Response inhibition is typically considered a brain mechanism selectively triggered by particular “inhibitory” stimuli or events. Based on recent research, an alternative non-selective mechanism was proposed by several authors. Presumably, the inhibitory brain activity may be triggered not only by the presentation of “inhibitory” stimuli but also by any imperative stimuli, including Go stimuli, when the context is uncertain. Earlier support for this notion was mainly based on the absence of a significant difference between neural activity evoked by equiprobable Go and NoGo stimuli. Equiprobable Go/NoGo design with a simple response time task limits potential confounds between response inhibition and accompanying cognitive processes while not preventing prepotent automaticity. However, previous neuroimaging studies used classical null hypothesis significance testing, making it impossible to accept the null hypothesis. Therefore, the current research aimed to provide evidence for the practical equivalence of neuronal activity in the Go and NoGo trials using Bayesian analysis of functional magnetic resonance imaging (fMRI) data. Thirty-four healthy participants performed a cued Go/NoGo task with an equiprobable presentation of Go and NoGo stimuli. To independently localize brain areas associated with response inhibition in similar experimental conditions, we performed a meta-analysis of fMRI studies using equal-probability Go/NoGo tasks. As a result, we observed overlap between response inhibition areas and areas that demonstrate the practical equivalence of neuronal activity located in the right dorsolateral prefrontal cortex, parietal cortex, premotor cortex, and left inferior frontal gyrus. Thus, obtained results favour the existence of non-selective response inhibition, which can act in settings of contextual uncertainty induced by the equal probability of Go and NoGo stimuli. It is generally accepted in the literature that response inhibition works in close relation to other processes associated with cognitive control, such as working memory, voluntary attention, conflict monitoring, and action selection7. Moreover, response inhibition is thought to represent a multifaceted phenomenon rather than a unitary brain mechanism. A distinction is made between action cancellation and action restraint9 as well as between reactive and proactive response inhibition mechanisms14.Response inhibition is the ability to suppress inappropriate, automatic, reflexive, or habitual prepotent responses to produce a controlled goal-directed response18. However, in several cases, the concept of selective response inhibition fails to explain observed behavioural and neurophysiological phenomena. Manipulating the probability of the occurrence of “inhibitory” stimuli and the subjects’ awareness of the probability of the appearance may slow down the motor response25. Moreover, when necessary to rapidly suppress a specific action, the action is inhibited along with all other potential actions. That is, such inhibition can affect the entire motor system32. In an attempt to explain the effects mentioned above, several authors proposed the concept of non-selective  response inhibition34. It is thought that these non-selective mechanisms serve to prevent inappropriate or premature responses at the expense of the speed of execution of correct actions. First, response inhibition mechanisms may non-selectively inhibit all potential responses to further selectively execute an appropriate response (non-selective inhibition of multiple concurrent motor responses). Second, inhibition may be triggered not only by the presentation of “inhibitory” stimuli but also by the occurrence of any imperative stimuli instructing on the necessity to suppress or execute a prepared action (non-selectivity of inhibitory stimulus perception). In the present work, we consider the latter mechanism.According to the conventional view, the response inhibition process is selectively triggered by “inhibitory” stimuli that result in increased neuronal activity in brain structures responsible for inhibitory control33 and included the cortico-subthalamic “hyper-direct” pathway36 which is capable of rapidly and non-selectively suppress all potential response options. As the research area developed, it was hypothesized that the model might be applied not only to tasks with multiple concurrent response options but also to simpler tasks where the subject has to choose between executing and refraining from an action34. The authors used a cued equiprobable Go/NoGo task, wherein a preparatory cue stimulus indicated the probability of a NoGo stimulus occurrence. A simple equiprobable Go/NoGo was chosen instead of a complex Go/NoGo task to limit confounds between response inhibition and the accompanying cognitive processes6. Complex Go/NoGo tasks usually utilize the low probability of NoGo stimulus, difficulties in identifying NoGo signals, high attentional or working memory loads. Although one of the possible ways to build up a prepotent response tendency is to bias the Go/NoGo probabilities in favour of Go stimuli, it is not necessary when the design involves a simple speeded reaction time task with a single response and reduces the complexity of the identification of Go and NoGo signals to a single bit of information18. In these conditions, a stimulus that does not require a response elicits subthreshold automatic motor activations that do not become overt because they are counteracted by fast automatic response inhibition40.A tentative neurophysiological model of the non-selective or “global” response inhibition involved in the resolution of interference between several competing response options was proposed by Frank34 and the level of neuronal activity measured by functional magnetic resonance imaging (fMRI)18. At the same time, a significant difference in early ERP amplitudes (peaked at 170 ms) was found in uncertain equiprobable NoGo and Go trials compared to certain Go-control trials, where no inhibition is required34. The authors considered this fact as evidence for the presence of a “non-selective” inhibitory mechanism that is not specific to the processing of NoGo signals. The analysis of early ERP components suggests that non-selective inhibition may blindly suppress any automatic response when the context is uncertain, acting as a gating mechanism controlling the initiation of a prepared, prepotent response.Within the framework of the selective inhibition model, the inhibition process would only be triggered by the identification of the NoGo stimulus. According to the hypothesis of non-selective inhibition, when the context is uncertain , the need for response inhibition arises for both NoGo and Go trials. Experimental assessment of the hypotheses revealed no statistically significant differences between the NoGo and Go trials in both the amplitude of the early components of event-related potentials (ERP)42.However, a critical limitation of the above-mentioned studies was that the authors could not accept the null hypothesis, so they did not provide direct proof of the practical equivalence of neuronal activity level between Go and NoGo trials in inhibition-related brain structures. Indeed, within the framework of classical (frequentist) null hypothesis significance testing (NHST), we cannot accept the null hypothesis based on the absence of a significant difference. We can only reject it. Thus, the question of experimental support for non-selective response inhibition remains unanswered, and answering this question requires overcoming the methodological limitation of NHST, which is possible using Bayesian statisticsTherefore, the present study aimed to verify the non-selective response inhibition hypothesis by using the fMRI data from an equiprobable Go/NoGo task. Based on the results of previous studies, it may be suggested that if the hypothesis on the non-selectivity of inhibition is correct, then the brain structures responsible for response inhibition will demonstrate practically equivalent levels of neuronal activity in equiprobable Go and NoGo trials. Bayesian parameter inference (BPI) was applied to assess this prediction. Instead, if the hypothesis on the selectivity of response inhibition is correct, then activation of inhibition-related brain structures will be observed in the NoGo conditions compared to the Go conditions.34 and Criaud et al.18, compared neuronal activity in the condition of equiprobable presentation of Go and NoGo stimuli with the control Go condition, in which the subject did not need to inhibit the prepared action. We hypothesized that inhibition would be non-selectively elicited by both equiprobable Go and NoGo-stimuli when the context is uncertain. To test the non-selective inhibition hypothesis, we considered similar Go/NoGo tasks with an equiprobable presentation of Go and NoGo-stimuli. We did not consider Go/NoGo tasks with rare NoGo-stimuli, since studies using them have previously shown the involvement of selective response inhibition17.Given that a practically equivalent level of neuronal activity can be identified not only for response inhibition-related structures, but also, for example, those related to sensory processing of visual stimuli, working memory, and attention, we conducted a meta-analysis of fMRI studies to independently localize brain structures associated with response inhibition. Studies using equal probability Go/NoGo tasks were selected for meta-analysis. We searched for studies that, similar to the investigation by Albares et al.We searched for these studies in the PubMed database in the period from 01/01/2000 to 15/07/2019 using the following keywords: “((fmri) OR ) AND ((nogo) OR (no-go)).” Four additional studies were identified through manual searches. As a result, 726 papers were identified. At the first stage of selection, we excluded reviews, meta-analyses and papers repeatedly reporting the results of fMRI studies  with a total of 210 foci ; required Go-control condition (100% probability of the Go stimulus presentation) was not used; the coordinates for the contrast of interest “50/50% Go/NoGo > 100% Go-control” within a group of healthy subjects were not reported. In 11 out of the 31 remaining studies, healthy volunteer subjects under the age of 18 (children and adolescents) were studied. These articles were also excluded from the analysis because this study focused on brain activity of healthy, adult subjects. In all remaining studies except one, only block designs were used. To make our sample more homogeneous, we excluded the only eligible study with an event-related design48 implemented in the GingerALE 3.0.2 software  and Seed-based d Mapping with Permutation of Subject Images (SDM-PSI) algorithm49 implemented in the SDM-PSI 6.22 software (http://www.sdmproject.com). Two different CBMA algorithms were used for the cross-method validation.Coordinate-based meta-analysis (CBMA) was performed using the random-effects activation likelihood estimation (ALE) algorithm42. An ALE map is obtained by computing the union of activation probabilities across studies for each voxel and tested against a null distribution of random spatial convergence between studies43. The empirical validations51 showed that the optimal FWHM largely depends on the data and may be substantially larger than proposed in45. The spatial smoothness of our fMRI data after preprocessing was 13.5 mm, as estimated by the SPM12  software package (http://www.fil.ion.ucl.ac.uk/spm). Since our objective was to find an overlap between the results of the meta-analysis and the Bayesian analysis of the obtained fMRI data, we used an