earlier efforts using different explant types such as protoplasts, cotyledon, epicotyl, and embryonic segments, yielded low-transformation efficiencies.9−13 Puonti-Kaerlas et al.10 were the first to generate fertile transgenic plants in two pea genotypes, Stivo and Puget, using hygromycin as a selection agent in the medium, and the obtained plants were tetraploids. Three cultivars, Greenfeast, Rondo, and Puget, were transformed using the BAR gene with phosphinothricin (PPT) as the selection agent, with transformation frequencies of 1.5 and 2.5%.9,12 More cultivars, Bolero, Hadlee, Courier, and Crown, were transformed using kanamycin selection, and the transformation frequency ranged from 0.9 to 3.4%.14 Two other cultivars, Crown and Lincoln, were transformed using hygromycin selection with an efficiency of 0.6 to 2.8%.15,16
Since the work of Gregor Mendel, pea has provided a superb understanding of genetic traits.17 Access to its completely sequenced genome18 will certainly expedite our understanding of the genetic and molecular bases of agronomically important traits while enabling progress toward the pea crop improvements to nutritional value and food security.18 However, questions still remain on how to exploit sequenced genes for specific trait improvements in pea, particularly through genetic transformation. The absence of an efficient genetic transformation system has hampered the progress in pea genetics and functional genomics. Therefore, refinement in genetic transformation is required to revolutionize pea breeding with advanced gene-editing methods such as CRISPR-Cas.
The present study aimed to develop a highly efficient transformation system in field pea. Modifying media formulations could substantially improve pea transformation, as previously observed in response to increased levels of copper and 6-benzylaminopurine (BAP) in the medium used for cereals transformation.19,20 Herein, we present the optimization of all media required, from callus induction to rooting medium and other transformation variables, to develop an efficient and reproducible system in pea.
Materials and Methods
Plant Materials
Six pea genotypes were used in this study: CN 42819, CN 43016, CN 31237, Greenfeast (TMP 25969), Rondo (TMP 21877), and Snap. These genotypes were selected on the basis of seed color, as light-colored seeds produce fewer phenolic compounds, a desirable trait for tissue culture. The seed coat color was light green for all the selected genotypes, and all genotypes had similar flowering times. Two genotypes, CN 42819 and Rondo, produced round seeds while all others produced wrinkled. Seeds were acquired from Plant Gene Resources of Canada (Saskatoon, SK, Canada). All pea donor plants were grown in pots filled with G6 Agro Mix (Teris, Laval, QC, Canada) and a sand mixture (10:1) in the research greenhouse at McGill University’s Macdonald Campus. Every 2 weeks, plants were fertilized (20:20:20) with all-purpose fertilizer (Plant Products Inc., Leamington, ON, Canada). Temperatures were kept at approximately 25 °C during the day and 20 °C at night. Plants were grown at a light intensity between 1000 and 1400 μmol/m2 s with a 16 h photoperiod.
Green Callus Induction and Its Further Proliferation
Seed pods were collected when grown to maximum size but still green. Immature green seeds were isolated from pods and surface sterilized with 20% bleach for 20 min, followed by 3–4 washes with autoclaved water. Seeds were cut into two halves. The embryonic axis and cotyledon were used for callus induction. Callus was initially induced on different media (D′, CIM, and P1) as described previously9,21,22 and from mature seeds. Callus initiated on D′ and CIM media was further proliferated and maintained on DBC3 media by moving the calli to fresh media every 2 weeks.21 Similarly, callus initiated on P1 was further maintained on P1. The calli were maintained under dim light conditions (approximately 10–30 μmol/m2 s and a 16-h photoperiod). Calli were additionally induced on a new medium, “KREG”. The composition of all media used in this study is described in Table 1. Callus was multiplied until enough explant was acquired for genetic transformation and regeneration experiments.
Table 1 Composition of Media for Tissue Culture
D′21 4.4 g/L MS salts (Phytotechnology Laboratories, Shawnee Mission, KS, catalogue number M524), 0.25 g/L myo-inositol, 1.0 g/L casein hydrolysate, 1.0 mg/L thiamine HCL, 2.0 mg/L 2,4-D, 30 g/L maltose, 0.69 g/L l-proline, 0.025 mg/L cupric sulfate, 3.5 g/L phytagel, pH 5.8
CIM22 4.4 g/L MS salts, 0.5 mg/L nicotinic acid, 0.5 mg/L pyridoxine HCl, 1.0 mg/L thiamine HCl, 0.1 g/L myoinositol, 2.0 mg/L 2,4-D, 30 g/L sucrose, 3.5 g/L phytagel, pH 5.8
P19 4.4 g/L MS salts, 1.0 mg/L nicotinic acid, 1.0 mg/L pyridoxine HCl, 10.0 mg/L thiamine HCl, 0.1 g/L myoinositol, 30g/L sucrose, 2 mg/L BAP and 2 mg/L NAA, 3.5 g/L phytagel, pH 5.8
P29 P1 medium with changes, 4.5 mg/L BAP and 0.02 mg/L NAA
DBC321 4.4 g/L MS salts, 0.25 g/L myo-inositol, 1.0 g/L casein hydrolysate, 1.0 mg/L thiamine HCL, 1.0 mg/L 2,4-D, 30 g/L maltose, 0.69 g/L l-proline, 1.22 mg/L cupric sulfate, 0.5 mg/L BAP, 3.5 g/L phytagel, pH 5.8
LREG23 4.4 g/L MS salts, 1.0 mg/L nicotinic acid, 1.0 mg/L pyridoxine HCl, 10.0 mg/L thiamine HCl, 0.1 g/L myoinositol, 30g/L sucrose, 1 mg/L BAP, 1 mg/L IAA and 0.16 mg/L CuSO4, 3.5 g/L phytagel, pH 5.8
KREG 4.4 g/L MS salts, 0.5 mg/L nicotinic acid, 0.5 mg/L pyridoxine HCl, 1 mg/L thiamine HCl, 30 g/L sucrose, 0.16 mg/L CuSO4, 1 mg/L BAP and 1 mg/L IAA, 0.5 g/L phytagel, pH 5.8
KREG-OM KREG medium with 0.2 M Sorbitol, 0.M Mannitol, no sucrose
B59 4.4 g/L MS salts, 1.0 mg/L nicotinic acid, 1.0 mg/L pyridoxine HCl, 10.0 mg/L thiamine HCl, 0.1 g/L myoinositol, 10 g/L glucose, 20 g/L sucrose, 2 g/L MES, with 100 μM acetosyringone pH 5.8
KREG-IN KREG medium with 68.5 g/L sucrose, 36 g/L glucose, with 100 μM acetosyringone added before use
KROOTING KREG medium without BAP with 1 mg/L, NAA, 1 mg/L IBA
Standardization of Regeneration of Plantlets from the Green Callus
To ensure the quality of callus for regeneration, 10 pieces of 2-month old green tissues were placed on three different regeneration media, LREG,23 P2,9 and KREG, and exposed to higher light intensity (45–55 μmol/m2 s). KREG media was used as the sole media for callus induction to the regeneration of the full plants with the different pea genotypes.
Rooting media (KROOTING) was optimized by modifying KREG with respect to MS salt strength and growth hormone (IBA, IAA, and NAA) concentrations. Four different rooting media formulated and investigated: LMS, L1/2MS, PMS, and P1/2MS (P with BAP). Two media, L1/2MS and P1/2MS, contained MS salts at half strength, the others contained MS salts at full strength, with and without 1 mg/L BAP. For root induction, well-formed shoots were sliced off the callus and planted in solidified rooting media (KROOTING, Table 1) in plastic sundae cups. After approximately 4–5 weeks, plantlets with well-established roots were transferred to G6 Agro Mix potting mix and grown in a CMP4030-Conviron growth chamber with 23 °C day/18 °C night under 16-h photoperiod. Plants were covered for 4–5 days with plastic domes to acclimatize and grow there until maturity at a light intensity of 200–250 μmol/m2 s.
Gene Constructs and Their Confirmation
Two binary vectors pJP3502 and pJP367924 that contain the DGAT1 and WRI1 genes from Arabidopsis thaliana as well as OLEOSIN from Sesamum indicum for co-expression were kindly provided by Dr. Surinder Singh (CSIRO Canberra, Australia). Genes in pJP3502 are driven by leaf-specific promoters and a double enhancer region 35S promoter controls the NPTII kanamycin resistance gene. Genes in pJP3679 are driven by seed-specific promoters and a double enhancer region 35S promoter controls the PAT bialaphos and PPT resistance gene. Both plasmids were cloned into Escherichia coli (DH5α cells) and grown overnight at 37 °C in Luria–Bertani (LB) media with 100 mg/L kanamycin to isolate DNA for bombardments. The plasmids were confirmed by restriction digestion with enzymes and PCR amplification.
Standardization of Appropriate Selectable Markers in Genetic Transformation
Calli (25–30 pieces each) from all pea genotypes were subcultured on DBC3 media with 2 mg/L PPT, DBC3 with 20 mg/L hygromycin and DBC3 with 20 and 40 mg/L kanamycin. Concentrations of PPT and kanamycin were further investigated using different media (LREG, P2, and KREG) with PPT (2.5, 5.0, and 7.5 mg/L) and kanamycin (50, 100, and 150 mg/L).
Particle Gun Bombardment
Five pea genotypes (CN 43016, CN 31237, Snap, Greenfeast, and Rondo) were used for particle gun bombardment. Highly regenerative green calli proliferated on KREG media for pea were placed on osmotic media (KREG-OM), KREG without sucrose and enriched with 0.2 M (36.4 g/L) mannitol and 0.2 M (36.4 g/L) sorbitol prior to bombardment for 4 h. Approximately, 25 callus pieces were used per plate, and 4–6 plates were used for each pea genotype and each plasmid. A total of 25 μg plasmid DNA was coated on 0.6 μm gold particles (INBIO GOLD, Hurstbridge, Victoria, Australia) and treated with spermidine and calcium chloride prior to bombardment. Bombardment was carried out with a PDS-1000He biolistic device (Bio-Rad, Hercules, CA) at 1100 psi.20 Bombarded calli were moved to KREG 16–18 h after bombardment and grown at 24 °C under higher intensity light (45–55 μmol/m2 s). After 6–7 days, tissues were transferred to the KREG medium with selection. The selection system was 5–10 mg/L PPT (Phytotech Labs, Lenexa, KS, US) in the KREG regeneration medium for pJP3679 and 100–150 mg/L kanamycin (Bio Basic, Markham ON, Canada) for pJP3502. Calli were subcultured at 2–3 weeks intervals on the same selective medium. After 3–4 selection rounds, surviving shoots were placed on rooting media with 50–100 mg/L kanamycin. Plantlets with developed roots were transferred to the soil and grown under similar conditions as described above.
