Note de synthèse : La Prosocialité Humaine et les Mécanismes de Coopération

Cette note de synthèse explore les thèmes principaux et les idées clés issues des extraits de la conférence "L'expérience sociale la plus intéressante de ces dernières décennies".

Elle se concentre sur la nature de la coopération humaine, ses déclencheurs, ses freins et les mécanismes sociaux développés pour la maintenir.

1. La Nature Intrinsèque de la Prosocialité Humaine

Le discours débute par la description du "jeu du bien public", une expérience courante en économie expérimentale qui révèle des insights fondamentaux sur le comportement humain.

Dans ce jeu, les participants reçoivent une somme d'argent (par exemple, 20 €) et peuvent miser une partie de cette somme dans un pot commun qui sera ensuite doublé et redistribué équitablement.

• Coopération spontanée et initiale : Contrairement à l'hypothèse de l'Homo Economicus purement égoïste et rationnel, les humains, même entre inconnus et sous anonymat, tendent à coopérer spontanément au premier tour.

"En général la moitié des gens participent enfin les gens participent spontanément même entre inconnus même quand il y a des avec de l'anonymat même s'ils sont entre personnes qui qui n'ont jamais vu au premier tour ils vont quand même participer à hauteur de la moitié de de ce qu'ils ont". Cette tendance est observée "partout dans le monde".

• L'Homo Economicus comme modèle de laboratoire :

Le modèle de l'humain rationnel et égoïste est qualifié d'"animal de laboratoire", un "modèle théorique qui aurait dû juste rester au laboratoire". L'être humain est "beaucoup plus prosocial que ce que dit le modèle".

• L'intuition au service de la coopération : Une expérience de Harvard montre que lorsque les participants sont contraints de répondre rapidement et intuitivement ("dépêchez-vous de répondre réfléchissez pas vous avez 2 secondes pour répondre et pour miser ou pas"), ils misent davantage dans le pot commun.

À l'inverse, lorsque le mode "rationnel" est activé ("prenez le temps réfléchissez répondez pas trop vite"), la participation diminue.

"Plus on réfléchit plus on est dans le mental plus on se méfie moins on participe".

• Stress et prosocialité : Le stress peut également augmenter la coopération.

Les participants à qui on annonçait une prise de parole en public stressante par la suite "ont plus misé dans le mot peau commun que quand que si on que à ceux qu'on avait dit qu'ils allaient pas parler en public".

• L'empathie comme fondement : Cette prosocialité est "très ancré en nous" et découle de notre capacité à l'empathie.

L'existence de "neurones de miroir" permet de "vivre ce que l'autre sent", et cette capacité n'est pas limitée aux humains, s'étendant à d'autres espèces et même à des "bouts de bois" ou de simples symboles visuels.

• Altruisme précoce chez les bébés : Des études sur les enfants et les bébés montrent que "les capacités prosociales d'empathie et d'altruisme se retrouvent chez les bébés jusqu'à 6 mois même 5 mois".

Avant même le langage et le raisonnement, les bébés peuvent distinguer les coopérateurs des non-coopérateurs et chercher à aider, et même une récompense peut "démotiver à aider", soulignant une nature intrinsèquement altruiste.

2. L'Érosion de la Coopération et les Mécanismes de Stabilisation

Malgré cette tendance initiale à la coopération, le jeu du bien public montre que "au fil des bah des tours (...) l'entraide s'effrite et puis la la confiance s'effrite et puis finalement on se retire bien commun".

C'est le défi : "comment on fait pour ne pas que ça s'effrite avec le temps".

Les cultures ont développé des "systèmes des mécanismes sociaux pour stabiliser l'entraide et pour stimuler l'entraide".

• La réciprocité renforcée : C'est le mécanisme le plus courant et le plus efficace.

• Récompenser les altruistes : Encourager et reconnaître ceux qui contribuent positivement.

• Punir les tricheurs et les égoïstes : L'introduction de cette règle dans l'expérience du bien public a eu des "effets miraculeux", faisant exploser et stabiliser les niveaux de prosocialité.

Les humains sont prêts à dépenser de l'argent ("punition altruiste") pour punir les non-coopérateurs, "ça va même jusqu'à une une grande proportion du salaire mensuel c'est une passion".

• Plaisir neuronal associé : Coopérer, voir autrui coopérer, ou même anticiper un acte d'altruisme, active le circuit de la récompense dans le cerveau, procurant un "vrai plaisir", même chez les enfants.

Inversement, le "circuit de dégoût" est activé par la punition d'un altruiste ou la récompense d'un égoïste/tricheur (ex: "quand au hasard quelqu'un du gouvernement est mise en examen pour corruption et est relâché").

Cela montre l'importance de la justice perçue pour la coopération (ex: plaisir à payer des impôts si l'argent est bien dépensé).

La réciprocité indirecte et la réputation :

• Ce mécanisme implique que l'aide donnée à une personne peut inciter une tierce personne à aider le donneur initial, ou qu'un acte altruiste est observé par des témoins, ce qui étend l'entraide dans le groupe.

• Les "ragots et les Cancans" comme moteur : Ces interactions sociales informelles sont cruciales car elles "créent la réputation". Avoir une "bonne réputation" est un "capital social" précieux qui renforce la coopération.

• L'expérience de la réputation : Une expérience a montré que lorsque le jeu du bien public est alterné avec un jeu de réputation, les niveaux de coopération restent élevés.

Cependant, si les participants apprennent que la fin du jeu est proche et que la réputation n'aura plus d'importance, la coopération s'effondre ("ils se sont mis à en profiter à mort ils en avaient plus rien à foutre la réputation n'était pas en plus en jeu").

• Le sentiment d'être observé : Se sentir observé ("Big Brother", les religions avec un Dieu omniscient) augmente significativement la coopération. Même de simples points évoquant un visage sur un mur peuvent avoir cet effet inconscient.

3. Les Fondements Profonds de la Relation Humaine et l'Élargissement du Cercle d'Empathie

La distinction entre interagir avec un humain ou un ordinateur est fondamentale : la coopération avec un ordinateur n'active pas le circuit de la récompense, indiquant que "c'est quelque chose de profondément humain".

• La relation "Je et Tu" : Le philosophe Martin Buber est cité avec son concept de "Je et Tu" par opposition à "Je et ça".

La relation "Je et Tu" implique une reconnaissance mutuelle de l'autre comme sujet doté d'empathie, créant une "relation de miroir" infinie.

• La déshumanisation : L'horreur survient lorsque l'on "sort quelqu'un de notre champ d'empathie", transformant une relation "Je et Tu" en "Je et ça", et déshumanisant l'autre.

"C'est ce qui s'est passé pour les juifs pendant la guerre au Rwanda avec les les utou et les tutti et probablement en Ukraine dans toutes les guerres on on peut arriver basculer dans l'horreur lorsqu'on sort les humains de notre champ d'empathie ça peut arriver très vite".

• Élargir le cercle d'empathie : Le défi contemporain est d'élargir ce cercle d'empathie au-delà des seuls humains (souvent limité aux animaux domestiques), pour inclure les animaux et les plantes.

Considérer le monde non pas comme "entouré d'objets mais entouré de sujets" permettrait de "retrouver des relations de réciprocité et donc de prosocialité et donc tous les circuits vont s'enclencher et ça va faire un un monde totalement différent".

En conclusion, la prosocialité est une caractéristique fondamentale et spontanée de l'être humain, ancrée dans l'empathie et activée par l'intuition.

Bien qu'elle puisse s'effriter avec le temps, des mécanismes sociaux tels que la récompense des altruistes, la punition des tricheurs et l'importance de la réputation sont essentiels pour stabiliser et renforcer la coopération.

Le maintien et l'élargissement de notre "cercle d'empathie" sont cruciaux pour prévenir la déshumanisation et construire un monde plus coopératif et juste.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public review):

Weaknesses:

The technical approach is strong and the conceptual framing is compelling, but several aspects of the evidence remain incomplete. In particular, it is unclear whether the reported changes in connectivity truly capture causal influences, as the rank metrics remain correlational and show discrepancies with the manipulation results.

We agree that our functional connectivity ranking analyses cannot establish causal influences. As discussed in the manuscript, besides learning-related activity changes, the functional connectivity may also be influenced by neuromodulatory systems and internal state fluctuations. In addition, the spatial scope of our recordings is still limited compared to the full network implicated in visual discrimination learning, which may bias the ranking estimates. In future, we aim to achieve broader region coverage and integrate multiple complementary analyses to address the causal contribution of each region.

The absolute response onset latencies also appear slow for sensory-guided behavior in mice, and it is not clear whether this reflects the method used to define onset timing or factors such as task structure or internal state.

We believe this may be primarily due to our conservative definition of onset timing. Specifically, we required the firing rate to exceed baseline (t-test, p < 0.05) for at least 3 consecutive 25-ms time windows. This might lead to later estimates than other studies, such as using the latency to the first spike after visual stimulus onset (Siegle et al., 2021) or the time to half-max response (Goldbach, Akitake, Leedy, & Histed, 2021).

The estimation of response onset latency in our study may also be affected by potential internal state fluctuations of the mice. We used the time before visual stimulus onset as baseline firing, since firing rates in this period could be affected by trial history, we acknowledge this may increase the variability of the baseline, thus increase the difficulty to statistically detect the onset of response.

Still, we believe these concerns do not affect the observation of the formation of compressed activity sequence in CR trials during learning.

Furthermore, the small number of animals, combined with extensive repeated measures, raises questions about statistical independence and how multiple comparisons were controlled.

We agree that a larger sample size would strengthen the robustness of the findings. However, as noted above, the current dataset has inherent limitations in both the number of recorded regions and the behavioral paradigm. Given the considerable effort required to achieve sufficient unit yields across all targeted regions, we wish to adjust the set of recorded regions, improve behavioral task design, and implement better analyses in future studies. This will allow us to both increase the number of animals and extract more precise insights into mesoscale dynamics during learning.

The optogenetic experiments, while intended to test the functional relevance of rank increasing regions, leave it unclear how effectively the targeted circuits were silenced. Without direct evidence of reliable local inhibition, the behavioral effects or lack thereof are difficult to interpret.

We appreciate this important point. Due to the design of the flexible electrodes and the implantation procedure, bilateral co-implantation of both electrodes and optical fibers was challenging, which prevented us from directly validating the inhibition effect in the same animals used for behavior. In hindsight, we could have conducted parallel validations using conventional electrodes, and we will incorporate such controls in future work to provide direct evidence of manipulation efficacy.

Details on spike sorting are limited.

We have provided more details on spike sorting in method section, including the exact parameters used in the automated sorting algorithm and the subsequent manual curation criteria.

Reviewer #2 (Public review):

Weaknesses:

I had several major concerns:

(1) The number of mice was small for the ephys recordings. Although the authors start with 7 mice in Figure 1, they then reduce to 5 in panel F. And in their main analysis, they minimize their analysis to 6/7 sessions from 3 mice only. I couldn't find a rationale for this reduction, but in the methods they do mention that 2 mice were used for fruitless training, which I found no mention in the results. Moreover, in the early case, all of the analysis is from 118 CR trials taken from 3 mice. In general, this is a rather low number of mice and trial numbers. I think it is quite essential to add more mice.

We apologize for the confusion. As described in the Methods section, 7 mice (Figure 1B) were used for behavioral training without electrode array or optical fiber implants to establish learning curves, and an additional 5 mice underwent electrophysiological recordings (3 for visual-based decision-making learning and 2 for fruitless learning).

As we noted in our response to Reviewer #1, the current dataset has inherent limitations in both the number of recorded regions and the behavioral paradigm. Given the considerable effort required to achieve high-quality unit yields across all targeted regions, we wish to adjust the set of recorded regions, improve behavioral task design, and implement better analyses in future studies. These improvements will enable us to collect data from a larger sample size and extract more precise insights into mesoscale dynamics during learning.

(2) Movement analysis was not sufficient. Mice learning a go/no-go task establish a movement strategy that is developed throughout learning and is also biased towards Hit trials. There is an analysis of movement in Figure S4, but this is rather superficial. I was not even sure that the 3 mice in Figure S4 are the same 3 mice in the main figure. There should be also an analysis of movement as a function of time to see differences. Also for Hits and FAs. I give some more details below. In general, most of the results can be explained by the fact that as mice gain expertise, they move more (also in CR during specific times) which leads to more activation in frontal cortex and more coordination with visual areas. More needs to be done in terms of analysis, or at least a mention of this in the text.

Due to the limitation in the experimental design and implementation, movement tracking was not performed during the electrophysiological recordings, and the 3 mice shown in Figure S4 (now S5) were from a separate group. We have carefully examined the temporal profiles of mouse movements and found it did not fully match the rank dynamics for all regions, and we have added these results and related discussion in the revised manuscript. However, we acknowledge the observed motion energy pattern could explain some of the functional connection dynamics, such as the decrease in face and pupil motion energy could explain the reduction in ranks for striatum.

Without synchronized movement recordings in the main dataset, we cannot fully disentangle movement-related neural activity from task-related signals. We have made this limitation explicit in the revised manuscript and discuss it as a potential confound, along with possible approaches to address it in future work.

(3) Most of the figures are over-detailed, and it is hard to understand the take-home message. Although the text is written succinctly and rather short, the figures are mostly overwhelming, especially Figures 4-7. For example, Figure 4 presents 24 brain plots! For rank input and output rank during early and late stim and response periods, for early and expert and their difference. All in the same colormap. No significance shown at all. The Δrank maps for all cases look essentially identical across conditions. The division into early and late time periods is not properly justified. But the main take home message is positive Δrank in OFC, V2M, V1 and negative Δrank in ThalMD and Str. In my opinion, one trio map is enough, and the rest could be bumped to the Supplementary section, if at all. In general, the figure in several cases do not convey the main take home messages. See more details below.

We thank the reviewer for this valuable critique. The statistical significance corresponding to the brain plots (Figure 4 and Figure 5) was presented in Figure S3 and S5 (now Figure S5 and S7 in the revised manuscript), but we agree that the figure can be simplified to focus on the key results.

In the revised manuscript, we have condensed these figures to focus on the most important comparisons to make the visual presentation more concise and the take-home message clearer.

(4) The analysis is sometimes not intuitive enough. For example, the rank analysis of input and output rank seemed a bit over complex. Figure 3 was hard to follow (although a lot of effort was made by the authors to make it clearer). Was there any difference between the output and input analysis? Also, the time period seems redundant sometimes. Also, there are other network analysis that can be done which are a bit more intuitive. The use of rank within the 10 areas was not the most intuitive. Even a dimensionality reduction along with clustering can be used as an alternative. In my opinion, I don't think the authors should completely redo their analysis, but maybe mention the fact that other analyses exist

We appreciate the reviewer’s comment. In brief, the input- and output-rank analyses yielded largely similar patterns across regions in CR trials, although some differences were observed in certain areas (e.g., striatum) in Hit trials, where the magnitude of rank change was not identical between input and output measures. We have condensed the figures to only show averaged rank results, and the colormap was updated to better covey the message.

We did explore dimensionality reduction applied to the ranking data. However, the results were not intuitive as well and required additional interpretation, which did not bring more insights. Still, we acknowledge that other analysis approaches might provide complementary insights.

Reviewer #3 (Public review):

Weaknesses:

The weakness is also related to the strength provided by the method. It is demonstrated in the original method that this approach in principle can track individual units for four months (Luan et al, 2017). The authors have not showed chronically tracked neurons across learning. Without demonstrating that and taking advantage of analyzing chronically tracked neurons, this approach is not different from acute recording across multiple days during learning. Many studies have achieved acute recording across learning using similar tasks. These studies have recorded units from a few brain areas or even across brain-wide areas.

We appreciate the reviewer’s important point. We did attempt to track the same neurons across learning in this project. However, due to the limited number of electrodes implanted in each brain region, the number of chronically tracked neurons in each region was insufficient to support statistically robust analyses. Concentrating probes in fewer regions would allow us to obtain enough units tracked across learning in future studies to fully exploit the advantages of this method.

Another weakness is that major results are based on analyses of functional connectivity that is calculated using the cross-correlation score of spiking activity (TSPE algorithm). Functional connection strengthen across areas is then ranked 1-10 based on relative strength. Without ground truth data, it is hard to judge the underlying caveats. I'd strongly advise the authors to use complementary methods to verify the functional connectivity and to evaluate the mesoscale change in subnetworks. Perhaps the authors can use one key information of anatomy, i.e. the cortex projects to the striatum, while the striatum does not directly affect other brain structures recorded in this manuscript

We agree that the functional connectivity measured in this study relies on statistical correlations rather than direct anatomical connections. We plan to test the functional connection data with shorter cross-correlation delay criteria to see whether the results are consistent with anatomical connections and whether the original findings still hold.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) The small number of mice, each contributing many sessions, complicates the  interpretation of the data. It is unclear how statistical analyses accounted for the small  sample size, repeated measures, and non-independence across sessions, or whether  multiple comparisons were adequately controlled.

We realized the limitation from the small number of animal subjects, yet the difficulty to achieve sufficient unit yields across all regions in the same animal restricted our sample size. Though we agree that a larger sample size would strengthen the robustness of the findings, however, as noted below the current dataset has inherent limitations in both the scope of recorded regions and the behavioral paradigm.

Given the considerable effort required to achieve sufficient unit yields across all targeted regions, we wish to adjust the set of recorded regions, improve behavioral task design, and implement better analyses in future studies. This will allow us to both increase the number of animals and extract more precise insights into mesoscale dynamics during learning.

(2) The ranking approach, although intuitive for visualizing relative changes in  connectivity, is fundamentally descriptive and does not reflect the magnitude or  reliability of the connections. Converting raw measures into ordinal ranks may obscure  meaningful differences in strength and can inflate apparent effects when the underlying  signal is weak.

We agree with this important point. As stated in the manuscript, our motivation in taking the ranking approach was that the differences in firing rates might bias cross-correlation between spike trains, making raw accounts of significant neuron pairs difficult to compare across conditions, but we acknowledge the ranking measures might obscure meaningful differences or inflate weak effects in the data.

We added the limitations of ranking approach in the discussion section and emphasized the necessity in future studies for better analysis approaches that could provide more accurate assessment of functional connection dynamics without bias from firing rates.

(3) The absolute response onset latencies also appear quite slow for sensory-guided  behavior in mice, and it remains unclear whether this reflects the method used to  determine onset timing or factors such as task design, sensorimotor demands, or  internal state. The approach for estimating onset latency by comparing firing rates in  short windows to baseline using a t-test raises concerns about robustness, as it may  be sensitive to trial-to-trial variability and yield spurious detections.

We agree this may be primarily due to our conservative definition of onset timing. Specifically, we required the firing rate to exceed baseline (t-test, p < 0.05) for at least 3 consecutive 25-ms time windows. This might lead to later estimates than other studies, such as using the latency to the first spike after visual stimulus onset (Siegle et al., 2021) or the time to half-max response (Goldbach, Akitake, Leedy, & Histed, 2021).

The estimation of response onset latency in our study may also be affected by potential internal state fluctuations of the mice. We used the time before visual stimulus onset as baseline firing, since firing rates in this period could be affected by trial history, we acknowledge this may increase the variability of the baseline, thus increase the difficulty to statistically detect the onset of response.

Still, we believe these concerns do not affect the observation of the formation of compressed activity sequence in CR trials during learning.

(4) Details on spike sorting are very limited. For example, defining single units only by  an interspike interval threshold above one millisecond may not sufficiently rule out  contamination or overlapping clusters. How exactly were neurons tracked across days  (Figure 7B)?

We have added more details on spike sorting, including the processing steps and important parameters used in the automated sorting algorithm. Only the clusters well isolated in feature space were accepted in manual curation.

We attempted to track the same neurons across learning in this project. However, due to the limited number of electrodes implanted in each brain region, the number of chronically tracked neurons in each region was insufficient to support statistically robust analyses.

This is now stated more clearly in the discussion section.

(5) The optogenetic experiments, while designed to test the functional relevance of  rank-increasing regions, also raise questions. The physiological impact of the inhibition  is not characterized, making it unclear how effectively the targeted circuits were  actually silenced. Without clearer evidence that the manipulations reliably altered local  activity, the interpretation of the observed or absent behavioral effects remains  uncertain.

We appreciate this important point. Due to the design of the flexible electrodes and the implantation procedure, bilateral co-implantation of both electrodes and optical fibers was challenging, which prevented us from directly validating the inhibition effect in the same animals used for behavior. In hindsight, we could have conducted parallel validations using conventional electrodes, and we will incorporate such controls in future work to provide direct evidence of manipulation efficacy. 

(6) The task itself is relatively simple, and the anatomical coverage does not include  midbrain or cerebellar regions, limiting how broadly the findings can be generalized to more flexible or ethologically relevant forms of decision-making.

We appreciate this advice and have expanded the existing discussion to more explicitly state that the relatively simple task design and anatomical coverage might limit the generalizability of our findings.

(7) The abstract would benefit from more consistent use of tense, as the current mix of  past and present can make the main findings harder to follow. In addition, terms like  "mesoscale network," "subnetwork," and "functional motif" are used interchangeably in  places; adopting clearer, consistent terminology would improve readability.

We have changed several verbs in abstract to past form, and we now adopted a more consistent terminology by substituting “functional motif” as “subnetwork”. We still feel the use of

“mesoscale network” and “subnetwork” could emphasize different aspects of the results according to the context, so these words are kept the same.

(8) The discussion could better acknowledge that the observed network changes may  not reflect task-specific learning alone but could also arise from broader shifts in  arousal, attention, or motivation over repeated sessions.

We have expanded the existing discussion to better acknowledge the possible effects from broader shifts in arousal, attention, or motivation over repeated sessions.

(9) The figures would also benefit from clearer presentation, as several are dense and  not straightforward to interpret. For example, Figure S8 could be organized more  clearly to highlight the key comparisons and main message

We have simplified the over-detailed brain plots in Figure 4-5, and the plots in Figure 6 and S8 (now S10 in the revised manuscript).

(10) Finally, while the manuscript notes that data and code are available upon request,  it would strengthen the study's transparency and reproducibility to provide open access  through a public repository, in line with best practices in the field.

The spiking data, behavior data and codes for the core analyses in the manuscript are now shared in pubic repository (Dryad). And we have changed the description in the Data Availability secition accordingly.

Reviewer #2 (Recommendations for the authors):

(A) Introduction:

(1) "Previous studies have implicated multiple cortical and subcortical regions in visual  task learning and decision-making". No references here, and also in the next sentence.

The references were in the following introduction and we have added those references here as well.

We also added one review on cortical-subcortical neural correlates in goal-directed behavior (Cruz et al., 2023).

(2) Intro: In general, the citation of previous literature is rather minimal, too minimal.  There is a lot of studies using large scale recordings during learning, not necessarily  visual tasks. An example for brain-wide learning study in subcortical areas is Sych et  al. 2022 (cell reports). And for wide-field imaging there are several papers from the  Helmchen lab and Komiyama labs, also for multi-area cortical imaging.

We appreciate this advice. We included mainly visual task learning literature to keep a more focused scope around the regions and task we actually explored in this study. We fear if we expand the intro to include all the large-scale imaging/recording studies in learning field, the background part might become too broad.

We have included (Sych, Fomins, Novelli, & Helmchen, 2022) for its relevance and importance in the field.

(3) In the intro, there is only a mention of a recording of 10 brain regions, with no  mention of which areas, along with their relevance to learning. This is mentioned in the  results, but it will be good in the intro.

The area names are now added in intro.

(B) Results:

(1) Were you able to track the same neurons across the learning profile? This is not  stated clearly.

We did attempt to track the same neurons across learning in this project. However, due to the limited number of electrodes implanted in each brain region, the number of chronically tracked neurons in each region was insufficient to support statistically robust analyses.

We now stated this more clearly in the discussion section.

(2) Figure 1 starts with 7 mice, but only 5 mice are in the last panel. Later it goes down  to 3 mice. This should be explained in the results and justified.

We apologize for the confusion. As described in the Methods section, 7 mice (Figure 1B) were used for behavioral training without electrode array or optical fiber implants to establish learning curves, and an additional 5 mice underwent electrophysiological recordings (3 for visual-based decision-making learning and 2 for fruitless learning).

(3) I can't see the electrode tracks in Figure 1d. If they are flexible, how can you make  sure they did not bend during insertion? I couldn't find a description of this in the  methods also.

The electrode shanks were ultra-thin (1-1.5 µm) and it was usually difficult to recover observable tracks or electrodes in section.

The ultra-flexible probes could not penetrate brain on their own (since they are flexible), and had to be shuttled to position by tungsten wires through holes designed at the tip of array shanks. The tungsten wires were assembled to the electrode array before implantation; this was described in the section of electrode array fabrication and assembly. We also included the description about the retraction of the guiding tungsten wires in the surgery section to avoid confusion.

As an further attempt to verify the accuracy of implantation depth, we also measured the repeatability of implantation in a group of mice and found a tendency for the arrays to end in slightly deeper location in cortex (142.1 ± 55.2 μm, n = 7 shanks), and slightly shallower location in subcortical structure (-122.6 ± 71.7 μm, n = 7 shanks). We added these results as new Figure S1 to accompany Figure 1.