additional FWHM of 4 mm for the ALE meta-analysis. It allowed us to achieve a median kernel FWHM of 13.5 mm. We used a cluster-level extend threshold of 0.05 corrected for family-wise error (FWE) with an uncorrected cluster-forming threshold of 0.001 (5000 threshold permutations) recommended for the ALE analysis44.The ALE algorithm assesses the spatial convergence between neuroimaging studies by modelling spatial uncertainty of activation foci using isotropic three-dimensional Gaussian probability functions with the full-width at half-maximum (FWHM) inversely related to the square root of the sample size of the original study52. It has been shown that the recreation substantially improved by fully anisotropic kernels and did not depend on the FWHM52. We performed the SDM-PSI meta-analysis using the same coordinates as for the ALE meta-analysis adding peak t-values . We used the default kernel  and threshold (threshold-free cluster enhancement FWE-corrected threshold of 0.05 with 5000 permutations) recommended by SDM developers52.The alternative CBMA algorithm, SDM-PSI, considers not only the coordinates of the activation foci, but also the effect sizes. The SDM-PSI algorithm recreates 3D effect size images for each study using t-values of the peak coordinates and anisotropic kernels53. The subject volunteers signed a written informed consent to participate in the study and were paid for their participation. All procedures were performed in accordance with the Declaration of Helsinki and were approved by the Ethics Committee of the N.P. Bechtereva Institute of the Human Brain of the Russian Academy of Sciences.The recruitment of subjects for the present fMRI study was carried out in two stages. At the first stage, the sample size was 20 subjects  23.9 ± 4.6). To protect from the potential impact of relatively small sample size on the observed null-effects a retrospective power analysis was carried out, which suggested increasing the sample size up to 34 subjects. Therefore, the final sample consisted of 34 healthy, right-handed volunteer subjects . An Oldfield test was used to determine the dominant arm55 as it contains fMRI data from a relatively large cohort (N = 115) of healthy subjects performing a stop-signal task  with a power of 0.9 . To provide evidence for the null hypothesis, Bayesian inference or frequentist equivalence testing should be used60. Accordingly, in the current study, all conclusions on the non-selective response inhibition were made only on the basis of the Bayesian analysis and its overlap with the meta-analysis. Results for the classical NHST were added only to illustrate how the more familiar frequentist inference relates to Bayesian inference.However, it is worth noting that obtaining non-significant results with high retrospective power (based on an independent dataset) or high prospective power would not provide direct evidence for the null hypothesis61. This task was originally developed to dissociate cognitive processes, such as response inhibition, conflict monitoring, sensory mismatch and category discrimination63. Given that imperative Go and NoGo stimuli were presented in an equiprobable manner this modification of the Go/NoGo task was used for the purposes of the current study. Each trial consisted of the two consequently presented stimuli. The first preparatory cue stimulus warned the subjects on the presentation of the second, imperative stimulus, or indicated no need for any response to the second stimulus. The study included two variants of the experiment’s instructions. According to the first instruction . Accordingly, it was assumed that there is no inhibition of the prepared action in “Ignore” conditions. The “Ignore” trials were included in the adopted Go/NoGo task to assess preparatory brain activity, such as preparing to receive a relevant stimulus  and preparing to make a movement (motor set), by comparing them to Go and NoGo trials. However, the issue related to the preparatory processes is beyond the scope of the present study. Fifty pairs of each type of stimuli were randomly presented in each experiment. The order of following the instructions was counterbalanced among the subjects. Two variants of the present Go/NoGo task allowed us to control the differences in the load on working memory between A-P/A-A–Go/NoGo stimulinon-selectivity of response inhibition in the current study, since a similar task design was previously used for the same purpose by Criaud et al.18. The imperative Go and NoGo stimuli were presented after the preparatory stimulus with equal probability, as reported in event-related studies by64. To build up prepotent tendency to react, subjects were instructed to press the button with their right thumb as quickly as possible. This is a case of a simple speeded reaction time task with a single response and only one bit of information. It is known that these conditions elicit subthreshold automatic motor activations18. According to G.A. Miller: “One bit of information is the amount of information that we need to make a decision between two equally likely alternatives”65. Here, it refers to discrimination between “animal” and “plant” stimuli.The fMRI data obtained using this experimental task allowed us to test the hypothesis on the 18. Second, such a design enables the exclusion of effects associated with a low frequency of the NoGo stimuli presentation  confounding the effect of inhibition64. Third, it creates maximum uncertainty regarding the probability of the presentation of an imperative stimulus, thus minimizes the conflict between two response types, making it possible to distinguish between error monitoring and conflict resolution processes on the one hand and response inhibition processes on the other66.Such an equally probable presentation of imperative stimuli provides several advantages. First, it minimizes the difference in cognitive load between Go and NoGo conditions arising from task complexityIn total, 100 NoGo trials, 100 Go trials, 100 P-A Ignore trials, and 100 P-P Ignore trials were presented over two experimental sessions. In the absence of stimulation, a fixation cross was displayed in the centre of the screen. The stimuli were presented for 100 ms, and the interstimulus interval was 1000 ms. The intertrial interval jittered from 2800 to 3200 ms with an increment step of 100 ms. Additionally, to improve design efficiency, 100 zero events (fixation crosses) were randomly inserted between the stimuli pairs , and their duration jittered from 3000 to 5000 ms with an increment size of 500 ms. The action to be performed consisted of pressing a button with the right thumb. The duration of one task session was 17.5 min. Before starting the fMRI study, the subjects performed a training task. The Invivo’s Eloquence fMRI System  was used to deliver the stimuli, synchronize with fMRI acquisition, and record reaction times of subjects’ bottom pressing. The task presentation sequence and all temporal parameters of the stimuli presentation were programmed using the E-prime 2.0 software package .A Philips Achieva 3.0 Tesla scanner  was used for the study. The structural T1-images were registered with the following parameters: field of view (FOV)—240 × 240 mm, repetition time (TR)—25 ms, echo time (TE)—2.2 ms, 130 axial slices with a thickness of 1 mm and pixel size of 1 × 1 mm, flip angle—30°. For the registration of T2*-images, a single-pulse echo planar imaging (EPI) sequence was used. The period of data registration from 31 axial slices was 2 s . The following parameters were employed: FOV—200 × 186 mm, flip angle—90°, voxel size—3 × 3x3 mm. Two dummy scans were performed prior to each session. To minimize head movements, we used an MR-compatible soft cervical collar and foam padding.67. The mean FD across two runs ranged from 0.07 to 0.36 mm (group mean ± standard deviation was 0.19 ± 0.07 mm). The average percentage of time points with FD over 0.9 mm was 1 ± 2%. We used all time points for the analysis since all subjects had > 90% volumes under the FD threshold of 0.9 mm69.Image preprocessing included the following: realignment to the first image of the session, slice time correction, co-registration, segmentation, normalization to an MNI template, and spatial smoothing (8 mm FWHM). Preprocessing and statistical analyses of the images were performed using an SPM12. To assess head motion during the scanning, we calculated the framewise displacement (FD) as the sum of the absolute values of the derivatives of the six realignment parameters70. The equiprobable Go/NoGo study by Hong et al.71 showed an increase of the right-hand target-related LRP (“C3 minus C4” ERP difference) in NoGo trials compared to Ignore trials, which was considered as evidence of a prepotent response tendency. The fMRI analogue of LRP is the Lateralized BOLD response (LBR)73. Therefore, we used the LBR to index prepotent motor activations under task settings of the present study. The right-hand LBR was calculated as the mean BOLD signal in the left sensorimotor cortex (SM) minus the right SM cortex (“L SM minus R SM” BOLD difference). BOLD signal was estimated within 0.5 s time bins from the target onset time using finite impulse response models (time window from 1 to 12 s). Regions of interest (ROIs) located in the SM cortex were defined as the overlap between anatomical and a priori functional masks74. Functional masks were obtained using the Neurosynth platform for an automatic meta-analysis (https://neurosynth.org). We used a uniformity test with a default false discovery rate (FDR) corrected p < 0.01 threshold for the “motor” term (2565 studies). To obtain anatomical masks, we used voxels with the maximum probability for “precentral gyrus” and “precentral gyrus” labels according to the probabilistic Harvard–Oxford atlas75. ROI analysis was performed using one-sample and paired-sample t-tests with FDR correction for multiple comparisons.One of the electrophysiological indicators of overt and covert motor activations is the Lateralized Readiness Potential (LRP)78. In general, using Bayesian analysis on the second level does not presuppose Bayesian parameter estimation at the first level of analysis. One can combine computationally less demanding frequentist parameter estimation for single subjects with Bayesian estimation and inference at the group level78. Onset times of second stimuli presentation , erroneous button pressing, and missing the responding in Go trials were used to create regressors of the general linear model (GLM) for each subject. Events were impulse responses with a duration of zero convolved with the canonical haemodynamic response function (HRF). We also performed an additional analysis with the temporal and dispersion derivatives for all task regressors to take into account the unknown BOLD delay. Low-frequency drift was removed by temporal high-pass filtering with a cut-off frequency of 1/128 Hz. Six head motion parameters were included in the GLM as nuisance regressors to account for the movement artefacts79. Beta coefficients reflecting an increase in the blood oxygenation level-dependent (BOLD) signal in the experimental condition relative to the implicit baseline were scaled to percent signal change (PSC) following the procedure recommended in . To this end, the beta coefficients for each condition were divided by the mean value of the beta coefficients for the constant term and multiplied by 100 and a scaling factor (SF) needed so that the peak of an isolated BOLD response is equal to one (SF = 0.21). Two linear contrasts of scaled beta coefficients were calculated: (1) 0.5 × [“A-P NoGo” + “A-A NoGo”]—0.5 × [“A-P Go” + “A-A Go”] (the “NoGo vs. Go” comparison); (2) 0.25 × [“A-P NoGo” + “A-A NoGo” + “A-P Go” + “A-A Go”]—0.25 × [“P-A Ignore Exp1” + “P-P Ignore Exp1” + “P-A Ignore Exp2” + “P-P Ignore Exp2”] (the “Go + NoGo vs. Ignore” comparison). The sum of positive contrast weights was equal to one. The contrasts from 34 subjects were used as variables to verify the hypotheses on selective and non-selective response inhibition at the second level of analysis. Only grey matter voxels were included in the second-level analysis, as we did not expect to detect the BOLD signal changes in white matter associated with the Go/NoGo task performance. To that end, a mask was created based on the segmentation of each subject’s structural T1-images.The first level of analysis was conducted using frequentist parameter estimation. The second level of analysis was performed using both the frequentist and Bayesian parameter estimation34. In particular, we did not consider non-selectivity related to decision mechanisms that involve the selection between multiple alternative responses, which confound response inhibition processes in choice reaction time tasks18. The former type of non-selectivity implies that any imperative stimuli (both Go and NoGo stimuli) would trigger response inhibition when the context is uncertain because of equal probability of Go and NoGo stimuli. The latter type of non-selectivity, usually called “global” inhibition, implies that inhibition affects all alternative responses, including the selected response27.As in previous studies employing equiprobable Go/NoGo task with a single prepotent motor response, in the current study, non-selectivity refers to the perceptual decision mechanisms involved in the detection, discrimination, or identification of sensory stimuliselectivity of response inhibition, we expect to find a selective increase in the neuronal activity in response to the presentation of NoGo stimuli compared to Go stimuli (“NoGo > Go”). In the case of the non-selectivity of response inhibition, we expect to find a practically equivalent increase in the neuronal activity in response to the presentation of both NoGo and Go stimuli (“NoGo = Go”) in the brain areas related to the response inhibition, which were independently localized by the current meta-analysis (“50/50% Go/NoGo blocks > 100% Go-control blocks”). Additionally, we used the “Go + NoGo vs. Ignore” comparison to distinguish between the brain areas that are simply not activated in current task settings (“Go + NoGo = Ignore”) from the brain areas activated in equiprobable Go and NoGo trials compared to Ignore trials, where no inhibition is required (“Go + NoGo > Ignore”). Thus, to identify the non-selective response inhibition in the settings of contextual uncertainty induced by the equal probability of Go and NoGo stimuli, it was necessary to show a three-way overlap between (1) inhibitory-related brain areas according to the meta-analysis (“50/50% Go/NoGo blocks > 100% Go-control blocks”), (2) brain areas with practically equivalent neuronal activity in Go and NoGo trials (“NoGo = Go”) and (3) brain areas activated in equiprobable Go and NoGo trials (“Go + NoGo > Ignore”). This conjunction analysis was performed by binarization and multiplication of thresholded images81.We tested the hypotheses on selective and non-selective response inhibition using the “NoGo vs. Go” comparison. In the case of the β, or something more extreme under the null hypothesis that the experimental effect is θ = cβ = 0. The null hypothesis can never be accepted using NHST because the p-value does not represent the probability of the null hypothesis, and the probability that an effect equals exactly zero is itself zero76. A non-significant result can be obtained in two cases82: (1) there is no effect, and our data are against the alternative hypothesis; or (2) our data are insufficient to distinguish alternative hypothesis from the null hypothesis, and we cannot confidently make any inference . When the result obtained is not significant, it is recommended to use frequentist equivalence testing62 or Bayesian inference83 to provide evidence for the null hypothesis. In the present study, Bayesian parameter inference (BPI) was used83.To reveal brain areas with practically equivalent neuronal activity in the Go and NoGo trials (“NoGo = Go”), one has to provide evidence for the null hypothesis. The classical NHST approach estimates the probability  of obtaining actual data’s best-fitting parameter value, (PP) distribution of the effect θ, given the data using the likelihood and the prior knowledge. For the second-level Bayesian analysis, SPM12 implements the hierarchical parametric empirical Bayes approach with the global shrinkage prior77. It represents a prior belief that, on average, in the whole brain, there is no global experimental effect. If the posterior probability of the effect exceeding the effect size threshold, γ, is greater than the predefined probability threshold, α = 95%, then the hypothesis on the presence of “NoGo > Go” effect will be accepted  that expresses which effect size values (PSC) are equivalent to the null value for current practical purposes41.The interval  with the PP > 0.95. Thus, for these voxels, the data obtained are sufficient to make an inference confidently. This decision rule is also known as the “ROPE-only” decision rule . Among different response inhibition tasks, several meta-analyses showed that an action-restraint (Go/NoGo task) elicits stronger activation in the right DLPFC and parietal cortex compared to another inhibition process, namely, action cancellation 9. Furthermore, the “response uncertainty” tasks induced by the equal probability of Go and NoGo stimuli evoked more activity in the right DLPFC  IFG was damaged125. According to these findings, the left IFG can also participate in inhibitory control during action restraint, which is also confirmed by our results.Another prefrontal structure related to the non-selective response inhibition in the current study was the left IFG . At the same time, the right IFG is commonly associated with response inhibition126. According to Duque et al.127, premotor cortex function is associated with inhibition of any premature actions and control of the time of action execution. The authors refer to this brain mechanism as “impulse control”, a concept that is similar to the non-selective response inhibition18.Regarding the practically equivalent participation of PMC in both the Go and NoGo trials, it was previously shown to participate in the planning and coordination of actions because its electrical stimulation results in involuntary motor actions128. Recently, Criaud et al.18 supposed the possibility of involvement of non-selective response inhibition not only for NoGo stimuli but also for Go stimuli. In the current study, the practical equivalence of BOLD signals evoked by equiprobable NoGo and Go stimuli was demonstrated for a number of brain areas associated with response inhibition in an uncertain context, according to our meta-analysis. The present study results proved that response inhibition can act as a non-selective mechanism of action inhibition when the context is uncertain. Thus, one promising area of further research of brain mechanisms of response inhibition  is the study of the interplay between selective and non-selective inhibition as a function of the contextual uncertainty degree.In summary, it can be noted that the selectivity of response inhibition has been studied more extensively to date than its non-selectivity. A considerable amount of research on inhibitory control has used tasks with a low probability of inhibitory stimuli. However, over time, it has become apparent that under several conditions, non-selectivity of response inhibition can, in principle, occur. Studies on non-selectivity of response inhibition typically focus on the possible brain mechanisms for non-selective  inhibition of motor responses in the settings of interference among multiple concurrent response optionsFor the first time, combining a meta-analysis and second-level Bayesian analysis yielded results favouring the existence of non-selective response inhibition in equiprobable Go/NoGo task settings. In the present work, selectivity means that inhibition is triggered only by an inhibitory stimulus. The overlap between brain areas previously associated with response inhibition in uncertain context and brain areas demonstrating the practical equivalence of neuronal activity in equiprobable Go and NoGo trials was observed in the right DLPFC, IPL, TPJ, FEF, PMC, and left IFG (AIFO). When a subject was waiting for an equiprobable Go or NoGo stimulus, a non-selective inhibitory control process occurred in both Go trials and NoGo trials in opposition to the model of selective response inhibition. This type of response inhibition prevents the performance of any premature motor actions and operates in a non-selective, “global” mode. Its involvement is favoured by contextual uncertainty caused by the equally probable presentation of Go and NoGo stimuli. Presumably, upon the identification of a Go stimulus, the inhibition is released and the process of action execution is initiated, i.e., it acts as a gating mechanism for accessing a prepared motor program. At the same time, we do not discard the opportunity of involvement of selective inhibitory mechanisms in a less uncertain context. Therefore, future research should address the issue of how brain mechanisms of selective and non-selective response inhibition are inter-related.Generally, it is assumed that the non-selective response inhibition is a relatively early transient process. Therefore, one of the limitations is associated with a low temporal resolution of the fMRI method. Further studies of the early temporal dynamics of neural activity are needed using EEG, MEG, and intracranial recordings in similar uncertain context settings. In particular, evidence for the practical equivalence of early ERP amplitudes (170 ms) revealed previously in equiprobable NoGo and Go trials should be provided in a future research. It is also important to note that we looked for practically equivalent increases in regional brain activity as measured by BOLD-signal. Firstly, it is possible to look for the similarity of local activity patterns by employing multivariate classification techniques in future studies. Secondly, future research should consider how non-selective response inhibition is mediated by interactions between the revealed cortical structures and subcortical structures that form inhibitory and excitatory cortico-striato-thalamo cortical circuits.98. Future research should use objective electrophysiological measures, such as LRPs, subthreshold electromyographic activity, and motor evoked potentials elicited by transcranial magnetic stimulation to rigorously demonstrate prepotent motor activity in equiprobable cue-target Go/NoGo task settings.Despite the fact that revealed fast reaction times and the LBR increase in NoGo trials evidenced the existence of prepotent response tendency, the low error rate might be a potential limitation of the current study because of the high automaticity of induced behaviour. At the same time, such low commission error rates have been repeatedly reported in several equiprobable Go/NoGo studiesThe other important challenge for the current study and the field in general is the question of segregation of the brain activity elicited by non-selective response inhibition from that elicited by non-inhibitory processes, like attentional or executive processes. One possible solution may be to look for brain activity that is causally related to inhibitory effects at the level of the primary motor cortex, spinal cord, and effector muscles.Supplementary Information.