Agrobacterium Transformation
Five pea genotypes (CN 43016, CN 31237, Greenfeast, Rondo, and Snap pea), two constructs (pJP3679 and pJP3502), and the Agrobacterium tumefaciens strain AGL1 were used for Agrobacterium transformation experiments. Agro-transformation was also performed with a GFP reporter gene containing plasmid (pActinIntGFPnos25). Seeds were sterilized and embryonic axes were excised from cotyledons to use as explants. The root part was cut off and the remaining region (shoot) was used for agro-infections. Mature seeds were soaked in water for 1–2 days before inoculation. AGL1 was grown overnight at 28 °C in the LB medium supplemented with 100 mg/L kanamycin with shaking at 200 rpm. The bacterial culture was centrifuged at 5000 rpm for 5 min and the pellet was suspended (A600 = 0.4) in an inoculation medium.
Agrobacterium transformation was performed by two different methods. In initial experiments, embryonic axes were sliced longitudinally into three to four segments and plated on a B5-H medium, after inoculation with the A. tumefaciens suspension in B5-I.9,26 Both media (B5-I and B5-H) were supplemented with 100 μM acetosyringone after autoclaving. After 4 days of cocultivation, explants were subcultured on the callus induction medium P1, after washing with sterile water containing 300 mg/L Timentin to remove overgrown AGL1.9
In the majority of agro-infection experiments, the bacterial pellet was suspended in the inoculation liquid medium, KREG with 36 g/L glucose, 68 g/L sucrose, and 100 μm acetosyringone without a solidifying agent (KREG-In). The embryonic axis was cut into two cross segments and plated on the KREG medium. Sliced segments were immersed in the bacterial suspension. For each agro-inoculation, embryonic regions of approximately 20 seeds/genotype were soaked in the Agrobacterium culture for 15 min and placed on co-cultivation agar media, KREG-Co (solidified KREG-In), for 2–3 days at 24 °C under light. After 2–3 days of co-cultivation, the segments were transferred to KREG media containing the agro-inhibiting antibiotic Timentin (150 mg/L). After 6–7 days, initiating young shoots were removed and subcultured along with calli on the same media amended with Timentin and 3–4 mg/L PPT for pJP3679, and kanamycin (100–150 mg/L) for pJP3502. Shoots surviving after two to three rounds of selection were subcultured on the rooting medium with kanamycin (50–100 mg/L) and 3 mg/L PPT for pJP3502 and pJP3679 putative events, respectively. The surviving plants with roots from different transformation events were transferred to pots. In pots, they were grown in a CMP4030-Conviron Growth Chamber at 23 °C day temperature and 18 °C night temperature with a 16-h photoperiod and 50–60% relative humidity. Most plantlets transferred to soil in a controlled environment survived.
Characterization of Transgenic Events
PCR Analysis
DNA from leaf samples of the putative transgenic plants was extracted and analyzed by PCR to verify the presence of selection marker genes PAT (in pJP3679) and NPT (in pJP3502). This amplification was done using either a Phire Plant Direct PCR Master Mix (F-160L Thermo Fisher Scientific, Baltics UAB, Vilnius, Lithuania) or Go Taq G2 Green Master Mix (M7822, Promega, Madison, WI, US) according to the manufacturer’s instructions. The PCR reaction volume was 15 μL, including 7.5 μL Phire master mix, 0.25 μM both forward and reverse primers (PATF and PATR), 0.5 μL DNA obtained with dilution buffer, and 6 μL molecular biology grade water. Amplification conditions for the PAT gene were 98 °C for 5 min, followed by 40 cycles at 98 °C for 5 s, 58 °C for 5 s, and 72 °C for 20 s, with final extension on 72 °C for 1 min. Temperatures and timing were controlled using a Thermal cycler C1000 from Bio-Rad. The NPT gene was amplified with primers NPTII-11F and NPTII-488R under similar conditions, except that annealing was done at 56 °C. PCR amplifications were also conducted using the reverse primer JIPR11 with PATF and NPTII-11F as forward primers. Sequences of the PCR primers are listed in Table 2. PCR products were analyzed by gel electrophoresis in 0.8% agarose gels.
Table 2 Primers Used for PCR and qRT-PCR Amplification
primer sequence 5′ to 3′
PATF GACCTGCTACTGCTGCTGATATGG
PATR TCGTTAGGGAGTCCGATAACAGC
NPTII-11F AAGATGGATTGCACGCAGGT
NPTII-488R TGATGCTCTTCGTCCAGATC
JIPR11 GCGCGCGATAATTTATCC
HMGF ATGGCAACAAGAGAGGTTAA
HMGR TGGTGCATTAGGATCCTTAG
RTDGATF GCTGGTGTTACTACCGTTAC
RTDGATR CTGCTGGAGAACCAGAAATC
RTOLEOF GAGAACTGCTCAGCCCTATC
RTOLEOR TGTGCTTTCCGGACTTACTC
HMGIIIF AGGGGTAGGCCGAAGAAGAT
HMGIIIR TGAGGCTTCACCTTAGGAGG
qRTDGATF CTAACCTCGCTGGTGATAAC
qRTDGATR TGGCCTGTAGGTGAAAGTAG
qRTOLEOF GGTTCCAGCAAGAGTAAGTC
qRTOLEOR GGCTCCACATCTTCAACTTC
PPTF ATACAAGCACGGTGGATGG
PPTR TGGTCTAACAGGTCTAGGAGGA
In the case of Go Taq G2 Green Master Mix, PCR reactions contained 50 ng (1 μL) genomic DNA, 0.5 μM each primer (forward and reverse), 10 μL Master Mix, and ddH2O to reach a final volume of 20 μL. PCR was performed with an initial denaturation at 95 °C for 2 min, followed by 35 cycles of 95 °C for 30 s, 58 °C for 45 s, and 72 °C for 45 s, with a final extension on 72 °C for 5 min.
Expression Analysis
Primary screening of the transformation events obtained with pJP3679 was conducted by leaf painting with the Liberty herbicide (20% glufosinate ammonium; 0.25% v/v with 0.1% Tween) as described previously by Singh et al.25 For gene expression analysis, total RNA was isolated from young leaves using the TRIzol (Ambion RNA by life technologies Carlsbad, Ca, US) method. RNA integrity and concentration were assessed by spectrophotometry with ND1000 (NanoDrop, Wilmington, DE) and a total of 1 μg RNA was treated with DNase I (Catalogue number M198A, Promega, Madison, WI, US) prior to cDNA synthesis using the AffnityScript cDNA Synthesis Kit (Agilent technology, Cedar Creek, Texas, US). RTPCR and qRTPCR were carried out to detect the expression of DGAT, OLEO, and WRI genes as described by Singh and Singh.27 HMG-I/Y was used as a reference gene. For RTPCR primers, HMGF and HMGR were used to amplify 350 bp and 570 bp fragments (DNA/cDNA).28 RT-PCR amplification with cDNA was carried out for the DGAT gene using primers RTDGATF and RTDGATR, and the OLEO gene was amplified with primers RTOLEOF and RTOLEOR (Table 2). 12 μL of the RT-PCR products were analyzed by electrophoresis on 1.2% agarose gels.
Primer pairs qRTDGAT, qRTOLEO, and PPT (Table 2) were used to amplify the DGAT, OLEO, and PPT genes to analyze quantitative transgene expression in the transgenic plants. To normalize expression of these transgenes, the pea housekeeping gene, HMG-I/Y was used as a reference with primers HMGIII-F and HMGIII-R amplifying a 164-bp product for the quantitative expression.28
qRT-PCR was performed in optical strip tubes using the Mx3000 qPCR system (Stratagene, US). qRT-PCR was performed in optical strip tubes using the Mx3000 qPCR system (Stratagene, US). The qRTPCR mixture (20 μL) contained 0.8 μL from 10 μm stock of both forward and reverse primers, 10 μL of Advanced qPCR Master Mix (Cat: 800-435-UL Wisent Inc., St-Bruno, QC, Canada), and 2 μL cDNA of each sample. Amplification conditions were an initial denaturation step at 95 °C for 2 min, followed by 40 cycles of 95 °C for 5 s, 60 °C for 30 s, and 72 °C for 30 s, with a final melting curve analysis step of 95 °C for 1 min, 60 °C for 30 s, and 95 °C for 30 s. The qRTPCR data were automatically collected and analyzed. The relative level of gene expression was analyzed using the 2–ΔΔCq method.29 Expression values were represented as the average values of three biological repeats and error bars represented standard deviations. Asterisks indicated significant differences revealed by the Student’s t-test using JMPpro 16 software.
Results
Standardization of Tissue Culture Process
For any successful tissue culture experiment to produce reliable embryogenic callus, the choice of an appropriate explant is extremely crucial. Callus initiation was observed 7–10 days from when cotyledons and embryonic regions of immature/mature seeds were excised for all the genotypes used in this study (Figure 1A,B). However, the growth and proliferation behavior of calli from different genotypes was dramatically different. Calli for genotype CN 42819 was light green in color and the fastest growing. Calli from all other genotypes were slow in growth, with a compact nodular shape and a darker green color (Figure 1C). To check regenerability, calli obtained from different genotypes were transferred to known regeneration media including LREG and P2.9,23 No shoot regeneration was observed on LREG, but root production was observed (Figure 1D). With the P2 medium, very few calli did demonstrate some shoot growth (2–3 shoots per callus) in the genotype Greenfeast. We observed that calli from almost all genotypes could not survive more than 3–4 weeks on the P2 medium. These findings prompted us to identify new media components for the regeneration of pea calli and new medium, “KREG”, was formulated by modifying concentrations of vitamins and growth regulators and supplementing with copper sulfate (Table 1). Callus initiation, multiplication, and regeneration on the KREG medium was superb for all pea genotypes (Figure 1E). Once shoots emerged to 1–2 cm, they were transferred to rooting media.
Figure 1 Standardization of tissue culture steps for regeneration of field pea plants. (A) Embryonic axis attached to cotyledon from immature seeds; (B) callus initiation from cotyledon and embryonic segments on the D medium in 2–3 weeks; (C) development and proliferation of green callus on DBC3; (D) failed green callus regeneration on LREG; (E) regenerating callus on KREG; and (F) plantlets with roots on rooting media.
Different rooting media were modified by altering the composition of growth hormones and MS salt strength and then subsequently investigated. Shoots could not survive on 1/2 MS strength media. Shoots multiplied on MS media with BAP, but root initiation was hampered. Roots were successfully generated on the rooting medium with full strength MS salts without BAP (Figure 1F). The best response was observed in Greenfeast, followed by CN 43016, Rondo, and CN 31237.
Reliability of Appropriate Selectable Markers in Genetic Transformation
For any successful genetic transformation system, reliable selection markers are required for the recovery of viable transgenic plants. Three selective agents, hygromycin, kanamycin, and PPT were optimized for the pea genotypes. All green calli began to display hygromycin stress symptoms on DBC3 with 20 mg/L hygromycin within 1 week of culturing. Calli became necrotic, the media turned brown in 2–3 weeks, and calli were unable to survive. Contrarily, calli proliferated slowly on the DBC3 medium containing 40 mg/L kanamycin and 2 mg/L PPT. Concentrations of PPT and kanamycin were further optimized in LREG, P2, and KREG media by supplementing with 2.5–7.5 mg/L PPT and 50–150 mg/L kanamycin. It was noted that PPT between 2.5–5.0 mg/L and kanamycin 100–150 mg/L were effective as selective agents for all pea genotypes.