(4) In the spike rater in 1E, there seems to be ~20 cells in V2L, for example, but in 1F,  the number of neurons doesn't go below 40. What is the difference here? 

We checked Figure 1F, the plotted dots do go below 40 to ~20. Perhaps the file that reviewer received wasn’t showing correctly?

(5) The authors focus mainly on CR, but during learning, the number of CR trials is  rather low (because they are not experts). This can also be seen in the noisier traces  in Figure 2a. Do the authors account for that (for example by taking equal trials from  each group)? 

We accounted this by reconstructing bootstrap-resampled datasets with only 5 trials for each session in both the early stage and the expert stage. The mean trace of the 500 datasets again showed overall decrease in CR trial firing rate during task learning, with highly similar temporal dynamics to the original data.

The figure is now added to supplementary materials (as Figure S3 in the revised manuscript).

(6) From Figure 2a, it is evident that Hit trials increase response when mice become  experts in all brain areas. The authors have decided to focus on the response onset  differences in CRs, but the Hit responses display a strong difference between naïve  and expert cases.

Judged from the learning curve in this task the mice learned to inhibit its licking action when the No-Go stimuli appeared, which is the main reason we focused on these types of trials.

The movement effects and potential licking artefacts in Hit trials also restricted our interpretation of these trials.

(7) Figure 3 is still a bit cumbersome. I wasn't 100% convinced of why there is a need  to rank the connection matrix. I mean when you convert to rank, essentially there could  be a meaningful general reduction in correlation, for example during licking, and this  will be invisible in the ranking system. Maybe show in the supp non-ranked data, or  clarify this somehow

We agree with this important point. As stated in the manuscript and response to Reviewer #1, our motivation in taking the ranking approach was that the differences in firing rates could bias cross-correlation between spike trains, making raw accounts of significant neuron pairs difficult to compare across conditions, but we acknowledge the ranking measures might obscure meaningful differences or inflate weak effects in the data.

We added the limitations of ranking approach in the discussion section and emphasized the necessity in future studies for better analysis approaches that could provide more accurate assessment of functional connection dynamics without bias from firing rates.

(8) Figure 4a x label is in manuscript, which is different than previous time labels,  which were seconds.

We now changed all time labels from Figure 2 to milliseconds.

(9) Figure 4 input and output rank look essentially the same.

We have compressed the brain plots in Figures 4-5 to better convey the take-home message.

(10) Also, what is the late and early stim period? Can you mark each period in panel A? Early stim period is confusing with early CR period. Same for early respons and late response.

The definition of time periods was in figure legends. We now mark each period out to avoid confusion.

(11) Looking at panel B, I don't see any differences between delta-rank in early stim,  late stim, early response, and late response. Same for panel c and output plots.

The rankings were indeed relatively stable across time periods. The plots are now compressed and showed a mean rank value.

(12) Panels B and C are just overwhelming and hard to grasp. Colors are similar both  to regular rank values and delta-rank. I don't see any differences between all  conditions (in general). In the text, the authors report only M2 to have an increase in  rank during the response period. Late or early response? The figure does not go well  with the text. Consider minimizing this plot and moving stuff to supplementary.

The colormap are now changed to avoid confusion, and brain plots are now compressed.

(13) In terms of a statistical test for Figure 4, a two-way ANOVA was done, but over  what? What are the statistics and p-values for the test? Is there a main effect of time  also? Is their a significant interaction? Was this done on all mice together? How many  mice? If I understand correctly, the post-hoc statistics are presented in the  supplementary, but from the main figure, you cannot know what is significant and what  is not.

For these figures we were mainly concerned with the post-hoc statistics which described the changes in the rankings of each region across learning.

We have changed the description to “t-test with Sidak correction” to avoid the confusion.

(14) In the legend of Figure 4, it is reported that 610 expert CR trials from 6 sessions,  instead of 7 sessions. Why was that? Also, like the previous point, why only 3 mice?

Behavior data of all the sessions used were shown in Figure S1. There were only 3 mice used for the learning group, the difficulty to achieve sufficient unit yields across all regions in the same animal restricted our sample size

(15) Body movement analysis: was this done in a different cohort of mice? Only now  do I understand why there was a division into early and late stim periods. In supp 4,  there should be a trace of each body part in CR expert versus naïve. This should also  be done for Hit trials as a sanity check. I am not sure that the brightness difference  between consecutive frames is the best measure. Rather try to calculate frame-to frame correlation. In general, body movement analysis is super important and should  be carefully analyzed.

Due to the limitation in the experimental design and implementation, movement tracking was not performed during the electrophysiological recordings, and the 3 mice shown in Figure S4 (now S5) were from a separate group. We have carefully examined the temporal profiles of mouse movements and found it did not fully match the rank dynamics for all regions, and we have added these results and related discussion in the revised manuscript. However, we acknowledge the observed motion energy pattern could explain some of the functional connection dynamics, such as the decrease in face and pupil motion energy could explain the reduction in ranks for striatum.

Without synchronized movement recordings in the main dataset, we cannot fully disentangle movement-related neural activity from task-related signals. We have made this limitation explicit in the revised manuscript and discuss it as a potential confound, along with possible approaches to address it in future work.

(16) For Hit trials, in the striatum, there is an increase in input rank around the  response period, and from Figure S6 it is clear that this is lick-related. Other than that,  the authors report other significant changes across learning and point out to Figure 5b,c. I couldn't see which areas and when it occurred.

We did naturally expect the activity in striatum to be strongly related to movement.

With Figure S6 (now S7) we wished to show that the observed rank increase for striatum could not simply be attributed to changes in time of lick initiation.

As some readers may argue that during learning the mice might have learned to only intensely lick after response signal onset, causing the observed rise of input rank after response signal, we realigned the spikes in each trial to the time of the first lick, and a strong difference could still be observed between early training stage and expert training stage.

We still cannot fully rule out the effects from more subtle movement changes, as the face motion energy did increase in early response period. This result and related discussion has been added to the results section of revised manuscript.

(17) Figure 6, again, is rather hard to grasp. There are 16 panels, spread over 4 areas,  input and output, stim and response. What is the take home message of all this?  Visually, it's hard to differentiate between each panel. For me, it seems like all the  panels indicate that for all 4 areas, both in output and input, frontal areas increase in  rank. This take-home message can be visually conveyed in much less tedious ways.  This simpler approach is actually conveyed better in the text than in the figures  themselves. Also, the whole explanation on how this analysis was done, was not clear  from the text. If I understand it, you just divided and ranked the general input (or  output) into individual connections? If so, then this should be better explained.

We appreciate this advice and we have compressed the figures to better convey the main message.The rankings for Figure 6 and Figure S8 (now Figure S9) was explained in the left panel of Figure 3C. Each non-zero element in the connection matrix was ranked to value from 1-10, with a value of 10 represented the 10% strongest non-zero elements in the matrix.

We have updated the figure legends of Figure 3, and we have also updated the description in methods (Connection rank analyses) to give a clearer description of how the analyses were applied in subsequent figures.

(18) Figure 7: Here, the authors perform a ROC analysis between go and no-go  stimuli. They balance between choice, but there is still an essential difference between  a hit and a FA in terms of movement and licks. That is maybe why there is a big  difference in selective units during the response period. For example, during a Hit trial  the mouse licks and gets a reward, resulting in more licking and excitement. In FAs,the mouse licks, but gets punished, which causes a reduction in additional licking and  movements. This could be a simple explanation why the ROC was good in the late  response period. Body movement analysis of Hit and FA should be done as in Figure  S4.

We appreciate this insightful advice.

Though we balanced the numbers of basic trial types, we couldn’t rule out the difference in the intrinsic movement amount difference in FA trials and Hit trials, which is likely the reason of large proportion of encoding neurons in response period.

We have added this discussion both in result section and discussion section along with the necessity of more carefully designed behavior paradigm to disentangle task information.

(19) The authors also find selective neurons before stimulus onset, and refer to trial  history effects. This can be directly checked, that is if neurons decode trial history.

We attempted encoding analyses on trial history, but regrettably for our dataset we could not find enough trials to construct a dataset with fully balanced trial history, visual stimulus and behavior choice.

(20) Figure 7e. What is the interpretation for these results? That areas which peaked  earlier had more input and output with other areas? So, these areas are initiating  hubs? Would be nice to see ACC vs Str traces from B superimposed on each other.  Having said this, the Str is the only area to show significant differences in the early  stim period. But is also has the latest peak time. This is a bit of a discrepancy.

We appreciate this important point.

The limitation in the anatomical coverage of brain regions restricted our interpretation about these findings. They could be initiating hubs or earlier receiver of the true initiating hubs that were not monitored in our study.

The Str trace was in fact above the ACC trace, especially in the response period. This could be explained by the above advice 18: since we couldn’t rule out the difference in the intrinsic movement amount difference in FA trials and Hit trials, and considering striatum activity is strongly related to movement, the Str trace may reflect more in the motion related spike count difference between FA trials and Hit trials, instead of visual stimulus related difference.

This further shows the necessity of more carefully designed behavior paradigm to disentangle task information.

The striatum trace also in fact didn’t show a true double peak form as traces in other regions, it ramped up in the stimulus region and only peaked in response period. This description is now added to the results section.

In the early stim period, the Striatum did show significant differences in average percent of encoding neurons, as the encoding neurons were stably high in expert stage. The striatum activity is more directly affected Still the percentage of neurons only reached peak in late stimulus period.

(21) For the optogenetic silencing experiments, how many mice were trained for each  group? This is not mentioned in the results section but only in the legend of Figure 8. This part is rather convincing in terms of the necessity for OFC and V2M

We have included the mice numbers in results section as well.

(C) Discussion

(1) There are several studies linking sensory areas to frontal networks that should be  mentioned, for example, Esmaeili et a,l 2022, Matteucci et al., 2022, Guo et a,l 2014,Gallero Salas et al, 2021, Jerry Chen et al, 2015. Sonja Hofer papers, maybe. Probably more.

We appreciate this advice. We have now included one of the mentioned papers (Esmaeili et al., 2022) in the results section and discussion section for its direct characterization of the enhanced coupling between somatosensory region and frontal (motor) region during sensory learning.The other studies mentioned here seem to focus more on the differences in encoding properties between regions along specific cortical pathways, rather than functional connection or interregional activity correlation, and we feel they are not directly related to the observations discussed.

(2) The reposted reorganization of brain-wide networks with shifts in time is best  described also in Sych et al. 2021.

We regret we didn’t include this important research and we have now cited this in discussion section.

(3) Regarding the discussion about more widespread stimulus encoding after learning,  the results indicate that the striatum emerges first in decoding abilities (Figure 7c left  panel), but this is not discussed at all.

We briefly discussed this in the result section. We tend to attribute this to trial history signal in striatum, but since the structure of our data could not support a direct encoding analysis on trial history, we felt it might be inappropriate to over-interpret the results.

(4) An important issue which is not discussed is the contribution of movement which  was shown to have a strong effect on brain-wide dynamics (Steinmetz et al 2019;  Musall et al 2019; Stringer et al 2019; Gilad et al 2018) The authors do have some movement analysis, but this is not enough. At least a discussion of the possible effects of movement on learning-related dynamics should be added.

We have included these studies in discussion section accordingly. Since the movement analyses were done in a separate cohort of mice, we have made our limitation explicit in the revised manuscript and discuss it as a potential confound, along with possible approaches to address it in future work.

(D) Methods

(1) How was the light delivery of the optogenetic experiments done? Via fiber  implantation in the OFC? And for V2M? If the red laser was on the skull, how did it get  to the OFC?

The fibers were placed on cortex surface for V2M group, and were implanted above OFC for OFC manipulation group. These were described in the viral injection part of the methods section.

(2) No data given on how electrode tracking was done post hoc

As noted in our response to the advice 3 in results section, the electrode shanks were ultra-thin (1-1.5 µm) and it was usually difficult to recover observable tracks or electrodes in section.

As an attempt to verify the accuracy of implantation depth, we measured the repeatability of implantation in a group of mice and found a tendency for the arrays to end in slightly deeper location in cortex (142.1 ± 55.2 μm, n = 7 shanks), and slightly shallower location in subcortical structure (-122.6 ± 71.7 μm, n = 7 shanks). We added these results as new Figure S1 to accompany Figure 1.

Reviewer #3 (Recommendations for the authors):

(1) The manuscript uses decision-making in the title, abstract and introduction.  However, nothing is related to decision learning in the results section. Mice simply  learned to suppress licking in no-go trials. This type of task is typically used to study behavioral inhibition. And consistent with this, the authors mainly identified changes  related to network on no-go trials. I really think the title and main message is  misleading. It is better to rephrase it as visual discrimination learning. In the  introduction, the authors also reviewed multiple related studies that are based on  learning of visual discrimination tasks.

We do view the Go/No-Go task as a specific genre of decision-making task, as there were literature that discussed this task as decision-making task under the framework of signal detection theory or updating of item values (Carandini & Churchland, 2013; Veling, Becker, Liu, Quandt, & Holland, 2022).

We do acknowledge the essential differences between the Go/No-Go task and the tasks that require the animal to choose between alternatives, and since we have now realized some readers may not accept this task as a decision task, we have changed the title to visual discrimination task as advised.

(2) Learning induced a faster onset on CR trials. As the no-go stimulus was not  presented to mice during early stages of training, this change might reflect the  perceptual learning of relevant visual stimulus after repeated presentation. This further  confirms my speculation, and the decision-making used in the title is misleading. 

We have changed the title to visual discrimination task accordingly.

(3) Figure 1E, show one hit trial. If the second 'no-go stimulus' is correct, that trial  might be a false alarm trial as mice licked briefly. I'd like to see whether continuous  licking can cause motion artifacts in recording. 

We appreciate this important point. There were indeed licking artifacts with continuous licking in Hit trials, which was part of the reason we focused our analyses on CR trials. Opto-based lick detectors may help to reduce the artefacts in future studies.

(4) What is the rationale for using a threshold of d' < 2 as the early-stage data and d'>3  as expert stage data?

The thresholds were chosen as a result from trade-off based on practical needs to gather enough CR trials in early training stage, while maintaining a relatively low performance.

Assume the mice showed lick response in 95% of Go stimulus trials, then d' < 2 corresponded to the performance level at which the mouse correctly rejected less than 63.9% of No-Go stimulus trials, and d' > 3 corresponded to the performance level at which the mouse correctly rejected more than 91.2% of No-Go stimulus trials.

(5) Figure 2A, there is a change in baseline firing rates in V2M, MDTh, and Str. There  is no discussion. But what can cause this change? Recording instability, problem in  spiking sorting, or learning?

It’s highly possible that the firing rates before visual stimulus onset is affected by previous reward history and task engagement states of the mice. Notably, though recorded simultaneously in same sessions, the changes in CR trials baseline firing rates in the V2M region were not observed in Hit trials.

Thus, though we cannot completely rule out the possibility in recording instability, we see this as evidence of the effects on firing rates from changes in trial history or task engagement during learning.

References:

Carandini, M., & Churchland, A. K. (2013). Probing perceptual decisions in rodents. Nat Neurosci, 16(7), 824-831. doi:10.1038/nn.3410.

Cruz, K. G., Leow, Y. N., Le, N. M., Adam, E., Huda, R., & Sur, M. (2023).Cortical-subcortical interactions in goal-directed behavior. Physiol Rev, 103(1), 347-389. doi:10.1152/physrev.00048.2021

Esmaeili, V., Oryshchuk, A., Asri, R., Tamura, K., Foustoukos, G., Liu, Y., Guiet, R., Crochet, S., & Petersen, C. C. H. (2022). Learning-related congruent and incongruent changes of excitation and inhibition in distinct cortical areas. PLOS Biology, 20(5), e3001667. doi:10.1371/journal.pbio.3001667

Goldbach, H. C., Akitake, B., Leedy, C. E., & Histed, M. H. (2021). Performance in even a simple perceptual task depends on mouse secondary visual areas. Elife, 10, e62156. doi:10.7554/eLife.62156.

Siegle, J. H., Jia, X., Durand, S., Gale, S., Bennett, C., Graddis, N., Heller, G.,Ramirez, T. K., Choi, H., Luviano, J. A., Groblewski, P. A., Ahmed, R., Arkhipov, A., Bernard, A., Billeh, Y. N., Brown, D., Buice, M. A., Cain, N.,Caldejon, S., Casal, L., Cho, A., Chvilicek, M., Cox, T. C., Dai, K., Denman, D.J., de Vries, S. E. J., Dietzman, R., Esposito, L., Farrell, C., Feng, D., Galbraith, J., Garrett, M., Gelfand, E. C., Hancock, N., Harris, J. A., Howard, R., Hu, B.,Hytnen, R., Iyer, R., Jessett, E., Johnson, K., Kato, I., Kiggins, J., Lambert, S., Lecoq, J., Ledochowitsch, P., Lee, J. H., Leon, A., Li, Y., Liang, E., Long, F., Mace, K., Melchior, J., Millman, D., Mollenkopf, T., Nayan, C., Ng, L., Ngo, K., Nguyen, T., Nicovich, P. R., North, K., Ocker, G. K., Ollerenshaw, D., Oliver, M., Pachitariu, M., Perkins, J., Reding, M., Reid, D., Robertson, M., Ronellenfitch, K., Seid, S., Slaughterbeck, C., Stoecklin, M., Sullivan, D., Sutton, B., Swapp, J., Thompson, C., Turner, K., Wakeman, W., Whitesell, J. D., Williams, D., Williford, A., Young, R., Zeng, H., Naylor, S., Phillips, J. W., Reid, R. C., Mihalas, S., Olsen, S. R., & Koch, C. (2021). Survey of spiking in the mouse visual system reveals functional hierarchy. Nature, 592(7852), 86-92. doi:10.1038/s41586-020-03171-x

Sych, Y., Fomins, A., Novelli, L., & Helmchen, F. (2022). Dynamic reorganization of the cortico-basal ganglia-thalamo-cortical network during task learning. Cell Rep, 40(12), 111394. doi:10.1016/j.celrep.2022.111394

Veling, H., Becker, D., Liu, H., Quandt, J., & Holland, R. W. (2022). How go/no-go training changes behavior: A value-based decision-making perspective. Current Opinion in Behavioral Sciences, 47,101206.

doi:https://doi.org/10.1016/j.cobeha.2022.101206.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public review):

The authors' goal was to arrest PsV capsids on the extracellular matrix using cytochalasin D. The cohort was then released, and interaction with the cell surface, specifically with CD151, was assessed.

The model that fragmented HS associated with released virions mediates the dominant mechanism of infectious entry has only been suggested by research from a single laboratory and has not been verified in the 10+ years since publication. The authors are basing this study on the assumption that this model is correct, and these data are referred to repeatedly as the accepted model despite much evidence to the contrary.

We stated in the introduction on line 65/66 ´Two release mechanisms are discussed, that mutually are not exclusive´. This implies that we do not consider the shedding model as ‘the accepted model’. Furthermore, we do not state in the discussion neither that the shedding model is the preferred one. However, we referred to the shedding model in the discussion, because we find HS associated with transferred PsVs, which is in line with this model.

The discussion in lines 65-71 concerning virion and HSPG affinity changes is greatly simplified. The structural changes in the capsid induced by HS interaction and the role of this priming for KLK8 and furin cleavage have been well researched. Multiple laboratories have independently documented this. If this study aims to verify the shedding model, additional data need to be provided.

Our findings are compatible with both models, and we do not aim to verify the shedding model neither want to disprove the priming model. However, as we understand, the referee wishes more visibility of the priming model. Therefore, using inhibitors previously used in the field, we tested whether inhibition of KLK8 or furin reduces PsV translocation to the cell body (after CytD wash off). Leupeptin blocks transport, while Furin inhibitor I still allows some initial translocation. We incorporated this new data as Figure 2 (line 265): “…we would expect that inhibition of L1 processing during the CytD incubation prevents the recovery of PsV translocation from the ECM to the cell body (Figure 2A and D). To test for this possibility, as employed in earlier studies, the protease inhibitor leupeptin was used to inhibit proteases including KLK8 which is required for L1 cleavage (Cerqueira et al. 2015). Employing this inhibitor, the PCC between PsV-L1 and F-actin staining remains negative after CytD removal, showing that for translocation indeed the action of proteases is required (Figure 2B and D). In contrast, inhibition of L2 cleavage by a furin specific inhibitor has no effect on the PCC (Figure 2C and D). However, it should be noted that we occasionally observe PsVs not completely translocating but accumulating at the border of the F-actin stained area (for example see Figure 2C (60 min)). This results in an increase of the PCC almost equal to complete translocation, explaining why the PCC remains unaffected despite a furin inhibitory effect. Hence, furin inhibition may have some effect on translocation that, however, is undetected in this type of analysis.’

Moreover, we have added a paragraph discussing how our data integrates into the established model of the HPV infection cascade (line 604): ‘HPV infection is the result of several steps, starting with the initial binding of virions via electrostatic and polar interactions (Dasgupta et al. 2011) to the primary attachment site HS (Richards et al. 2013), which induces capsid modification (Feng et al. 2024; Cerqueira et al. 2015) and HS cleavage (Surviladze et al. 2015), enabling the virion to be released from the ECM or the glycocalyx. Next, virions bind to the cell surface to a secondary receptor complex that forms over time, and become internalized via endocytosis, before they are trafficked to the nucleus (Ozbun and Campos 2021; Mikuličić et al. 2021). Regarding the transition from the primary attachment site to cell surface binding, as already outlined in the introduction, two models are discussed. In one model, proteases cleave the capsid proteins. After priming, the capsids are structurally modified and the virion can dissociate from its HS attachment site. It has been suggested that capsid priming is mediated by KLK8 (Cerqueira et al. 2015) and furin (Richards et al. 2006). In our system, KLK8 inhibition blocks PsV transport, while furin inhibition has some effect that, however, cannot be detected in this analysis (Figure 2) suggesting furin engagement at later steps in the infection cascade. This is in line with earlier in vitro studies on the role of cell surface furin (Surviladze et al. 2015; Day et al. 2008; Day and Schiller 2009). In any case, our results align with both models of ECM detachment: one involving HS cleavage (HS co-transfer) and another involving capsid modification (by e.g., KLK8).’

The model should be fitted into established entry events,…

Please see our reply above.

or at minimum, these conflicting data, a subset of which is noted below, need to be acknowledged.

(1) The Sapp lab (Richards et al., 2013) found that HSPG-mediated conformational changes in L1 and L2 allowed the release of the virus from primary binding and allowing secondary receptor engagements in the absence of HS shedding.

(2) Becker et al. found that furin-precleaved capsids could infect cells independently of HSPG interaction, but this infection was still inhibited with cytochalasin D.

(3) Other work from the Schelhaas lab showed that cytochalasin D inhibition of infection resulted in the accumulation of capsids in deep invaginations from the cell surface, not on the ECM

(4) Selinka et al., 2007, showed that preventing HSPG-induced conformational changes in the capsid surface resulted in noninfectious uptake that was not prevented with cytochalasin D.

(5) The well-described capsid processing events by KLK8 and furin need to be mechanistically linked to the proposed model. Does inhibition of either of these cleavages prevent engagement with CD151?

The authors need to consider an explanation for these discrepancies.

We do not see any discrepancies; our observations are compatible with aspects of both the shedding and the priming model. That PsVs carry HS-cleavage products doesn´t imply that HS cleavage is sufficient or required for infection, or that the priming model would be wrong. We do not view our data as being in conflict with the priming model. Most of the above-mentioned papers are now cited.

Altogether, we acknowledge that the study gains importance by directly testing the priming model within our experimental system. We are thankful for the above comments and addressed this issue.

Other issues:

(1) Line 110-111. The statement about PsVs in the ECM being too far away from the cell surface to make physical contact with the cell surface entry receptors is confusing. ECM binding has not been shown to be an obligatory step for in vitro infection.

Not obligatory, but strongly supportive (Bienkowska-Haba et al., Plos Path., 2018; Surviladze et al., J. Gen. Viro., 2015). As recently published by the Sapp lab (Bienkowska-Haba et al., Plos Path., 2018), ´Direct binding of HPV16 to primary keratinocytes yields very inefficient infection rates for unknown reasons.´ Moreover, the paper shows that HaCaT cell ECM binding of PsVs increases the infection of NHEK by 10-fold and of HFK by almost 50-fold.

This idea is referred to again on lines 158-159 and 199. The claim (line 158) that PsV does not interact with the cell within an hour needs to be demonstrated experimentally and seems at odds with multiple laboratories' data. PsV has been shown to directly interact with HSPG on the cell surface in addition to the ECM. Why are these PsVs not detected?

The reviewing editor speculated that HaCaT cells may be a model system in which the in vivo relevant binding to the ECM can be better studied as in non-polarized cell types. This is because binding to the ECM cannot be bypassed by direct cell surface binding. The observation that only few PsVs bind to the basal cell membrane indeed suggests restricted diffusional access of PsVs to binding receptors of the basal membrane. The reviewing editor asked for an experiment showing that more PsVs bind after cell detachment. We performed this experiment and indeed find more PsVs binding to the cell surface of detached cells. This point is very important for the understanding of the study and now we mention it in several sections of the manuscript, as outlined in the following.