Moringa oleifera accessions with antimethanogenesis to identify potential MIFs that were responsible for high and low methane inhibition from ruminants. Plant extracts from 12 Moringa accessions were evaluated at a 50 mg/kg DM feed for gas production and methane inhibition. Subsequently, the accessions were classified into low and high enteric methane inhibition groups. Four of twelve accessions (two the lowest and two the highest methane inhibitors), were used to characterize them in terms of MIFs. A total of 24 samples (12 from lower and 12 from higher methane inhibitors) were selected according to their methane inhibition potential, which ranged from 18% to 29%. Ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) and untargeted metabolomics with univariate and multivariate statistical analysis with MetaboAnalyst were used in the study. Although 86 MIFs showed (p < 0.05) variation between higher and lower methane inhibition groups and lay within the detection ranges of the UPLC-MS column, only 14 were significant with the volcano plot. However, Bonferroni correction reduced the candidate MIFs to 10, and their R2-value with methane production ranged from 0.39 to 0.64. Eventually, MIFs 4.44_609.1462 and MIF 4.53_433.1112 were identified as bioactive MIFs associated with higher methane inhibition, whereas MIF 9.06_443.2317 and 15.00_487.2319 were associated with lower methane inhibition with no significant effect on in vitro organic matter digestibility of the feed. These MIFs could be used by plant breeders as potential markers to develop new M. oleifera varieties with high methane inhibition characteristics. However, further investigation on identifying the name, structure, and detailed biological activities of these bioactive metabolites needs to be carried out for future standardization, commercialization, and application as dietary methane mitigation additives.This study evaluated the relationship of secondary bioactive plant metabolite ion-features (MIFs) of  The intention was to identify potential m/z ion-features that could be used as markers to select breeding varieties with high methane inhibition characteristics. These could then be used as dietary additives to the ruminants’ feeding system.The inclusion of flavonoids such as flavone, myricetin, naringin, rutin, quercetin, and kaempferol at 4.5%  mL/g DM . Luteolicatechin . However control . Dependimulation ,37,38,39mulation . Plant mmulation ,41,42,43mulation ,44,45,46M. oleifera and one Moringa stenopetala accession were grown from October 2018 to February 2019 in the subtropical climate of Pretoria at Roodeplaat experimental site of the Agricultural Research Council (ARC), Pretoria in South Africa, which is located at 25°44′30′′ S, 28°15′30′′ E. Prior to this study, plant extracts from these accessions were evaluated for in vitro gas production and methane-reducing potential when used as dietary additives. The accessions were classified as low and high methane inhibition groups based on their CH4 inhibition potential. Subsequently, four accessions—that is, two accessions that contained 12 samples that exhibited the lowest antimethanogenic effect, and another two that constituted 12 samples of the highest antimethanogenic potential—were selected for this metabolomics study [Eleven cs study  (unpubliThe leaves were freeze-dried for about five days and milled to a 1 mm sieve size in a milling machine. About 50 g of the dried leaf powder was suspended in 500 mL of methanol at a ratio of 1:10 (mass/volume)  for 96 hIVOMD) of Eragrostis curvula hay, and CH4/IVOMD treated with these Moringa accession leaf extracts were determined in a previous study and imported into this part of the study [4HCO3) and sodium bicarbonate (NaHCO3), while the macromineral solution was prepared from sodium hydrogen phosphate dibasic (Na2HPO4), potassium phosphate monobasic (KH2PO4), and magnesium chloride hexahydrate (MgCl2 × 6H2O). The micromineral solution was composed of calcium chloride dihydrate (CaCl2 × 2H2O), manganese chloride tetrahydrate (MnCl2 × 4H2O), cobalt chloride hexahydrate (CoCl2 × 6H2O), and ferric chloride hexahydrate (FeCl3 × 6H2O) [w/v) solutions were mixed with tryptone and distilled water. Then, the mixed solution was bathed at 39 °C and continuously bubbled with CO2 until the incubation process was completed to keep the anaerobic condition. Before morning feeding of the incubation day, the rumen fluid was collected from three ruminally cannulated Pinzyl (Pinzgauer cross Nguni) steers and adapted for 14 days, following procedures approved by the Animal Ethics Committee of the University of Pretoria (No. EC039-18), South Africa. L-cysteine and sodium sulphide nonahydrate (Na2S × 9H2O) were added to the medium 10 min before the addition of rumen fluid [The total gas, antimethanogenic potential, in vitro organic matter digestibility  . Early ien fluid  except fx = VjPpsi × 0.068004084, where Vx is the gas produced at 39 °C in mL; Vj is the headspace of the incubation bottle in mL; and Ppsi is the pressure recorded by the gas monitor system software [The total gas produced during incubation was measured with a pressure transducer attached to a digital data logger at 3, 6, 12, 24, and 48 h of incubation, and recorded in pounds-force per square inch (psi) . The gassoftware .4 samples were taken at 3, 6, 12, 24, and 48 h of incubation and analyzed by gas chromatography (GC)  equipped with a flame ionization detector. The CH4 area obtained from the GC was converted into parts per million (ppm) using a standard curve and then changed into a percentage. Next, the CH4 concentration was converted to ml by multiplying the total gas produced (mL) with the percentage of CH4 in the sample as CH4 (mL) = total gas produced (mL) × % CH4 concentration. Eventually, the antimethanogenic potential of the plant extract was expressed as a mL/g DM incubated feed [m/z ion-features of Moringa accessions with methane inhibition and identify the MIFs responsible for their antimethanogenic potential.Similarly, CHted feed . In addiIVOMD was determined with the modified two digestion phase techniques [E. curvula grass hay, extracts of the accessions (50 mg extract/kg DM feed), artificial saliva solution, and urea and rumen fluid were incubated under anaerobic conditions for 48 h at 39 °C using a test tube, which was fitted with modified stoppers. Artificial saliva was prepared from potassium chloride (KCl), disodium hydrogen phosphate (Na2HPO4), sodium bicarbonate (NaHCO3), sodium chloride (NaCl), magnesium sulphate heptahydrate (MgSO4·7H2O), and calcium chloride (CaCl2) before the incubation day and kept in the refrigerator until use. In the second phase, an acid and pepsin-containing solution were prepared from 20 mL of 32% HCl and 8 g of pepsin by dissolving them in 2000 mL of distilled water. Then, 20 mL of this solution was added after gently decanting the liquid on the top of the tubes and incubating for another 48 h at 39 °C. After 96 h of total anaerobic incubation, the residual plant materials were oven-dried at 100 °C for 18–24 h, ashed, and weighed, and the percentage IVOMD was calculated as follows: In addition, the chniques ,54 and iTo study metabolomics, about 100 mg of the crude extract used to determine in vitro methane was re-dissolved with 10 mL of 70% methanol (10 mg/mL concentration) in the Department of Chemistry, University of Pretoria, South Africa. The mixture was then vortexed and centrifuged for 10 min. Then, 10 µL of the sample supernatant was taken and diluted again with 990 µL of 70% methanol. About 1 mL of solution (0.1 mg/mL) was filtered with 0.2-micron syringe filters and transferred to the injection vial of UPLC-MS for analysis. The UPLC-MS analyses were carried out with a Waters Synapt G2 quadrupole time-of-flight (QTOF) MS connected to a Waters Acquity UPLC .4 inhibition, two accessions (A3 and A11), which contained 12 samples and showed the best in vitro CH4 inhibition (hereafter ‘high’), and another two accessions (A1 and A2), which comprised 12 samples and recorded the lowest CH4 inhibition, were selected as ‘low’ methane inhibitors. Then, before the metabolomics analysis, the low and high CH4 inhibition labelling of the samples was added to the UPLC-MS data. The MIFs in the column regeneration step (after 17 min) and the void volume (0–2 min) of the detector were poorly resolved from each other and ignored in all statistical analyses. Thus, the focus of the study was MIFs detected in the chromatogram between 2 and 17 min. A data pre-processing strategy for metabolomics was carried out to reduce the masking effect in data analysis. The values that were missing because of the limits of quantification in the detector were managed with a modified 80% rule. Thus, variables were excluded when the proportion of non-missing elements accounted for less than 80% in each biological group [t-test, fold change, volcano plot, and principal component analysis (PCA) with MetaboAnalyst 5.0. Initially, PCA was performed to explore the maximum variation to obtain the overview and classification. The volcano plot visualized the predictive component loading and identified important MIFs by the fold change and t-tests. After analyzing the significant variation of each MIF intensity by the t-test (p < 0.05), the Bonferroni correction was applied to minimize the multiple testing problem or false discovery rate [2-value) of MIF intensity with methane production was determined with scatter plot analysis. The samples with the injection vials were subjected to both the positive and negative ion mode detector of the UPLC-MS. The untargeted metabolomics data were transferred to Microsoft Excel and a MetaboAnalyst 5.0 system for univariate and semi-quantitative multivariate statistical analysis. After clustering the accessions with their antimethanogenic potential as high or low CHal group . Besidesery rate . The rel4 production, and IVOMD among these accessions using SAS version 9.4 [t-tests, fold change, the volcano plot, and PCA were carried out with MetaboAnalyst 5.0. The relationship  between MIF intensity and CH4 production, total gas production, and IVOMD was determined with scatter plot analysis. The Bonferroni correction was also calculated and applied at the corrected p ≤ 6.25 × 10−3 in the negative ion mode and p ≤ 6.25 × 10−4 in the positive ion mode [One-way analysis of variance (ANOVA) and least significant difference (LSD) for randomized complete block design (RCBD) were conducted to determine the variations in terms of total gas, CHsion 9.4 . To inveion mode . E. curvula hay, which was used for in vitro fermentation and methane determination, was a relatively poor-quality roughage that contained about 93.08% dry matter (DM). Its composition was 8.57% crude protein (CP), 80.02% neutral detergent fibre (NDF), 41.01% acid detergent fiber (ADF), 8.34% acid detergent lignin (ADL), and 3.92% ash content on a DM basis. The total gas and total CH4 and CH4 yield per unit of organic matter (OM) digestibility of E. curvula hay treated with the leaf extracts were significantly (p < 0.01) varied in the study. Their total methane production and methane yield per unit of OM digestibility ranged from 4.52 mL/g DM (A11) to 5.23 mL/g DM (A1) and 7.61 (A3) to 8. 