Recovery of Transgenic Events via Agrobacterium Transformation and Particle Gun Bombardment
Poor callus response was observed when longitudinally cut embryonic axes were used for inoculation and subcultured on the B5 medium. Heavy AGL1 growth was observed, which covered all segments in 4 days. Therefore, calli was unable to establish on the P1 induction medium. Interestingly, callus response was superb when embryonic segments were cross-sectioned (cut horizontally) and inoculation and co-cultivation media were supplemented with copper sulfate, glucose, and a higher concentration of sucrose in all agro-transformation experiments (Figure 2A,B). Callus proliferated faster and was highly regenerable on media enriched with copper sulfate (0.16 mg/L) in combination with cytokinins (1 mg/L BAP and 1 mg/L IAA) (Table 1), leading to the regeneration of 5–6 plantlets per putative transgenic event. This observation was made for all pea genotypes used in this transformation experiment (Figures 1E and 2D,E). Upon transfer to the regeneration medium with a selective agent and with the surviving shoots to rooting medium with selective agents, different genotypes produced transformants at varying efficiencies (Figure 2E,F). The frequency of transformed embryos with pActinIntGFPnos expressing GFP was examined 2 d, 14 d, and 28 d after co-cultivation. A negative correlation was observed between the number of GFP-expressing calli and days after co-cultivation. As expected, the size of the GFP-expressing spots became larger as the number of days increased after inoculation (Figure 2C). This expression pattern indicated the presence of stable transformation events. Sixty-two putative transgenic events in pea for both plasmids (pJP3502 and pJP3679) through agro-infection were able to survive through kanamycin and PPT selection (Figure 2E,F).
Figure 2 Agrobacterium-mediated transformation in field pea. (A) Embryonic axis of immature seeds after 2 days of inoculation with Agrobacterium strain AGL1 on KREG-CO; (B) callus initiation of inoculated embryonic on KREG after 1 week; (C) GFP expression in 4 weeks old callus; (D) selection of transgenic calli on KREG using 100–150 mg/L kanamycin; (E) transgenic plants on rooting media; and (F) healthy transgenic plant.
Transformation efforts through particle gun bombardment were successful but low transformation frequency was recorded. In different transformation experiments, where 1500 pieces from three genotypes (CN 43016, Greenfeast, and Rondo), were bombarded, only 16 calli survived through selection and only two transgenic plants from CN 43016 were obtained (Figure 2F).
Molecular Confirmation of Transgenic Events
PCR analysis confirmed the presence of the NPT and PAT transgenes in 58 pea events (T0) for 4 pea genotypes, and for 15 events in T1 to T3 generations (Figures 3, 4 and 5). Most tissues were resistant to kanamycin or PPT and the regenerated T0 plants were positively amplified for their respective genes. PCR analyses of T1, T2, and T3 plants using the same set of primers confirmed stable inheritance of the transgene. Transformation frequency varied strikingly for different pea genotypes (Table 3). The maximum number of transgenic events was obtained in Greenfeast with a transformation frequency of 47% (30 events), followed by snap peas (22%; 9 events), CN 43016 (13%; 14 events), and CN 31237 (4%; 5 events). No events were recovered in Rondo (Table 3). transformation frequency via bombardment was extremely low; only two transgenic events were confirmed in the CN 43016 pea genotype.
Figure 3 PCR amplification of the KAN gene from plants pJP3502; (A) T0 events. (B) T1 plants. Lane 1: 1 kb plus ladder; lanes 2–17: transformants; lane 18: non-transgenic control; lane 19: water; lane 20: plasmid.
Figure 4 PCR amplification of the PAT gene from Greenfeast transformed with pJP3679. (A) T0 events. Lane 1: Marker; lanes 2–16: transgenic events; lane 17: non-transgenic control; lane 18: +ve transgenic control; lane 19: water; lane 20: plasmid DNA. (B) T1 plants. Lane 1: marker; lanes 2–17: transgenic plants; lane 18: non-transgenic control, lane 19: water; and lane 20: plasmid DNA.
Figure 5 PCR amplification of the KAN gene from CN 43016 transformed with particle gun bombardment. (A) T0 events. Lane 1. Marker; lanes 2–11 transgenic events; lane 12: plasmid DNA: lane 13: non-transgenic control; lane 14: water. (B) Progeny of T1 transgenic plants. Lane 1: marker; lanes 2–15 T2 plants; lanes 16–17 T1 transgenic plants; lane 18: non-transgenic control; lane 19: plasmid DNA; and lane 20: water.
Table 3 Frequency of Transformation in Different Genotypes
genotype construct delivery system no of explants no of transgenic events transformation frequency (%)
CN 31237 agro-infection 120 5 4.1
CN 43016 agro-infection 113 15 13.2
particle gun bombardment 500 2 0.4
Greenfeast agro-infection 64 30 46.8
snap agro-infection 40 9 22.5
Transcript Abundance of Transgenes
Expression of the PAT gene using pJP3679 in transformed events and their subsequent generations was examined using the leaf paint assay with the Liberty herbicide. Plants containing the PAT gene remained healthy after herbicide treatment (Figure 6). The plasmids used to develop transgenic plants also contain the OLEO gene, in addition to the PPT selective marker. The selected T2 transgenic plants from Greenfeast were further analyzed by qRT-PCR and semi-quantitative RT-PCR. The transcript of both genes was detected in the RT-PCR. Transcript abundance for both genes were further observed by qRT-PCR in the selected T2 transgenic and non-transgenic (control) plants. Expression folds for PPT and OLEO were normalized with the HMG reference gene. Analysis of the selected transgenic events indicates a 3- to 8-fold transcript abundance of PPT and OLEO, respectively (Figure 7). The expressions of PPT and OLEO were significant different from the expression in the wild-type control (Greenfeast) using the student t-test. This analysis further verified the transgenic status of the pea genotypes.
Figure 6 Leaf paint assay of the transgenic events (pJP3679) showing Liberty herbicide tolerance, with a representative image showing the reaction of Liberty herbicide on pea leaves after 1 week. (A) Plants resistant to Basta and (B) susceptible (control).
Figure 7 Expression analysis of different genes in T2 transgenic Greenfeast plants using real-time quantitative PCR; (A) expression of selectable marker PPT; (B): expression of the DGAT gene; and (C): expression of the OLEO gene. The statistical significance of relative expression in transgenic plants and Greenfeast wild control was determined with a student t-test (*p < 0.05).
Discussion
Efforts to improve field pea through genetic modifications are hindered because of poor transformation efficiency. Choosing the best explant to produce reliable embryogenic callus and an appropriate selection strategy both play critical roles in genetic transformation. A maximum frequency of transformation of 3.4% has been reported in some pea genotypes, using embryonic regions and cotyledons as explants with the selection agents hygromycin, PPT, and kanamycin.9,14,30 Other studies that indicate a higher frequency of transgenic calli that could be selected failed to regenerate calli into transgenic plants.30 Therefore, regenerating plants from the selected calli is one of the most critical factors for obtaining transgenic plants. Since 2000, there has not been any known constructive effort made to improve transformation efficiency in pea. However, new approaches for precise manipulation, through gene-editing for crop improvement, have reinvigorated the need and potential of plant tissue culture and transformation. This study has made substantial progress in devising new media formulations for high-quality callus induction, efficient regeneration from cotyledon and embryonic segments, and efficient genetic transformation in five different pea genotypes, a plant species that is historically recalcitrant to transformation.
Shiny compact green callus with highly embryogenic structures were originally obtained on three different callus-inducing media, after culturing embryonic regions and cotyledons from five pea genotypes in a few weeks. Calli were unable to regenerate on these previously optimized media. Similar media have been successfully utilized in other crop plants, including barley, oat, and sorghum crops.21,22,31 For cereals, calli initiated and maintained on these media are highly regenerable with large numbers of shoots that can be maintained for more than a year with minimal loss in regenerability. Previous efforts for the regeneration of the callus in pea have unsuccessful32−35 or with very limited success.30 Optimization of the media for callus induction, regeneration, and rooting was critical for transformation success presented in this work.
The effective regeneration of calli in our experiments and seems dependent on new media formulation (Figures 1 and 2). This high-quality callus was highly regenerable on media enriched with copper sulfate, in combination with the cytokinins BAP and IAA (Table 1). This observation was true for all pea genotypes used for transformation in this study. The importance of BAP and NAA in legumes to increase shoot bud differentiation and regeneration has been reported previously.36 Similarly, high levels of copper present in the medium enhance the callus induction and regeneration ability in cereals.19,20 An efficient regeneration ability in different pea genotypes allowed us to perform effective and efficient genetic transformation in the field pea. High-efficiency pea transformation was obtained, between 4% (CN 31237) to 47% (Greenfeast). The number of these events in different pea genotypes includes 30 for Greenfeast, 14 for CN 43016, 9 for snap pea, and 5 for CN 31237 (Table 3).
In agro-transformation experiments, only 1–2 cross-sectioned segments of the embryonic axis were used (next to the root shoot node) for agro-infection over 3 d on the co-cultivation medium KREG-Co, followed by regeneration on KREG media. Our co-cultivation media contains higher amount of sugars (glucose and sucrose) and CuSO4, which seems crucial for calli health during agro-infection. Similar observations for callus response with high concentrations of sugars are reported in cereals.22,37,38 Agro-mediated transformation using different types of pea explants such as epicotyl segments, protoplasts, lateral cotyledonary meristems, nodulus explants, and embryonic axis, have been reported previously.9,14 However, low transformation frequency has continued to be a major hurdle.9,14,30 The reproducibility of this protocol was demonstrated by the consistently high transformation frequencies generated from embryonic regions across multiple experiments with five pea genotypes. Using this optimized protocol with Agrobacterium strain AGL1, the transformation frequency was as high as 47% for the pea genotype Greenfeast, which is substantially higher than previously reported efficiencies.12,13 Nevertheless, our transformation experiments with biolistic have limited success, where only two stable transgenic plants were generated. To our knowledge, this is the first study where particle gun bombardment has been used for transformation in field pea. Out of five pea genotypes used in the transformation experiment, only one genotype, CN 43016, was successfully transformed. This was surprising, as bombardment is considered to have less genotypic dependence;39 as such, this particle gun bombardment strategy needs further refinement.
By using modified media and honing our transformation protocol, 62 independent transgenic pea events were obtained, 15 of which were advanced to the T3 generation. PCR and qRT-PCR confirmed transgene inheritance. Although genotype selection is of the utmost importance to efficient tissue culture systems, we have established a highly efficient reproducible tissue culture system for recalcitrant pea genotypes, now available for further pea improvements through modern genetic editing tools.
This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC CRDPJ 477365–14), Lefsrud Seed and Processors Ltd, Harbin Seed Farm Ltd. and Agrocentre Belcan Inc.
The authors declare no competing financial interest.
Acknowledgments
Special thanks to Dr. Surinder Singh from CSIRO, Plant Industry Canberra, Australia for providing specific constructs. Also acknowledge the assistance of Dr. Rajiv Kumar Tripathi and Sukhjiwan K Kadoll, McGill University, for their help in QRT-PCR. The authors gratefully acknowledge proofreading of the manuscript by Dr. Sarah MacPherson.