Line 125: ‘Many PsVs that bind to the ECM may locate distal from the cell surface and are thus unable to establish direct contact with entry receptors. However, they are capable of migrating by an actindependent transport along cell protrusions towards the cell body (Smith et al. 2008; Schelhaas et al. 2008). We aimed for blocking this transport in HaCaT cells, a cell line that is widely used as a cell culture model for HPV infection. HaCaT cells closely resemble primary keratinocytes in key aspects: they are not virally transformed and produce large amounts of ECM that facilitates infection (Bienkowska-Haba et al. 2018; Gilson et al. 2020). In addition, HaCaT cells exhibit cellular polarity that enforces binding of virus particles to the ECM, as the virions cannot bind to receptors/entry components, such as CD151, Itgα6 and HSPGs that co-distribute on the basolateral membrane of polarized keratinocytes (Sterk et al. 2000; Cowin et al. 2006; Mertens et al. 1996), making them inaccessible by diffusion.’

Line 205: ‘During the CytD incubation, PsVs bind to HSPGs of the basolateral membrane for 5 h. Still, in the cell body area hardly any PsVs are present (0.14 PsV/µm², Supplementary Figure 1B). In the control, the PsV density is several-fold larger (Supplementary Figure 1B). This is expected, as the PsVs bind to the ECM and translocate to the cell body. We wondered whether there are more binding sites at the basal membrane that remain inaccessible to PsVs by diffusion because of the insufficient space between glass-coverslip and basolateral membrane. For clarification, we incubated EDTA detached HaCaT cells in suspension with PsVs for 1 h at 4 °C, followed by re-attachment for 1 h. Under these conditions, we find a PsV density 12.4-fold larger than after 5 h of CytD incubation of adhered cells (Supplementary Figure 1B and D). However, it should be noted that these values cannot be directly compared. Aside from the different treatments, another difference lies in the size of the basal membrane, as re-attachment of cells is not complete after only 1 h (compare size of adhered membranes in Supplementary Figure 1A and C). Therefore, the imaged membranes are likely strongly ruffled, which results in the underestimation of the size of the adhered membrane. As a result, we overestimate the PsVs per µm² (please note that we cannot re-attach cells for longer times as we would then lose PsVs due to endocytosis). On the other hand, we would underestimate the PsV density at the basal membrane if after re-attachment we image in part also some apical membrane. In any case, the experiment suggests that PsVs bind more efficiently if membrane surface receptors are accessible by diffusion. This is in support of the above notion that the basal membrane may provide more entry receptors than one would expect from the low density of PsVs bound after 5 h CytD (Supplementary Figure 1B). This suggests that under our assay conditions, PsVs cannot easily bypass the translocation from the ECM to the cell body by diffusing directly to the basal membrane. Hence, the large majority of PsVs that enter the cell were previously bound to the ECM. Therefore, HaCaT cells serve as an ideal model for studying the transfer of ECM bound HPV particles to the cell surface, which is similar to in vivo infection of basal keratinocytes after binding to the basement membrane (Day and Schelhaas 2014; Kines et al. 2009; Schiller et al. 2010; Bienkowska-Haba et al. 2018).’

Line 529: ‘Filopodia usage not only facilitates infection but also increases the likelihood of virions to reach their target cells during wound healing, namely the filopodia-rich basal dividing cells. In fact, several types of viruses exploit filopodia during virus entry (Chang et al. 2016), hinting at the possibility that for HPV and other types of viruses actin-driven virion transport may play a more important role than it is currently assumed. If this is the case, sub-confluent HaCaT cells, or even better single HaCaT cells, would be an ideal model system for the study of these very early infection steps that involve ECM attachment and subsequent filopodia-dependent transport. As shown in Supplementary Figure 1, HaCaT cells have many binding sites for the HPV16 PsVs. However, as they are polarized and the binding receptors are only at the basal membrane, they remain relatively inaccessible by diffusion. Therefore, the ECM binding that is also observed in vivo (Day and Schelhaas 2014) and subsequent transport via filopodia are used upon infection of HaCaT cells that locate at the periphery of cell patches. Here, PsVs bind to the ECM which strongly enhances infection of primary keratinocytes (Bienkowska-Haba et al. 2018). In contrast, HPV can readily bind to HSPGs on the cell surface of nonpolarized cells, and by this bypasses ECM mediated virus priming and the filopodia dependency. We propose that HaCaT cells are a valuable system for studying the very early events in HPV infection that allows for dissecting capsid interaction with ECM resident priming factors and cell surface receptors.’

Finally, please note that in the previous version of the manuscript, we did not question that in many cellular systems PsVs interact with heparan sulfate proteoglycans (HSPGs) present on the cell surface, or both on the cell surface and the ECM. We stated on line 59 ´While in cell culture virions bind to HS of the cell surface and the ECM, it has been suggested that in vivo they bind predominantly to HS of the extracellular basement membrane (Day and Schelhaas, 2014; Kines et al., 2009; Schiller et al., 2010).´

We hope that after adding the above explanations and the experiment requested by the reviewing editor it is now clear why only few PsVs bind directly (not via the ECM) to the cell surface. We appreciate the reviewer’s and the reviewing editor’s input that has significantly improved the manuscript.

(2) The experiments shown in Figure 5 need to be better controlled. Why is there no HS staining of the cell surface at the early timepoints? This antibody has been shown to recognize N-sulfated glucosamine residues on HS and, therefore, detects HSPG on the ECM and cell surface.

There is staining. However, as the staining at the periphery is stronger and images are shown at the same settings of brightness and contrast, the impression is given that the cell surface is not stained. We have added more images showing HS cell surface staining.

(i) Supplementary Figure 4C shows an enlarged view of the CytD/0 min cell shown in Figure 6A. In the area stained by Itgα6, that marks the cell body, HS staining is present, although less abundant in comparison to the ECM.

(ii) In Figure 8, CytD/30 min, a cell is shown with abundant HS in the cell body region (compare cyan and green LUT).

(iii) In newly added Figure 3A, lower panel, another cell with HS in the cell body region is shown.

Please note that the staining is highly variable. We indicate this by stating on Line 373: ‘The pattern of the HS staining (cyan LUT) and the overlap of HS with PsVs and Itgα6 are highly variable (Figure 6A).’

Therefore, the conclusion that this confirms HS coating of PsV during release from the ECM (line 430431) is unfounded. How do the authors distinguish between "HS-coated virions" and HSPG-associated virions?

The transient increase in the PCC at CytD/30 min can be interpreted as PsV/HS co-transport or as direct binding of PsVs to cell surface HSPGs. However, two arguments support co-transport.

First, we find that CytD/PsVs increases the HS intensity (see newly added Figure 3, confirming old Figure 5 that is now Figure 6). We state on line 290 ‘… that without actin-dependent PsV translocation HS cleavage products are retained in the ECM, consistent with the hypothesis that cleaved HS remains associated with PsVs (Ozbun and Campos 2021).

Second, the distance between HS and Itgα6 (the cell body marker) decreases over time after CytD removal, which suggests movement of HS to the cell body (Supplementary Figure 8D). We state on line 422: ‘The movement of HS towards the cell body after removal of CytD, which indirectly demonstrates that PsVs are coated with HS, is suggested by a shortening of the HS-Itgα6 distance over time (Supplementary Figure 8D).’

It is difficult to comprehend how the addition of 50 vge/cell of PsV could cause such a global change in HS levels.

Some areas are covered with confluent cells, to which hardly any PsVs are bound, because accessing their basolateral membrane is nearly impossible, and PsVs do not bind to the exposed apical membrane as well. We assume this is a major difference to cultures of unpolarized cells, where PsVs should distribute more or less equally over cells. This means that in our experiments the vge/cell is not a suitable parameter for relating the magnitude of an effect to a defined number of PsVs. In the ECM, the PsV density is very high, enabling one cell to collect, in theory, several hundred PsVs, much more than expected from the 50 vge/cell.

We state on line 135: ‘Frequently, we observe patches of confluent cells which are common to HaCaT cells. Cells at the center of these patches are dismissed during imaging, because there are no anterogradely migrating PsVs at these cells. A second reason for our dismissal of these cells is that hardly any PsVs are bound to them, possibly because their basal membranes are inaccessible by diffusion. Instead, we focus on isolated HaCaT cells or cells at the periphery of cell patches. In these cells, we find more PsVs per cell than one would expect from the employed 50 viral genome equivalents (vge) per cell, indicating that PsVs are unequally distributed between the cells.’

The claim that the HS levels are decreased in the non-cytochalasin-treated cells due to PsV-induced shedding needs to be demonstrated.

We did not claim that PsVs induce shedding, we rather believe they retain shedded HS. Without PsVs, the shedded HS is washed off from the ECM. We have reproduced the observation made in old Figure 5 (now Figure 6) in the newly added Figure 3 that also shows that PsVs alone have no effect on the HS intensity, only when present together with CytD. We state on line 277: ‘As outlined above, during the 5 h incubation with CytD, proteases in the ECM are expected to cleave HS chains. These cleavage products should be able to diffuse out of the ECM, unless they remain associated with nontranslocating PsVs. In the control, PsV associated HS cleavage products would leave the ECM through PsV translocation…. Using an antibody that reacts with an epitope in native heparan sulfate chains, only after CytD and if PsVs are present, the level of HS staining is significantly increased (Figure 3B). As shown in Figure 3A, stronger HS staining at PsVs (open arrows) and as well in PsV free areas (closed arrows) was observed… Collectively, our findings indicate that without actin-dependent PsV translocation HS cleavage products are retained in the ECM, consistent with the hypothesis that cleaved HS remains associated with PsVs (Ozbun and Campos 2021).’

If HS is actually shed, staining of the cell periphery could increase with the antibody 3G10, which detects the HS neoepitope created following heparinase cleavage.

We have tested the antibody by which we obtain only a very weak staining (Supplementary Figure 2), not allowing to differentiate between an increase in the cell periphery and the cell body area. We still include the experiment as it suggests that CytD has no effect on HS processing. We state on line 286: ‘As additional control and shown in Supplementary Figure 2, we use an antibody that reacts with a HS neo-epitope generated by heparitinase-treated heparan sulfate chains (Yokoyama et al. 1999; for details see methods). This neo-epitope staining is independent of the presence of CytD and the incubation time, suggesting that CytD does not directly affect HS processing.’

Reviewer #2 (Public review):

Summary:

Massenberg and colleagues aimed to understand how Human papillomavirus particles that bind to the extracellular matrix (ECM) transfer to the cell body for later uptake, entry, and infection. The binding to ECM is key for getting close to the virus's host cell (basal keratinocytes) after a wounding scenario for later infection in a mouse vaginal challenge model, indicating that this is an important question in the field.

Strengths:

The authors take on a conceptually interesting and potentially very important question to understand how initial infection occurs in vivo. The authors confirm previous work that actin-based processes contribute to virus transport to the cell body. The superresolution microscopy methods and data collection are state-of-the art and provide an interesting new way of analysing the interaction with host cell proteins on the cell surface in certain infection scenarios. The proposed hypothesis is interesting and, if substantiated, could significantly advance the field.

Weaknesses:

As a study design, the authors use infection of HaCaT keratinocytes, and follow virus localisation with and without inhibition of actin polymerisation by cytochalasin D (cytoD) to analyse transfer of virions from the ECM to the cell by filopodial structures using important cellular proteins for cell entry as markers.

First, the data is mostly descriptive besides the use of cytoD, and does not test the main claim of their model, in which virions that are still bound to heparan sulfate proteoglycans are transferred by binding to tetraspanins along filopodia to the cell body.

The study identifies a rapid translocation step from the ECM to CD151 assemblies. We have no data that demonstrates a physical interaction between PsVs and CD151. In the model figure, we draw CD151 as part of the secondary receptor complex. We are sorry for having raised the impression that PsVs would bind directly to CD151 and have modified the model Figure accordingly. In the new model figure (Figure 9), the first contact established is to a CD151 free receptor.

Second, using cytoD is a rather broad treatment that not only affects actin retrograde flow, but also virus endocytosis and further vesicular transport in cells, including exocytosis. Inhibition of myosin II, e.g., by blebbistatin, would have been a better choice as it, for instance, does not interfere with endocytosis of the virus.

As we focus on early events, we are not concerned about CytD blocking as well late steps in the infection cascade, like endocytosis. However, we agree that a comparison between CytD and blebbistatin would be very interesting. We added Figure 8, showing that blebbistatin only partially stops migration.

Line 429: ‘Actin retrograde transport, which underlies the here observed virion transport, is the integrative result of three components (Smith et al. 2008; Schelhaas et al. 2008)…. As CytD broadly interferes with F-actin dependent processes, we investigated the effects upon inhibition of only one of the three components, namely the myosin II mediated retrograde movement towards the cell body. Instead of CytD, we employed in the 5 h preincubation the myosin II inhibitor blebbistatin. For the control (0 min), we show in Figure 8A one example of a cell with comparatively many PsVs at the periphery (as mentioned above, the PsV pattern is highly variable) to better illustrate the difference to the PsV pattern occasionally seen with blebbistatin. After blebbistatin treatment (0 min), PsVs are still distal to the cell body but less dispersed than after CytD treatment, seemingly as if translocation started but stopped in the midst of the pathway (Figure 8A, blebbistatin). The PCC between PsVs and HS, like after CytD (Figure 6C), is elevated after blebbistatin, albeit the effect is not significant (Figure 8C). The cell body PCC, is not at 30 min (CytD) but already at 0 min elevated (compare Figure 6D to Figure 8D), which can be explained by partial translocation. This is further supported by the fact that only 8% of PsVs are closely associated with HS (Figure 8E; blebbistatin, 0 min) compared to 15% after CytD treatment (Figure 6E; 0 min). Furthermore, after 0 min PsV incubation with blebbistatin we observe no effect on the HS intensity (compare Figure 8B to Figure 3B and Figure 6B). Hence, in contrast to CytD, blebbistatin does not trap the PsVs in the ECM where they associate with HS, but ongoing actin polymerization pushes actin filaments along with PsVs towards the cell body.’

Third, the authors aim to study transfer from ECM to the cell body and the effects thereof. However, there are substantial, if not the majority of, viruses that bind to the cell body compared to ECM-bound viruses in close vicinity to the cells.

Please see our detailed reply to referee #1 that has raised the same issue. In brief, we agree that in multiple cell culture systems viruses bind preferentially to the cell surface directly. However, in HaCaT cells, the majority of PsVs does not bind directly to the basal membrane but gets there after initial binding to the ECM. Thus, we believe our system appropriately models the physiologically relevant scenario of ECM-to-cell transfer, as also speculated by the reviewing editor that has suggested an experiment showing that more PsVs bind to detached cells (please see above).

This is in part obscured by the small subcellular regions of interest that are imaged by STED microscopy, or by the use of plasma membrane sheets. As a consequence, the obtained data from time point experiments is skewed, and remains for the most part unconvincing due to the fact that the origin of virions in time and space cannot be taken into account. This is particularly important when interpreting association with HS, the tetraspanin CD151, and integral alpha 6, as the low degree of association could originate from cell-bound and ECM-transferred virions alike.

As already stated above, we observe massive binding of PsVs to the ECM, in contrast to very few PsVs that diffuse beneath the basolateral membrane of the polarized HaCaT cells and do bind directly to the cell surface. In other cellular systems, cells may hardly secrete ECM, are not polarized, and therefore virions can easily bypass ECM binding. Therefore, it is reasonable to assume that in HaCaT cells the large majority of PsVs found on the cell body originates from the ECM.

Fourth, the use of fixed images in a time course series also does not allow for understanding the issue of a potential contribution of cell membrane retraction upon cytoD treatment due to destabilisation of cortical actin. Or, of cell spreading upon cytoD washout.

The newly added blebbistatin experiment suggests that the initial translocation is exclusively dependent on retrograde actin flow. However, we agree that we are not able to unravel more details regarding the different possible contributions to the movement. Importantly, the lack of PCC increase after CytD/leupeptin removal (Figure 2D) suggest there is not much cell spreading into the area of accumulated PsVs. Please see our more detailed reply to the same issue raised by the same referee in the recommendations for the authors.

The microscopic analysis uses an extension of a plasma membrane stain as a marker for ECM-bound virions, which may introduce a bias and skew the analysis.

The dye TMA-DPH stains exclusively cellular membranes and not the ECM. The stain is actually used to delineate the cell body from the ECM area (please see Figure 1).

Fifth, while the use of randomisation during image analysis is highly recommended to establish significance (flipping), it should be done using only ROIs that have a similar density of objects for which correlations are being established.

We agree that the way of how randomization is done is very important. Regarding the association of PsVs with CD151 and HS, we corrected for random background association, which is now explained in more detail in in the Figure legend of Supplementary Figure 7: “On flipped images, we often find values more than half of the values of the original images, demonstrating that many PsVs have a distance ≤ 80 nm to CD151 merely by chance (background association)… (C) Each time point in (A) and (B) obtained from flipped images is the average of three biological replicates. We use these altogether 24 data points, plotting the fraction of closely associated PsVs against the CD151 maxima density. The fraction increases with the maxima density, as the chance of random association increases with the maxima density. The fitted linear regression line describes the dependence of the background association from the maxima density. As a result, the background association (y) can be calculated for any maxima density (x) in original images with the equation y = 2.04x. Please note that the CytD/0 min may be overcorrected as we subtract background association with reference to the CD151 maxima density of the entire ROI (for an example ROI see Supplementary Figure 6A), although the local maxima density at distal PsVs is lower. On the other hand, PsVs at the cell border may have a larger local CD151 maxima density and consequently are undercorrected.’

For instance, if one flips an image with half of the image showing the cell body, and half of the image ECM, it is clear that association with cell membrane structures will only be significant in the original.

We are aware of this problem. For instance, it would produce ‘artificially’ low PCCs after flipping images of PsV/HS stainings (please see negative PCC value after flipping in Supplementary Figure 8). In this case, we do not use as argument that in flipped images the PCC is lower. Instead, we would argue that over time the PCC changes in the original images. We still provide the PCC values of flipped images, as additional information, showing that in most cases we obtain after flipping a PCC of zero, as expected

Hence, we fully agree that careful controls in image analysis is required, and used the above-described method for the correction of background association when the fraction of closely associated PsVs is analyzed. We do not use a lower PCC value in flipped images as argument if not appropriate.

I am rather convinced that using randomisation only on the plasma membrane ROIs will not establish any clear significance of the correlating signals.

Figure 6D and 8D show the PCC specifically of the cell body (only of plasma membrane ROIs). In flipped images (not shown in the previous version for clarity), we obtain significantly lower PCCs (Supplementary Figure 8F/G and Supplementary Figure 10C/D. We propose that in this case it would be appropriate to use a lower PCC of flipped images as argument for specific association. Still, also in this experiment we argue with a change in the PCC over time, and not with a PCC of zero after flipping. As above, we still provide the PCC values of flipped images as additional information.

Also, there should be a higher n for the measurements.

One replicate is based on the average of 14-15 cells for each condition (more for figure 4). Hence, in a typical experiment (Control and CytD with 4 time points) about 120 cells are analyzed, which is a broad basis for the averages of one replicate.

We realize that with three biological replicates we find significant effects only if we have strong effects or moderate effects with very low variance.

Recommendations for the authors:

Reviewing Editor:

The focus on the events of HPV infection between ECM binding and keratinocyte-specific receptor binding is unique and interesting. However, I agree with the reviewers that some of the conclusions could use more experimental support, as detailed in their comments. The failure to detect direct binding of the PsV to HSPGs on the cell surface in in vitro assays contradicts much of the published literature. For example, others have found that HPV capsids bind cultured cell lines in suspension, i.e, in the absence of ECM. Do EDTA-suspended HaCaT cells bind PsV? Is the binding HSPG dependent? If the authors think that failure to detect direct cell binding of HaCaTs is an unusual feature of these cell lines or culture condition,s then it would be helpful to provide an explanation. However, it is worth noting that an in vitro system where the cells do not directly bind capsids through HSPG interactions would be a much better model for studying the stages of HPV infection that are the focus of this study, since there is no direct binding of keratinoctyes in vivo.

We are thankful for this comment that had a strong influence on the revision. The suggested experiment has been incorporated as new Supplementary Figure 1. It shows that many more PsVs bind to the cell surface of cells in suspension than to adhered cells. As suggested by the reviewing editor, we explain now that HaCaT cells are a suitable model system for studying the in vivo transport from the ECM to the cell body that in these cells, due to their polarization, cannot be bypassed (for more details please see our replies above addressing these issues).

Because conclusions drawn regarding HS interactions are largely based on experiments using a single HS mAb, it is important that the specificity of this mAb is described in more detail, either based on the literature or further experimentation.

We provide now detailed information about the HS antibodies used in the study. We state on line 282 ‘Using an antibody that reacts with an epitope in native heparan sulfate chains…’ and on line 286 ‘we use an antibody that reacts with a HS neo-epitope generated by heparitinase-treated heparan sulfate chains…’ and in the methods section ‘For Heparan sulfate (HS) a mouse IgM monoclonal antibody (1:200) (amsbio, cat# 370255-S) was used that reacts with an epitope in native heparan sulfate chains and not with hyaluronate, chondroitin or DNA, and poorly with heparin (mAb 10E4 (David et al., 1992)). For HS neo-epitope (Yokoyama et al., 1999) detection, a mouse monoclonal antibody (1:200) (amsbio, cat#370260-S) was used that reacts only with heparitinase-treated heparan sulfate chains, proteoglycans, or tissue sections, and not with heparinase treated HSPGs. The antibody recognizes desaturated uronic acid residues (mAb 3G10 (David et al., 1992)).’

Reviewer #1 (Recommendations for the authors):

(1) The phrase "tight association" or similar is repeatedly used and is not acceptable for microscopic studies; use "close association", which has no affinity connotations.

Has been changed as suggested by the referee.

(2) Why are lysine-coated coverslips used for microscopy? HaCaT cells adhere tightly to untreated glass, and this coating could affect the distribution of ECM and extracellular PsV.

We believe a tight association of the basal cell membrane to its substrate, as in vivo, where the basal membrane is tightly adhered to other cells, is important in these experiments. In weakly adherent cells more PsVs may bind to the cell surface, bypassing the transport step. Hence, although HaCaT cells may not require the coat and would be able to adhere to glass, the association may not be tight enough to mimic in vivo conditions.

(3) What is the reason to use detection of the pseudogenome for some of the experiments instead of L1 detection throughout? The process of EdU detection is sufficiently denaturing to affect some protein epitopes. The introduction of this potential artifact doesn't seem warranted for capsid detection experiments.

The L1 and the Itgα6 antibody are from the same species, wherefore we have used in Figures 4 and 6 click-labeling of the reporter plasmid. We do not disagree with the notion of the referee, that EdU detection may denature the epitope of some proteins. For instance, we have observed a different staining pattern for CD151; for Itgα6 and HS we saw no obvious difference in the staining patterns. In double staining experiments using L1 antibody and click-labeling, both staining patterns overlapped very well, indicating that click-labeling is suitable to visualize PsVs.

(4) What concentration of TMA-DPH was used?

TMA-DPH is a poorly water-soluble dye that becomes strongly fluorescent upon insertion into a membrane. Because of its poor water solubility, a precise concentration cannot be given. We added 50 µl of a saturated TMA-DPH solution in PBS to 1 ml of PBS in the imaging chamber. We state this now in the methods section.

(5) Line 419: This statement is misleading. Although PsV interaction with HSPG on the ECM is crucial for infectious transfer to cells, the majority of the PsV binding on the ECM has been attributed to interaction with laminin 332. Treatment of PsV with heparin causes sequestration to the ECM.

We are sorry for the confusion and have removed the misleading statement.

(6) Some reference choices are poor:

Line 54: Ozbun and Campos, this is not the correct reference

In the review we cited, in the introduction it is stated that PsVs establish infection via a break in the epithelial barrier? However, we have replaced this reference by a review that focuses more on epithelial wounding: ‘Ozbun, Michelle A. (2019): Extracellular events impacting human papillomavirus infections: Epithelial wounding to cell signaling involved in virus entry. In Papillomavirus research (Amsterdam, Netherlands) 7, pp. 188–192. DOI: 10.1016/j.pvr.2019.04.009.’

Line 2012: Doorbar et al., this is not the correct reference.

Thank you for pointing this out (..we assume the referee refers to line 104 and not line 2012). We have noticed this error during revision. As it is difficult to get a specialized review on this topic, we now cite Ozbun and Campus, 2021 that states PsVs are ‘structurally and immunologically indistinguishable from lesion- and tissue-derived HPVs.’

Minor issues:

(1) It is difficult to appreciate the ECM and cell surface binding pattern from the provided images, which do not even contain an entire cell. We need to see a few representative field views with the ECM delineated with laminin 332 staining, as HS antibodies stain both the ECM and cell surface.

We now provide overview images in Supplementary Figure 4. The only experiment requiring a clear delineation between ECM and cell surface is the experiment of Figure 4. Here, we do not use the HS as a reference staining because it stains both the ECM and the cell surface.