81 mL (A1) per OM digestibility, respectively. Thus, the total methane production and methane yield per unit of OM digestibility were significantly (p < 0.01) decreased in all accession extracts compared with the CH4 produced in the control . Compared with the control, the recorded antimethanogenic potential (% CH4 inhibition) of the accessions ranged from 18% (A1) to 29% (A11). Thus, the hierarchical cluster analysis (HCA) grouped the study samples into two broad cluster trees as high and low CH4 inhibition with 80% similarity  and A11 (Pretoria) lay in a higher CH4 inhibition cluster tree, whereas the other 12 samples of A1 (bulk) and A2 (07229) belonged to the lower CH4 inhibition cluster tree . Thus, the variations in these parameters might be attributed to the differences in the active compounds in the extracts, which were expressed as SPM m/z ion-features. The substrate feed, milarity  using thotential ; Table 1ter tree . In addip < 0.01) different. This antimethanogenic variation was expected because of the difference in composition and concentration of the plant components, which were represented as MIFs in this study. A large number of MIFs were detected at the initial step of the screening from the UPLC-MS data of the accessions (data not shown). However, only 161 (33 in the negative and 128 in the positive ion mode) were selected for MetaboAnalyst using the detection range of 2 to 7 min and a significant level of their intensity between the two methane inhibition groups before data processing (p < 0.05). The MetaboAnalyst output of the MIFs containing data obtained from UPLC-MS in the positive and negative ion mode is illustrated in m/z ion-features in the samples, as well as a 95% confidence interval around the median of each group and their mean abundance in each group.A significant decrease in methane production was obtained from all accessions of Morinaga leaf extracts compared with the control. Hence their antimethanogenic potential was significantly . In addition, the volcano plot that is drawn from fold change threshold (x) 2 and the t-tests threshold (y) 0.05 selected 14 important MIFs (six in the negative ion mode and eight in the positive ion mode) that significantly varied between high and low inhibition samples (p ≤ 6.25 × 10−3) and seven MIFs in the positive ion mode (corrected p ≤ 6.4 × 10−4) were significantly different between the two methane inhibition groups. Thus, the MIFs that showed significant variations in all the tests were promoted to the next stage of associating them with higher and lower methane inhibition potential. However, MIFs that did not vary significantly with Bonferroni correction, but showed a significant variation on the other tests, were considered in the selection because of their higher relative abundance for easy practical application and higher relationship value  of the intensity with methane inhibition potential. Among the 161 markers uploaded into MetaboAnalyst, eight MIFs in the negative ion mode and 78 MIFs in the positive ion mode were significantly different between high and low CH samples . Using tm/z ion-features in a and b are identical to the MIFs shown in box plots of The red circles/dots in this volcano plot represent ion-features above the threshold. The Bonferroni corrected value of the negative ion mode (0.00625) in the graph indicates 2.2, whereas the positive ion mode (0.00064) is 3.2. The direction of comparison is ‘high methane inhibition’/‘low methane inhibition’. The overlapped m/z ion-features responsible for high methane inhibition and their summary data are shown in 2-value) of MIFs intensity that allied to higher methane inhibition with CH4 volume, total gas, IVOMD, and methane yield per OM digestibility was calculated with a scatter plot (p < 0.05) and two MIFS in the positive ion mode (corrected p ≤ 6.4 × 10−4) were selected as candidates for higher methane inhibition potentials of the accessions. The relationship value determined with scatter plot analysis between the MIF intensities with CH4 produced per DM feed incubated and CH4 yield per OM digested were 0.42 and 0.41 in MIF 4.44_609.1462, and 0.54 and 0.43 in MIF 4.53_433.1112, respectively (p < 0.01) for the MIFs 4.44_609.1462 and MIF 4.53_433.1112 intensities with total methane production and methane yield per OM digestibility  and correlation coefficient (−0.66). Hence, the higher signal strength obtained in MIF 4.44_609.1462 might provide an advantage for analysis and practical application, though concentration is not the sole determinant factor of their antimethanogenic potential. It was proven from MIF 4.53_433.1112, however, its level of intensity was low, and it exhibited a greater relationship  with CH4 production than was observed in MIFs with greater intensity. This result indicated not only that metabolite concentration played a crucial role in their antimethanogenic potential, but that the specific action of the metabolites and their thresholds of maximum and minimum dose-dependent influence might also determine their biological activities, including antimethanogenesis. The potential MIFs identified for their contribution to higher CH4 inhibition were found in high and low methane inhibition sample groups of the study. The variations in their effect on the two groups might be attributed to differences in their concentration. However, several MIFs seemed to contribute positively to the higher CH4 inhibition potential of the samples with different levels of contribution. The three MIFs illustrated in 4 inhibition potentials of Moringa accessions. However, MIF 4.53_433.1112 showed significant variations after the Bonferroni correction at corrected p ≤ 6.4 × 10−4 and seemed to have the greatest responsibility for the higher inhibition of methane recorded in the study. However, MIF 4.44_609.1462 could be a promising candidate in the future because of its larger intensity, which might be easier for analysis and practical applicability. It also had a relatively good R2-value and correlation coefficient.The potential candidate ter plot . Howeverectively . Besidestibility . In termIVOMD to see their overall effect on fermentation kinetics and animal performances. Thus, the MIFs selected for their contribution to higher CH4 inhibition correlated negatively and significantly with methane production, TGP and CH4/IVOMD; however, MIF 14.22_682.3577 was not correlated significantly with CH4/IVOMD at p < 0.01 (p > 0.05) correlated with IVOMD, except MIF 14.22_682.3577, which correlated negatively and significantly (p < 0.05) with IVOMD. Thus, the MIF 14.22_682.3577 is not the interest of this study because of its negative effect on digestibility and non-significant reduction of CH4 yield per unit of OM digestibility, which might decrease the animal’s performance during its application. The relationship scatter plot analysis between MIF 4.44_609.1462 and MIF 4.53_433.1112 intensities versus TGP (mL/g DM), which showed R2-values of 0.1132 and 0.4221  and p < 0.01 . Howeverd 0.4221 . Thus, tt-test, fold change, PCA, and volcano plot, these candidate MIFs, for their contribution to lower methane inhibition, are presented in p < 0.05) different between higher and lower CH4 inhibition in the t-test, most MIFs intensity and total CH4 production were associated positively with lower methane inhibition characteristics of the samples with R2-values ranging from 0.03 to 0.64. However, the volcano plot and Bonferroni correction were used to explore the MIFs most responsible for lower CH4 inhibition. Only seven MIFs (one in the negative ion mode and six in the positive ion mode) were varied between the two sample groups at the corrected p ≤ 6.25 × 10−3 in the negative ion mode and p ≤ 6.4 × 10−4 in the positive ion mode. Thus, the Bonferroni correction reduced the candidates to seven and selected the MIFs that showed an R2-value above 0.39  of CH4 production with MIF intensity were obtained in MIF 15.00_487.2319  and MIF 15.00_487.2319 , respectively . After analyses of the ove 0.39 . Howeveranalysis  and signectively . The MIF4 inhibition were also analyzed for their correlation and relationship with TGP, IVOMD, and CH4 per unit of OM digestibility . However, they did not (p > 0.05) correlate with IVOMD of the substrate feed. Relationship values  of 0.6929 and 0.0798 were obtained from the scatter plot analysis of MIF 9.06_443.2317 and MIF 15.00_487.2319 intensities with TGP across samples of the study. These MIFs also positively and significantly (p < 0.01) correlated with methane yield per OM digested and showed a relationship  of 0.4812 and 0.5586, respectively. However, the non-significant correlation of these MIFs intensities with IVOMD proved that they did not negatively affect fermentation kinetics and productivity of the animals. Therefore, the MIFs selected for their contribution to lower inhibitors of the accessions may have an antagonistic effect on the MIFs that are involved in higher methane inhibition or may stimulate methanogens during ruminal fermentation with no effect on OM digestibility. However, this study did not investigate enough evidence of the bioactivity and mechanism of action of the selected MIFs. The seven m/z ion-features would have generally been selected as potential candidates, individually or combined, and might be the responsible MIFs, although MIF 9.06_443.2317 and 15.00_487.2319 could be the most promising ion-features for the lower methane inhibitor Moringa accessions. However, identification of their names, structure, and detailed biological activities will be needed in the future.The seven MIFs selected for their contribution to lower CHtibility . Thus, am/z ion-features selected after screening (three contributed to higher CH4 inhibition and seven to lower CH4 inhibition), all were included in both sample groups of the study with different concentration levels. However, the intensity of some MIFs within the group was below the limits of detection and recorded a zero value in the detector. Similarly, among the 122 metabolite constituents identified in a previous study of Moringa accessions grown in China and India, 118 were shared components. However, four compounds were detected in only one of the accessions [Plant secondary metabolites are produced by stress and defense response signaling in plant growth , though cessions . m/z ion-features in this study affected methanogenesis in different ranges by suppressing the bioactivity of active compounds involved in CH4 inhibition or by changing the pathways that encourage methane production. Thus, the MIFs in higher methane inhibitors reduced methane per unit of organic matter (methane yield) because of their effect on increasing OM digestibility or not having an effect on OM digestibility. On the other hand, an increased total methane production and methane yield were found in the lower methane inhibitor accessions compared with the higher inhibitor group. However, all samples reduced the total CH4 production and CH4 yield per unit of organic matter digestibility with improved OM digestibility of the substrate feed compared with the control, and the magnitude of antimethanogenic effect varied within accessions. This might be because of the cumulative effect of all the compounds in the sample. Thus, the biological activity of the m/z ion-features that contributed to higher inhibition might have dominated those that were responsible for lower CH4 inhibition. The benefit of decreasing total methane production and methane yield per OM digestibility were recorded in all accessions with a significant improvement in OM digestibility. Thus, it is apparent that this antimethanogenic variation and improvement in OM digestibility are attributed to the difference in the compounds that existed in the plants and their extracts. This is supported by several studies in which the SPM were reported as antioxidant and antimicrobial agents that act against bacteria, protozoa, and fungi [The bioactive metabolites denoted by nd fungi ,62,63,64m/z ion-features in samples of the study might be attributed to the variation in the dose-dependent influence of the metabolites and their thresholds of maximum and minimum activities [m/z ion-features having larger intensity. Of the 10 potential m/z ion-features selected (three for higher methane inhibition and seven for lower inhibition), all were found in both high and low inhibition sample groups. Thus, their variation on the effect of antimethanogenesis might be associated with the level of concentration in the samples. Consequently, along with the type, structure, and functional group of the metabolites affecting their antimethanogenic potential, the concentration of the specific m/z ion-feature also influenced their effect on CH4 production substantially.The effects of tivities ,66,67. T4 inhibition may also be associated with differences in selective direct inhibition and toxic effects  of the compounds, or depression of the microbial metabolic processes involved in methanogenesis [4 inhibition potential and for lower methane inhibitors did not reduce the IVOMD of the feed significantly. Hence, they would not affect the productivity of ruminant animals negatively during their ultimate application. Furthermore, volatile fatty acids can also be used as a substrate during methanogenesis by methanogens [2) into propionate production via lactate, fumarate, and malate pathways, which decrease the flow of H2 into CH4 production; whereas, some metabolites are involved in favoring H2 into the acetyl CoA and formate pathway, which produces more acetate and may increase CH4 [4 production. Therefore, the observed higher CH4 inhibition without a negative effect on the OM digestibility indicates that the inhibition potential obtained in these accessions might be partly associated with changing the H2 pathway towards propionate production, or may play a role in the direct inhibition of the methanogens involved in methane production. This feature increases the interest of the MIFs selected in this study; however, further detailed investigations are warranted to annotate these m/z ion-features and elucidate their pathway and/or mechanism of action on methane production. The variations in CHogenesis ,68,69. Sogenesis ,71,72. Hhanogens . Hence, ease CH4 . Howeverease CH4 ,20, whicm/z ion-features in the study will be used as markers for the selection and standardization of commercial Moringa varieties that will be used as a source of plant extracts to mitigate enteric methane from ruminants. However, further detailed investigations are warranted to annotate these m/z ion-features and elucidate their pathway and/or mechanism of action on methane production before their application to the mitigation strategies. Besides, their effectiveness in live animals and applicability at the farmers’ level needs more future research.To produce healthy and safe animal products free from long-term residual effects of synthetic feed additives, and for efficient ruminant animal production-friendly with the environment, the study results have several practical implications and future research perspectives. The study approach can be used as an initiative to investigate more effective bioactive metabolites from different medicinal plants. The selected bioactive M. oleifera accession plant extracts in m/z ion-features intensity, composition, and antimethanogenesis (18%–29%) even when grown in the same environment: the subtropical climate of Pretoria, Gauteng Province, South Africa. However, several secondary plant m/z ion features were detected in the initial stage of screening; three m/z ion-features for high CH4 inhibition and seven for low CH4 inhibition, individually or combined, are potentially contributing to the antimethanogenic variation in the studied accessions. Among them, the bioactive m/z ion-features 4.53_433.1112 and 4.44_609.1462 were for higher CH4 inhibition, whereas the m/z ion-features 9.06_443.2317 and 15.00_487.2319 for lower CH4 inhibition were selected as potential markers for Moringa. The promising m/z ion-features selected for both higher and lower methane inhibitors did not also adversely affect the fermentation and productivity of the animals. Hence, the use of these m/z ion-features as potential markers to standardize and select through breeding varieties for high methane inhibition characteristics will increase the benefit without compromising the feed quality when used as dietary additives in ruminant feeding. However, further detailed investigations on matching their name, structure, and biological activities need to be confirmed in the future using a multidisciplinary approach before the application in methane mitigation strategies.The study confirmed that there are variations of


Fmr1 knock-out mouse embryonic fibroblasts, and it showed dysfunctional protein kinase and phosphatase signaling hubs in the brain. This suggests that the further use of phosphoproteomics approaches in Fragile X syndrome holds promise for identifying novel targets for kinase inhibitor therapies.Absence of the Fragile X Messenger Ribonucleoprotein 1 (FMRP) causes autism spectrum disorders and intellectual disability, commonly referred to as the Fragile X syndrome. FMRP is a negative regulator of protein translation and is essential for neuronal development and synapse formation. FMRP is a target for several post-translational modifications (PTMs) such as phosphorylation and methylation, which tightly regulate its cellular functions. Studies have indicated the involvement of FMRP in a multitude of cellular pathways, and an absence of FMRP was shown to affect several neurotransmitter receptors, for example, the GABA receptor and intracellular signaling molecules such as Akt, ERK, mTOR, and GSK3. Interestingly, many of these molecules function as protein kinases or phosphatases and thus are potentially amendable by pharmacological treatment. Several treatments acting on these kinase-phosphatase systems have been shown to be successful in preclinical models; however, they have failed to convincingly show any improvements in clinical trials. In this review, we highlight the different protein kinase and phosphatase studies that have been performed in the Fragile X syndrome. In our opinion, some of the paradoxical study conclusions are potentially due to the lack of insight into integrative kinase signaling networks in the disease. Quantitative proteome analyses have been performed in several models for the FXS to determine global molecular processes in FXS. However, only one phosphoproteomics study has been carried out in  Fragile X Messenger Ribonucleoprotein 1 gene (FMR1) [FMR1 promotor [Fragile X Syndrome (FXS) is the most prevalent form of inherited intellectual disability (ID) and autism spectrum disorders (ASD), and globally affects approximately 1:4000 males and 1:8000 females . Patiente (FMR1) . This expromotor . Loss ofpromotor .Fmr1 knock-out mice which, in turn, could contribute to impaired dendritic spine morphology and altered neuronal signaling [Fmr1 knock-out mice hippocampus. Aberrant GSK3β signaling has also been linked to structural changes in dendritic spines [FMRP is an RNA-binding protein that binds to polyribosomes supporting local protein synthesis . Generalignaling . MMP9 alc spines . Anotherc spines . Cleavinc spines . Besidesc spines . These fc spines .Fmr1 knock-out mice, enhancing phosphorylation of multiple downstream targets such as ribosomal protein S6 kinase beta-1 (S6K), 4E-binding protein (4E-BP), and eukaryotic initiation factor complex 4F (eIF-4E) [Most of the intracellular signaling pathways pivotal in the FXS are tightly regulated by protein kinases, which phosphorylate their protein targets . Phospho(eIF-4E) . In addi(eIF-4E) . In thisFMR1 gene [FMR1 shows a high conservation in both rodents and primates [FMR1 gene starts early during embryonic development, with the highest levels in brain, testes, ovaries, and thymus, concomitant with several symptoms underlying the FXS [FMR1 gene has 17 exons which are alternatively spliced into 12 transcriptional variants [FMR1 mRNA levels. However, FMRP expression is slightly reduced [FMR1. Here, methylation of CpG-specific cytosines covers the entire promotor region, leading to heterochromatin formation [The FXS is an X-linked monogenetic disorder that is caused by a CGG trinucleotide repeat expansion in the 5′ untranslated region (UTR) of the MR1 gene . The genMR1 gene . During primates . Express the FXS . The FMRvariants . Within variants . Normal variants . In contvariants . Premuta reduced . The fulormation .FMR1 gene result in transcriptional downregulation and an absence of FMRP [The full repeat expansion and subsequent hypermethylation of the  of FMRP . Mammali of FMRP . A final of FMRP .6-methyladenosine (m6A), a modification regulating mRNA function. Reading of the m6A modification enables shuttling of methylated mRNA targets between the nucleus and the cytoplasm during neural differentiation. Moreover, FMRP also interacts with another m6A reader YTH N6-methyladenosine RNA Binding Protein 2 (YTHDF2), which ensures degradation of the mRNAs previously stabilized by FMRP [FMRP plays a crucial role as a negative regulator of translation of proteins that are involved in synaptic function, connectivity, plasticity, and dendritic morphology . The way by FMRP ,46. In t by FMRP . The tra by FMRP .In response to ligand binding, several postsynaptic membrane receptors can be triggered, initiating an intracellular signaling cascade. Common receptors still under debate regarding their involvement in the FXS are the metabotropic glutamate receptors (mGluRs), AMPA receptors, GABA receptors type A and B, NMDA receptors, and TrkB receptors . Signal A receptor and the metabotropic GABAB receptor . Additionally, impairments of the GABAB receptor are also reported. Upon binding of GABA, presynaptic vesicles, carrying glutamate neurotransmitters, are inhibited from unloading their cargo, resulting in an inhibition of the postsynaptic mGluR signaling. In case of the FXS, there is an imbalance of neurotransmission, favoring excitatory mGluR5 signaling [In addition to the glutamatergic imbalance, the GABA receptor system is also impaired in FXS patients. GABA is the principal inhibitory neurotransmitter expressed in the adult mammalian brain and is strongly involved in the modulation of neuronal activity. Dysregulated GABAergic transmission is thought to play a role in behavioral abnormalities underlying the FXS. Epileptic seizures and sleeping disorders are common problems affecting FXS patients. At a cellular level, GABA can bind to two different types of receptors, being the ionotropic GABAreceptor . Here, Fignaling ,15.FMRP is a multifunctional protein that plays a role in translational repression through ribosomal stalling, where it is locally modulated by posttranslational modifications such as phosphorylation and methylation . InitialAs mentioned above, the FXS is characterized by abnormal receptor signaling and intracellular second messengers, which are modulated by a bidirectional system, being protein kinases (PKs) and protein phosphatases (PPs) . PKs canAkt) is a serine-threonine kinase, and its signaling is mainly activated by extracellular ligands such as growth factors [2 and PIP3 [Serine-Threonine Protein kinase B  is serine-threonine protein kinase that consists of a heterotrimeric complex, comprising a catalytic α-subunit and two regulator β and γ-subunits [Adenosine mono-phosphate-activated protein kinase  kinases consist of a family of effector enzymes that are responsive to calcium. They become