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PMC009xxxxxx/PMC9595388.txt |
==== Front
Sports Med Health Sci
Sports Med Health Sci
Sports Medicine and Health Science
2666-3376
Chengdu Sport University
S2666-3376(22)00063-4
10.1016/j.smhs.2022.10.004
Original Article
Athletes with mild post-COVID-19 symptoms experience increased respiratory and metabolic demands: Α cross-sectional study
Stavrou Vasileios T. vasileiosstavrou@hotmail.com
ab∗
Kyriaki Astara ac
Vavougios George D. ad
Fatouros Ioannis G. e
Metsios George S. f
Kalabakas Konstantinos b
Karagiannis Dimitrios b
Daniil Zoe a
I. Gourgoulianis Konstantinos a
Βasdekis George b
a Laboratory of Cardio-Pulmonary Testing and Pulmonary Rehabilitation, Respiratory Medicine Department, Faculty of Medicine, University of Thessaly, Larissa, Greece
b The Medical Project, Prevention, Evaluation and Recovery Center, Larissa, Greece
c Department of Neurology, 417 Army Equity Fund Hospital (NIMTS), Athens, Greece
d Department of Neurology, Faculty of Medicine, University of Cyprus, Lefkosia, Cyprus
e School of Physical Education and Sport Sciences, University of Thessaly, Trikala, Greece
f Department of Nutrition and Dietetics, University of Thessaly, Trikala, Greece
∗ Corresponding author. Laboratory of Cardio-Pulmonary Testing and Pulmonary Rehabilitation, Respiratory Medicine Department, Faculty of Medicine, University of Thessaly, Larissa, 38221, Greece. vasileiosstavrou@hotmail.com
23 10 2022
6 2023
23 10 2022
5 2 106111
22 7 2022
9 10 2022
17 10 2022
© 2022 Chengdu Sport University. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd.
2022
Chengdu Sport University
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Coronavirus Disease 2019 (COVID-19) has significantly affected different physiological systems, with a potentially profound effect on athletic performance. However, to date, such an effect has been neither addressed nor investigated. Therefore, the aim of this study was to investigate fitness indicators, along with the respiratory and metabolic profile, in post-COVID-19 athletes. Forty male soccer players, were divided into two groups: non-hospitalized COVID-19 (n = 20, Age: [25.2 ± 4.1] years, Body Surface Area [BSA]: [1.9 ± 0.2] m2, body fat: 11.8% ± 3.4%) versus [vs] healthy (n = 20, Age: [25.1 ± 4.4] years, BSA: [2.0 ± 0.3] m2, body fat: 10.8% ± 4.5%). For each athlete, prior to cardiopulmonary exercise testing (CPET), body composition, spirometry, and lactate blood levels, were recorded. Differences between groups were assessed with the independent samples t-test (p < 0.05). Several differences were detected between the two groups: ventilation (V˙E: Resting: [14.7 ± 3.1] L·min−1 vs. [11.5 ± 2.6] L·min−1, p = 0.001; Maximal Effort: [137.1 ± 15.5] L·min−1 vs. [109.1 ± 18.4] L·min−1, p < 0.001), ratio VE/maximal voluntary ventilation (Resting: 7.9% ± 1.8% vs. 5.7% ± 1.7%, p < 0.001; Maximal Effort: 73.7% ± 10.8% vs. 63.1% ± 9.0%, p = 0.002), ratioVE/BSA (Resting: 7.9% ± 2.0% vs. 5.9% ± 1.4%, p = 0.001; Maximal Effort: 73.7% ± 11.1% vs. 66.2% ± 9.2%, p = 0.026), heart rate (Maximal Effort: [191.6 ± 7.8] bpm vs. [196.6 ± 8.6] bpm, p = 0.041), and lactate acid (Resting: [1.8 ± 0.8] mmol·L-1 vs. [0.9 ± 0.1] mmol·L-1, p < 0.001; Maximal Effort: [11.0 ± 1.6] mmol·L-1 vs. [9.8 ± 1.2] mmol·L-1, p = 0.009), during CPET. No significant differences were identified regarding maximal oxygen uptake ([55.7 ± 4.4] ml·min−1·kg−1 vs. [55.4 ± 4.6] ml·min−1·kg−1, p = 0.831). Our findings demonstrate a pattern of compromised respiratory function in post-COVID-19 athletes characterized by increased respiratory work at both rest and maximum effort as well as hyperventilation during exercise, which may explain the reported increased metabolic needs.
Keywords
Cardiopulmonary exercise testing
Infected with COVID-19
Respiratory work
==== Body
pmcAbbreviations
COVID-19 Coronavirus Disease 2019
CPET cardiopulmonary exercise testing
FEV1 forced expiratory volume in the 1st second
HR heart rate
MVV maximal voluntary ventilation
PASC post-acute coronavirus disease 2019syndrome
PSQI Pittsburg sleep quality index
RER respiratory exchange ratio
SARS-CoV-2 severe acute respiratory syndrome coronavirus 2
VE ventilation
VE/MVV breathing reserve, ventilation/maximal voluntary ventilation ratio
V˙O2max maximal oxygen uptake
Δchest chest circumference difference between maximal inhalation and exhalation
ΔSpO2 difference values between resting and end of test in oxygen saturation measurement with pulse oximetry
% predicted percent of predicted values
bpm beats per minute
cm centimeters
kg kilograms
kg·m-2 kilogram per square meter
km·h−1 kilometers per hour
L·min−1 liters per minute
m2 square meter
mmol·L-1 millimoles per liter
μL microliter
ml·min−1·kg−1 milliliter per minute to kilograms
Introduction
The Coronavirus Disease 2019 (COVID-19) pandemic has led to an increase in morbidity and mortality globally.1 After five pandemic waves, attention has shifted to the post-COVID-19 era, in which residual or nascent syndromes formulate the spectrum of long-COVID-19.2 Emerging data support the existence of systematic long-lasting symptoms in COVID-19 survivors, beyond the respiratory system, which is collectively described under the term of post-acute COVID-19 syndrome (PASC).3 PASC may manifest as breathlessness, impaired breathing, increased oxygen requirements, post-viral cough, cardiovascular muscular changes,4 sleep disorders, chronic fatigue, cognitive impairment,5 and sarcopenia.2,6 The multi-organ sequelae may extend up to 6 months post-COVID, making the prioritization of follow-up essential,7 regardless of comorbidity risk status and severity of illness. Along with PASC, a rather sedentary lifestyle prevailed due to local prohibiting legislation to prevent the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which worsened the disease burden.8 Recent studies have shown that even post-COVID-19 athletes, who have enhanced physical condition, as well as, no previous history of illness or comorbidities, may, face challenges upon their return to their training programs.9 In the absence of relevant data, the aim of this study was to investigate fitness indicators, along with their respiratory and metabolic profile, in post-COVID-19 athletes.
Materials and methods
Participants
Forty male professional soccer players from the Greek Super League 1 and 2 volunteered for this study and were divided into two groups: previously infected with SARS-CoV-2, but non-hospitalized (i.e. mild COVID-19) versus healthy ones (Table 1). All athletes previously infected with SARS-CoV-2 (Omicron variant), were without symptoms (e.g. chest pain, fever, runny nose, cough, sore throat, headaches, muscle pain, fatigue, etc.) and included in our study two days after polymerase chain reaction negative test. The total duration of virus positivity in athletes was (6.1 ± 1.1) days. Athletes were recruited between November 2021 to January 2022. For all athletes' inclusion criteria were: age ≥ 20 to ≤ 30 years, training age ≥ 6 years, without recent injury (> 12 months). Exclusion criteria were: the lack of medical history and recent athletes' transcripts (< 30 days),10 while for post-COVID-19 athletes were difference (Δ) value in blood oxygen saturation (SpO2) between resting and at the end of cardiopulmonary exercise testing (ΔSpO2 > 4%), hospitalization and self-reported symptoms (chest pain, fatigue and/or dyspnea).11 The study's protocol was approved by the Institutional Review Board/Ethics Committee of the University Hospital of Larissa, Greece (approval reference number: N◦ 13463). All athletes provided written informed consent, in accordance with the Helsinki declaration.Table 1 Athletes’ characteristics. Data are expressed as mean ± standard deviation (SD).
Table 1 Post-COVID-19 Healthy p value
Age years 25.2 ± 4.1 25.1 ± 4.4 0.971
Body mass kg 75.0 ± 7.6 77.7 ± 7.4 0.257
Body mass index kg·m-2 23.2 ± 1.8 23.9 ± 1.3 0.147
Body surface area m2 1.9 ± 0.2 2.0 ± 0.3 0.333
Body fat % 11.8 ± 3.4 10.8 ± 4.5 0.448
Muscle mass kg 62.4 ± 4.3 64.1 ± 3.7 0.174
Lean body mass kg 59.3 ± 4.2 60.6 ± 4.7 0.388
Total body water % 63.0 ± 3.0 62.4 ± 3.4 0.574
Δchest cm 7.0 ± 1.5 6.6 ± 1.5 0.301
FEV1 % predicted 109.2 ± 4.5 111.7 ± 5.8 0.139
PSQI score 6.1 ± 3.2 3.0 ± 1.7 < 0.001
Abbreviations: % = percentage, % predicted = percent of predicted values, cm: centimeters, COVID-19 = Coronavirus Disease 2019, FEV1 = forced expiratory volume in the 1st second, kg·m-2 = kilogram per square meter, kg = kilograms, m2 = square meter, PSQI = Pittsburg sleep quality index, Δchest = chest circumference difference between maximal inhalation and exhalation.
Data collection
The study protocol initiated with the assessment of anthropometrical characteristics (i.e. body height (Seca 700, Seca Deutschland, Hamburg, Germany), chest circumference difference between maximal inhalation and exhalation (Δchest, Seca 201, Hamburg, Germany), body composition (Tanita MC-980, Tanita Europe BV, Amsterdam, The Netherlands) and calculated the percentage of body fat (from seven skinfold points measurement, Harpenden, Baty International Ltd, Burgess Hill, UK)12 and body surface area according to Mosteller's formula.13 All participants underwent standard spirometry and lung volume measurements in the sitting position using the MasterScreen-CPX pneumotachograph (VIASYS HealthCare, Germany). For each pulmonary function test, three maximal flow-volume loops were obtained to determine forced expiratory volume in the 1st second (FEV1) according to the American Thoracic Society/European Respiratory Society guidelines.14 Prior to the procedures, all athletes answered the Pittsburgh Sleep Quality Index (PSQI) questionnaire10,15 and it was recorded in their medical history. Cardiopulmonary exercise testing (CPET) was performed on a treadmill (h/p/Cosmos, Nussdorf-Traunstein, Germany). All participants initiated the CPET at a speed of 7 km·h−1. Thereafter the speed of the treadmill increased by 1 km·h−1 every minute until volitional exhaustion was reached. Following CPET, all participants engaged in a 3-min active recovery i.e. walking on the treadmill.