(2) For Figure 1E, the cells were only infected for 24 hours. The half-time for infectious internalization of HaCaT cells was shown to be 8 hours for cell-associated PsV and closer to 20 hours for PsV that was associated with the ECM prior to cell association (Becker et al., 2018). Why was such a short infection time chosen?

During assay establishment it has been observed that after 24 h the luciferase activity is optimal.

(3) Figure 5, the staining of uninfected cells +/- cyto treatment needs to be included.

Now visible in new Figure 3.

I am confused by lines 54-57. It seems as if the authors are claiming that HSPGs are not present on the ECM. This sentence, as written, is misleading.

We agree, and state now on line 58 ‘Here, virions bind to the linear polysaccharide heparan sulfate (HS) that is present in the extracellular matrix (ECM) but as well on the plasma membrane surface. HS is attached to proteins forming so called heparan sulfate proteoglycans (HSPGs).’

Reviewer #2 (Recommendations for the authors):

There are further issues that are not pertaining to the study design that I find important.

(1) It remains speculative whether the virions that are transferred from the ECM are actually structurally modified.

The newly added Figure 2, showing that leupeptin blocks infection in our assay, suggests that virions indeed are primed.

(2) The origin of HS correlated with virions on the cell body after transfer is also not clear: does the virus associate with cell surface HS, or does it bring HS from the ECM? Simply staining HS against Nsulfated moieties does not allow such conclusions.

This issue has been already raised in the public review to which we replied above. In brief, we agree that the transient increase of the PCC between PsVs and HS in the cell body region can be also explained by PsVs coming from the ECM without HS and binding to cell surface HS, or from PsVs binding directly (not via the ECM) to cell surface HSPGs. However, there are two more arguments indicating that PsVs are coated with HS. Please see our detailed reply above.

(3) Figure 1: There are few, if any, filopodia in untreated cells. It would be good to quantify their abundance to substantiate that resting HaCat cells are indeed a good model for filopodial transport bs. membrane retraction / spreading. In HaCat ECM, the virus also binds to laminin-332 for a good part. Would this not also confound the analysis?

At first glance, the number of filopodia appears to be too low to account for such an efficient transport. However, please note that the formation of filopodia is very dynamic, and that they can form and disappear within minutes (see below). We also often observe many PsVs aligned at one filopodium. Moreover, not every cell periphery exhibits large accumulations of PsVs. Therefore, we believe it is in principle possible that filopodia are largely responsible for the transport. We cannot exclude that we overestimate the transport rate due to partial cell spreading after CytD removal, which, however, we consider as rather unlikely as in Figure 2 we observe no increase in the PCC when leupeptin was present during the CytD incubation. Under these conditions, PsVs do not translocate but cells could spread, and this would increase he PCC between PsVs and F-actin if cells would spread into the area of accumulated PsVs.

We now state on line 304: ‘This suggests that the half-time of PsV translocation from the periphery to the cell body is about 15 min. In fact, the half-time maybe longer, as we cannot exclude that cell spreading after CytD removal contributes to less PsVs measured in the cell periphery.’ and on line 477 ‘As mentioned above, the half-time could be longer if cell spreading is in part responsible for the translocation of PsVs onto the cell body. However, we assume that this is rather unlikely, as cell spreading would increase the PCC between PsVs and F-actin under a condition where filopodia mediated transport is blocked but not cell spreading, which is not the case (Figure 2B and D, CytD/leupeptin).’

(4) Figure 2: This would benefit from live cell analysis. There are considerable amounts of virions on the cell body, which partially contradicts statements from Figure 1.

Does the referee refer to the images shown in Figure 4 (old Figure 2)? Please note that at CytD/0 min there are hardly any PsVs in the cell body region, the fluorescence (magenta LUT) is autofluorescence (this is explained in the results section). Only at later time points PsVs are in the cell body region.

The fast transfer to the cell body after cyto D washout is based on the assumption that filopodia formation and transport along them (and not membrane extension) occur quickly. Is this reasonable?

We are no experts on filopodia, but one finds references suggesting that they grow at rates of several µm per minutes and have lifetimes between a few seconds and several minutes. Hence, within the 15 min we determine for the transport, cells may need a few minutes to recover from CytD, a few minutes to form filopodia that reach out into the ECM, and a few minutes for the transport itself. However, we agree that we cannot exclude membrane extension contributing to our observed transport, although we consider this as rather unlikely (see above).

(5) Figure 3: The rationale of claiming the existence of 'endocytic structures' needs to be better explained and quantified in the according supplementary figure.

We now state in the legend ‘We propose that the agglomerated CD151 maxima close to PsVs feature the characteristics of endocytic structures, as CD151 has been shown to co-internalize with PsVs (Scheffer et al. 2013), and as these structures invaginate into the cell, like PsV filled tubular organelles previously described by electron microscopy (Schelhaas et al. 2012).’ For a proper quantification of these highly variable structures a much larger sample would be required.

The formation of virus-filled tubules upon cytoD treatment has been previously reported. Are these viruses that come from the cell body or from the ECM?

With the new data and explanations that have been added to the manuscript, it should be clear that it is reasonable to assume that they come largely from the ECM.

(6) Figure 4: How are the subcellular ROIs chosen? Is there not a bias by not studying a full cell?

We now explain better how we chose cells for analysis. We state on line 138 ‘Instead, we focus on isolated HaCaT cells or cells at the periphery of cell patches. In these cells, we find more PsVs per cell than one would expect from the employed 50 viral genome equivalents (vge) per cell, as PsVs are unequally distributed between the cells. Moreover, these PsVs usually are not homogenously distributed around the cell but concentrate at one region. We investigate the translocation of PsVs from these regions, defining ROIs for analysis that cover PsVs at the periphery and the cell body (see Supplementary Figures 6A and 8A).’

(7) Figure 5/6: The data needs a better analysis on correlation by using randomisation as explained above.

Please see our reply to the same point of the public review raised by the same referee.

(8) Figure 7: This model involves CD151 being a mediator in transfer, but this has not been functionally shown. There are HaCaT CD151 KO cells available (from the Sonnenberg lab), it would be good to use those to test the model and whether transfer indeed involves CD151.

As already stated above, we are sorry for having raised the impression that PsVs bind directly to CD151. The model Figure has been modified. Please see our reply above.

(9) The manuscript would benefit from a number of experiments addressing the most crucial issues:

(a) As mentioned before, the use of blebbistatin, which blocks myosin II function and arrests actin retrograde flow within seconds of addition, would be a good inhibitor to control for transfer in at least some of the most crucial experiments.

In Figure 8 we have tested blebbistatin. Please see our reply above.

(b) Live cell analysis would allow for monitoring of whether membrane retraction upon cytoD treatment would have to be taken into account for the analysis of the data. The same is true for the cytoD washouts, upon which most cells exhibit pronounced membrane spreading. The latter is important to support filopodial transport rather than membrane ruffling and spreading, leading to the clearance of extracellular virions from the ECM.

We agree that this would be desirable. As replied above, we now discuss the issue of possible membrane spreading and reason why we consider it as rather unlikely.

(c) To rid oneself of the issue of plasma membrane-bound virions as a confounding factor, one could use cells treated by sodium chlorate, which leads to undersulfation of HS on the cell surface, and seed them onto ECM with functional HSPGs. This would then indeed establish that the HS and virus are transferred together.

We agree that this would be a smart experiment. As the main focus of our study is not clarifying whether PsVs are coated with HS or not, we gave other experiments priority.

(10) The manuscript is, while carefully and thoughtfully worded on the issue of microscopy analysis, for a good part, extrapolating too strongly from the authors' data and unsubstantiated assumptions to conclude on their model. It would be good if the authors would support their claims with previous or their own experimental work. Just two examples of several: the assumption that cell-bound virions are negligible should be substantiated, as the literature would indicate otherwise.

We determined the PsV density in adhered, CytD treated cells, and find around 0.14 per µm² (Supplementary figure 1B), which is 4 to 5-fold less when compared to the PsV density quantified in an area covering the cell body and the periphery (Figure 1B, see line 174 for PsVs/µm² values). Quantifying the PsV density only in the periphery would yield a severalfold larger difference. However, due to the limited resolution of the microscope we would strongly underestimate the PsV density in the accumulations. We prefer not to discuss this in detail, as exact numbers are difficult to obtain.

Line 129: Cyto D should not inhibit the enzymes modifying HS or proteins (including virions). This is true, but cytoD may limit their secretion and abundance.

We show in Figure 3 that CytD does not reduce HS staining (e.g., by limiting HS secretion, as suggested by the referee), suggesting that it rather does not limit secretion.

We thank the referee´s and the reviewing editor for their helpful comments!

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

Learn more at Review Commons

Reply to the reviewers

__Reviewer #1 __

*This study "Interpreting the Effects of DNA Polymerase Variants at the Structural Level" comprises an in-depth analysis of protein sequence variants in two DNA polymerase enzymes with particular emphasis on deducing the mechanistic impact in the context of cancer. The authors identify numerous variants for prioritisation in further studies, and showcase the effectiveness of integrating various data sources for inferring the mechanistic impact of variants. *

*All the comments below are minor, I think the manuscript is exceptionally well written. *

*> The main body of the manuscript has almost as much emphasis on usage of the MAVISp tool as analysis of the polymerase variants. I don't think this is an issue, as an illustrated example of proper usage is very handy. I do, however, think that the title and abstract should better reflect this emphasis. E.g. "Interpreting the Effects of DNA Polymerase Variants at the Structural Level with MAVISp". This would make the paper more discoverable to people interested in learning about the tool. *

We have changed the manuscript title according to the reviewer’s suggestions, and the current title is “Interpreting the Effects of DNA Polymerase Variants at the Structural Level using MAVISp and molecular dynamics simulations.”

• *

*> Figure 1. I don't believe there is much value in showing the intersection between the datasets (especially since the in-silico saturation dataset intersects perfectly with all the others). As an alternative, I suggest a flow-chart or similar visual overview of the analysis pipeline. *

• *

We moved the former Figure 1 to SI. We decided to keep it at least in SI because it provides guidance on the number of variants relative to the total reported across the different disease-related datasets annotated with the MAVISp toolkit. On the other hand, the suggestion of a visual scheme for the pipeline followed in the analyses is a great idea. We have thus added Figure 1, which illustrates the pipeline workflows for analysis of known pathogenic variants and for discovery of VUS and other unknown variants, as suggested by the reviewer.

*> Please note in the MAVISp dot-plot figure legends that the second key refers to the colour of the X-axis labels rather than the dots *

We have revised the code that produces the dotplot so the second key is placed closer to the x-axis and clearer to read.

Missing figure reference (Figure XXX) at the bottom of page 16

We apologize for this mistake. Figures, contents, and the order have changed significantly to address all reviewers’ comments; this statement is no longer included. Also, we have carefully proofread the final version of the manuscript before resubmitting it.

__Reviewer #2 __

• *

This manuscript reports a comprehensive study of POLE and POLD1 annotated clinical variants using a recently developed framework, MAVISp, that leverages scores and classifications from evolutionary-based variant effect predictors. The resource can be useful for the community. However, I have a number of major concerns regarding the methodology, the presentation of the results.

*** On the choice of tools in MAVISp and interpretation of their outputs *

- Based on the ProteinGym benchmark: https://proteingym.org/benchmarks*, GEMME outperforms EVE for predicting the pathogenicity of ClinVar mutations, with an AUC of 0.919 for GEMME compared to 0.914 for EVE. Thus, it is not clear for me why the authors chose to put more emphasis on EVE for predicting mutation pathogenicity. It seems that GEMME can better predict this property, without any adaptation or training on clinical labels. *

• *

We appreciate this comment, but we should not exclude EVE entirely from our data collection or from VEP coverage under MAVISp, based on a difference in AUC of 0.005. It was not our intention to place more emphasis on EVE predictions, and we have revised it accordingly. We would like to clarify the workflow we use for applications of the MAVISp framework in “discovery mode,” i.e., for variants not reported as pathogenic in ClinVar. This relies on AlphaMissense to prioritize the pathogenic variants and then retain further only the ones that also have an impact according to DeMaSk, which provides further indication for loss/gain-of-fitness. DeMaSk nicely fits the MAVISp framework, as it was trained on data from experimental deep mutational scans, which we generally import in the EXPERIMENTAL_DATA module. We have revised the text to make this clearer. GEMME and EVE (or REVEL) can be used for complementary analysis in the discovery workflow. Other users of MAVISp data might want to combine them with a different design, and they have access to all the original scores in the MAVISp database CSV file and the code for downstream analysis to do so. The choice for our MAVISp discovery workflow is mainly dictated by the fact that we have noticed we do not always have full coverage of all variants in many protein instances for EVE, GEMME, and REVEL. In particular, since the reviewer highlights GEMME over EVE, GEMME is currently unavailable for a few cases in the MAVISp database. This is because we need to rely on an external web server to collect the data, which slows down data collection on our end.

Additionally, we have encountered instances where GEMME was unable to provide an output for inclusion in the MAVISp entries. When we designed the workflow for variant characterization in focused studies, we also made practical considerations. We are also exploring the possibility of using pre-calculated GEMME scores from

https://datadryad.org/dataset/doi:10.5061/dryad.vdncjsz1s, but we encountered some challenges at the moment that deserve further investigations and considerations. For example, MAVISp annotations rely on the canonical isoform as reported in Uniprot, which can lead to mismatches with the GeMME pre-computed scores. So far, we have identified a couple of entries whose canonical isoforms no longer match the one in the pre-computed GEMME score dataset. Another limitation is the absence of the original MSA files in the dataset, which we would need for a more in-depth comparison with the ones we used for our calculations. We are facing some challenges in reproducing the MSA output from MMseq2-based ColabFold protocol in this context that need to be solved first. Overall, the dataset shows potential for integration into MAVISp, but we need to define the inclusion criteria and compare it with the existing results in more detail.

Additionally, since the principle behind MAVISp is to provide a framework rooted in protein structure, AlphaMissense was the most reasonable choice for us as the primary indicator among the VEPs for our discovery workflow, and it has performed reasonably well in this case study and others.

Of course, our discovery design is one of the many applications and designs that could be envisioned using the data provided and collected by MAVISp. We also include all raw scores in the database's final CSV files, allowing other end users to decide how to use them in their own computational design. The design choice we made for the discovery phase of focused studies, using MAVISp to identify variants of interest for further studies, has been applied in other publications (see https://elelab.gitbook.io/mavisp/overview/publications-that-used-mavisp-data) in some cases together with experiments. It is also a fair choice for the application, as the ultimate goal is to provide a catalog of variants for further studies that may have a potentially damaging impact, along with a corresponding structural mechanism.

We have now revised the results section text where Table 1 is cited to clarify this. We also revised the terminology because we are using the VEPs' capability to predict damaging variants, rather than the pathogenic variants themselves. Experiments on disease models should validate our predictions before concluding whether a variant is pathogenic in a disease context, and we want to avoid misunderstandings among readers regarding our stance on this matter.

- Which of the predictors, among AM, EVE, GEMME, and DeMaSK, provide a classification of variants and which ones provide continuous scores? This should be clarified in the text. If some predictors do not output a classification, then evaluating their performance on a classification task is unfair. The MAVISp framework sets thresholds on the predicted scores to perform the classification and it is unclear from reading the manuscript whether these thresholds are optimal nor whether using universal cutoff values is pertinent. For instance, for GEMME, a recent study shows that fitting a Gaussian mixture to the predicted score distribution yields higher accuracy than setting a universal threshold (https://doi.org/10.1101/2025.02.09.637326*). Along this line, for predictors that do not provide a classification, I am not convinced of the benefit for the users of having access to only binary labels, instead of the continuous scores. The users currently do not have any idea of whether each variant is borderline (close to theshold) or confident (far from threshold). *

We agree with the reviewer, and this is due to us not being sufficiently clear in the manuscript. We have now revised the first part of the results to clarify this and to explain how we use the MAVISp data for application to focused studies, where the goal is to identify the most interesting variants that are potentially damaging and have a linked structural mechanism. Of course, there are other applications for leveraging the data in the database. We do offer scores to variants instead of just classification labels in the MAVISp csv file. They can be accessed, together with the full dataset, through the MAVISp website and reused for any applications.

Additionally, we used the scores in the revised manuscript for the VUS variant ranking (Figure 5), applying a strategy recently designed as an addition to the downstream analysis tool kit of MAVISp (​​https://github.com/ELELAB/MAVISp_downstream_analysis), thereby allowing the scores themselves to be taken into account. Also, in the final part of the manuscript, the VEP scores have been used to introduce the ACMG-like classification of the variants in response to reviewer 3 (Figure 9 and Tables S3-S4). We absolutely agree that it is informative to keep the continuous scores, and we have never overlooked this aspect. However, we also need a strategy with a simpler classification to highlight the most interesting variants among thousands or more to start an exploration. This is why we included the support with dotplots and lolliplots, for example. Our purpose here is to identify, among many cases, those with a potentially damaging signature (and thus we need a binary classification for simplicity). Next, we evaluate whether this signature entails a fitness effect (with DeMaSk), and finally, retain only the cases we can identify with a structural mechanism to study further.

The thresholds we set as the default for data analysis of dotplots in GEMME and DeMaSk are discussed in __Supplementary Text S3 __of the original MAVISp article. In brief, we carried out an ROC analysis against the scores for known pathogenic and benign variants in ClinVar with review status higher than 2. For applicative purposes, one could design other strategies to analyze the MAVISp data too; it is not limited to the workflow we decided to set as the primary one for our focused studies, as already mentioned above.

We have now also included classification based on the GMM model applied to GEMME scores for POLE and POLD1, so it can be evaluated against other designs for our protein of interest (see Table 1 in the revised version). The method section has been revised to include this part, and the ProteoCast pre-print is cited as a reference. We have not yet officially included this classification in the MAVISp database because we must first follow internal protocols to meet the inclusion criteria for new methods or analyses. We will do so by performing a similar comparison on the entire MAVISp dataset and focusing on high-quality variants, as ClinVar annotations, as we did to set the current thresholds for GEMME in Supplementary Table S3 of the original MAVISp article. We need to allocate time and resources to this pilot, which is scheduled for Q1 2026.

** On the presentation and impact of the results

• While reading the manuscript, it is difficult to grasp the main messages. The text contains abundant discussion about the potential caveats of the framework, the care that should be taken in interpreting the results, and the dependency on the clinical context. Although these aspects are certainly important, this extensive discussion (spread throughout the manuscript) obscures the results. Moreover, the way variants are catalogued throughout the text makes it difficult to grasp key highlights. The reader is left unsure about whether the framework can actually help the clinical practitioners.

We have revised the text to make it easier to read, including additional MD simulations of three variants of interest and more downstream analyses to clarify the mechanisms of action. We also added a recap of the most interesting variants and their associated mechanisms, along with the ranking of the variants using the different features available in the MAVISp csv file for the VUS. We hope that this makes it more accessible and valuable. In the original publication, Table 2 aimed to provide a summary of the interesting variants, and we have revised it now in light of the ranking results and the additional analyses that allow us to clarify the mechanisms of action further. We have also introduced__ Figure 9 and Tables S3 and S4__, which present data on ACMG-like classification for VUS that can fall into the likely pathogenic or benign categories.

• In many cases, the authors state that experimental validation is required to validate the results. Could they be more explicit on the experimental design and the expected outcome?

We have added a section on the point above at pages 21 and 30, where, alongside the summary of mechanisms per variant, we propose the experimental readouts to use based on known MAVE assays or assays that could be designed.

• AlphaMissense seems to tend to over-predict pathogenicity. Could the authors comment on that?

We are unsure whether this comment relates to our specific case or to a general feature of AlphaMissense.

In the latest iteration of our small benchmarking dataset for POLE and POLD1 (as shown in the paper), we achieve a sensitivity of 1 and a balanced specificity of 0.96 for AlphaMissense, which suggests that AlphaMissense does not over-predict pathogenicity very significantly in these proteins, predicting true negatives (i.e., non-pathogenic) mutations quite accurately. As performance was sufficient in our case, we deemed recalibrating the classification threshold for AlphaMissense unnecessary.

We are aware that this is not necessarily the case for every gene, e.g., it has been shown that AlphaMissense shows lower specificity in some cases (see e.g. 10.3389/fgene.2024.1487608, 10.1038/s41375-023-02116-3). This is also why we found it essential to evaluate its performance with its recommended classification on a gene-specific basis, as done here. In the future, we will keep a critical eye on our predictors to understand whether they are suitable for the specific case of study, or whether they require threshold recalibration or the use of a different predictor.

** On specific variants

• The mention of H1066R, H1068, and D1068Y is very confusing. There seems to be a confusion between residue numbers and amino acid types.

We have revised the text for typos and errors. This part of the text changed, so these specific variants are no longer mentioned.

• A major limitation of the 3D modeling is this impossibility to include Zn2+ coordination by cysteine residues. This limitation holds for both POLE and POLD1. Could the authors comment on the implication of this limitation for interpreting the mechanistic impact of variants. In particular, there are several variants reported in the study that consist in gain of cysteines. The authors discuss the potential impact of some of these mutations on the structural stability but not that on Zn coordination or the formation of disulphide bridges.

This is a great suggestion. We had, for a long time, a plan in the pipeline to include a module to tackle changes in cysteines. We have now used this occasion to include a new module that allows identifying mutations: 1) that are likely to disrupt native disulphide bridges and annotate them as damaging or 2) potential de novo formation of disulphide bridges upon a mutation of a residue to a cysteine, also annotated as damaging with respect to the original functionality. We also included a step that evaluates if the protein target is eligible for the analysis based on the cellular localization, since in specific compartments the redox condition (such as the nucleus) would not favour disulfide bridges. The module has been added to MAVISp, and we are collecting data with the module for the existing entries in the database to be able to release them at one of the following updates. More details are on the website in the Documentation section (https://services.healthtech.dtu.dk/services/MAVISp-1.0/). We could not apply the module to POLE and POLD1 since they are nuclear proteins, and it would not be meaningful to look into this structural aspect either in connection with loss of native cysteines or de novo disulfide bridge formation upon mutations that change a wild-type residue to a cysteine.

We would like to clarify that the structures we use, as it is a focused study rather than high-throughput data collection for the first inclusion in the MAVISp database, have been modelled with zinc at the correct position. It is just the first layer of high-throughput collection with MAVISp, which uses models without cofactors unless the biocurator attempts to model them or we move to collect further data for research studies (as done here). Prompted by this confusion, we have now added a field to the metadata of a MAVISp entry indicating the cofactor state. Nevertheless, the RaSP stability prediction does not account for the cofactor's presence, even when it is bound in the model. This is discussed in the Method Section. We thus did not further analyze the variants in sites directly coordinating the metal groups due to these limitations.

• MAVISp does not identify any mechanistic effect for a substantial portion of variants labelled as pathogenic. Could the authors comment on this point?

We are not sure how to interpret this question. It can be read two ways. Either the reviewer is asking about the known pathogenic ClinVar variants without mechanistic indicators, or more generally, the ones that we label “pathogenic” in discovery (we actually refer to more usually damaging in the dotplots), and for which we cannot associate a mechanism.

Overall, as a general consideration, it would be challenging to envision a mechanism for each variant predicted to be functionally damaging. For example, in the case of POLE and POLD1, we still lack models of complexes that did not meet the quality-control and inclusion criteria for the binding-free-energy scheme used by the LOCAL INTERACTION module. Also, when it comes to effects on catalysis or to analyzing effects in more detail at the cofactor sites, we could miss effects that would require QM/MM calculations. Other points we have not yet covered include cases related to changes in protein abundance due to degron exposure for degradation, which is one of the mechanistic indicators we are currently developing. Moreover, we used only unbiased molecular simulations of the free protein, and we would need future studies with enhanced sampling approaches and longer timescales to better address conformational changes and changes in the population of different protein conformational states induced by the mutation (including DNA). This can be handled formally by the MAVISp framework using metadynamics approaches, but it would be outside the scope of this work and is a direction for future studies on a subset of variants to investigate in even greater detail.

Furthermore, modifications related to PTM differ from phosphorylations. Anyway, our scope is to use the platform to provide structure-based characterization of either known pathogenic variants or potentially damaging ones predicted by VEPs, and focus on more detailed analyses of those. As we develop MAVISp further and design new modules, we will also be able to tackle other mechanistic aspects. This discussion, however, is more relevant to the MAVISp method paper itself.

Moreover, none of the variants discussed are associated with allosteric effect. Is this expected?

.

In general, allosteric mutations are rare. Nevertheless, in these case studies, the size of the proteins under investigation also poses some challenges for the underlying coarse-grain model used in the simple mode to generate the allosteric signalling map, as we have found it performs best on protein structures below 1000 residues

__Reviewer #3 (Evidence, reproducibility and clarity (Required)): __

The manuscript utilized the MAVISp framework to characterize 64,429 missense variants (43,415 in POLE and 21,014 in POLD1) through computational saturation mutagenesis. The authors integrate protein stability predictions with pathogenicity predictors to provide mechanistic insights into DNA polymerase variants relevant to cancer predisposition and immunotherapy response. There are discussions of known PPAP-associated variants and somatic cancer mutations in the context of known data and some proposed variants of interest (which are not validated).