active when intracellular calcium levels are elevated, subsequently phosphorylating various downstream protein substrates that are involved in synaptic vesicle formation, ion channel signaling, gene expression, and synaptic plasticity, memory, and learning [2+/calmodulin (CaM) complex, an isoform variable sequence, and ultimately an oligomerization domain at the carboxy terminus, allowing inter-subunit assembly [Calcium/calmodulin-dependent protein kinase type II subunit alpha  are a family of serine-threonine protein kinases that play a central role in cell cycle regulation. However, their function is not limited to mitotic cell division, as they also have crucial functions in other molecular processes such as migration, senescence, apoptosis, gene expression, and metabolism [Cyclin-dependent kinase 4  are ubiquitously expressed enzymes, mediating a rate-limiting step in the diacylglycerol (DAG) signaling [3 to IP3 and DAG mediated by phosphatidylinositol 4,5-bisphosphate by phospholipase C (PLC) [Diacylglycerol kinases  . The met C (PLC) . The str C (PLC) . The DGK C (PLC) . Moreove C (PLC) .Among the CGK family members, the CGK kappa isoform has received the most interest. In an attempt to identify target mRNAs of FMRP, crosslinking immunoprecipitation experiments in cortical neurons demonstrated that CGKκ was a unique candidate. The loss of FMRP disrupted the CGKκ activity and expression, which is dependent upon group 1 metabotropic glutamate receptors. The decreased expression and activity of DGKκ correlated with dendritic spine abnormalities, synaptic plasticity aberrations, and behavioral abnormalities. Overexpression of DGKκ, to restore the reduced protein levels, rescued Fmr1 knock-out neurons from the dendritic spine defects, suggesting that DGKκ contributes to FXS pathology . SimilarERKs) enable cells to detect external stimuli such as growth factors, cytokines, stress factors, GPCRs, and intracellular calcium, allowing receptor-induced intracellular signaling [Extracellular signal-regulated kinases  to evoke mGluR-dependent long-term depression, indicated that synthesis and degradation of FMRP were required as a bifunctional regulatory system controlling synaptic plasticity. Upon rapid stimulation of mGluRs, translation of mRNAs bound to FMRP is initiated and is excessive in Fmr1 knock-out mice. Here, the expression of ERK, whose mRNA is not bound to FMRP, was found to not be significantly different in Fmr1 wild-type compared to knock-out mice. Next, the activation, hence phosphorylation, of ERK was assessed, which is necessary for mGluR-LTP. Increased phosphorylation of ERK was found in Fmr1 knock-out mice, compared to wild-type mice. As ERKs play a pivotal role in protein synthesis, depending on mGluRs activation, it was investigated whether inhibition of the ERK pathway could affect the LTP. However, it was found that inhibition of the ERK pathway could not reverse the enhanced mGluR-LTP in Fmr1 knock-out mice . AnotherFocal adhesion kinases (FAK) and related proline rich tyrosine kinase 2 (Pyk2) comprise a family of non-receptor tyrosine kinases characterized by high sequence identity, conserved phosphorylation sites, and similar domain structures . FAKs reThe role of FAK was only reported to a minor extent in FXS. Research performed by Sidhu et al. (2014) showed that mRNA of matrix metalloproteinase-9 (MMP9) is locally regulated by FMRP. Hence, loss of FMRP resulted in higher MMP9 activity in the Fmr1 knock-out mouse . To furtGSK3) is a key enzyme involved in the regulation of glycogen metabolism, by phosphorylation of glycogen synthase [Glycogen synthase kinase 3  is a serine/threonine kinase that is involved in the regulation of the actin cytoskeleton, which is predominantly expressed in neuronal tissues such as the hippocampus, where it plays a critical role in memory and learning [LIM Domain Kinase 1  mRNA was identified as a target of FMRP. Absence of FMRP, which is the case in the FXS, increased intracellular BMPR2 levels and activated LIMK1. Treating postnatal Fmr1 knock-out mice by pharmacological inhibition of LIMK1 corrected morphological abnormalities in the FX model. Moreover, BMPR2 and markers for LIMK1 activity were studied in post-mortem prefrontal tissue from FXS patients and were found to be elevated in FXS patients, suggesting that BMPR2-LIMK1 signaling is a possible target for future therapy of the FXS . Later, MNK) is involved in regulation of mRNA translation and comprises two members, being MNK1 and MNK2 [The family of MAPK Interacting Serine/Threonine Kinases  is a serine-threonine kinase and acts as a master regulator of diverse biological processes such as ribosome biogenesis, mRNA translation, cell growth, and neuronal plasticity. The main function of mTOR is regulation of the translational machinery after activation of ligands, growth factors, or insulin, and protein kinases such as Akt and PI3K. It fine-tunes translation by activating p70 ribosomal S6 kinase (p70S6K) and inhibiting eIF4E binding protein (4E-BP1) [Mechanistic Target of Rapamycin Kinase ((4E-BP1) . The mTO(4E-BP1) . The mTO(4E-BP1) . The mTO(4E-BP1) . The mTO(4E-BP1) .As mTOR controls several nervous system functions such as development, plasticity, memory, and cognition, it is no surprise that neurological disorders such as FXS report abnormal mTOR signaling . An earlRAC1) Activated Kinases (PAKs) consist of a diverse group of serine and threonine kinases that act as main effectors of Rho GTPases Cdc42 and Rac. They function as a molecular switch, regulating the cytoskeleton and neuronal activity [The family of p21  comprise a group of plasma membrane-associated lipid kinases, which phosphorylate a hydroxyl group on position three of the ring structure of inositol glycerophospholipids, thereby creating 3′ phosphoinositides (PIs) [Phosphatidylinositol-4,5-Bisphosphate 3-Kinases (es (PIs) . PI3Ks ces (PIs) . The fames (PIs) . PI3Ks oes (PIs) ,185. Thees (PIs) . The difes (PIs) ,187. Actes (PIs) ,188. Onces (PIs) . Counteres (PIs) .As mentioned above, PI3Ks are interconnected with signaling pathways involving Akt and mTOR. For this reason, PI3K activity is thought to be compromised in FXS. An early study by Hu et al. (2007) reported abnormalities in the Ras-PI3K-Akt signaling pathway in a mouse model for the FXS. The activity of PI3K was indirectly determined by western blotting of 3-phosphoinositide-dependent protein kinase-1 (PDK1), as it is a master kinase facilitating phosphorylation of Akt. Global expression of PDK1 was slightly increased in CA1 cells of Fmr1 knock-out mice compared to wild-types, however not significantly. Administration of histamine, a neuromodulator, increased phosphorylation of PDK1 at Thr308 in wild-type cultures, but failed to elevate phosphorylation of PDK1 in knock-out cultures, suggesting that the Ras-PI3K-Akt pathway is seriously impaired in the Fmr1 knock-out mouse . A next PKA) is a highly conserved cyclic adenosine monophosphate (cAMP)-dependent kinase, which catalyzes the phosphorylation of serine, threonine, and tyrosine amino acids [Protein Kinase cAMP-Activated Catalytic Subunit Alpha  circuit. However, FMRP overexpression positively correlated with elevated PKA activity. Moreover, loss of Rugose, a large A-Kinase Anchor Protein (AKAP) which can bind PKA, resulted in a reduction in PKA activity. As PKA signaling affects cytoskeleton dynamics, F-actin regulation was investigated. In the FXS fly model, F-actin was found to be accumulated in MB cells, and loss of Rugose also resulted in similar F-actin accumulation. These data suggest a FMRP-Rugose-PKA signaling mechanism that involves regulation of cytoskeleton dynamics, and proposes PKA as a new target for therapy in the FXS [Drosophila and human FMRP enhance PKA activity in brain regions involved in memory and learning. Conversely, enhancing neuronal PKA activity negatively correlated with FMRP expression, demonstrating an FMRP-PKA negative feedback loop. Moreover, patient-derived R138Q point mutations of FMRP increased PKA activity in dendritic arbors, generated oxidative stress, and disrupted the neuronal cell architecture. The activation of PKA was also assessed by western blotting for its catalytic subunit, where phosphorylation of Thr197-198 was found to be increased, indicative of higher activity. Deletion of the RNA-binding RGG box of human FMRP (hFMRP) also reduced Drosophila FMRP (dFMRP), suggesting that FMRP does not exclusively regulate binding of its own mRNA in a direct negative self-regulation loop. The hFMRP-RGG mutant formed protein aggregates with colocalized PKA activity in neuronal cells. These data indicate that FMRP is a regulator of PKA activity and acts via a negative feedback loop regulating learning and memory [The emphasis of PKA signaling in FXS has mainly focused on cAMP metabolism, and the protein kinase itself has been poorly studied in contrast to the other kinases mentioned above. In platelets derived from FXS patients, inconsistent results were found for cAMP accumulation. Some patients showed overlap with controls, while others showed reduced cAMP levels, reflecting the variability in phenotype of patients with the syndrome . Later o the FXS . Findingd memory . Only red memory .PKC) represents a family of serine and threonine kinases that signal in a lipid-dependent manner, e.g., binding of phosphatidylserine. Currently, ten PKC isoforms have been identified and can be divided into three groups: (1) the conventional isoforms , which become active after binding of second messengers Ca2+ and diacylglycerol (DAG), (2) novel isoforms  that only depend on DAG for activation, and (3) atypical isoforms  which are regulated by protein–protein interactions independent of Ca2+ and DAG [Protein kinase C  reported that activation of the GABAB receptor results in Fmrp expression by upregulation of cAMP response element binding protein (CREB) in primary mouse cultures by two mechanisms: (1) transactivation of insulin-like growth factor-1 receptor (IGF-1R), and (2) activation of PKC. To assess PKC activity, phosphorylation of the downstream substrate MARCKS was investigated by western blotting. Phosphorylation of MARCKS at Ser152/156 was increased in a time-dependent manner upon baclofen stimulation but was reduced after treatment with the FAK inhibitor PF573228, suggesting that PKC acts downstream of FAK. Next, the effects of three different PKC inhibitors were tested on CREB activation. All three were capable of reducing CREB phosphorylation, indicating that PKC is required for CREB activation after GABAB receptor stimulation. Next, the effect of GABAB receptor activation was studied for induction of FMRP expression. Upon GABAB receptor stimulation with baclofen, FMRP synthesis was decreased. Similar findings were reported for IGF-1R, suggesting that both IGF-1R and PKC are crucial players in the expression of Fmrp after GABAB receptor activation. Unfortunately, there are no reports of PKC activity studies in Fmr1-deficient cultures [A receptor subunits in vitro and in vivo in Fmr1 knock-out and wild-type mice. It found a significant decrease in the expression of α1 GABAA receptor and PKC in Fmr1 knock-out mice compared to wild-type mice. Moreover, phosphorylation of α1 GABAA receptor at Ser408/409 and PKC at Thr497 were both reduced in cortical neurons and brains from Fmr1 knock-out mice, indicative of a lower activity. To address whether α1 GABAA receptor is mediated by PKC, a specific inhibitor calphostin C was used and showed a threefold decrease in α1 GABAA receptor phosphorylation. The effect of the PKC activator PDBu was also tested in cortical cultures and showed a time-dependent