Prior to testing, 2-min familiarization sessions were provided for all participants; after the end of the maximal test (start of test with 7 km per min and increase 1 km per min until 18th km per min), they performed a 3-min walking (3 km·h−1) for recovery purposes. Analysis of breathing gases (Fitmate MED Cosmed, Italy) was used for all respiratory parameters while heart rate was recorded via Polar H10 (USA).10 Predicted values for oxygen uptake at peak (V˙O2max) was calculated according to Wasserman et al.'s formula V˙O2max (mL·min-1) = (Height [cm] – Age [years]) x 20 and maximum heart rate (HR) was calculated according the formula (HRmax [bpm] = 207 − 0.7 × Age [years]).16, 17, 18 Each trial was terminated when the participant reached symptom-limited maximum exercise, which was confirmed by the presence of respiratory exchange ratio (RER) > 1.10, HR ≥ 80% of predicted HRmax, and/or plateau of oxygen consumption with increasing workload.19 A sample of 0.5 μL of peripheral blood taken from the fingertip was drawn from each participant before, at the end and the 1st minute of recovery after the CPET for the evaluation of blood lactate levels. Blood lactate concentrations were evaluated with enzymatic amperometry detection method (Lactate Scout+, EKF diagnostic, Leipzig, Germany).
All sessions were performed at The Medical Project Center (Larissa, Greece), with the environmental temperature at (22.1 ± 1.1) °C and humidity at (32.6% ± 4.1%). The evaluation of patients was performed between 11:00 a.m. to 1:00 p.m.
Statistical analysis
Data are presented as mean ± standard deviation (SD) and percentage (%) where appropriate. Data normality was assessed via the Kolmogorov-Smirnov One Sample test. Independent Samples t-Test was used to assess differences between groups (post-COVID-19 versus healthy controls). For all tests, a p-value of < 0.05 was considered statistically significant. The IBM SPSS 21 statistical package (SPSS inc., Chicago, Illinois, USA) was used for all statistical analyses.
Results
Table 1 presents athletes’ characteristics while the results of respiratory parameters during CPET are presented in Fig. 1, Fig. 2, Fig. 3. HR in the maximal effort was significantly different between the groups. COVID-19 athletes demonstrated significantly lower HR during maximal effort ([191.6 ± 7.8] bpm versus [196.6 ± 8.6] bpm, t[38] = −2.120, p = 0.041, Fig. 4) compared to the healthy group. However, mean arterial blood pressure did not reveal significant differences between groups during resting, maximal effort, or the 1st min of recovery (p > 0.05).Fig. 1 Ventilation alteration in cardiopulmonary exercise testing during resting phase, main test and recovery, between the groups. ∗p < 0.05.
Abbreviations: COVID-19 = Coronavirus Disease 2019, km·h−1 = kilometers per hour, L·min−1 = liters per minutes.
Fig. 1
Fig. 2 Breathing reserve alteration in cardiopulmonary exercise testing during resting phase, main test and recovery, between the groups. ∗p < 0.05, #p < 0.001.
Abbreviations: % = ventilation/maximal voluntary ventilation ratio, COVID-19 = Coronavirus Disease 2019, km·h−1 = kilometers per hour.
Fig. 2
Fig. 3 Ventilation to body surface area ratio alteration in cardiopulmonary exercise testing during resting phase, main test and recovery, between the groups. ∗p < 0.05.
Abbreviations: % = ventilation/body surface area ratio, COVID-19 = Coronavirus Disease 2019, km·h−1 = kilometers per hour.
Fig. 3
Fig. 4 Heart rate alteration in cardiopulmonary exercise testing during resting phase, maximal effort and recovery, between the groups. ∗p < 0.05.
Abbreviations: bpm = beats per minutes, COVID-19 = Coronavirus Disease 2019.
Fig. 4
Blood lactate concentration was also significantly different between the groups. In specific, post-COVID-19 athletes showed higher values of blood lactate concentration in resting ([1.8 ± 0.8] mmol·L-1 versus [0.9 ± 0.1] mmol·L-1, t[38] = −4.695, p < 0.001, Fig. 5), during maximal effort ([11.0 ± 1.6] mmol·L-1 versus [9.8 ± 1.2] mmol·L-1, t[38] = 2.742, p = 0.009, Fig. 5) and the 1st min of recovery ([10.0 ± 1.6] mmol·L-1 versus [8.9 ± 1.2] mmol·L-1, t[38] = 2.441, p = 0.019, Fig. 5) compared to the healthy group.Fig. 5 Blood lactate concentration alteration during the cardiopulmonary exercise testing between the groups. #p < 0.001, ∗p < 0.05.
Abbreviations: COVID-19 = Coronavirus Disease 2019, mmol·L-1 = millimoles per liter.
Fig. 5
Oxygen uptake was not significantly different between groups in the resting condition (post-COVID-19: [5.4 ± 0.6] ml·min−1·kg−1 versus Healthy [5.1 ± 0.8] ml·min−1·kg−1, p > 0.05, Fig. 6): or maximal effort (post-COVID-19: [55.7 ± 4.4] ml·min−1·kg−1, 131.4% ± 9.3% of predicted versus Healthy: [55.4 ± 4.6] ml·min−1·kg−1, 131.8% ± 9.0% of predicted, p > 0.05, Fig. 6), between groups.Fig. 6 Oxygen uptake differentiation during cardiopulmonary exercise testing between the groups.
Abbreviations: COVID-19 = coronavirus disease 2019, ml·min−1·kg−1 = milliliter per minute to kilograms.
Fig. 6
Sleep quality, as assessed by PSQI, revealed significant differences between the athlete groups with COVID-19 survivors demonstrating higher values compared to the healthy group (post-COVID-19: [6.1 ± 1.7] score versus Healthy: [3.0 ± 1.7] score, t[38] = 3.814, p < 0.001).
Oxygen saturation, self-assessed dyspnea, and leg fatigue also did not show significant differences between groups.
Discussion
To our knowledge, this is the first study to investigate differences in respiratory and metabolic parameters in post-COVID-19 athletes. Our study has indicated that, despite the non-significant differences in performance, it becomes evident that athletes surviving COVID-19 exhibit significant respiratory and musculature strain during exercise. Despite mild illness, these athletes display significant aerometric burdens, in order to achieve the same training performance, in contrast to non-infected ones. This may be due to the fact that COVID-19 transitions to long-COVID-19 regardless of the severity of the illness.20 Even in athletes, who are considered to have greater cardiorespiratory system capacity when compared to their age-matched sedentary controls, studies have reported a regression in the onset of the aerobic threshold, as well as lower V˙O2max as a result of COVID-19 infection.21,22 In our study, despite the relatively equal V˙O2max in both groups, increased respiratory work at both rest and maximum effort as well as hyperventilation during exercise, were documented. CPET was integrated with spirometry to offer a deeper understanding of lung function.
Moreover, blood lactate concentration was found significantly increased in post-COVID-19 athletes. In buffering potential hypoxia, hyperventilation is expected to occur at the expense of CO2. However, as relevant studies show, diffusing capacity of the lungs for carbon monoxide is impaired in post-COVID-19 patients.23 As the physiological process of gas exchange is hindered, hypoxia is perpetuated resulting in the recruitment of the anaerobic metabolism. The anaerobic energy pathways have higher rates of adenosine triphosphate production, but a smaller amount of total adenosine triphosphate production, compared to the aerobic ones.24 Anaerobic metabolism yields excessive levels of blood lactate concentration, disproportionately to pyruvate's (lactate/pyruvate ratio > 10).25 Therefore, we hypothesize that there exists a systematic deceleration in O2 utilization, amenable to impaired gas exchange capacity, secondary to SARS-CoV-2 infection.
Hypoxia depends on two elements: tissue-level oxidative metabolism and the supply of oxygen in the circulation (hyperventilation and tachycardia). As mentioned above, the shift to dominant-energy pathways via aerobic metabolism is delayed, while ventilatory response amplified metabolic demands and stress in post-infected athletes. However, the cardiovascular response did not yield similar differences between the two groups. One would expect that heart rate would be concomitantly increased, in line with ventilation. In a recent study, post-COVID-19 cases of previously hospitalized patients exhibited significantly increased heart rates during exercise assessment.11 Athletes with increased cardiac capacity, along with no previous history of cardiovascular pathology that could undermine ejection fraction (e.g. infection), may, indeed, require screening before returning to their prior training program. Małek et al.26 demonstrated that in a small proportion of professional post-COVID-19 athletes with mild or even asymptomatic infection, non-specific cardiac abnormalities could be identified by magnetic resonance imaging. Despite the overall low risk for cardiac involvement,27 engaging in competitive sports increases the risk of fatality and as such, guidelines for the safe return of athletes after COVID-19 infection have been published.28 It is worth noting that screening for such abnormalities should be streamlined with a personalized rehabilitation regimen for post-COVID-19 patients. Due to local restrictive legislation for COVID-19, medicine has shifted towards rehabilitation remotely supervised or even unsupervised.11,29
A consequence of the harmful effects of the SARS-CoV-2 infection, in conjunction with the aforementioned cardiorespiratory complications, is sleep disturbances. In the long-COVID-19 setting, athletes report higher scores in PSQI, suggesting the presence of persistent underlying sleep disorders. Sleep quality is essential for maximal performance, as it has been implicated in cognitive implications.30 Sleep-deprived athletes are prone to both injuries and affected perceptual ability with slower reaction times.10,31 It becomes obvious that long-COVID-19 manifests as a chronic burden, in which non-invasive means, like exercise, could be proved beneficial.32 Rehabilitation is advisable to extend to sleep hygiene intervention in the setting of holistic approaches.
Limitations, strengths, and context
Our study should be interpreted within the context of its potential limitations. The study included solely soccer athletes, whose sport combines the capability to perform in aerobic conditions for prolonged periods of time. The context and potential limitation here is that this conditioning provides a reserve against hypoxia and other noxious effects on stamina and oxygenation affected by COVID-19. The latter was reflected in our findings, indicating increased respiratory work at rest and maximum effort as well as hyperventilation. These effects would impact the performance of athletes in similar sports, and less so than others. Another caveat stemming from this is that other athletes from sports that require strength such as weightlifting would not be represented by our population. Another potential limitation is that omicron was associated with less severe respiratory illness, and thus cannot account for COVID-19 survivors infected with other variants. As a final potential limitation, the higher PSQI scores may indicate that sleep disturbances were an intermediate step in retracting from the athletes’ maximum capabilities, and thus may not be a specific effect of SARS-CoV-2.
Conclusion
In conclusion, a phenotype of post-COVID-19 implications was outlined in mild cases of previously infected athletes. The post-COVID-19 pattern was characterized by increased respiratory work at both rest and maximum effort as well as hyperventilation during exercise, which may have increased metabolic needs and mechanical stress. Such implications are not benign and require a carefully curated rehabilitation program, which could take into consideration principally the hindered oxygen supply, as well as the asymptomatic cases of myocarditis, which are gradually revealed in the post-COVID-19 era.
Submission statement
All authors have read and agree with manuscript content. In addition, as long as this manuscript is being reviewed for this journal, it will not be submitted elsewhere for review and publication.