Major comments:

I was unaware of the MAVISp framework. It concerns me that alebit this paper has a lot of technical details about the framework, its not the paper about the framework. I did look into the paper https://www.biorxiv.org/content/10.1101/2022.10.22.513328v5 which keeps benign updated (version five now) for three years, but I do not see a peer reviewed version. It would be unfair of me to peer review the underlying framework of the work but together with the previous comments, I am a bit concerned.

We have intentionally left the MAVISp resource paper as a living pre-print until we have sufficient data in the database that could be useful to the rest of the community. We have been actively revising the manuscript, thanks to comments from users in previous versions, to ensure it provides a solid resource. We had attempted approximately one and a half years ago a submission to a high-impact journal and even addressed the reviewers’ comments there. Still, we did not receive feedback for a long time, and ultimately, we were not sent to the reviewers again despite more than six months of work on our side. After that, we realized that we would benefit from collecting a larger dataset, and we invested time and effort in that and submitted again for revision, this time through Review Commons in the Summer of 2025. Anyway, the paper has been peer-reviewed by three reviewers through Review Commons. We submitted the revised version and response to reviewers, and it is now under revision with Protein Science. The reviewers’ comments and our responses can be found in the “Latested Referred Preprints” on the Review Commons website with the date of 17th of October 2025.

We would also like to clarify another point on this. In our experience, it is common practice to keep sofware on BioRxiv even for a long and to bring it to a more complete form in parallel with the community already applying it. This allows feedback from peers in a broad manner. We had similar experiences with MoonlightR, where the first publications with applications within the TCGA-PanCancer papers came before the publication of the tool itself, and the same has been for any of our main workflows, such as MutateX or RosettaDDGPrediction, which are widely used by the community. Finally, it can be considered that the MAVISp framework has already been used in different published peer-review studies (since 2023), attesting to its integrity and potential. Here, the reviewer can read more about the studies that used MAVISp data or modules: https://elelab.gitbook.io/mavisp/overview/publications-that-used-mavisp-data

For example, the authors are using AlphaFold models to predict DDG values. Delgado et al. (2025, Bioinformatics) explicitly tested FoldX on such models and concluded that "AlphaFold2 models are not suitable for point mutation ΔΔG estimation" after observing a correlation of 0.06 between experimental and calculated values. AlphaFold's own documentation states it "has not been validated for predicting the effect of mutations". Pak et al. (2023, PLOS ONE) showed correlation between AlphaFold confidence metrics and experimental ΔΔG of -0.17. Needless to say that these concerns seriously undermine the validity of a major part of the study.

We appreciate the reviewer’s comments and would like to clarify a point regarding the MAVISp STABILITY module, which we believe may have been misunderstood. Based on the studies cited by the reviewer, which critique the use of AF-generated mutant structures for assessing stability effects, we understand that this assumption may have led to the concern.

The STABILITY module utilises three in silico tools (FoldX, Rosetta, and RaSP) to assess changes in protein stability resulting from missense mutations. Importantly, the input to these assessments consists of AF models of the WT protein structures, not of AF-generated mutant structures. The mutants are generated using the FoldX and Rosetta protocols, along with estimates of the changes in free energy. For further details and clarification, we kindly refer the reviewer to the MAVISp original publication.

Also, one should consider the goal of our use of free energy calculations: not to identify the exact ΔΔG values, but to correlate with data from in vitro or biophysical experiments, such as those from cellular experiments like MAVE. We, other researchers, have shown that we have a good agreement in the MAVISp paper (case study on PTEN as an example in the original MAVISp publication and https://pmc.ncbi.nlm.nih.gov/articles/PMC5980760/ https://pubmed.ncbi.nlm.nih.gov/28422960/,10.7554/eLife.49138). Also, we had, before even designing the STABILITY module for MAVISp, verified that we can use WT structures from AlphaFold (upon proper trimming and quality control with Prockech) instead of experimental structure without compromising accuracy in the publications of the two main protocols of the STABILITY module (MutateX and RosettaDDGPrediction and a case study on p53, https://doi.org/10.1093/bib/bbac074,https://doi.org/10.1002/pro.4527). In the focused studies, we also carefully consider whether the prediction is at a site with a low pLDDT score or surrounded by other sites with a low pLDDT score before reaching any conclusions. The pLDDT score is reported in the MAVISp csv file exactly to be used for flagging variants or looking closer at them, as we discuss in this study (see, for example, Figure 2). Additionally, it should be noted that we employ a consensus approach across the two classes of methods in MAVISp to account for their limitations arising from their empirical energy function or backbone stiffness. Furthermore, in the focused studies, we also collected molecular dynamics simulations for the ensemble mode and reassessed the stability on different conformations from the trajectory to compensate for the issues with backbone stiffness of FoldX, RaSP, and Rosetta ΔΔG protocols.

I have to add that this is also true for the technical choices: Several integrated predictors (DeMaSk, GEMME) are outperformed by newer methods according to benchmarking studies (https://www.embopress.org/doi/full/10.15252/msb.202211474). AlphaMissense, while state-of-the-art, shows substantial overcalling of pathogenic variants. could ensemble meta-predictors (REVEL, BayesDel) improve accuracy?

The MAVISP framework includes REVEL as one of the VEPs available for data analysis. In this way, we were representing one of the ensemble meta-predictors. This is explained in the MAVISp original paper. We were not aware of BayesDel, which we will consider for one of the next pilot projects to assess new tools for the framework (see more details below on how we generally proceed). Currently, we cannot use REVEL for all variants because we do not necessarily have genomic coordinates for them. We retrieve genomic-level variants corresponding to our protein variants from mutation databases, where available (e.g., ClinVar, COSMIC, or CbioPortal). However, as we strive to cover every possible mutation, several of the variants in MAVISp are not in the database, which means we do not have the corresponding genomic variation for those, limiting our ability to annotate them with VEPs. In the future (see GitHub issue https://github.com/ELELAB/cancermuts/issues/235), we will revise the code to identify the genomic variants that could give rise to each protein mutation of interest, thereby increasing the coverage of VEP annotations.

We can see from the work cited by the reviewer that ESM-1v, EVE, and DeepSequence are among the top performers, whereas reviewer 2 cited another work in which GEMME outperforms EVE. We have been covering all of them, except ESM-1v, in our framework. We are planning to evaluate for inclusion in MAVISP some of the new top-performing predictors, including ESM-1v, in Q2 2026 (according to the protocol described later in this answer), which is why it is not available yet.

In our discovery protocol (i.e., when we work on VUS or variants not classified in ClinVar), we generally use AlphaMissense as the first indicator of potentially damaging variants. EVE, REVEL, or GEMME could be used in the case that AlphaMissense data are missing or as a second layer of evidence in the case we want, for example, to select a smaller pool of variants for experimental validation in a protein target with too many uncharacterized variants and too many that pass the evaluation with our discovery workflow. Finally, we rely on DeMaSk, as it also provides information on possible loss- or gain-of-fitness signatures to further filter the variant of interest for the search of mechanistic indicators. Since the MAVISp framework is modular, other users may want to use the data differently and design a different workflow. They have access to them (scores and classifications) through the web portal. The fact that we combine AlphaMissense with DeMaSk could yield final results after further variant filtering and mitigate the issue that AlphaMissense risks over-predicting pathogenicity.

In general, we work to keep MAVISp up-to-date, and we have developed a protocol for the inclusion of new methodologies in the available module before generating and releasing data with new tools in the database. In particular, we perform comparative studies using data already available in the database to evaluate the performance of new approaches against that of the tools already included. Depending on the module, we use different golden standards that we are also curating in parallel, and it would make sense to apply for that specific module. For example, if the question is to evaluate VEP, we would compare it against ClinVar known variants with good review status. If the VEP performs better than the currently included ones, we can include it as an additional source of annotations and evaluate whether we could change the protocol for the discovery/characterization of variants. We operate similarly for the structural modules. For example, for stability, we are importing experimental data from MAVE assays on protein abundance and use them as a golden standard where we evaluate new approaches against the current FoldX and Rosetta-based consensus for changes in folding free energies. Instead, If we find evidence that suggests switching to a new method or integrating it would be beneficial, we will do so as a result of these investigations. An example of our working mode for evaluating tools for inclusion in the framework is illustrated by how we handled the comparison between RaSP and Rosetta in the MAVISp original article (Supplementary file S2) before officially switching to RaSP for high-throughput data collection. We still maintain Rosetta, especially in focused studies, to validate further variants classified as uncertain.

*Further, I found the web site of the framework, where I looked for the data on these models, rather user unfriendly. Selecting POLD1, POLD2, or POLE tells me I am viewing entries A2ML1, ABCB11, ABCB6 respectively, when I search for POL and then click: these are the first three entries of the table, bot the what I click on. displaying the whole table and clicking on POLD1, gets me to POLD1. However, when I selected "Damaging mutations on structure" I get "Could not fetch protein structure model from the AlphaFold Protein Structure Database". Many other features are not working (Safari or Chrome, in a Mac). That is a concern for the usability of the dataset. *

• *

We have been able to reproduce the bugs identified by the reviewer and have fixed them. The second was connected to recent updates on the AlphaFold Protein Structure Database. We are not really sure how to work and act on the “other features that are not working” due to lack of specificity in this comment. Still, we have worked to make the website more robust: the coauthors of this work and other colleagues in the MAVISp team have extensively tested it across different proteins and with various browsers and operating systems, and we have fixed all identified issues. We also have a GitHub repository where users can open issues to share problems they have been experiencing with the website, which we will fix as promptly as we can (https://www.github.com/ELELAB/MAVISp), as we do for any of the tools we develop and maintain. If the reviewer were to come across other specific problems with the website, we recommend to (anonymously) open issues on the MAVISp repository so that they can be described more in detail and dealt with appropriately.

This comment seems more related to the MAVISP paper itself than to the POLE and POLD1 entries. We have been doing several revisions to the web app to improve it over time. We are also afraid that the reviewer consulted it during one of these changes, and we hope it will be better now. For POLE and POLD1, the CSV files were, in any case, also available through the MAVISp website itself (https://services.healthtech.dtu.dk/services/MAVISp-1.0/), as well as in the OSF repository connected to this paper (https://osf.io/z8x4j/overview), in case the reader needed to consult them or as a reference for the analyses reported in this paper.

Albeit this is a thorough analysis with the existing tools, and the authors make some sparse attempts to put the mutants classification in context with examples, the work stays descriptive for know effects in literature, or point out that e.g. "further functional and in vitro assays are required". The examples are not presented in a systematic way, or in an appealing manner. Thus, what this manuscript adds to the web site is unclear. It is a description of content, which could be at least more appealing if examples woudl be more clearly outlined in a conceptual framework, and illustrated more consistently. For exmaple I read in the middle of mage 16 "One such example is the F931S (p.Phe931Ser) variant (Figure 5A)" and then I see "F931 forms contacts with D626, a critical residue for the coordination of Mg2+ which is essential for the correct orientation of the incoming nucleotide (Figure XXX)". Figure 5B is not XXX as this has just many mutations labeled. These issues are very discouraging. I woudl recommend to put much more effort in examples, put them in clearer paragraphs, and decribe results rather than the methodology. Doing both in an intemigled way, clearly does not work for me.

We have revised the storyline to make it more straightforward for the reader, focusing on the essential messages and avoiding excessive description in the results section, instead conveying the key points directly. We also included new simulation data on three variants and downstream analyses of other variants. We revised the section to focus less on methodologies and more on the actual biological results. We have also added a ranking approach for the VUS and an ACMG-like classification to facilitate the identification of the most important results.

Additionally, we included a summary Table (Table 2) and Figure 9 that present the main findings on the VUS, and we discussed in the text the possible associated experimental validation.

We also do not fully understand the reviewer’s comment “the work stays descriptive for know effects in literature”. We agree that we should make a better effort to write the results in a logical and easy-to-follow manner, without risking the reader getting lost in too many details, and with more dedicated subsections. However, the paper does not describe just known effects in the literature. We had, in the previous version, a section aimed at identifying mechanistic indicators for ClinVar-reported variants that are also (in some cases) functionally characterized. This is true, but it is the very first part of the results, and it is still adding structure-based knowledge to these variants. After this, we also reported predicted results with mechanisms for VUS and variants in other databases. We took the opportunity in this revised version to elaborate more on the results of the variants reported in COSMIC and cBioPortal.

We are afraid that we also do not fully understand the reviewer's comment on the fact that “Thus, what this manuscript adds to the website is unclear.” We have generated POLE and POLD1 data with the MAVISp toolkit in both ensemble and simple mode, and the whole pool of local interactions with other proteins and DNA, specifically for this publication. It should be acknowledged that we have generated new data in ensemble mode, which relies on all-atom microsecond molecular dynamics simulations, and additional modules for the simple mode, including calculations with the flexddg protocol of Rosetta, which is also computationally demanding, to provide a comprehensive overview of the effects of variants in POLE and POLD1. The two proteins were available in the database only in simple mode with the basic default modules, and the remaining data were collected during this research article. This can also be inferred by the references in the csv file of the ensemble mode, which refer only to the DOI of the pre-print of this article. This entails a substantial effort in computing and analysis. The website is the repository for data that researchers collect using the MAVISp protocols or modules; in our opinion, it cannot replace a research project. We designed the database to store the data generated by the framework for others to consult and use for various purposes (e.g., biological studies, preparing datasets for benchmarking approaches against existing ones, or using features for machine learning applications). The entry point in the database is the simple mode, along with some compulsory modules (VEPs, STABILITY, PTM, EFOLDMINE, SASA). After this initial entry point, a biocurator or a team of researchers can decide to expand data coverage by moving into the other modules. Still, at some point, one would need to design focused studies to have a comprehensive overview of the effects on specific targets, as we did here, or, for example, in the publication https://doi.org/10.1016/j.bbadis.2024.167260.

Furthermore, there are analyses here, especially in the simulations, that are not directly available from consulting the database; in these cases, one needs to use other resources beyond MAVISp to investigate further the mechanisms underlying the predicted mechanistic indicators. We also included simulations of mutant variants to validate the hypothesis further. And another example is the analysis of the effects on the splicing site that is not covered by a structure-based framework, such as MAVISp, but is still an essential aspect in the analysis of the variants' effects.

Will the community find this analysis useful?

The analysis provided here will be helpful, especially for researchers interested in experimental studies of these enzymes, because they have throughout the study an extensive portfolio of structural data to consult, including a ranked list of variants by class of effect. We originally started designing MAVISp because we realized it was needed by our experimental collaborators, both in cellular biology and in more clinical research, whenever they needed to predict or simulate variants, and we expanded the concept into a robust, versatile framework for broader use. Especially for those genes where extensive MAVE data are not available (as in this case), having a set of variants to test experimentally is crucial support, as it provides the potential mechanism behind the predicted damaging variant.

How many ClinVar VUS could be reclassified using MAVISp data under current ACMG/AMP guidelines?

• *

The ACMG/AMP variant classification guidelines, to the best of our knowledge, include computational evidence (PP3/BP4) and well-established functional studies (PS3/BS3). Because MAVISp provides multi-level mechanistic predictions derived from structural modelling, these data formally fall within the PP3/BP4 computational category. They cannot be used to reclassify ClinVar VUS independently under ACMG/AMP rules. This is not really the goal of our framework, which is to provide a structure-based framework for investigating potentially damaging variants predicted by VEPs. However, the suggestion of the reviewer is something we wanted to explore too in general with MAVISp data, and we failed because of a lack of time. We checked the requirements for PP3, BP4, and PM1 and developed a classifier for VUS reported in ClinVar, using MAVISp features in accordance with the ACMG/AMP guidelines. Using ClinVar pathogenic and benign variants with at least a review status of 1 for calibration, we obtained thresholds for all MAVISp-supported VEPs (REVEL, AlphaMissense, EVE, GEMME, and DeMaSk). These thresholds were then applied to all ClinVar VUS to determine PP3 (pathogenic-supporting) and BP4 (benign-supporting) evidence. In parallel, we constructed a PM1-like mechanistic evidence category that integrates MAVISp structural stability, protein–protein interactions, DNA interactions, long-range allosteric paths, functional sites, and PTM-mediated regulatory effects. Variants classified as damaging in MAVISp according to such criteria were assigned PM1-like support. These evidence tags provide mechanistic insight to support VUS classification for polymerase proofreading genes. The workflow and complete annotated VUS table are now included in the revised manuscript and in the OSF repository. Although these findings cannot formally reclassify variants under ACMG/AMP criteria, they provide prioritization for PS3/BS3 experimental validation and highlight variants that are likely to be reclassified once supporting functional evidence becomes available.

How do MAVISp predictions meet calibrated thresholds, as in https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-023-01234-y* for the exonuclease domain of POLE and POLD1? *

• *

Mur et al. (Genome Medicine 2023) restricted their ACMG/AMP recommendations to the exonuclease domain (ED) because (i) nearly all known pathogenic germline variants in POLE/POLD1 cluster within the ED, (ii) the ED has a well-characterised structure–function architecture, and (iii) sufficient pathogenic and benign variants exist only within the ED to support empirical calibration. To mirror this approach, we performed the calibration workflow exclusively on ED variants (POLE residues 268–471; POLD1 residues 304–533). For these ED-restricted variants, we recalibrated all MAVISp-derived computational predictors (REVEL, AlphaMissense, EVE, GEMME, DeMaSk) using ClinVar P/LP and B/LB variants. We applied the resulting POLE/POLD1-specific thresholds to all ClinVar VUS within the ED. We also applied our PM1-like structural/functional evidence exclusively to ED variants. The results of this ED-specific analysis are now reported in the revised manuscript (Figure 9 Supplementary Tables S3 and S4), as also explained in the response to the previous question. This ensures that MAVISp predictions are applied in a manner that is consistent with the principles of Mur et al. and ACMG/AMP variant interpretation.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

Referee #3

Evidence, reproducibility and clarity

The manuscript used the MAVISp framework to characterize 64,429 missense variants (43,415 in POLE, 21,014 in POLD1) through computational saturation mutagenesis. The authors integrate protein stability predictions with pathogenicity predictors to provide mechanistic insights into DNA polymerase variants relevant to cancer predisposition and immunotherapy response. There are discussions of known PPAP-associated variants and somatic cancer mutations in the context of known data and some proposed variants of interest (which are not validated).

Major comments:

I was unaware of the MAVISp framework. It concerns me that alebit this paper has a lot of technical details about the framework, its not the paper about the framework. I did look into the paper https://www.biorxiv.org/content/10.1101/2022.10.22.513328v5 which keeps benign updated (version five now) for three years, but I do not see a peer reviewed version. It would be unfair of me to peer review the underlying framework of the work but together with the previous comments, I am a bit concerned. For example, the authors are using AlphaFold models to predict DDG values. Delgado et al. (2025, Bioinformatics) explicitly tested FoldX on such models and concluded that "AlphaFold2 models are not suitable for point mutation ΔΔG estimation" afte observing a correlation of 0.06 between experimental and calculated values. AlphaFold's own documentation states it "has not been validated for predicting the effect of mutations". Pak et al. (2023, PLOS ONE) showed correlation between AlphaFold confidence metrics and experimental ΔΔG of -0.17. Needless to say that these concerns seriously undermine the validity of a major part of the study. I have to add tha this is also true for toher technical choices: Several integrated predictors (DeMaSk, GEMME) are outperformed by newer methods according to benchmarking studies (https://www.embopress.org/doi/full/10.15252/msb.202211474). AlphaMissense, while state-of-the-art, shows substantial overcalling of pathogenic variants. could ensemble meta-predictors (REVEL, BayesDel) improve accuracy?

Further, I found the web site of the framework, where I looked for the data on these models, rather user unfriendly. Selecting POLD1, POLD2, or POLE tells me I am viewing entries A2ML1, ABCB11, ABCB6 respectively, when I search for POL and then click: these are the first three entries of the table, bot the what I click on. displaying the whole table and clicking on POLD1, gets me to POLD1. However, when I selected "Damaging mutations on structure" I get "Could not fetch protein structure model from the AlphaFold Protein Structure Database". Many other features are not working (Safari or Chrome, in a Mac). That is a concern for the usability of the dataset.

Albeit this is a thorough analysis with the existing tools, and the authors make some sparse attempts to put the mutants classification in context with examples, the work stays descriptive for know effects in literature, or point out that e.g. "further functional and in vitro assays are required". The examples are not presented in a systematic way, or in an appealing manner. Thus, what this manuscript adds to the web site is unclear. It is a description of content, which could be at least more appealing if examples woudl be more clearly outlined in a conceptual framework, and illustrated more consistently. For exmaple I read in the middle of mage 16 "One such example is the F931S (p.Phe931Ser) variant (Figure 5A)" and then I see "F931 forms contacts with D626, a critical residue for the coordination of Mg2+ which is essential for the correct orientation of the incoming nucleotide (Figure XXX)". Figure 5B is not XXX as this has just many mutations labeled. These issues are very discouraging. I woudl recommend to put much more effort in examples, put them in clearer paragraphs, and decribe results rather than the methodology. Doing both in an intemigled way, clearly does not work for me.

Will the community find this analysis useful? How many ClinVar VUS could be reclassified using MAVISp data under current ACMG/AMP guidelines? How do MAVISp predictions meet calibrated thresholds as in https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-023-01234-y for the exonuclease domain of POLE and POLD1? Such questions might undermien teh appear of the work and coudl been looked into.

Referee cross-commenting

I agree with all the comments raised by reviewer 2; she/he elaborates more on some issues I brought up too briefly (e.g. the choice of GEMME) while other issues that I made more comments about are also mentioned. I only want to note that the statement "A major limitation of the 3D modeling is this impossibility to include Zn2+ coordination by cysteine residues" is not accurate, as there are many 3D structure prediction tools and modeling tools that are capable og handling zinc ions coordinated by cysteines.

While I respect that Referee 1 is clearly more positive and less concerned by methodological issues, I note that while I agree that "The authors identify numerous variants for prioritisation in further studies" (albeit in a sparse and not well organised manner in my view), I am not convinced by the present manuscript that "the effectiveness of integrating various data sources for inferring the mechanistic impact of variants" is really shown: there are hypotheses generated, but none are tested, so the effectiveness of the approach remains to be proven in my view.

I still view this as a thorough study and a very brave attempt to be integrative and inclusive, but several methodological limitations and lack of concrete novel insight, seriously dampen my enthusiasm.

Significance

Strengths:

A very comprehensive analysis of POLE and POLD1 missense variants (64,429 total), approximately 600-fold more coverage than the ~100 experimentally characterized variants in the PolED database. The multi-layered MAVISp approach provides mechanistic interpretability beyond simple pathogenic/benign classifications, potentially valuable for understanding variant effects on stability, DNA binding, protein interactions, and allosteric communication. The clinical context is highly relevant given POLE/POLD1 roles in disease.

Limitations:

The methodological concerns were outlined above. No solid new insight examples in a validated manner. Examples of how the datasets can be really used are not well-organised as they appear in the context of the approach in perplexed manner.

Advance:

The advance is primarily technical and database-driven rather than conceptually novel. Scale, Multi-dimensional assessment, Mechanistic insight and consideration of Clinical framework integration is a clear advance.

Audience:

The audience is the POLDPOLE experts; I however doubt if clinical scientists will find the paper useful, especially in the context of the absence of a dedicated resource and the fact that the entried in the MAVISp web-toold are not easily and intuitively accessible and clinical requirements(eg Integration with ACMG/AMP classification frameworks) are not clearly met.

Reviewer expertise: I am a structural biologist with experience in structure analysis of experimental and predicted models, but no specific expertise or interest in polymerases.

this is half a plan. The LoRA stuff is to be able to keep comms within the group up if they fail. And the group is then expected to bootstrap general connectivity. The rest of the page only deals with LoRa and choosing devices. No word on the actual work of achieving internet resilience, other than 'collect a group of internet-y people'.

Author response:

eLife Assessment

This study provides valuable mechanistic insight into the mutually exclusive distributions of the histone variant H2A.Z and DNA methylation by testing two hypotheses: (i) that DNA methylation destabilizes H2A.Z nucleosomes, thereby preventing H2A.Z retention, and (ii) that DNA methylation suppresses H2A.Z deposition by ATP-dependent chromatin remodeling complexes. Through a series of well-designed and carefully executed experiments, findings are presented in support of both hypotheses. However, the evidence in support of either hypothesis is incomplete, so that the proposed mechanisms underlying the enrichment of H2A.Z on unmethylated DNA remain somewhat speculative.