increase in phosphorylation of the α1 GABAA receptor. Thus, studies with a PKC inhibitor as well as an activator suggest that phosphorylation of the α1 GABAA receptor is mediated by PKC [NaP), which is mediated by an increased mGluR5-PLC-PKC signaling. Therefore, PKC activity was investigated for the influence of the INaP using two potent and selective PKC inhibitors, calphostin C and PKC19–36. Using a recording pipette, administration of both calphostin C and PKC19–36 reduced the excess INaP current of the Fmr1 knock-out mouse back to baseline wild-type levels. These findings indicate INaP current deregulation in the FXS and hyperexcitability, which can be targeted with specific PKC inhibitors [Recently, changes in PKC signaling have also been reported in FXS. Treatment with the GABAcultures . In 2015d by PKC . Anotherhibitors . In 2020hibitors .RSKs) are a serine-threonine kinase family that play a role in various cellular processes such as cell growth, motility and migration, survival, and proliferation, thereby functioning as downstream mediators of the MAPK signaling cascade [The p90 ribosomal protein S6 kinases  reported higher activity of ERK-induced p90 ribosomal S6 kinase activity in Fmr1 knock-out mice. Here, defective ERK signaling was studied outside of the hippocampus, where ERK appears normal with no change in basal activity ,190. SubS6Ks) are part of a conserved serine-threonine kinase family that mainly function as downstream effectors of the mTOR pathway [The p70 ribosomal protein S6 kinases  investigated altered signaling mechanisms in lymphocytes from FXS patients and healthy subjects. First, phosphorylation of S6K1 was assessed by western blotting, resulting in a stronger Thr389 phosphorylation quantified to total S6K1 expression in FXS lymphocytes compared to controls. Next, a direct S6K substrate, the ribosomal protein S6, was investigated, resulting in a higher phosphorylation at Ser 235/236 in FXS lymphocytes with no difference in expression of total S6. Controversially, after investigating mTOR, phosphorylation of Ser2448, a site that also includes S6K action, remained unaffected between patients and controls. To determine whether the higher phosphorylation of S6K was able to induce translation, eIF4E phosphorylation was investigated, as it is a substrate of S6K and participates in translation initiation. Here, a massive increase in phosphorylation of eIF4E at Ser209 was found in FXS patients, while total eIF4E expression was not affected. These findings were further explored in post-mortem brain tissue of FXS patients and healthy age-matched controls. Similarly to findings in lymphocytes, S6K1 phosphorylation was increased at Thr389 in brains of FXS patients. No alterations in phosphorylation of mTOR were observed. Phosphorylation of effector substrate eIF4E at Ser209 was extremely low in FXS patients, possibly explained by high phosphatase activity in the brain during the post-mortem interval. Total eIF4E abundance was unchanged between patients and controls and was easy to detect. These results indicate abnormal translation in lymphocytes and post-mortem brain tissue from FXS patients . ConcurrTrks) are a family of receptor protein tyrosine kinases (RTKs) consisting of the members tropomyosin-related kinase A (TrkA), B (TrkB), and C (TrkC) [2+ pathway, ultimately resulting in transcription of genes involved in neuronal differentiation, proliferation, synaptic plasticity, growth, and cell survival [The tropomyosin-related kinases (C (TrkC) . The famC (TrkC) . The TrkC (TrkC) . ActivatC (TrkC) . Tyr816 C (TrkC) . Recruitsurvival ,237. Expsurvival ,237,238.2A receptor (A2AR) antagonist, revealed a reduction in synaptic and cognitive abnormalities in Fmr1 knock-out mice. Similarly to a behavioral rescue, overactive signaling pathways such as the mTOR, TrkB, and STEP signaling were corrected. Here, the truncated form of TrkB was found to be increased in the hippocampus of Fmr1 knock-out mice, which could be normalized by A2AR antagonization. TrkB receptor truncation could inhibit downstream tyrosine kinase signaling, thereby affecting multiple downstream signaling processes. Ultimately, reduced BDNF levels were found in the hippocampus of Fmr1 knock-out mice, and reduction of the truncated-TrkB receptor partially rescued the Fmr1 mouse phenotype [Initially, FMRP was discovered to be regulated by the neurotrophin BDNF. In vitro administration of BDNF to cultured rat hippocampal neurons for 24 h resulted in a downregulation of FMR1. Moreover, transgenic mice which overexpressed the active TrkB receptor showed a slight downregulation of Fmr1 mRNA in the hippocampus. However, expression of BDNF and TrkB remained unchanged in Fmr1 knock-out and control mice. Activity of the TrkB receptor was not assessed in Fmr1-deficient mice . Later, henotype .p < 0.0001). Here, FMRP is involved in a tightly related network of protein kinases with its main interactors being LIMK1, CaMKIIα, Akt1, S6K1, GSK3β, and TrkB. For DGKk, no functional associations with other protein kinases or with FMRP were predicted.Mapping of kinase interaction networks is essential for understanding the cellular processes that protein kinases participate in, and subsequently could provide information about disease-associated signaling pathways . For thiDrosophila models for the FXS, which were developed to identify differences in synaptic protein expression. Stable isotope labeling by amino acids in cell culture (SILAC)-based proteomics in Fmr1 knock-out and wild-type cortical neurons resulted in identification of 132 differentially expressed proteins in absence of FMRP, related to changes in synaptic structures, neurotransmission, and dendritic mRNA transport, together with autism- and epilepsy-related proteins [Fmr1 knock-out and wild-type hippocampal synapses, using isobaric tags for relative and absolute quantitation (iTRAQ). A series of 23 proteins were significantly different in expression with groups of proteins known to be involved in cellular differentiation, neurite outgrowth, and synaptic vesicle release [dfmr1 Drosophila model resulted in profiling of 1617 proteins, identifying several proteins which were altered in expression such as actin-binding protein profilin and microtubule-associated protein futsch [Fmr1 knock-out and wild-type mice at adolescent and adult stages of life. Over 100 proteins were upregulated in Fmr1-deficient mice at adolescence, but this was no longer the case in adult mice. The differentially expressed proteins were involved in processes affecting brain development, ASD, and intellectual disability, which were further integrated in an interactome showing a central role for PSD95 [Fmr1 knock-out and wild-type fibroblastic cell lines derived from Fmr1-deficient embryos to identify proteins and phosphorylation sites dysregulated as a consequence of FMRP loss. FMRP-related changes in the levels of 5023 proteins and events were initially identified and mapped onto major signaling transduction pathways. This study confirmed the global downregulation of the MAPK/ERK pathway and a decrease in the phosphorylation level of ERK1/2 in the absence of FMRP, which is connected to attenuation of long-term potentiation. Additionally, this study revealed phosphorylation abnormalities in several additional pathways, as it detected differential expression and phosphorylation of proteins involved in pre-mRNA processing and nuclear transport, as well as Wnt and calcium homeostasis signaling, which involves signaling by PLC, PKC, NFAT, and cPLA2 [Intracellular signaling is mostly mediated through phosphorylation of protein kinases, which are dysregulated in the FXS. However, despite great advances in studies of protein kinases in the FXS, an understanding of their dysfunction is far from clear and needs an integrative approach. For this reason, identification of phosphorylated proteins can be exerted by mass spectrometry-based phosphoproteomics. In this way, the activity of protein kinases can be indirectly quantified with phosphoproteomics as changes in protein phosphorylation result from dynamic alterations in protein kinase signaling . Initialproteins . Another release . Anothern futsch . A finalor PSD95 . Besidesnd cPLA2 .TM , Pamchip  or multiplexing phosphorylation assays  may provide novel perspectives on kinase network signaling defects which contribute to the FXS physiopathology. In addition, several of the identified kinases in the Fmr1 knock-out mouse model can be integrated with pathway analysis tools to find new targets for kinase inhibitor treatment. Additionally, translational studies are required to extrapolate the findings from the Fmr1 knock-out mouse model to different FXS patient sample materials. However, the capacity of FMRP to bind directly to different kinase mRNAs, e.g., DGKκ, could contribute to a potential feedback regulation, limiting kinase inhibitor therapy. Currently, specific kinase inhibitors such as a selective PI3K subunit inhibitor or different GSK3 isoform inhibitors have already been implemented for treatment with great success in the Fmr1 knock-out mouse model. Nevertheless, clinical trials in FXS patients tend to fail, possibly due to a lack of a proper outcome measure, indicating a high demand for objective assessment of FXS clinical trials. Thus, designing a generalized and patient-specific kinase signature could provide insights into the molecular signaling networks of the syndrome and could even help to stratify these within the wide heterogeneous group of FXS patients. One possible way to approach this would be to test different FXS patient-derived cell lines in response to a selection of kinase inhibitors on a phosphorylation array. In this way, an accurately informed inhibitor response profile can be created for controls and FXS patients, providing a first step towards personalized medicine. Furthermore, stratifications of patients, as well as defining kinase inhibitor responder profiles associated with different patient backgrounds, remains necessary for the development of personalized medicines in FXS. Additional research is required to obtain a global kinome tree activity map to facilitate future implementation of kinase inhibitors and kinase activity-based biomarkers for personalized FXS treatment.In this review, we summarized the current knowledge on protein kinase signaling, phosphorylation abnormalities, and phosphoproteomics-based approaches in the FXS, highlighting the complexity of its pathophysiology. We demonstrate the interplay of post-translational modifications (PTMs) involved in the regulation of FMRP, necessary for strict translational control in healthy neurons. However, further investigations are required to determine the effects of other PTMs besides phosphorylation, and whether these PTMs also result in dramatic phenotypic effects in the absence of FMRP. Subsequently, we have extensively discussed the protein kinases and phosphatases involved in the FXS. This summary highlights the complexity of capturing dynamic changes in kinase signaling networks which sometimes may yield conflicting results at the single kinase level. On top of this, many studies on protein kinases have been performed on different neurodevelopmental stages of life, model organisms, sample material, and techniques, which challenges data integration to better understand the mechanisms governing the FXS. We also explored a gap in the FXS literature, being the lack of integrative phosphoproteomics studies, which could provide novel molecular insights into the etiology of the disease. Besides phosphoproteomics approaches, integrative kinase activity assays such as NanoBret