Ethical approval statement
All participants provided informed consent, and the study's protocol was approved by the Institutional Review Board/Ethics Committee of the University Hospital of Larissa, Greece (approval reference number: N◦ 13463).
Authors’ contributions
VTS, IGF, GSM, KK and DK collected the data, VTS ran statistical analyses, VTS, KA, GDV, IGF and GSM drafted the manuscript and ZD, KIG and GB supervised the whole protocol. All authors reviewed the paper and agreed on the final form of submission.
Funding
The current research protocol was not financially supported.
Conflict of interest
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.
Acknowledgements
We thank all the participants from the Medical Project, Prevention, Evaluation and Recovery Center, for volunteering in the current research protocol.
==== Refs
References
1 Gebru A.A. Birhanu T. Wendimu E. Global burden of COVID-19: situational analyis and review Hum Antibodies 29 2 2021 139 148 10.3233/HAB-200420 32804122
2 Deer R.R. Rock M.A. Vasilevsky N. Characterizing long COVID: deep phenotype of a complex condition EBioMedicine 74 2021 103722 10.1016/j.ebiom.2021.103722
3 Carfì A. Bernabei R. Landi F. Gemelli against COVID-19 post-acute care study group. Persistent symptoms in patients after acute COVID-19 JAMA 324 6 2020 603 605 10.1001/jama.2020.12603 32644129
4 Stavrou V.T. Vavougios G.D. Boutlas S. Physical fitness differences, amenable to hypoxia-driven and sarcopenia pathophysiology, between sleep apnea and COVID-19 Int J Environ Res Publ Health 19 2 2022 669 10.3390/ijerph19020669
5 Vavougios G.D. Stavrou V.T. Papayianni E. Investigating the prevalence of cognitive impairment in mild and moderate COVID-19 patients two months post-discharge: associations with physical fitness and respiratory function Alzheimers Dement 17 suppl 6 2021 e057752 10.1002/alz.057752
6 Greenhalgh T. Knight M. A'Court C. Buxton M. Husain L. Management of post-acute covid-19 in primary care BMJ 370 2020 m3026 10.1136/bmj.m3026 32784198
7 Nalbandian A. Sehgal K. Gupta A. Post-acute COVID-19 syndrome Nat Med 27 4 2021 601 615 10.1038/s41591-021-01283-z 33753937
8 Woods J.A. Hutchinson N.T. Powers S.K. The COVID-19 pandemic and physical activity Sports Med Health Sci 2 2 2020 55 64 10.1016/j.smhs.2020.05.006 34189484
9 Hull J.H. Wootten M. Moghal M. Clinical patterns, recovery time and prolonged impact of COVID-19 illness in international athletes: the UK experience Br J Sports Med 56 1 2022 4 11 10.1136/bjsports-2021-104392 34340972
10 Stavrou V.T. Astara K. Daniil Z. The reciprocal association between fitness indicators and sleep quality in the context of recent sport injury Int J Environ Res Publ Health 17 13 2020 4810 10.3390/ijerph17134810
11 Stavrou V.T. Tourlakopoulos K.N. Vavougios G.D. Eight weeks unsupervised pulmonary rehabilitation in previously hospitalized of SARS-CoV-2 Infection J Personalized Med 11 8 2021 806 10.3390/jpm11080806
12 Santos D.A. Dawson J.A. Matias C.N. Reference values for body composition and anthropometric measurements in athletes PLoS One 9 5 2014 e97846 10.1371/journal.pone.0097846
13 Mosteller R.D. Simplified calculation of body-surface area N Engl J Med 317 17 1987 1098 10.1056/NEJM198710223171717 3657876
14 Miller M.R. Hankinson J. Brusasco V. Standardisation of spirometry Eur Respir J 26 2 2005 319 338 10.1183/09031936.05.00034805 16055882
15 Stavrou V. Vavougios G.D. Bardaka F. Karetsi E. Daniil Z. Gourgoulianis K.I. The effect of exercise training on the quality of sleep in national-level adolescent finswimmers Sports Med Open 5 1 2019 34 10.1186/s40798-019-0207-y 31392589
16 Dafoe W. Principles of exercise testing and interpretation Can J Cardiol 23 4 2007 274
17 Stavrou V. Boutou A.K. Vavougios G.D. The use of cardiopulmonary exercise testing in identifying the presence of obstructive sleep apnea syndrome in patients with compatible symptomatology Respir Physiol Neurobiol 262 2019 26 31 10.1016/j.resp.2019.01.010 30684645
18 Tanaka H. Monahan K.D. Seals D.R. Age-predicted maximal heart rate revisited J Am Coll Cardiol 37 1 2001 153 156 10.1016/s0735-1097(00)01054-8 11153730
19 American Thoracic Society;American College of Chest Physicians ATS/ACCP Statement on cardiopulmonary exercise testing Am J Respir Crit Care Med 167 2 2003 211 277 10.1164/rccm.167.2.211 12524257
20 Townsend L. Dowds J. O'Brien K. Persistent poor health after COVID-19 is not associated with respiratory complications or initial disease severity Ann Am Thorac Soc 18 6 2021 997 1003 10.1513/AnnalsATS.202009-1175OC 33413026
21 Anastasio F. LA Macchia T. Rossi G. Mid-term impact of mild-moderate COVID-19 on cardiorespiratory fitness in élite athletes J Sports Med Phys Fit 62 10 2022 1383 1390 10.23736/S0022-4707.21.13226-8
22 Vonbank K. Lehmann A. Bernitzky D. Predictors of prolonged cardiopulmonary exercise impairment after COVID-19 Infection: a prospective observational study Front Med 8 2021 773788 10.3389/fmed.2021.773788
23 Fuschillo S. Ambrosino P. Motta A. Maniscalco M. COVID-19 and diffusing capacity of the lungs for carbon monoxide: a clinical biomarker in postacute care settings Biomarkers Med 15 8 2021 537 539 10.2217/bmm-2021-0134
24 Sahlin K. Tonkonogi M. Söderlund K. Energy supply and muscle fatigue in humans Acta Physiol Scand 162 3 1998 261 266 10.1046/j.1365-201X.1998.0298f.x 9578371
25 Alberti K.G. The biochemical consequences of hypoxia J Clin Pathol Suppl 11 1977 14 20 10.1136/jcp.s3-11.1.14
26 Małek Ł.A. Marczak M. Miłosz-Wieczorek B. Cardiac involvement in consecutive elite athletes recovered from COVID-19: a magnetic resonance study J Magn Reson Imag 53 6 2021 1723 1729 10.1002/jmri.27513
27 van Hattum J.C. Spies J.L. Verwijs S.M. Cardiac abnormalities in athletes after SARS-CoV-2 infection: a systematic review BMJ Open Sport Exerc Med 7 4 2021 e001164 10.1136/bmjsem-2021-001164
28 Greene D.N. Wu A.H.B. Jaffe A.S. Return-to-play guidelines for athletes after COVID-19 infection JAMA Cardiol 6 4 2021 479 10.1001/jamacardio.2020.5348
29 Wang T.J. Chau B. Lui M. Lam G.T. Lin N. Humbert S. Physical medicine and rehabilitation and pulmonary rehabilitation for COVID-19 Am J Phys Med Rehabil 99 9 2020 769 774 10.1097/PHM.0000000000001505 32541352
30 Astara K. Siachpazidou D. Vavougios G.D. Sleep disordered breathing from preschool to early adult age and its neurocognitive complications: a preliminary report Sleep Sci 14 Spec 2 2021 140 149 10.5935/1984-0063.20200098 35082983
31 Stavrou V.T. Astara K. Tourlakopoulos K.N. Sleep quality's effect on vigilance and perceptual ability in adolescent and adult athletes J Sports Med 2021 2021 5585573 10.1155/2021/5585573
32 Anderson E. Durstine J.L. Physical activity, exercise, and chronic diseases: a brief review Sports Med Health Sci 1 1 2019 3 10 10.1016/j.smhs.2019.08.006 35782456
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J Cell Biol
J Cell Biol
jcb
The Journal of Cell Biology
0021-9525
1540-8140
Rockefeller University Press
36656648
jcb.202301035
10.1083/jcb.202301035
Spotlight
Mammalian fertilization: Does sperm IZUMO1 mediate fusion as well as adhesion?
Mammalian fertilization
https://orcid.org/0000-0001-8124-7328
Bianchi Enrica 1
https://orcid.org/0000-0003-0537-0863
Wright Gavin J. 1
1 Department of Biology, Hull York Medical School, York Biomedical Research Institute, University of York , York, UK
Correspondence to Enrica Bianchi: enrica.bianchi@york.ac.uk
Gavin J. Wright: gavin.wright@york.ac.uk
06 2 2023
19 1 2023
222 2 e202301035© 2023 Bianchi and Wright
2023
Bianchi and Wright
https://creativecommons.org/licenses/by-nc-sa/4.0/ http://www.rupress.org/terms/ This article is distributed under the terms of an Attribution–Noncommercial–Share Alike–No Mirror Sites license for the first six months after the publication date (see http://www.rupress.org/terms/). After six months it is available under a Creative Commons License (Attribution–Noncommercial–Share Alike 4.0 International license, as described at https://creativecommons.org/licenses/by-nc-sa/4.0/).
Brukman and colleagues provide evidence that the sperm surface protein IZUMO1, which is essential for mammalian fertilization, can induce membrane fusion in cultured cells.
The molecular mechanism of sperm–egg fusion is a long-standing mystery in reproduction. Brukman and colleagues (2022. J. Cell Biol. https://doi.org/10.1083/jcb.202207147) now provide evidence that the sperm surface protein IZUMO1, which is essential for mammalian fertilization, can induce membrane fusion in cultured cells.
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pmcThe basic genetic principles of sexual reproduction are remarkably constant across a wide range of different organisms: diploid cells partition their genetic material into two haploid cells that, at some stage of their lifecycle, are segregated into different mating types or sexes which recognize one another and fuse at fertilization to recreate a single diploid cell (1). In mammals, the haploid cells differ significantly in their size and shape: the female egg is large and spherical, whereas the male sperm is small, elongated, and highly motile. For successful fertilization, these two cells must first recognize and adhere to each other before finally fusing. For many mammals—including humans—fertilization can successfully be performed in vitro, meaning that we have a good cellular description of this process; however, we are only just learning which molecules are involved and what their biological roles are.
IZUMO1 was the first sperm cell surface protein shown to be essential for mammalian fertilization and importantly passed the stringent in vivo test of demonstrating that male mice lacking a functional Izumo1 gene were infertile (2). Prior to this, targeted knock out of sperm cell surface proteins that had been suggested to be required for fertilization resulted in male mice that remained fertile (3). In the case of IZUMO1 knock outs, the sperm were ostensibly normal: they looked and moved like wild-type sperm but were unable to finally fuse and fertilize the egg.
Since this founding discovery, the molecular mechanism of IZUMO1 function as either mediating adhesion between the sperm and egg membranes or actively driving the fusion of the membranes has been a topic of debate. These two processes are likely to be distinct and ordered: adhesion must first align areas of apposing gamete plasma membranes to within around 10 nm, followed by the action of a fusogen to overcome the thermodynamic barrier that normally prevents inappropriate membrane fusion between neighboring cells (4).