We would like to thank the editor and reviewers for their critical assessments of our manuscript. While we do acknowledge the limitations of our work, we believe that our results provide important mechanistic insights into the long-standing question of how H2A.Z is preferentially enriched in hypomethylated genomic DNA regions. First, our structural and biochemical data suggest that DNA methylation increases the openness and physical accessibility of H2A.Z, albeit the effect is relatively subtle and is sequence-dependent. Second, using Xenopus egg extracts and synthetic DNA templates, we provide the first clear and direct evidence that DNA methylation-sensitive H2A.Z deposition is due to the H2A.Z chaperone SRCAP-C, corroborated by our discovery that SRCAP-C binding to DNA is suppressed by DNA methylation. Although the molecular details by which DNA methylation inhibits binding of SRCAP-C is an important area of future study, in our current manuscript, we do provide evidence that directly links the presence of SRCAP-C to the establishment of the DNA methylation/H2A.Z antagonism in a physiological system. Thanks to criticisms by the reviewers, we realized that we did not clearly state in our Abstract that the impact of DNA methylation on intrinsic H2A.Z nucleosome stability is relatively subtle, although we did explain these observations and limitations in the main text. In our revised manuscript, we are willing to edit the text to better clarify the criticisms raised by the reviewers.

Public Reviews:

Reviewer #1 (Public review):

Summary:

The authors considered the mechanism underlying previous observations that H2A.Z is preferentially excluded from methylated DNA regions. They considered two non-mutually exclusive mechanisms. First, they tested the hypothesis that nucleosomes containing both methylated DNA and H2A.Z might be intrinsically unstable due to their structural features. Second, they explored the possibility that DNA methylation might impede SRCAP-C from efficiently depositing H2A.Z onto these DNA methylated regions.

Their structural analyses revealed subtle differences between H2A.Z-containing nucleosomes assembled on methylated versus unmethylated DNA. To test the second hypothesis, the authors allowed H2A.Z assembly on sperm chromatin in Xenopus egg extracts and mapped both H2A.Z localization and DNA methylation in this transcriptionally inactive system. They compared these data with corresponding maps from a transcriptionally active Xenopus fibroblast cell line. This comparison confirmed the preferential deposition or enrichment of H2A.Z on unmethylated DNA regions, an effect that was much more pronounced in the fibroblast genome than in sperm chromatin. Furthermore, nucleosome assembly on methylated versus unmethylated DNA, along with SRCAP-C depletion from Xenopus egg extracts, provided a means to test whether SRCAP-C contributes to the preferential loading of H2A.Z onto unmethylated DNA.

Strengths:

The strength and originality of this work lie in its focused attempt to dissect the unexplained observation that H2A.Z is excluded from methylated genomic regions.

Weaknesses:

The study has two weaknesses. First, although the authors identify specific structural effects of DNA methylation on H2A.Z-containing nucleosomes, they do not provide evidence demonstrating that these structural differences lead to altered histone dynamics or nucleosome instability. Second, building on the elegant work of Berta and colleagues (cited in the manuscript), the authors implicate SRCAP-C in the selective deposition of H2A.Z at unmethylated regions. Yet the role of SRCAP-C appears only partial, and the study does not address how the structural or molecular consequences of DNA methylation prevent efficient H2A.Z deposition. Finally, additional plausible mechanisms beyond the two scenarios the authors considered are not investigated or discussed in the manuscript.

Although we acknowledge the limitations of our study and are willing to expand our discussion to more thoroughly discuss these points, we believe our manuscript provides several important mechanistic insights which this reviewer may not have fully appreciated.

Our first conclusion that H2A.Z nucleosomes on methylated DNA are more open and accessible compared to their unmethylated counterparts is supported by both our cryo-EM study and the restriction enzyme accessibility assay. Although the physical effect of DNA methylation is relatively subtle and is likely sequence dependent, as we clearly noted within the manuscript, the difference does exist and is valuable information for the chromatin field at large to consider.

The second major conclusion of our manuscript is that SRCAP-C exhibits preferential binding to unmethylated DNA over methylated DNA, and that SRCAP-C represents the major mechanism that can explain the biased deposition of H2A.Z to unmethylated DNA in Xenopus egg extracts. Furthermore, our experiments using Xenopus egg extract clearly demonstrated that H2A.Z is deposited by both DNA-methylation sensitive and insensitive mechanisms. Depletion of SRCAP-C almost completely eliminated the levels of DNA-methylation-sensitive H2A.Z deposition and reduced the total level of H2A.Z on chromatin to less than half of that seen in non-depleted extract. This result demonstrated that DNA methylation-sensitive H2A.Z loading is primarily regulated by SRCAP-C, at least in our experimental context where transcription, replication, and other epigenetic modifications are not involved. It is likely that additional mechanisms do further contribute, implicated by our sequencing experiments, particularly at regions with active transcription, and we have noted these possibilities and the rationale for their existence in the Discussion.

Our study also suggests that a SRCAP-independent, DNA methylation-insensitive mechanism of H2A.Z loading exists, which we suspect to be mediated by Tip60-C. In line with this possibility, our data suggest that Tip60-C binds DNA in a DNA methylation-insensitive manner in Xenopus egg extract. Since antibodies to deplete Tip60-C from Xenopus egg extract are currently unavailable, we were unable to directly test that hypothesis and decided not to include Tip60-C into our final model as we lacked experimental evidence for its role. However, whether or not Tip60-C is the complex responsible for the DNA methylation-insensitive pathway does not influence our final conclusion that SRCAP-C plays a major role in DNA methylation-sensitive H2A.Z loading. We are planning to edit our manuscript to more comprehensively discuss these points.

Please note that while Berta et al reported that DNA methylation increases at H2A.Z loci in tumors defective in SRCAP-C, they selected those regions based off where H2A.Z is typically enriched within normal tissues (Berta et al., 2021). They did not show data indicating whether H2A.Z is still retained specifically at those analyzed loci upon mutation of SRCAP-C subunits. Thus, although we greatly admire their work and are pleased that many of our findings align with theirs, their paper did not directly address whether SRCAP-C itself differentiates between DNA methylation status nor the impact that has on H2A.Z and DNA methylation colocalization. In contrast, our Xenopus egg extract system, where de novo methylation is undetectable (Nishiyama et al., 2013; Wassing et al., 2024) offers a unique opportunity to examine the direct impact of DNA methylation on H2A.Z deposition using controlled synthetic DNA substrates. Corroborated with our demonstration that DNA binding of SRCAP-C is suppressed by DNA methylation, we believe that our manuscript provides a specific mechanism that can explain the preferential deposition of H2A.Z at hypomethylated genomic regions.

Reviewer #2 (Public review):

This manuscript aims to elucidate the mechanistic basis for the long-standing observation that DNA methylation and the histone variant H2A.Z occupy mutually exclusive genomic regions. The authors test two hypotheses: (i) that DNA methylation intrinsically destabilizes H2A.Z nucleosomes, thereby preventing H2A.Z retention, and (ii) that DNA methylation suppresses H2A.Z deposition by ATP-dependent chromatin-remodelling complexes. However, neither hypothesis is rigorously addressed. There are experimental caveats, issues with data interpretation, and conclusions that are not supported by the data. Substantial revision and additional experiments, including controls, would be required before mechanistic conclusions can be drawn. Major concerns are as follows:

We appreciate the critical assessment of our manuscript by this reviewer. Although we acknowledge the limitations of our study and will revise the manuscript to better describe them, we would like to respectfully argue against the statement that our "conclusions […] are not supported by the data".

(1) The cryo-EM structure of methylated H2A.Z nucleosomes is insufficiently resolved to address the central mechanistic question: where the methylated CpGs are located relative to DNA-histone contact points and how these modifications influence H2A.Z nucleosome structure. The structure provides no mechanistic insights into methylation-induced destabilization.

The fact that the DNA resolution in the methylated structure was not high enough to resolve the positions of methylated CpGs despite a high overall resolution of 2.78 Å implies that 1) the Sat2R-P DNA was not as stably registered as the 601L sequence, requiring us to create two alternative Sat2R-P atomic models to account for the variable positioning in our samples, and 2) that the presence of DNA methylation increases that positional variability. We understand that one may prefer to see highly resolved density around each methylation mark, but we do believe that our inability to accomplish that is actually a feature rather than a weakness and has important biological implications. The decrease in local DNA resolution on the methylated Sat2R-P structure compared to its unmethylated counterpart is meaningful and suggests to us that DNA methylation weakens overall DNA wrapping and positioning on the nucleosome, supported by the increased flexibility seen at the linker DNA ends as well as an increase in the population of highly shifted nucleosomes amongst the methylated particles. Additionally, one major view in the DNA methylation/nucleosome stability field is that the presence of DNA methylation can make DNA stiffer and harder to bend, causing opening and destabilization of nucleosomes (Ngo et al., 2016). The increased opening of linker DNA ends and accessibility of methylated H2A.Z nucleosomes in our hands also aligns with such an idea, again suggesting decreased histone-DNA contact stability on methylated DNA substrates. We plan to revise the writing in our manuscript to better reflect these ideas.

The experimental system also lacks physiological relevance. The template DNA sequence is artificial, despite the existence of well-characterised native genomic sequences for which DNA methylation is known to inhibit H2A.Z incorporation. Alternatively, there are a number of studies examining the effect of DNA methylation on nucleosome structure, stability, DNA unwrapping, and positioning. Choosing one of these DNA sequences would have at least allowed a direct comparison with a canonical nucleosome. Indeed, a major omission is the absence of a cryo-EM structure of a canonical nucleosome assembled on the same DNA template - this is essential to assess whether the observed effects are H2A.Z-specific.

The reviewer raises a fair question about whether canonical H2A would experience the same DNA methylation-dependent structural effects. We had considered solving the H2A structures, however, ultimately decided against it for a few reasons. First, there already exists crystal structures of canonical H2A nucleosomes using a DNA sequence highly similar to our Sat2R-P with and without the presence of DNA methylation (PDB: 5CPI and 5CPJ). The authors of this study did not see any physical differences present in their structures (Osakabe et al., 2015). Additionally, we had included canonical H2A conditions within our restriction enzyme accessibility assay and did not see a significant impact of DNA methylation on those samples (Fig 3). Because of the previous report and our own negative data, we expected that only limited additional insights would be obtained from the canonical H2A structures and decided not to pursue that analysis.

One of the primary reasons we chose the Sat2R-P sequence was, as noted above, that there already was a published study examining how DNA methylation affects nucleosome structure using a variant of this sequence which we could compare to our results, as the reviewer has suggested. We did have to modify the sequence, namely by making it palindromic, in order to increase the final achievable resolution. We viewed the Sat2R-P sequence as an attractive candidate because it is physiologically relevant; the initial sequence was taken directly from human satellite II. Several modifications were made for technical reasons, including making the sequence palindromic as described above and also ensuring that each CpG is recognizable by a methylation-sensitive restriction enzyme so that we could be certain about the degree of methylation on our substrates. These practical concerns outweighed the necessity of maintaining a strict physiological sequence to us. However, we still believe the final Sat2R-P more closely mimics physiological sequences than Widom 601. Additionally, human satellite II is a highly abundant sequence in the human genome that is known to undergo large methylation changes on the onset of many disorders, like cancer, as well as during aging. Thus, there are interesting biological questions surrounding how the methylation state of this particular sequence affects chromatin structure. Furthermore, it has been reported that satellite II is devoid of H2A.Z (Capurso et al., 2012). Beyond those reasons, the satellite II sequence is generally interesting to our lab because we have been studying genes involved in ICF syndrome, where hypomethylation of satellite II sequences forms one of the hallmarks of this disorder (Funabiki et al., 2023; Jenness et al., 2018; Wassing et al., 2024). We understand that sequence context plays a large role in nucleosome wrapping and stability. This is why we strived to test multiple sequences in each of our assays. We do agree that it would be interesting to use DNA sequences where H2A.Z binding has already been described to be affected in a DNA methylation-dependent manner, forming an exciting future study to pursue.

Furthermore, the DNA template is methylated at numerous random CpG sites. The authors' argument that only the global methylation level is relevant is inconsistent with the literature, which clearly demonstrates that methylation effects on canonical nucleosomes are position-dependent. Not all CpG sites contribute equally to nucleosome stability or unwrapping, and this critical factor is not considered.

We did not argue that only the global methylation level is relevant. We also would appreciate it if the reviewer could provide specific references that "clearly demonstrates that methylation effects on canonical nucleosomes are position-dependent". We are aware of a series of studies conducted by Chongli Yuan's group, including one testing the effect of placing methylated CpGs at different positions along the Widom 601 sequence. In that study (Jimenez-Useche et al., 2013), they did find that positioning of mCpGs has differential impacts on the salt resistance of the nucleosomes, with 5 tandem mCpG copies at the dyad causing the most dramatic nucleosome opening whereas having mCpGs only at the DNA major grooves, but not elsewhere, increased nucleosome stability. However, they did also find that methylation of the original Widom 601 sequence also caused destabilization, albeit to a lesser degree, and another study by the same group (Jimenez-Useche et al., 2014) also found that CpG methylation decreased nucleosome-forming ability for all tested variants of the Widom 601 sequence, regardless of CpG density or positioning.

Other studies monitored how distribution of methylated CpGs correlates with nucleosome positioning (Collings et al., 2013; Davey et al., 1997; Davey et al., 2004). However, these studies assessed the sequence-dependent effects specifically on nucleosome assembly during in vitro salt dialysis, which is a different physical process than the one our manuscript focuses on, especially when considering the fact that H2A.Z is deposited onto preassembled H2A-nucleosome. Our cryo-EM analysis examines the structural changes induced by DNA methylation on already formed nucleosomes rather than the process of formation. Thus, probing accessibility changes using a restriction enzyme was the more appropriate biochemical assay to verify our structures.

We do very much agree that DNA context can influence nucleosome stability under different conditions. A study of molecular dynamics simulations concluded that the "combination of overall DNA geometrical and shape properties upon methylation" makes nucleosomes resistant to unwrapping (Li et al., 2022), while another modeling study suggests that DNA methylation impacts nucleosome stability in a manner dependent on DNA sequence, where "[s]trong binding is weakened and weak binding is strengthened" (Minary and Levitt, 2014). While G/C-dinucleotides are preferentially placed at major groove-inward positions in the nucleosomes in vivo (Chodavarapu et al., 2010; Segal et al., 2006) and G/C-rich segments are excluded from major groove-outward positions in Widom 601-like nucleosomes (Chua et al., 2012), methylated CpG dinucleotides are preferably, if not exclusively, located at major groove-outward positions in vivo. Mechanisms behind this biased mCpG positioning on the nucleosome remain speculative, likely caused by a combination of multiple factors, but the fact that we did not observe clear structural impacts using the Widom 601L sequence, where mCpGs are located at the major groove-outward and -inward positions ((Chua et al., 2012) and our structure), deserves a space for discussion. On the other hand, positioning of mCpG on satellite II-derived sequences that we used in this study was based on a physiological sequence, and thus it may not be appropriate to say that those CpGs are placed at multiple "random" positions. Although we decided not to discuss the position of 5mC on our Sat2R nucleosome structure due to ambiguous base assignments, neither of our two atomic models is consistent with an idea that DNA methylation repositions the CpG to the outward major grooves. As the potential contribution of how DNA methylation affects the nucleosome structure via modulating DNA stiffness has been extensively studied (Choy et al., 2010; Li et al., 2022; Ngo et al., 2016; Perez et al., 2012), we believe that it is appropriate to consider overall DNA properties along the whole DNA sequence, though we are willing to discuss potential positional effects in the revised manuscript.

Perhaps one of the most important points that we did not emphasize enough in our original manuscript was that in contrast to the subtle intrinsic effect of DNA methylation that was DNA sequence dependent, we observed SRCAP-dependent preferential H2A.Z deposition to unmethylated DNA over methylated DNA in both 601 and satellite II DNAs. In the revised manuscript, we will make the value of comparative studies on 601 and satellite II in two distinct mechanisms.

Finally, and most importantly, the reported increase in accessibility of the methylated H2A.Z nucleosome is negligible compared with the much larger intrinsic DNA accessibility of the unmethylated H2A.Z nucleosome. These data do not support the authors' hypothesis and contradict the manuscript's conclusions. Claims that methylated H2A.Z nucleosomes are "more open and accessible" must therefore be removed, and the title is misleading, given that no meaningful impact of DNA methylation on H2A.Z nucleosome stability is demonstrated.

We respectfully disagree with this reviewer's criticism. We investigated the potential impact of DNA methylation on nucleosome stability to the best of our abilities through complementary assays and reported our observations. The effect of DNA methylation is smaller than the difference between H2A.Z and H2A, but we were able to see an effect. It is also not uncommon for small differences to have functional impacts in biological systems. We agree that further testing is required to determine whether this subtle effect is functionally important, and it remains the subject of future research due to the many technical challenges associated with addressing said question. We would like to note that 18 years have passed since Daniel Zilberman first reported the antagonistic relationship between H2AZ and DNA methylation (Zilberman et al., 2008) but very few studies have since directly tested specific mechanistic hypotheses. We believe that our study lays the groundwork for exciting future investigation that better elucidates the pathways that contribute to this antagonism and will have meaningful impacts on the field in general. However, thanks to the reviewer's criticism, we realized that we did not clearly state in the Abstract the relatively subtle effect of DNA methylation on the intrinsic H2A.Z nucleosome stability. Therefore, we will accordingly revise the Abstract to make this point clearer.

(2) The cryo-EM structures of methylated and unmethylated 601L H2A.Z nucleosomes show no detectable differences. As presented, this negative result adds little value. If anything, it reinforces the point that the positional context of CpG methylation is critical, which the manuscript does not consider.

We believe the inclusion and factual reporting of negative data is important for the scientific community as one of the major issues currently in biology research is biased omission of negative data. We considered eLife as a venue to publish this work for this reason. We understand that the reviewer believes our 601L structures may detract from the overall message of our manuscript. We believe this data rather emphasizes the importance of DNA sequence context, something that the reviewer also rightfully notes. It is standard practice in the nucleosome field to use the Widom 601 sequence, along with its variants. Our experience has shown that use of an artificially strong positioning sequence may mask weaker physical effects that could play a physiological role. Thus, we were careful to validate all further assays with multiple DNA sequences and believed it important to report these sequence-dependent effects on nucleosome structure.

(3) Very little H3 signal coincides with H2A.Z at TSSs in sperm pronuclei, yet this is neither explained nor discussed (Supplementary Figure 10D). The authors need to clarify this.

Our H3 signal, which represents the global nucleosome population, is more broadly distributed across the genome than H2A.Z, which is known to localize at specific genomic sites. Since both histone types were sequenced to similar read depths, H3 peaks are generally shallower than H2A.Z and peak heights cannot be directly compared (i.e. they should be represented in separate appropriate data ranges).

(4) In my view, the most conceptually important finding is that H2A.Z-associated reads in sperm pronuclei show ~43% CpG methylation. This directly contradicts the model of strict mutual exclusivity and suggests that the antagonism is context-dependent. Similarly, the finding that the depletion of SRCAP reduces H2A.Z deposition only on unmethylated templates is also very intriguing. Collectively, these result warrants further investigation (see below).

(5) Given that H2A.Z is located at diverse genomic elements (e.g., enhancers, repressed gene bodies, promoters), the manuscript requires a more rigorous genomic annotation comparing H2A.Z occupancy in sperm pronuclei versus XTC-2 cells. The authors should stratify H2A.Z-DNA methylation relationships across promoters, 5′UTRs, exons, gene bodies, enhancers, etc., as described in Supplementary Figure 10A.

(below is response to (4) and (5) together)

We agree that the substantial presence of co-localized H2A.Z and DNA methylation specifically in the sperm pronuclei samples and the changes in pattern between nuclear types are highly interesting and require further investigation. However, we faced technical challenges in our sequencing experiments that made us refrain from conducting a more detailed analysis for fear of over-interpreting potential artifacts. These challenges mainly stemmed from the difficulties in collecting enough material from Xenopus egg extracts and Tn5’s innate bias towards accessible regions of the genome. Because of this, open regions of the genome tend to be overrepresented in our data (as noted in our Discussion), making it challenging to rigorously compare methylation profiles and H2A.Z/H3 associated genomic elements.

While the degree of separation seems to be dependent on nuclei type, we still believe the antagonism exists in both the sperm pronuclei and XTC-2 samples when comparing H2A.Z methylation profiles to the corresponding H3 condition. Our study also demonstrates that H2A.Z is preferentially deposited to hypomethylated DNA in a manner dependent of SRCAP-C (the loss of SRCAP only reduces H2A.Z on unmethylated substrates) but an additional methylation-insensitive H2A.Z deposition mechanism also exists. We realized that this interesting point was not clearly highlighted in Abstract, so we will revise it accordingly.

(6) Although H2A.Z accumulates less efficiently on exogenous methylated substrates in egg extract, substantial deposition still occurs (~50%). This observation directly challenges the strong antagonistic model described in the manuscript, yet the authors do not acknowledge or discuss it. Moreover, differences between unmethylated and methylated 601 DNA raise further questions about the biological relevance of the cryo-EM 601 structures.

As depicted in Figure 6 and described in the Discussion, we clearly indicated that both methylation-sensitive and methylation-insensitive pathways exist to deposit H2A.Z within the genome. We also directly stated in our Discussion that a substantial proportion of H2A.Z colocalizes with DNA methylation both in our study as well as in previous reports, which is of major interest for future study. Additionally, we further discussed how the absence of transcription in Xenopus eggs is a likely reason for the more limited effect of DNA methylation restricting H2A.Z deposition in our egg extract system.

As noted in our response to (2), the lack of a clear impact on our 601L structures implies that this is due to the extraordinarily strong artificial nucleosome positioning capacity of the 601 sequence and its variants. Since 601 is heavily used in chromatin biology, including within DNA methylation research, such negative data are still useful to include and publish.

(7) The SRCAP depletion is insufficiently validated i.e., the antibody-mediated depletion of SRCAP lacks quantitative verification. A minimum of three biological replicates with quantification is required to substantiate the claims.

We are willing to address this concern. However, please note that our data showed that methylation-dependent H2A.Z deposition is almost completely erased upon SRCAP depletion, indicating functionally effective depletion. The specificity of the custom antibody against Xenopus SRCAP was verified by mass spectrometry. Additionally, we have obtained the same effect using another commercially available SRCAP antibody, though we did not include this preliminary result in our original manuscript. Due to its relatively low abundance and high molecular weight, SRCAP western blot signals are weak, making it challenging to quantify the degree of depletion. We also believe that the value of quantification in this context, with the points noted above, is rather limited. In the past, our lab has published papers on depleting the H3T3 kinase Haspin from Xenopus egg extracts (Ghenoiu et al., 2013; Kelly et al., 2010) but were never able to detect Haspin via western blot. This protein was only detected by mass spectrometry specifically on nucleosome array beads with H3K9me3 (Jenness et al., 2018). However, depletion of Haspin was readily monitored by erasure of H3T3ph, the enzymatic product of Haspin. In these experiments, it was impossible, and not critical, to quantitatively monitor the depletion of Haspin protein in order to investigate its molecular functions. Similarly, in this current study, the important fact is that depletion of SRCAP suppressed methylation-sensitive H2A.Z deposition and quantifying the degree of SRCAP depletion would not have a major impact on this conclusion.

(8) It appears that the role of p400-Tip60 has been completely overlooked. This complex is the second major H2A.Z deposition complex. Because p400 exhibits DNA methylation-insensitive binding (Supplementary Figure 14), it may account for the deposition of H2A.Z onto methylated DNA. This possibility is highly significant and must be addressed by repeating the key experiments in Figure 5 following p400-Tip60 depletion.

We are aware that the Tip60 complex is a very likely candidate for mediating DNA methylation-insensitive H2A.Z deposition, which is why we tested whether DNA binding of p400 is methylation sensitive. Therefore, the reviewer's statement that we "completely overlooked" Tip60-C’s role does not fairly report on our efforts. We wished to test the potential contribution of Tip60-C, but, unfortunately, the antibodies we currently have available to us were not successful in depleting the complex from egg extract. Since we had no direct experimental evidence indicating the role Tip60-C plays, we decided to take a conservative approach to our model and leave the methylation-insensitive pathway as mediated by something still unidentified. While further investigating Tip60-C’s contribution to this pathway is of definite value, we do not believe that it impacts our major conclusion that SRCAP-C is the main mediator responsible for H2A.Z deposition on unmethylated DNA and thus remains a subject for future study.

(9) The manuscript repeatedly states that H2A.Z nucleosomes are intrinsically unstable; however, this is an oversimplification. Although some DNA unwrapping is observed, multiple studies show that H3/H4 tetramer-H2A.Z/H2B interactions are more stable (important recent studies include the following: DOI: 10.1038/s41594-021-00589-3; 10.1038/s41467-021-22688-x; and reviewed in 10.1038/s41576-024-00759-1).

We understand that the H2A.Z stability field is highly controversial. We have introduced the many conflicting reports that have been published in the field but can further expand on the controversies if desired. We also understand that the term “nucleosome stability” is broad and encompasses many physical aspects. As noted in a prior response, we will better specify our use of the term within the manuscript. In our assays, we are most focused on the DNA wrapping stability of the nucleosome and have consistently seen in our hands that H2A.Z nucleosomes are much more open and accessible compared to canonical H2A on satellite II-derived sequences, regardless of methylation status. However, we do understand that many groups have observed the opposite findings while others have obtained results similar to us. We reported on our findings of the general H2A.Z stability with the hopes to help clarify some of the field’s controversies.

In summary, the current manuscript does not present a convincing mechanistic explanation for the antagonism between DNA methylation and H2A.Z. The observation that H2A.Z can substantially coexist with DNA methylation in sperm pronuclei, perhaps, should be the conceptual focus.

We appreciate this reviewer’s advice. However, please note that the first author who led this project has already successfully defended their PhD thesis primarily based on this project, making it impractical and unrealistic to completely change the focus of this manuscript to include an entirely new avenue of research. We believe that our data provide important insights into the mechanisms by which H2A.Z is excluded from methylated DNA, particularly via the DNA methylation-sensitive binding of SRCAP-C, which has never been described before. We agree that many questions are still left unanswered, including the exact molecular mechanism behind how DNA methylation prevents SRCAP-C binding. We have preliminary data that suggest none of the known DNA-binding modules of SRCAP-C, including ZNHIT1, by themselves can explain this sensitivity. This implies that domain dissection in the context of the holo-SRCAP complex is required to fully address this question. We believe this represents a very exciting future avenue of study; however, it does not negate our finding that SRCAP-C itself is important for maintaining the DNA methylation/H2A.Z antagonism. Therefore, we respectfully disagree with this reviewer's summary statement, which misleadingly undermines the impact of our work.

Reviewer #3 (Public review):

Summary:

Histone variant H2A.Z is evolutionarily conserved among various species. The selective incorporation and removal of histone variants on the genome play crucial roles in regulating nuclear events, including transcription. Shih et al. aimed to address antagonistic mechanisms between histone variant H2A.Z deposition and DNA methylation. To this end, the authors reconstituted H2A.Z nucleosomes in vitro using methylated or unmethylated human satellite II DNA sequence and examined how DNA methylation affects H2A.Z nucleosome structure and dynamics. The cryo-EM analysis revealed that DNA methylation induces a more open conformation in H2A.Z nucleosomes. Consistent with this, their biochemical assays showed that DNA methylation subtly increases restriction enzyme accessibility in H2A.Z nucleosomes compared with canonical H2A nucleosomes. The authors identified genome-wide profiles of H2A.Z and DNA methylation using genomic assays and found their unique distribution between Xenopus sperm pronuclei and fibroblast cells. Using Xenopus egg extract systems, the authors showed SRCAP complex, the chromatin remodelers for H2A.Z deposition, preferentially deposit H2A.Z on unmethylated DNA.

Strengths:

The study is solid, and most conclusions are well-supported. The experiments are rigorously performed, and interpretations are clear. The study presents a high-resolution cryo-EM structure of human H2A.Z nucleosome with methylated DNA. The discovery that the SRCAP complex senses DNA methylation is novel and provides important mechanistic insight into the antagonism between H2A.Z and DNA methylation.

We are grateful that this reviewer recognizes the importance of our study.

Weaknesses:

The study is already strong, and most conclusions are well supported. However, it can be further strengthened in several ways.

(1) It is difficult to interpret how DNA methylation alters the orientation of the H4 tail and leads to the additional density on the acidic patch. The data do not convincingly support whether DNA methylation enhances interactions with H2A.Z mono-nucleosomes, nor whether this effect is specific to methylated H2A.Z nucleosomes.

The altered H4 tail orientation and extra density seen on the acidic patch were incidental findings that we thought could be interesting for the field to be aware of but decided not to follow up on as there were other structural differences that were more directly related to our central question. We do believe that the above two differences are linked to each other because we used a highly purified and homogenous sample for cryo-EM analysis and the H4 tail/acidic patch interaction is a well characterized contact that mediates inter-nucleosome interactions. Additionally, other groups have reported that the presence of DNA methylation causes condensation of both chromatin and bare DNA (cited within our manuscript), though the mechanics behind this phenomenon remain to be elucidated. We believed that our structure data may also align with those findings. However, the reviewer is fair in pointing out that we do not provide further experimental evidence in verifying the existence of these increased interactions. We can revise our writing to clarify that these points are currently hypotheses rather than validated results.

(2) It remains unclear whether DNA methylation alters global H2A.Z nucleosome stability or primarily affects local DNA end flexibility. Moreover, while the authors showed locus-specific accessibility by HinfI digestion, an unbiased assay such as MNase digestion would strengthen the conclusions.

We would like to thank the reviewer for bringing up these issues. Although our current data cannot explicitly clarify these possibilities, we favor an idea that DNA methylation specifically alters histone to DNA contacts and that this effect is felt globally across the entire nucleosome rather than only at specific locations. The intrinsic flexibility of linker DNA ends means that that region tends to exhibit the greatest differences under different physical influences, hence the focus on characterizing that area; flexibility of a thread on a spool is most pronounced at the ends. However, we also found that the DNA backbone of H2A.Z on methylated DNA had a lower local resolution compared to its unmethylated counterpart, despite that structure having a higher global resolution, which suggested to us that DNA positioning along the nucleosome is overall weaker under the presence of DNA methylation. This is corroborated by the increased population of open/shifted structures in our classification analysis. The reviewer raises a fair point about the use of a specific restriction enzyme versus MNase. We agree that our accessibility assay is highly influenced by the position of the restriction site and have previously seen that moving the cut site too close to the linker DNA end will abolish any DNA methylation-dependent differences. We did initially attempt an MNase digestion-based assay, but the data were not as reproducible as with the use of a specific restriction enzyme. We do not know the reason behind this irreproducibility though we believe that the processivity of MNase could make it difficult to capture subtle effects like those induced by DNA methylation on already highly accessible H2A.Z nucleosomes. Overall, while we believe that DNA methylation does exert a physical effect, its subtlety may explain the many contradictory studies present within the DNA methylation and nucleosome stability field.

References

Berta, D.G., H. Kuisma, N. Valimaki, M. Raisanen, M. Jantti, A. Pasanen, A. Karhu, J. Kaukomaa, A. Taira, T. Cajuso, S. Nieminen, R.M. Penttinen, S. Ahonen, R. Lehtonen, M. Mehine, P. Vahteristo, J. Jalkanen, B. Sahu, J. Ravantti, N. Makinen, K. Rajamaki, K. Palin, J. Taipale, O. Heikinheimo, R. Butzow, E. Kaasinen, and L.A. Aaltonen. 2021. Deficient H2A.Z deposition is associated with genesis of uterine leiomyoma. Nature. 596:398–403.

Capurso, D., H. Xiong, and M.R. Segal. 2012. A histone arginine methylation localizes to nucleosomes in satellite II and III DNA sequences in the human genome. BMC Genomics. 13:630.

Chodavarapu, R.K., S. Feng, Y.V. Bernatavichute, P.Y. Chen, H. Stroud, Y. Yu, J.A. Hetzel, F. Kuo, J. Kim, S.J. Cokus, D. Casero, M. Bernal, P. Huijser, A.T. Clark, U. Kramer, S.S. Merchant, X. Zhang, S.E. Jacobsen, and M. Pellegrini. 2010. Relationship between nucleosome positioning and DNA methylation. Nature. 466:388–392.

Choy, J.S., S. Wei, J.Y. Lee, S. Tan, S. Chu, and T.H. Lee. 2010. DNA methylation increases nucleosome compaction and rigidity. J Am Chem Soc. 132:1782–1783.

Chua, E.Y., D. Vasudevan, G.E. Davey, B. Wu, and C.A. Davey. 2012. The mechanics behind DNA sequence-dependent properties of the nucleosome. Nucleic Acids Res. 40:6338–6352.

Collings, C.K., P.J. Waddell, and J.N. Anderson. 2013. Effects of DNA methylation on nucleosome stability. Nucleic Acids Res. 41:2918–2931.

Davey, C., S. Pennings, and J. Allan. 1997. CpG methylation remodels chromatin structure in vitro. J Mol Biol. 267:276–288.

Davey, C.S., S. Pennings, C. Reilly, R.R. Meehan, and J. Allan. 2004. A determining influence for CpG dinucleotides on nucleosome positioning in vitro. Nucleic Acids Res. 32:4322–4331.

Funabiki, H., I.E. Wassing, Q. Jia, J.D. Luo, and T. Carroll. 2023. Coevolution of the CDCA7-HELLS ICF-related nucleosome remodeling complex and DNA methyltransferases. Elife. 12.

Ghenoiu, C., M.S. Wheelock, and H. Funabiki. 2013. Autoinhibition and polo-dependent multisite phosphorylation restrict activity of the histone h3 kinase haspin to mitosis. Mol Cell. 52:734–745.

Jenness, C., S. Giunta, M.M. Muller, H. Kimura, T.W. Muir, and H. Funabiki. 2018. HELLS and CDCA7 comprise a bipartite nucleosome remodeling complex defective in ICF syndrome. Proc Natl Acad Sci U S A. 115:E876–E885.

Jimenez-Useche, I., J. Ke, Y. Tian, D. Shim, S.C. Howell, X. Qiu, and C. Yuan. 2013. DNA methylation regulated nucleosome dynamics. Sci Rep. 3:2121.

Jimenez-Useche, I., D. Shim, J. Yu, and C. Yuan. 2014. Unmethylated and methylated CpG dinucleotides distinctively regulate the physical properties of DNA. Biopolymers. 101:517–524.

Kelly, A.E., C. Ghenoiu, J.Z. Xue, C. Zierhut, H. Kimura, and H. Funabiki. 2010. Survivin reads phosphorylated histone H3 threonine 3 to activate the mitotic kinase Aurora B. Science. 330:235–239.

Li, S., Y. Peng, D. Landsman, and A.R. Panchenko. 2022. DNA methylation cues in nucleosome geometry, stability and unwrapping. Nucleic Acids Res. 50:1864–1874.

Minary, P., and M. Levitt. 2014. Training-free atomistic prediction of nucleosome occupancy. Proc Natl Acad Sci U S A. 111:6293–6298.

Ngo, T.T., J. Yoo, Q. Dai, Q. Zhang, C. He, A. Aksimentiev, and T. Ha. 2016. Effects of cytosine modifications on DNA flexibility and nucleosome mechanical stability. Nat Commun. 7:10813.

Nishiyama, A., L. Yamaguchi, J. Sharif, Y. Johmura, T. Kawamura, K. Nakanishi, S. Shimamura, K. Arita, T. Kodama, F. Ishikawa, H. Koseki, and M. Nakanishi. 2013. Uhrf1-dependent H3K23 ubiquitylation couples maintenance DNA methylation and replication. Nature. 502:249–253.

Osakabe, A., F. Adachi, Y. Arimura, K. Maehara, Y. Ohkawa, and H. Kurumizaka. 2015. Influence of DNA methylation on positioning and DNA flexibility of nucleosomes with pericentric satellite DNA. Open Biol. 5.

Perez, A., C.L. Castellazzi, F. Battistini, K. Collinet, O. Flores, O. Deniz, M.L. Ruiz, D. Torrents, R. Eritja, M. Soler-Lopez, and M. Orozco. 2012. Impact of methylation on the physical properties of DNA. Biophys J. 102:2140–2148.

Segal, E., Y. Fondufe-Mittendorf, L. Chen, A. Thastrom, Y. Field, I.K. Moore, J.P. Wang, and J. Widom. 2006. A genomic code for nucleosome positioning. Nature. 442:772–778.

Wassing, I.E., A. Nishiyama, R. Shikimachi, Q. Jia, A. Kikuchi, M. Hiruta, K. Sugimura, X. Hong, Y. Chiba, J. Peng, C. Jenness, M. Nakanishi, L. Zhao, K. Arita, and H. Funabiki. 2024. CDCA7 is an evolutionarily conserved hemimethylated DNA sensor in eukaryotes. Sci Adv. 10:eadp5753.

Zilberman, D., D. Coleman-Derr, T. Ballinger, and S. Henikoff. 2008. Histone H2A.Z and DNA methylation are mutually antagonistic chromatin marks. Nature. 456:125–129.

DOI: 10.1038/s41598-025-32379-y

Resource: RRID:Addgene_124797

Curator: @scibot

SciCrunch record: RRID:Addgene_124797

What is this?

DOI: 10.1038/s41598-025-32379-y

Resource: RRID:Addgene_12259

Curator: @scibot

SciCrunch record: RRID:Addgene_12259

What is this?

DOI: 10.1038/s41598-025-32379-y

Resource: RRID:Addgene_52961

Curator: @scibot

SciCrunch record: RRID:Addgene_52961

What is this?

DOI: 10.1038/s41598-025-32379-y

Resource: RRID:Addgene_12260

Curator: @scibot

SciCrunch record: RRID:Addgene_12260

What is this?

DOI: 10.1038/s41467-026-68605-y

Resource: (BCRC Cat# 60343, RRID:CVCL_0547)

Curator: @scibot

SciCrunch record: RRID:CVCL_0547

What is this?

DOI: 10.1038/s41467-026-68605-y

Resource: (DSMZ Cat# ACC-563, RRID:CVCL_2061)

Curator: @scibot

SciCrunch record: RRID:CVCL_2061

What is this?

DOI: 10.1038/s41467-026-68605-y

Resource: (Thermo Fisher Scientific Cat# A18853, RRID:AB_2535630)

Curator: @scibot

SciCrunch record: RRID:AB_2535630

What is this?

DOI: 10.1038/s41467-026-68605-y

Resource: (RRID:CVCL_0419)

Curator: @scibot

SciCrunch record: RRID:CVCL_0419

What is this?

DOI: 10.1038/s41467-026-68605-y

Resource: (RRID:CVCL_5J31)

Curator: @scibot

SciCrunch record: RRID:CVCL_5J31

What is this?

DOI: 10.1038/s41467-026-68605-y

Resource: (IZSLER Cat# BS TCL 215, RRID:CVCL_1561)

Curator: @scibot

SciCrunch record: RRID:CVCL_1561

What is this?

DOI: 10.1038/s41467-026-68605-y

Resource: (DSMZ Cat# ACC-72, RRID:CVCL_1362)

Curator: @scibot

SciCrunch record: RRID:CVCL_1362

What is this?

DOI: 10.1038/s41467-026-68605-y

Resource: (ATCC Cat# HTB-30, RRID:CVCL_0033)

Curator: @scibot

SciCrunch record: RRID:CVCL_0033

What is this?

DOI: 10.1038/s41467-026-68605-y

Resource: (DSMZ Cat# ACC-369, RRID:CVCL_1711)

Curator: @scibot

SciCrunch record: RRID:CVCL_1711

What is this?

DOI: 10.1038/s41467-026-68605-y

Resource: (KCB Cat# KCB 2010183YJ, RRID:CVCL_1603)

Curator: @scibot

SciCrunch record: RRID:CVCL_1603

What is this?

DOI: 10.1038/s41467-026-68605-y

Resource: (ECACC Cat# 06072604, RRID:CVCL_1324)

Curator: @scibot

SciCrunch record: RRID:CVCL_1324

What is this?

DOI: 10.1038/s41467-026-68605-y

Resource: (KCLB Cat# 9S1569, RRID:CVCL_1255)

Curator: @scibot

SciCrunch record: RRID:CVCL_1255

What is this?

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public review):

The Reviewer structured their review such that their first two recommendations specifically concerned the two major weaknesses they viewed in the initial submission. For clarity and concision, we have copied their recommendations to be placed immediately following their corresponding points on weaknesses.

Strengths:

Studying prediction error from the lens of network connectivity provides new insights into predictive coding frameworks. The combination of various independent datasets to tackle the question adds strength, including two well-powered fMRI task datasets, resting-state fMRI interpreted in relation to behavioral measures, as well as EEG-fMRI.

Weaknesses:

Major:

(R1.1) Lack of multiple comparisons correction for edge-wise contrast:

The analysis of connectivity differences across three levels of prediction error was conducted separately for approximately 22,000 edges (derived from 210 regions), yet no correction for multiple comparisons appears to have been applied. Then, modularity was applied to the top 5% of these edges. I do not believe that this approach is viable without correction. It does not help that a completely separate approach using SVMs was FDR-corrected for 210 regions.

[Later recommendation] Regarding the first major point: To address the issue of multiple comparisons in the edge-wise connectivity analysis, I recommend using the Network-Based Statistic (NBS; Zalesky et al., 2010). NBS is well-suited for identifying clusters (analogous to modules) of edges that show statistically significant differences across the three prediction error levels, while appropriately correcting for multiple comparisons.

Thank you for bringing this up. We acknowledge that our modularity analysis does not evaluate statistical significance. Originally, the modularity analysis was meant to provide a connectome-wide summary of the connectivity effects, whereas the classification-based analysis was meant to address the need for statistical significance testing. However, as the reviewer points out, it would be better if significance were tested in a manner more analogous to the reported modules. As they suggest, we updated the Supplemental Materials (SM) to include the results of Network-Based Statistic analysis (SM p. 1-2):

“(2.1) Network-Based Statistic

Here, we evaluate whether PE significantly impacts connectivity at the network level using the Network-Based Statistic (NBS) approach.[1] NBS relied on the same regression data generated for the main-text analysis, whereby a regression is performed examining the effect of PE (Low = –1, Medium = 0, High = +1) on connectivity for each edge. This was done across the connectome, and for each edge, a z-score was computed. For NBS, we thresholded edges to |Z| > 3.0, which yielded one large network cluster, shown in Figure S3. The size of the cluster – i.e., number of edges – was significant (p < .05) per a permutation-test using 1,000 random shuffles of the condition data for each participant, as is standard.[1] These results demonstrate that the networklevel effects of PE on connectivity are significant. The main-text modularity analysis converts this large cluster into four modules, which are more interpretable and open the door to further analyses”.

We updated the Results to mention these findings before describing the modularity analysis (p. 8-9):

“After demonstrating that PE significantly influences brain-wide connectivity using Network-Based Statistic analysis (Supplemental Materials 2.1), we conducted a modularity analysis to study how specific groups of edges are all sensitive to high/low-PE information.”

(R1.2) Lack of spatial information in EEG:

The EEG data were not source-localized, and no connectivity analysis was performed. Instead, power fluctuations were averaged across a predefined set of electrodes based on a single prior study (reference 27), as well as across a broader set of electrodes. While the study correlates these EEG power fluctuations with fMRI network connectivity over time, such temporal correlations do not establish that the EEG oscillations originate from the corresponding network regions. For instance, the observed fronto-central theta power increases could plausibly originate from the dorsal anterior cingulate cortex (dACC), as consistently reported in the literature, rather than from a distributed network. The spatially agnostic nature of the EEG-fMRI correlation approach used here does not support interpretations tied to specific dorsal-ventral or anterior-posterior networks. Nonetheless, such interpretations are made throughout the manuscript, which overextends the conclusions that can be drawn from the data.

[Later recommendation] Regarding the second major point: I suggest either adopting a source-localized EEG approach to assess electrophysiological connectivity or revising all related sections to avoid implying spatial specificity or direct correspondence with fMRI-derived networks. The current approach, which relies on electrode-level power fluctuations, does not support claims about the spatial origin of EEG signals or their alignment with specific connectivity networks.

We thank the reviewer for this important point, which allows us to clarify the specific and distinct contributions of each imaging modality in our study. Our primary goal for Study 3 was to leverage the high temporal resolution of EEG to identify the characteristic frequency at which the fMRI-defined global connectivity states fluctuate. The study was not designed to infer the spatial origin of these EEG signals, a task for which fMRI is better suited and which we addressed in Studies 1 and 2.

As the reviewer points out, fronto-central theta is generally associated with the dACC. We agree with this point entirely. We suspect that there is some process linking dACC activation to the identified network fluctuations – some type of relationship that does not manifest in our dynamic functional connectivity analyses – although this is only a hypothesis and one that is beyond the present scope.

We updated the Discussion to mention these points and acknowledge the ambiguity regarding the correlation between network fluctuation amplitude (fMRI) and Delta/Theta power (EEG) (p. 24):

“We specifically interpret the fMRI-EEG correlation as reflecting fluctuation speed because we correlated EEG oscillatory power with the fluctuation amplitude computed from fMRI data. Simply correlating EEG power with the average connectivity or the signed difference between posterior-anterior and ventral-dorsal connectivity yields null results (Supplemental Materials 6), suggesting that this is a very particular association, and viewing it as capturing fluctuation amplitude provides a parsimonious explanation. Yet, this correlation may be interpreted in other ways. For example, resting-state Theta is also a signature of drowsiness,[2] which may correlate with PE processing, but perhaps should be understood as some other mechanism. Additionally, Theta is widely seen as a sign of dorsal anterior cingulate cortex activity,3 and it is unclear how to reconcile this with our claims about network fluctuations. Nonetheless, as we show with simulations (Supplemental Materials 5), a correlation between slow fMRI network fluctuations and fast EEG Delta/Theta oscillations is also consistent with a common global neural process oscillating rapidly and eliciting both measures.”

Regarding source-localization, several papers have described known limitations of this strategy for drawing precise anatomical inferences,[4–6] and this seems unnecessary given that our fMRI analyses already provide more robust anatomical precision. We intentionally used EEG in our study for what it measures most robustly: millisecond-level temporal dynamics.

(R1.2a)Examples of problematic language include:

Line 134: "detection of network oscillations at fast speeds" - the current EEG approach does not measure networks.

This is an important issue. We acknowledge that our EEG approach does not directly measure fMRI-defined networks. Our claim is inferential, designed to estimate the temporal dynamics of the large-scale fMRI patterns we identified. The correlation between our fMRI-derived fluctuation amplitude (|PA – VD|) and 3-6 Hz EEG power provides suggestive evidence that the transitions between these network states occur at this frequency, rather than being a direct measurement of network oscillations.

To support the validity of this inference, we performed two key analyses (now in Supplemental Materials). First, a simulation study provides a proof-of-concept, confirming our method can recover the frequency of a fast underlying oscillator from slow fMRI and fast EEG data. Second, a specificity analysis shows the EEG correlation is unique to our measure of fluctuation amplitude and not to simpler measures like overall connectivity strength. These analyses demonstrate that our interpretation is more plausible than alternative explanations.

Overall, we have revised the manuscript to be more conservative in the language employed, such as presenting alternative explanations to the interpretations put forth based on correlative/observational evidence (e.g., our modifications above described in our response to comment R1.2). In addition, we have made changes throughout the report to state the issues related to reverse inference more explicitly and to better communicate that the evidence is suggestive – please see our numerous changes described in our response to comment R3.1. For the statement that the reviewer specifically mentioned here, we revised it to be more cautious (p. 7):

“Although such speed outpaces the temporal resolution of fMRI, correlating fluctuations in dynamic connectivity measured from fMRI data with EEG oscillations can provide an estimate of the fluctuations’ speed. This interpretation of a correlation again runs up against issues related to reverse inference but would nonetheless serve as initial suggestive evidence that spontaneous transitions between network states occur rapidly.”

(R1.2b) Line 148: "whether fluctuations between high- and low-PE networks occur sufficiently fast" - this implies spatial localization to networks that is not supported by the EEG analysis.

Building on our changes described in our immediately prior response, we adjusted our text here to say our analyses searched for evidence consistent with the idea that the network fluctuations occur quickly rather than searching for decisive evidence favoring this idea (p. 7-8):

“Finally, we examined rs-fMRI-EEG data to assess whether we find parallels consistent with the high/low-PE network fluctuations occurring at fast timescales suitable for the type of cognitive operations typically targeted by PE theories.”

(R1.2c) Line 480: "how underlying neural oscillators can produce BOLD and EEG measurements" - no evidence is provided that the same neural sources underlie both modalities.

As described above, these claims are based on the simulation study demonstrating that this is a possibility, and we have revised the manuscript overall to be clearer that this is our interpretation while providing alternative explanations.

Reviewer #2 (Public review):

Strengths:

Clearly, a lot of work and data went into this paper, including 2 task-based fMRI experiments and the resting state data for the same participants, as well as a third EEG-fMRI dataset. Overall, well written with a couple of exceptions on clarity, as per below, and the methodology appears overall sound, with a couple of exceptions listed below that require further justification. It does a good job of acknowledging its own weakness.

Weaknesses:

(R2.1) The paper does a good job of acknowledging its greatest weakness, the fact that it relies heavily on reverse inference, but cannot quite resolve it. As the authors put it, "finding the same networks during a prediction error task and during rest does not mean that the networks' engagement during rest reflects prediction error processing". Again, the authors acknowledge the speculative nature of their claims in the discussion, but given that this is the key claim and essence of the paper, it is hard to see how the evidence is compelling to support that claim.

We thank the reviewer for this comment. We agree that reverse inference is a fundamental challenge and that our central claim requires a particularly high bar of evidence. While no single analysis resolves this issue, our goal was to build a cumulative case that is compelling by converging on the same conclusion from multiple, independent lines of evidence.

For our investigation, we initially established a task-general signature of prediction error (PE). By showing the same neural pattern represents PE in different contexts, we constrain the reverse inference, making it less likely that our findings are a task-specific artifact and more likely that they reflect the core, underlying process of PE. Building on this, our most compelling evidence comes from linking task and rest at the individual level. We didn't just find the same general network at rest; we showed that an individual’s unique anatomical pattern of PE-related connectivity during the task specifically predicts their own brain's fluctuation patterns at rest. This highly specific, person-by-person correspondence provides a direct bridge between an individual's task-evoked PE processing and their intrinsic, resting-state dynamics. Furthermore, these resting-state fluctuations correlate specifically with the 3-6 Hz theta rhythm—a well-established neural marker for PE.