Perhaps surprisingly, experimentally distinguishing between an adhesive and fusogenic role is not trivial: they require specifically designed assays and their interdependence means that even when only adhesion is defective, cellular fusion will not occur. It was conceivable that IZUMO1 could act independently of other factors because some fusogens—such as those used by viruses—are able to act in a unidirectional manner meaning they only need to be present on one of the two fusing cells. By contrast, proteins mediating adhesion usually require a specific binding partner displayed on the apposing cell; however, identifying these extracellular interactions which are typified by very weak binding affinities is technically challenging (5).
Clues that IZUMO1 had an adhesive role came when a receptor displayed on eggs called JUNO was identified (6). Confirmatory studies have shown that both mice and rats lacking IZUMO1 produce normal-looking sperm that are unable to bind eggs (7). There is evidence, however, that IZUMO1 could have additional roles other than just JUNO binding (8). Could IZUMO1 also function as a fusogen?
In this issue, Brukman and colleagues showed that IZUMO1 could act directly as a fusogen in the Baby Hamster Kidney fibroblast (BHK) cell line (9). When IZUMO1 was overexpressed in BHKs, they observed cells with two or more nuclei, an effect that could not be ascribed to a failure in cytokinesis, and they also confirmed that cell fusion occurred using a content-mixing assay. The level of fusion events was low but comparable with that obtained with the established fusogen HAP2.
Using structure-guided mutagenesis, the authors separated the adhesive and fusogenic activities of IZUMO1: the single mutant W148A prevented binding to JUNO but did not affect fusion whereas the triple mutant (F28A, W88A, and W113A) retained the ability to bind JUNO but did not induce fusion. These data indicated that the adhesive and fusogenic roles are performed by two distinct and functionally separable regions of the IZUMO1 protein.
While the results obtained with the hamster cell line were clear, the picture became more complex when cells and gametes were mixed in fusion assays. The authors found that sperm fused only to JUNO-expressing BHK cells, indicating that the adhesion step driven by JUNO-IZUMO binding was required and that IZUMO1 was not sufficient to induce cell fusion in this assay. Remarkably, while IZUMO1 behaved as a unilateral fusogen in the transfected cells, the authors found that the heterologous expression of the protein was not sufficient to fuse cells and eggs. This is in agreement with previous reports that showed that IZUMO1 ectopically expressed on cell lines adhered to—but did not fuse with—eggs (10). In summary, the mixed cell–gamete assays suggested that JUNO-mediated binding was required for fusion of sperm and cells, but for eggs to fuse with IZUMO1-expressing cells, an additional factor is required. Sperm and eggs are terminally differentiated cells that must fuse with each other, but it is equally important that they do not fuse with other cells in the body. In sperm, this is probably achieved by sequestering IZUMO1 within the cell and making it available at the sperm surface only following the acrosome reaction as the sperm approaches the egg. The extracellular matrix surrounding the egg, the zona pellucida, shields the egg membrane from coming into direct contact with other cells, but it is conceivable that a molecule or perhaps intrinsic feature, such as the thick actin cortex that lines the oolemma, inhibit cell fusion.
The successful manipulation of murine and human gametes in vitro in the 1960s and 1970s (11) brought the promise of quick discoveries, but the dissection of the molecular mechanisms of fertilization has proven more difficult than anticipated (5). While we can biochemically investigate the interactions of cell surface proteins, studying their mechanisms of action in the context of the cell membrane remains challenging. Here, Brukman and colleagues use content mixing and multinucleation as a proxy for measuring cell fusion; consequently, fusion events could be the result of other phenomena such as endocytosis, trogocytosis, nondisjunction of sister cells, and transfer of cargo molecules via special structures like tunneling nanotubes (12, 13). The development of better techniques to monitor cell adhesion, cell fusion, and the dynamics of membrane proteins in real time will shed further light on the function of the sperm proteins during fertilization (14). Crucially, the lack of a suitable cell line for mammalian gametes represents a major limitation within the field meaning that transgenic animals remain necessary to confirm the validity of observations obtained from in vitro assays. For example, the generation of an IZUMO1 transgenic mouse line carrying the triple mutation that retained JUNO binding but lost fusogenic activity would provide important data to support the new role proposed for IZUMO1.
The work from Brukman and colleagues represents a valuable contribution to untangle the molecular mechanisms of fertilization. By unveiling a novel role of the sperm protein IZUMO1, it prompts the deployment of similar approaches to better understand the function of other essential sperm surface proteins such as the structurally similar SPACA6 and TMEM95 (15, 16) and to understand the mechanisms of cell fusion in somatic cells such as skeletal muscle and the placenta.
==== Refs
References
1 Bianchi, E., and G.J. Wright. 2016. Annu. Rev. Genet. 10.1146/annurev-genet-121415-121834
2 Inoue, N., . 2005. Nature. 10.1038/nature03362
3 Okabe, M. 2015. Asian J. Androl. 10.4103/1008-682X.153299
4 Hernández, J.M., and B. Podbilewicz. 2017. Development. 10.1242/dev.155523
5 Wright, G.J., and E. Bianchi. 2016. Cell Tissue Res. 10.1007/s00441-015-2243-3
6 Bianchi, E., . 2014. Nature. 10.1038/nature13203
7 Matsumura, T., . 2022. Front. Cell Dev. Biol. 10.3389/fcell.2021.810118
8 Inoue, N., . 2015. Nat. Commun. 10.1038/ncomms9858
9 Brukman, N.G., K.P. Nakajima, C. Valansi, K. Flyak, X. Li, T. Higashiyama, and B. Podbilewicz. 2023. J. Cell Biol. 10.1083/jcb.202207147
10 Noda, T., . 2020. Proc. Natl. Acad. Sci. USA. 10.1073/pnas.1922650117
11 Bavister, B.D. 2002. Reproduction. 10.1530/rep.0.1240181
12 Dagar, S., . 2021. Biochem. J. 10.1042/BCJ20210077
13 Rustom, A., . 2004. Science. 10.1126/science.1093133
14 Nakajima, K.P., . 2022. Sci. Rep. 10.1038/s41598-022-13547-w
15 Tang, S., . 2022. Proc. Natl. Acad. Sci. USA. 10.1073/pnas.2207805119
16 Vance, T.D.R., . 2022. Commun. Biol. 10.1038/s42003-022-03883-y
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PMC009xxxxxx/PMC9893807.txt |
==== Front
Sports Med Health Sci
Sports Med Health Sci
Sports Medicine and Health Science
2666-3376
Chengdu Sport University
S2666-3376(23)00005-7
10.1016/j.smhs.2023.01.005
Opinion
Sport and physical exercise in sustainable mental health care of common mental disorders: Lessons from the COVID-19 pandemic
Lange Klaus W. klaus.lange@ur.de
∗
Nakamura Yukiko
Reissmann Andreas
Faculty of Human Sciences (Psychology, Education and Sport Science), University of Regensburg, Regensburg, Bavaria, Germany
∗ Corresponding author. University of Regensburg, 93040 Regensburg, Germany. klaus.lange@ur.de
02 2 2023
6 2023
02 2 2023
5 2 151155
2 9 2022
25 1 2023
29 1 2023
© 2023 Chengdu Sport University. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd.
2023
Chengdu Sport University
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
The large-scale disruptions to physical activity during the coronavirus pandemic have been found to be a leading predictor of common mental disorders. In addition, regular physical exercise has been found to alleviate anxiety, sadness and depression during the pandemic. These findings, together with numerous studies published before the pandemic on the effects of physical activity on mental health, should be considered in the provision of mental health care following the pandemic. Cross-sectional research has revealed that all types of exercise and sport are associated with a reduced mental health burden. Therefore, the effectiveness of exercise and sport participation in sustainable mental health care as well as the causal relationship between exercise, psychosocial health and common mental disorders merit further investigation. Physical activity and sport, with their global accessibility, significant and clinically meaningful efficacy as well as virtual absence of adverse effects, offer a promising option for the promotion of mental health, including the prevention and treatment of common mental disorders. Physical exercise and sport are likely to become valuable public mental health resources in the future.
Keywords
Anxiety
Depression
Mental health
Physical exercise
COVID-19
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pmcAbbreviation
COVID-19 Coronavirus disease caused by SARS-CoV-2 virus
In addition to serious pulmonary and extrapulmonary manifestations of the infectious coronavirus disease caused by the SARS-CoV-2 virus (COVID-19), the pandemic has resulted in significant mental health effects.1 Public health measures introduced to curb the spread of the coronavirus, such as self-isolation, quarantine, lockdown and social distancing, have restricted physical activity and social contact. As a consequence, many people have experienced a decline in well-being, an increase in psychological stress as well as feelings of isolation, depressed mood and anxiety,2,3 with common mental disorders being major adverse effects.1,4 Common mental disorders, comprising different types of depression and anxiety, cause marked emotional distress and interfere with daily functioning, while they do not usually affect cognition or insight. The mental health effects of the pandemic may shape the health of entire populations for many years to come. Failure to address these mental health issues is likely to prolong their impact. Mental health experts have therefore emphasized the importance of developing approaches to supporting mental health at the population level.
Available evidence suggests that dietary factors and physical exercise should be considered in the promotion of mental health and the prevention of mental illness.5 These factors may also contribute to shifting the population distribution of infection risk and preventing severe outcomes of COVID-19.6, 7, 8 For example, a systematic review and meta-analysis of observational studies has found evidence for associations between ultra-processed food consumption and adverse mental health, with greater consumption of ultra-processed food being associated with elevated odds of depressive and anxiety symptoms.9 Furthermore, various nutrients and food bioactive have been proposed as effective measures in the management of common mental disorders.10,11 However, evidence of their clinical efficacy is lacking12,13 and their long-term administration may even be harmful.14
The large-scale disruptions to physical activity during the pandemic have been found to be a leading predictor of depression. At the onset of the pandemic, average steps per day in young adults plunged from around 10 000 to 4 60015 and the proportion of young adults at risk for clinical depression ranged from 46% to 61%, which represented an increase by 90% compared to the pre-pandemic rates.15 Beneficial acute and chronic effects of exercise on the immune system have been demonstrated,16 and regular physical activity has been promoted by the World Health Organization as a non-pharmacological and cost-effective approach to supporting mental health during the COVID-19 pandemic.6,17 Empirical evidence suggests that regular exercise is effective in maintaining or improving well-being and mental health18, and exercise has also been found to alleviate anxiety, sadness and depression during the coronavirus pandemic.19,20 This, together with the large number of studies published before the pandemic on the effects of physical activity on mental health,21, 22, 23, 24, 25 should be considered in the provision of mental health care following the pandemic.
The enhancement of physical health affected by exercise has been widely investigated, and regular exercise has been shown to be a key factor in good health.26 Physically active individuals have been found to benefit from improved levels of health-related fitness, reduced risks of developing a wide range of disabling chronic conditions, including obesity, diabetes, cardiovascular disease, stroke and cancer, and a decrease in overall mortality.27 The health benefits of exercise have also been widely acknowledged to encompass mental health. A cross-sectional study of more than 1.2 million adults in the United States, matched for various sociodemographic and physical health factors, found that people who exercised reported 1.49 (43.2%) fewer days of poor mental health in the past month than people who did not exercise, with all types of exercise and sport being associated with a reduced mental health burden.28 The largest significant associations were for periods of exercise lasting 45 min and exercise performed 3–5 times per week as well as for popular team sports, cycling, aerobic and gym activities.28 Engaging in sports involving interaction with other people may improve social confidence and skills and thus promote positive interpersonal relationships and psychosocial development.
While the association between physical activity and mental health is well established, causation remains a matter of debate. Unmeasured factors closely related to physical activity may play a role. In view of the social context of physical activity, the interplay between exercise and social interactions or other lifestyle habits may impact mental health. Disturbances of both mental well-being and physical activity may be influenced by a third variable, such as people's response to the pandemic. Individuals capable of maintaining their lifestyle during the pandemic may be better able to preserve their mental health and well-being. Before the pandemic, these individuals may have been more resistant to stress and less prone to depressed mood or anxiety.29 Reverse causation is also possible, with physical activity being driven by mental health rather than lifestyle. For example, changes in lifestyle could be early signs of depression. Establishing a causal relationship between exercise and mental well-being and health requires randomized controlled trials demonstrating the beneficial effects of exercise interventions.
Depression has become the leading cause of the burden of disability globally.30 Since the ability of pharmacological and psychological therapies to mitigate the cumulative burden of depression on society is limited,31 there is an urgent need to find modifiable factors that could become targets of health campaigns at the population level. Since adults with major depressive disorder have been reported to engage in low levels of physical activity and high levels of sedentary behavior,32 lifestyle interventions targeting the adoption and maintenance of exercise may be warranted. The debate surrounding the effects of physical exercise on depression has often been contentious, leading to uncertainty regarding the magnitude of these effects. Meta-analyses of randomized controlled trials have reported moderate to large antidepressant efficacy of exercise.33, 34, 35, 36 The varying effect sizes reported may have been influenced by inclusion criteria, heterogeneity of samples and diagnostic criteria as well as publication bias. Furthermore, various studies compared exercise in addition to an established therapy versus an established therapy alone.33 This approach assumes that the efficacies of treatments can be added and subtracted algebraically. However, this assumption does not take into account that exercise may at least partially overlap and interact with the mechanisms underlying pharmacological and psychological therapies of depression.37 In a meta-analysis adjusting for publication bias, large antidepressant effects of exercise on depression were found in comparison with non-active control conditions.24 The effect was particularly high for studies including people with major depressive disorder. Larger effect sizes were also found in patients with a diagnosis of depression but without other clinical co-morbidities. In the context of the high risk of social isolation in older individuals with depression,38 group exercise appears to be particularly effective. Future studies should therefore address exercise as a means of promoting social interaction. The question of whether exercise in combination with a conventional treatment produces better results than exercise or the other treatment alone remains at present a matter of debate owing to the limited data available. Furthermore, the currently established meta-analytic evidence showing the antidepressant efficacy of exercise in clinical settings, with relatively low attrition in people with major depressive disorder, needs to be complemented by randomized controlled trials providing ecological (i.e. real-life) evidence. These trials should examine the effectiveness of exercise interventions, defined as benefits produced in daily clinical practice, using wide inclusion criteria and functional health outcomes, such as quality of life. Exercise programs have also been proposed as a promising means of protecting against the development of depressive symptoms in young people,39 which may be particularly helpful since they are non-stigmatizing and have few side effects.40 Anxiety and depressive symptoms in adolescents have been found to be higher for low physical activity compared to moderate and high physical activity.41 Furthermore, some evidence from randomized controlled trials suggests a moderate positive effect of exercise interventions on the severity of adolescent depression.42,43
Anxiety disorders are the most prevalent mental disorders in the general population44 and have a significant societal and economic impact.45 Epidemiological findings suggest that individuals who are more physically active may be less likely to suffer from anxiety disorders.41,46 Current evidence, presented in several systematic reviews and meta-analyses, suggests that engaging in physical activity and sports may protect against anxiety symptoms and disorders across the lifespan.23,47, 48, 49, 50, 51, 52 Higher-intensity exercise regimens may be more effective than low-intensity exercise.49 Physical exercise as an add-on to cognitive behavioral psychotherapy appears to be feasible and beneficial, especially when performed regularly several times per week over extended periods of time.52 While exercise may be a viable option for the therapy of anxiety, the evidence for its effectiveness in anxiety disorders is equivocal and limited by a lack of data from methodologically rigorous randomized controlled trials with large sample sizes. Major limitations of the available studies include non-representativeness of samples, inadequate assessment of fitness and adherence levels, inconsistent adjustment for putative confounders, attrition bias and issues regarding exercise exposure and outcome measures. The available evidence is therefore insufficient to warrant a recommendation of exercise as a treatment in people with anxiety disorders. Nevertheless, in view of preliminary findings suggesting exercise as a useful, accessible and affordable therapeutic option for anxiety, further high-quality, long-term randomized controlled trials using robust experimental designs are called for. The lack of large samples and statistical power does not allow for the examination of exercise form, frequency, duration or intensity. It would be important to know whether certain types of exercise have advantages over others or whether an individual's favored sport can be chosen.
Mental health benefits of exercise and sport may be related more to the context of physical activity than the behavior of being active itself.53, 54, 55, 56 For example, participation in team sports and informal group activities has been reported to be inversely associated with depressive symptoms in comparison with individual physical activity.57 Furthermore, team sports appear to be associated with positive mental health regardless of the volume of physical activity.57 This suggests that features of the team sport context provide benefits to mental health and that increasing the volume of physical activity has no beneficial effects. The context of team sport is thought to be beneficial to mental health since it offers an opportunity for social interaction, thereby strengthening social networks and perceived feelings of support and integration.57, 58, 59
An umbrella review of systematic reviews and meta-analyses found a small positive impact of participation in organized sports on mental health in children and adolescents.60 This was not related to any specific type of organized sport activity. The findings of a recent study from Canada using a large and diverse sample of high school students suggest that sport participation should be promoted for the prevention and management of adolescent mental illness.61 Students participating in varsity sports were found to have lower symptoms of anxiety and depression in comparison with nonparticipants, with associations being strongest in those who participated in both varsity sports and sports outside school. Another recent study including data from over 11 000 children and adolescents in the United States reported that participation in team sports compared to no sport participation was associated with fewer mental health difficulties, while participation in exclusively individual sports was associated with greater difficulties.62 These findings suggest that team-based organized sports may be a means to support youth mental health. However, given the cross-sectional design of the study, the causal relationship between participation in organized sport activities and mental health remains to be established.
The physiological and biochemical mechanisms discussed to be involved in mediating the effects of exercise on mental health include, among others, endorphins,63 myokines,64 inflammation65 and stress modulated by the hypothalamic-pituitary-adrenal axis. The impact of stressful life events on mental health in general and major depressive episodes, in particular, has been firmly established.66,67 An increase in the prevalence of stress, anxiety and depression in the general population has been observed during the COVID-19 pandemic.68,69 Exercise interventions may improve the ability to deal with psychological stressors during and after the pandemic, since increased physical activity and fitness levels appear to be associated with an attenuated response to psychosocial stress.70 Exercise may therefore exert its beneficial effects on mental health through stress-buffering mechanisms.71 Furthermore, physical activity seems to be capable of modulating stress-induced changes in DNA methylation and gene expression, with positive epigenetic influences of exercise counteracting the negative influences of stress.72
Changing an inactive lifestyle and increasing physical activity is a challenge for many people. The outcomes of introducing a physical exercise regimen may be improved when various strategies are combined.73,74 Such strategies include starting with short periods of physical activity,75 improving self-regulation,76,77 strengthening non-conscious processes,78 improving accessibility to environments and facilities77,79 and using internet and smartphone apps.80 Maintaining a physical exercise regimen presents a further problem, since adherence to short-term prescribed exercise has been found to drop to 50% within six months.81,82 Failure of practitioners to provide specific exercise recommendations is common.83 Rates of adherence to exercise regimens can be enhanced when written prescriptions provide a detailed exercise plan, and suggestions for overcoming anticipated potential barriers are given.84 The effects of exercise counseling may be facilitated through pre-intervention educational programs, continued motivational support and improving the convenience of exercise facilities and opportunities.85,86 Furthermore, increased drop-out rates have been found in groups undertaking high-intensity training compared to low-intensity programs.87 Given the higher efficacy of high-intensity programs, exercise regimens need to be individually tailored in order to maximize the beneficial effects of exercise while minimizing the drop-out rate.
Exercise has long been underestimated as a variable contributing to mental health, and lessons from the current pandemic regarding mental health care include the value of physical exercise and sport in the management of common mental disorders. The findings of numerous studies support the antidepressant and anxiolytic efficacy of physical exercise and sport, indicating their promise as additional or alternative options for managing anxiety and depressive disorders. Physical exercise as a therapeutic option is relatively free from side effects and produces additional benefits for physical health, such as a decrease in blood pressure and weight loss. The potential improvement in mental health resulting from lifestyle changes incorporating increased physical activity should therefore be considered in the therapy regimens of individuals and in population-wide public health programs. Further interventional study of sustainable exercise effects in individuals with depression and anxiety is required to identify the specific types and optimum frequency, intensity and duration of physical activity. The financing of high-quality studies investigating the effects of exercise on mental health will pose challenges, since immediate pecuniary returns from such investigations cannot be expected. However, long-term gains include amelioration of the burden of mental illness and additional physical health benefits in respect of cardiovascular and metabolic diseases. Political interventions, public funding and support through non-profit organizations will be needed to help counter the challenges faced in the fostering and maintenance of public health. In addition to specialist care, task-shifting may be capable of providing cost-effective and sustainable mental health care worldwide, especially where health services are overwhelmed by the pandemic.88
In conclusion, the effectiveness of exercise and sport participation in sustainable mental health care as well as the causal relationship between exercise, psychosocial health and common mental disorders merit further investigation. Physical activity and sport, with their global accessibility, significant and clinically meaningful efficacy as well as the virtual absence of adverse effects, offer a promising, evidence-based option for the prevention and treatment of common mental disorders. Any increase in physical activity should therefore be encouraged by health practitioners in order to improve mental health. Physical exercise and sports are likely to become valuable public mental health resources in the future.
Submission statement
The work described has not been published previously, is not under consideration for publication elsewhere, its publication is approved by all authors and tacitly or explicitly by the responsible authorities where the work was carried out, and, if accepted, it will not be published elsewhere including electronically in the same form, in English or in any other language, without the written consent of the copyright-holder.
Authors' contributions
KWL contributed substantially to the conception of the work, drafted the work, provided approval for publication of the content and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. YN contributed substantially to the conception of the work, revised it critically for important intellectual content, provided approval for publication of the content and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. AR contributed substantially to the conception of the work, revised it critically for important intellectual content, provided approval for publication of the content and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
Conflict of interest
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.
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