While reverse inference remains a fundamental limitation for many studies on resting-state cognition, the aspects mentioned above, we believe, provide suggestive evidence, favoring our PE interpretation. Nonetheless, we have made changes throughout the manuscript to be more conservative in the language we use to describe our results, to make it clear what claims are based on correlative/observational evidence, and to put forth alternative explanations for the identified effects. Please find our numerous changes detailed in our response to comment R3.1.

(R2.2) Given how uncontrolled cognition is during "resting-state" experiments, the parallel made with prediction errors elicited during a task designed for that effect is a little difficult to make. How often are people really surprised when their brains are "at rest", likely replaying a previously experienced event or planning future actions under their control? It seems to be more likely a very low prediction error scenario, if at all surprising.

We (and some others) take a broad interpretation of PE and believe it is often more intuitive to think about PE minimization in terms of uncertainty rather than “surprise”; the word “surprise” usually implies a sudden emotive reaction from the violation of expectations, which is not useful here.

When planning future actions, each step of the plan is spurred by the uncertainty of what is the appropriate action given the scenario set up by prior steps. Each planned step erases some of that uncertainty. For example, you may be mentally simulating a conversation, what you will say, and what another person will say. Each step of this creates uncertainty of “what is the appropriate response?” Each reasoning step addresses contingencies. While planning, you may also uncover more obvious forms of uncertainty, sparking memory retrieval to finish it. A resting-state participant may think to cook a frozen pizza when they arrive home, but be uncertain about whether they have any frozen pizzas left, prompting episodic memory retrieval to address this uncertainty. We argue that every planning step or memory retrieval can be productively understood as being sparked by uncertainty/surprise (PE), and the subsequent cognitive response minimizes this uncertainty.

We updated the Introduction to include a paragraph near the start providing this explanation (p. 3-4):

“PE minimization may broadly coordinate brain functions of all sorts, including abstract cognitive functions. This includes the types of cognitive processes at play even in the absence of stimuli (e.g., while daydreaming). While it may seem counterintuitive to associate this type of cognition with PE – a concept often tied to external surprises – it has been proposed that the brain's internal generative model is continuously active.[12–14] Spontaneous thought, such as planning a future event or replaying a memory, is not a passive, low-PE process. Rather, it can be seen as a dynamic cycle of generating and resolving internal uncertainty. While daydreaming, you may be reminded of a past conversation, where you wish you had said something different. This situation contains uncertainty about what would have been the best thing to say. Wondering about what you wish you said can be viewed as resolving this uncertainty, in principle, forming a plan if the same situation ever arises again in the future. Each iteration of the simulated conversation repeatedly sparks and then resolves this type of uncertainty.”

(R2.3)The quantitative comparison between networks under task and rest was done on a small subset of the ROIs rather than on the full network - why? Noting how small the correlation between task and rest is (r=0.021) and that's only for part of the networks, the evidence is a little tenuous. Running the analysis for the full networks could strengthen the argument.

We thank the reviewer for this opportunity to clarify our method. A single correlation between the full, aggregated networks would be conceptually misaligned with what we aimed to assess. To test for a personspecific anatomical correspondence, it is necessary to examine the link between task and rest at a granular level. We therefore asked whether the specific parts of an individual's network most responsive to PE during the task are the same parts that show the strongest fluctuations at rest. Our analysis, performed iteratively across all 3,432 possible ROI subsets, was designed specifically to answer this question, which would be obscured by an aggregated network measure.

We appreciate the reviewer's concern about the modest effect size (r = .021). However, this must be contextualized, as the short task scan has very low reliability (.08), which imposes a severe statistical ceiling on any possible task-rest correlation. Finding a highly significant effect (p < .001) in the face of such noisy data, therefore, provides robust evidence for a genuine task-rest correspondence.

We updated the Discussion to discuss this point (p. 22-23):

“A key finding supporting our interpretation is the significant link between individual differences in task-evoked PE responses and resting-state fluctuations. One might initially view the effect size of this correspondence (r = .021) as modest. However, this interpretation must be contextualized by the considerable measurement noise inherent in short task-fMRI scans; the split-half reliability of the task contrast was only .08. This low reliability imposes a severe statistical ceiling on any possible task-rest correlation. Therefore, detecting a highly significant (p < .001) relationship despite this constraint provides robust evidence for a genuine link. Furthermore, our analytical approach, which iteratively examined thousands of ROI subsets rather than one aggregated network, was intentionally granular. The goal was not simply to correlate two global measures, but to test for a personspecific anatomical correspondence – that is, whether the specific parts of an individual's network most sensitive to PE during the task are the same parts that fluctuate most strongly at rest. An aggregate analysis would obscure this critical spatial specificity. Taken together, this granular analysis provides compelling evidence for an anatomically consistent fingerprint of PE processing that bridges task-evoked activity and spontaneous restingstate dynamics, strengthening our central claim.”

(R2.4) Looking at the results in Figure 2C, the four-quadrant description of the networks labelled for low and high PE appears a little simplistic. The authors state that this four-quadrant description omits some ROIs as motivated by prior knowledge. This would benefit from a more comprehensive justification.Which ROIs are excluded, and what is the evidence for exclusion?

Our four-quadrant model is a principled simplification designed to distill the dominant, large-scale connectivity patterns from the complex modularity results. This approach focuses on coherent, well-documented anatomical streams while setting aside a few anatomically distant and disjoint ROIs that were less central to the main modules. This heuristic additionally unlocks more robust and novel analyses.

The two low-PE posterior-anterior (PA) pathways are grounded in canonical processing streams. (i) The OCATL connection mirrors the ventral visual stream (the “what” pathway), which is fundamental for object recognition and is upregulated during the smooth processing of expected stimuli. (ii) The IPL-LPFC connection represents a core axis of the dorsal attention stream and the Fronto-Parietal Control Network (FPCN), reflecting the maintenance of top-down cognitive control when information is predictable; the IPL-LPFC module excludes ROIs in the middle temporal gyrus, which are often associated with the FPCN but are not covered here.

In contrast, the two high-PE ventral-dorsal (VD) pathways reflect processes for resolving surprise and conflict. (i) The OC-IPL connection is a classic signature of attentional reorienting, where unexpected sensory input (high PE) triggers a necessary shift in attention; the OC-IPL module excludes some ROIs that are anterior to the occipital lobe and enter the fusiform gyrus and inferior temporal lobe. (ii) The ATL-LPFC connection aligns with mechanisms for semantic re-evaluation, engaging prefrontal control regions to update a mental model in the face of incongruent information.

Beyond its functional/anatomical grounding, this simplification provides powerful methodological and statistical advantages. It establishes a symmetrical framework that makes our dynamic connectivity analyses tractable, such as our “cube” analysis of state transitions, which required overlapping modules. Critically, this model also offers a statistical safeguard. By ensuring each quadrant contributes to both low- and high-PE connectivity patterns, we eliminate confounds like region-specific signal variance or global connectivity. This design choice isolates the phenomenon to the pattern of connectivity itself (posterior-anterior vs. ventral-dorsal), making our interpretation more robust.

We updated the end of the Study 1A results (p. 10-11):

“Some ROIs appear in Figure 2C but are excluded from the four targeted quadrants (Figures 2C & 2D) – e.g., posterior inferior temporal lobe and fusiform ROIs are excluded from the OC-IPL module, and middle temporal gyrus ROIs are excluded from the IPL-LPFC modules. These exclusions, in favor of a four-quadrant interpretation, are motivated by existing knowledge of prominent structural pathways among these quadrants. This interpretation is also supported by classifier-based analyses showing connectivity within each quadrant is significantly influenced by PE (Supplemental Materials 2.2), along with analyses of single-region activity showing that these areas also respond to PE independently (Supplemental Materials 3). Hence, we proceeded with further analyses of these quadrants’ connections, which summarize PE’s global brain effects.

“This four-quadrant setup also imparts analytical benefits. First, this simplified structure may better generalize across PE tasks, and Study 1B would aim to replicate these results with a different design. Second, the four quadrants mean that each ROI contributes to both the posterior-anterior and ventral-dorsal modules, which would benefit later analyses and rules out confounds such as PE eliciting increased/decreased connectivity between an ROI and the rest of the brain. An additional, less key benefit is that this setup allows more easily evaluating whether the same phenomena arise using a different atlas (Supplemental Materials Y).”

(R2.5) The EEG-fMRI analysis claiming 3-6Hz fluctuations for PE is hard to reconcile with the fact that fMRI captures activity that is a lot slower, while some PEs are as fast as 150 ms. The discussion acknowledges this but doesn't seem to resolve it - would benefit from a more comprehensive argument.

We thank the reviewer for raising this important point, which allows us to clarify the logic of our multimodal analysis. Our analysis does not claim that the fMRI BOLD signal itself oscillates at 3-6 Hz. Instead, it is based on the principle that the intensity of a fast neural process can be reflected in the magnitude of the slow BOLD response. It’s akin to using a long-exposure photograph to capture a fast-moving object; while the individual movements are blurred, the intensity of the blur in the photo serves as a proxy for the intensity of the underlying motion. In our case, the magnitude of the fMRI network difference (|PA – VD|) acts as the "blur," reflecting the intensity of the rapid fluctuations between states within that time window.

Following this logic, we correlated this slow-moving fMRI metric with the power of the fast EEG rhythms, which reflects their amplitude. To bridge the different timescales, we averaged the EEG power over each fMRI time window and convolved it with the standard hemodynamic response function (HRF) – a crucial step to align the timing of the neural and metabolic signals. The resulting significant correlation specifically in the 3-6 Hz band demonstrates that when this rhythm is stronger, the fMRI data shows a greater divergence between network states. This allows us to infer the characteristic frequency of the underlying neural fluctuations without directly measuring them at that speed with fMRI, thus reconciling the two timescales.

Reviewer #3 (Public review):

Bogdan et al. present an intriguing and timely investigation into the intrinsic dynamics of prediction error (PE)-related brain states. The manuscript is grounded in an intuitive and compelling theoretical idea: that the brain alternates between high and low PE states even at rest, potentially reflecting an intrinsic drive toward predictive minimization. The authors employ a creative analytic framework combining different prediction tasks and imaging modalities. They shared open code, which will be valuable for future work.

(R3.1) Consistency in Theoretical Framing

The title, abstract, and introduction suggest inconsistent theoretical goals of the study.

The title suggests that the goal is to test whether there are intrinsic fluctuations in high and low PE states at rest. The abstract and introduction suggest that the goal is to test whether the brain intrinsically minimizes PE and whether this minimization recruits global brain networks. My comments here are that a) these are fundamentally different claims, and b) both are challenging to falsify. For one, task-like recurrence of PE states during resting might reflect the wiring and geometry of the functional organization of the brain emerging from neurobiological constraints or developmental processes (e.g., experience), but showing that mirroring exists because of the need to minimize PE requires establishing a robust relationship with behavior or showing a causal effect (e.g., that interrupting intrinsic PE state fluctuations affects prediction).

The global PE hypothesis-"PE minimization is a principle that broadly coordinates brain functions of all sorts, including abstract cognitive functions"-is more suitable for discussion rather than the main claim in the abstract, introduction, and all throughout the paper.

Given the above, I recommend that the authors clarify and align their core theoretical goals across the title, abstract, introduction, and results. If the focus is on identifying fluctuations that resemble taskdefined PE states at rest, the language should reflect that more narrowly, and save broader claims about global PE minimization for the discussion. This hypothesis also needs to be contextualized within prior work. I'd like to see if there is similar evidence in the literature using animal models.

Thank you for bringing up this issue. We have made changes throughout the paper to address these points. First, we have omitted reference to a “global PE hypothesis” from the Abstract and Introduction, in favor of structuring the Introduction in terms of a falsifiable question (p. 4):

“We pursued this goal using three studies (Figure 1) that collectively targeted a specific question: Do the taskdefined connectivity signatures of high vs. low PE also recur during rest, and if so, how does the brain transition between exhibiting high/low signatures?”

We made changes later in the Introduction to clarify that the investigation is based on correlative evidence and requires interpretations that may be debated (p. 5-7):

“Although this does not entirely address the reverse inference dilemma and can only produce correlative evidence, the present research nonetheless investigates these widely speculated upon PE ideas more directly than any prior work.

Although such speed outpaces the temporal resolution of fMRI, correlating fluctuations in dynamic connectivity measured from fMRI data with EEG oscillations can provide an estimate of the fluctuations’ speed. This interpretation of a correlation again runs up against issues related to reverse inference but would nonetheless serve as initial suggestive evidence that spontaneous transitions between network states occur rapidly.

Second, we examined the recruitment of these networks during rs-fMRI, and although the problems related to reverse inference are impossible to overcome fully, we engage with this issue by linking rs-fMRI data directly to task-fMRI data of the same participants, which can provide suggestive evidence that the same neural mechanisms are at play in both.”

We made changes throughout the Results now better describing the results as consistent with a hypothesis rather than demonstrating it (p. 12-19):

“In other words, we essentially asked whether resting-state participants are sometimes in low PE states and sometimes in high PE states, which would be consistent with spontaneous PE processing in the absence of stimuli.

These emerging states overlap strikingly with the previous task effects of PE, suggesting that rs-fMRI scans exhibit fluctuations that resemble the signatures of low- and high-PE states. 

To be clear, this does not entirely dissuade concerns about reverse inference, which would require a type of causal manipulation that is difficult (if not impossible) to perform in a resting state scan. Nonetheless, these results provide further evidence consistent with our interpretation that the resting brain spontaneously fluctuates between high/low PE network states.

These patterns are most consistent with a characteristic timescale near 3–6 Hz for the amplitude of the putative high/low-PE fluctuations. This is notably consistent with established links between PE and Delta/Theta and is further consistent with an interpretation in which these fluctuations relate to PE-related processing during rest.”

We have also made targeted edits to the Discussion to present the findings in a more cautious way, more clearly state what is our interpretation, and provide alternative explanations (p. 19-26):

“The present research conducted task-fMRI, rs-fMRI, and rs-fMRI-EEG studies to clarify whether PE elicits global connectivity effects and whether the signatures of PE processing arise spontaneously during rest. This investigation carries implications for how PE minimization may characterize abstract task-general cognitive processes. […] Although there are different ways to interpret this correlation, it is consistent with high/low PE states generally fluctuating at 3-6 Hz during rest. Below, we discuss these three studies’ findings.

Our rs-fMRI investigation examined whether resting dynamics resemble the task-defined connectivity signatures of high vs. low PE, independent of the type of stimulus encountered. The resting-state analyses indeed found that, even at rest, participants’ brains fluctuated between strong ventral-dorsal connectivity and strong posterior-anterior connectivity, consistent with shifts between states of high and low PE. This conclusion is based on correlative/observational evidence and so may be controversial as it relies on reverse inference.

These patterns resemble global connectivity signatures seen in resting-state participants, and correlations between fMRI and EEG data yield associations, consistent with participants fluctuating between high-PE (ventral-dorsal) and low-PE (posterior-anterior) states at 3-6 Hz. Although definitively testing these ideas is challenging, given that rs-fMRI is defined by the absence of any causal manipulations, our results provide evidence consistent with PE minimization playing a role beyond stimulus process.”

(R3.2) Interpretation of PE-Related Fluctuations at Rest and Its Functional Relevance. It would strengthen the paper to clarify what is meant by "intrinsic" state fluctuations. Intrinsic might mean taskindependent, trait-like, or spontaneously generated. Which do the authors mean here? Is the key prediction that these fluctuations will persist in the absence of a prediction task?

Of the three terms the reviewer mentioned, “spontaneous” and “task-independent” are the most accurate descriptors. We conceptualize these fluctuations as a continuous background process that persists across all facets of cognition, without requiring a task explicitly designed to elicit prediction error – although we, along with other predictive coding papers, would argue that all cognitive tasks are fundamentally rooted in PE mechanisms and thus anything can be seen as a “prediction task” (see our response to comment R2.2 for our changes to the Introduction that provide more intuition for this point). The proposed interactions can be seen as analogous to cortico-basal-thalamic loops, which are engaged across a vast and diverse array of cognitive processes.

The prior submission only used the word “intrinsic” in the title. We have since revised it to “spontaneous,” which is more specific than “intrinsic,” and we believe clearer for a title than “task-independent” (p. 1): “Spontaneous fluctuations in global connectivity reflect transitions between states of high and low prediction error”

We have also made tweaks throughout the manuscript to now use “spontaneously” throughout (it now appears 8 times in the paper).

Regardless of the intrinsic argument, I find it challenging to interpret the results as evidence of PE fluctuations at rest. What the authors show directly is that the degree to which a subset of regions within a PE network discriminates high vs. low PE during task correlates with the magnitude of separation between high and low PE states during rest. While this is an interesting relationship, it does not establish that the resting-state brain spontaneously alternates between high and low PE states, nor that it does so in a functionally meaningful way that is related to behavior. How can we rule out brain dynamics of other processes, such as arousal, that also rise and fall with PE? I understand the authors' intention to address the reverse inference concern by testing whether "a participant's unique connectivity response to PE in the reward-processing task should match their specific patterns of resting-state fluctuation". However, I'm not fully convinced that this analysis establishes the functional role of the identified modules to PE because of the following:

Theoretically, relating the activities of the identified modules directly to behavior would demonstrate a stronger functional role.

(R3.2a) Across participants: Do individuals who exhibit stronger or more distinct PE-related fluctuations at rest also perform better on tasks that require prediction or inference? This could be assessed using the HCP prediction task, though if individual variability is limited (e.g., due to ceiling effects), I would suggest exploring a dataset with a prediction task that has greater behavioral variance.

This is a good idea, but unfortunately difficult to test with our present data. The HCP gambling task used in our study was not designed to measure individual differences in prediction or inference and likely suffers from ceiling effects. Because the task outcomes are predetermined and not linked to participants' choices, there is very little meaningful behavioral variance in performance to correlate with our resting-state fluctuation measure.

While we agree that exploring a different dataset with a more suitable task would be ideal, given the scope of the existing manuscript, this seems like it would be too much. Although these results would be informative, they would ultimately still not be a panacea for the reverse inference issues.

Or even more broadly, does this variability in resting state PE state fluctuations predict general cognitive abilities like WM and attention (which the HCP dataset also provides)? I appreciate the inclusion of the win-loss control, and I can see the intention to address specificity. This would test whether PE state fluctuations reflect something about general cognition, but also above and beyond these attentional or WM processes that we know are fluctuating.

This is a helpful suggestion, motivating new analyses: We measured the degree of resting-state fluctuation amplitude across participants and correlated it with the different individual differences measures provided with the HCP data (e.g., measures of WM performance). We computed each participant’s fluctuation amplitude measure as the average absolute difference between posterior-anterior and ventral-dorsal connectivity; this is the average of the TR-by-TR fMRI amplitude measure from Study 3. We correlated this individual difference score with all of the ~200 individual difference measures provided with the HCP dataset (e.g., measures of intelligence or personality). We measured the Spearman correlation between mean fluctuation amplitude with each of those ~200 measures, while correcting for multiple hypotheses using the False Discovery Rate approach.[18]

We found a robust negative association with age, where older participants tend to display weaker fluctuations (r = -.16, p < .001). We additionally find a positive association with the age-adjusted score on the picture sequence task (r = .12, p_corrected = .03) and a negative association with performance in the card sort task (r = -.12, p_corrected = 046). It is unclear how to interpret these associations, without being speculative, given that fluctuation amplitude shows one positive association with performance and one negative association, albeit across entirely different tasks.  We have added these correlation results as Supplemental Materials 8 (SM p. 11):

“(8) Behavioral differences related to fluctuation amplitude 

To investigate whether individual differences in the magnitude of resting-state PE-state fluctuations predict general cognitive abilities, we correlated our resting-state fluctuation measure with the cognitive and demographic variables provided in the HCP dataset.

(8.1) Methods

For each of the 1,000 participants, we calculated a single fluctuation amplitude score. This score was defined as the average absolute difference between the time-varying posterior-anterior (PA) and ventral-dorsal (VD) connectivity during the resting-state fMRI scan (the average of the TR-by-TR measure used for Study 3). We then computed the Spearman correlation between this score and each of the approximately 200 individual difference measures provided in the HCP dataset. We corrected for multiple comparisons using the False Discovery Rate (FDR) approach.

(8.2) Results

The correlations revealed a robust negative association between fluctuation amplitude and age, indicating that older participants tended to display weaker fluctuations (r = -.16, p_corrected < .001). After correction, two significant correlations with cognitive performance emerged: (i) a positive association with the age-adjusted score on the Picture Sequence Memory Test (r = .12, p_corrected = .03), (ii) a negative association with performance on the Card Sort Task (r = -.12, p_corrected = .046). As greater fluctuation amplitude is linked to better performance on one task but worse performance on another, it is unclear how to interpret these findings.”

We updated the main text Methods to direct readers to this content (p. 39-40):

“(4.4.3) Links between network fluctuations and behavior

We considered whether the extent of PE-related network expression states during resting-state is behaviorally relevant. We specifically investigated whether individual differences in the overall magnitude of resting-state fluctuations could predict individual difference measures, provided with the HCP dataset. This yielded a significant association with age, whereby older participants tended to display weaker fluctuations. However, associations with cognitive measures were limited. A full description of these analyses is provided in Supplemental Materials 8.”

(R3.2b) Within participants: Do momentary increases in PE-network expression during tasks relate to better or faster prediction? In other words, is there evidence that stronger expression of PE-related states is associated with better behavioral outcomes?

This is a good question that probes the direct behavioral relevance of these network states on a trial-by-trial basis. We agree with the reviewer's intuition; in principle, one would expect a stronger expression of the low-PE network state on trials where a participant correctly and quickly gives a high likelihood rating to a predictable stimulus.

Following this suggestion, we performed a new analysis in Study 1A to test this. We found that while network expression was indeed linked to participants’ likelihood ratings: higher likelihood ratings correspond to stronger posterior-anterior connectivity, whereas lower ratings correspond to stronger ventral-dorsal connectivity (Connectivity-Direction × likelihood, β [standardized] = .28, p = .02). Yet, this is not a strong test of the reviewer’s hypothesis, and different exploratory analyses of response time yield null results (p > .05). We suspect that this is due to the effect being too subtle, so we have insufficient statistical power. A comparable analysis was not feasible for Study 1B, as its design does not provide an analogous behavioral measure of trialby-trial prediction success.

(R3.3) A priori Hypothesis for EEG Frequency Analysis.

It's unclear how to interpret the finding that fMRI fluctuations in the defined modules correlate with frontal Delta/Theta power, specifically in the 3-6 Hz range. However, in the EEG literature, this frequency band is most commonly associated with low arousal, drowsiness, and mind wandering in resting, awake adults, not uniquely with prediction error processing. An a priori hypothesis is lacking here: what specific frequency band would we expect to track spontaneous PE signals at rest, and why? Without this, it is difficult to separate a PE-based interpretation from more general arousal or vigilance fluctuations.

This point gets to the heart of the challenge with reverse inference in resting-state fMRI. We agree that an interpretation based on general arousal or drowsiness is a potential alternative that must be considered. However, what makes a simple arousal interpretation challenging is the highly specific nature of our fMRI-EEG association. As shown in our confirmatory analyses (Supplemental Materials 6), the correlation with 3-6 Hz power was found exclusively with the absolute difference between our two PE-related network states (|PA – VD|)—a measure of fluctuation amplitude. We found no significant relationship with the signed difference (a bias toward one state) or the sum (the overall level of connectivity). This specificity presents a puzzle for a simple drowsiness account; it seems less plausible that drowsiness would manifest specifically as the intensity of fluctuation between two complex cognitive networks, rather than as a more straightforward change in overall connectivity. While we cannot definitively rule out contributions from arousal, the specificity of our finding provides stronger evidence for a structured cognitive process, like PE, than for a general, undifferentiated state. 

We updated the Discussion to make the argument above and also to remind readers that alternative explanations, such as ones based on drowsiness, are possible (p. 24):

“We specifically interpret the fMRI-EEG correlation as reflecting fluctuation speed because we correlated EEG oscillatory power with the fluctuation amplitude computed from fMRI data. Simply correlating EEG power with the average connectivity or the signed difference between posterior-anterior and ventral-dorsal connectivity yields null results (Supplemental Materials 6), suggesting that this is a very particular association, and viewing it as capturing fluctuation amplitude provides a parsimonious explanation. Yet, this correlation may be interpreted in other ways. For example, resting-state Theta is also a signature of drowsiness,[2] which may correlate with PE processing, but perhaps should be understood as some other mechanism.”

(R3.4) Significance Assessment

The significance of the correlation above and all other correlation analyses should be assessed through a permutation test rather than a single parametric t-test against zero. There are a few reasons: a) EEG and fMRI time series are autocorrelated, violating the independence assumption of parametric tests;

Standard t-tests can underestimate the true null distribution's variance, because EEG-fMRI correlations often involve shared slow drifts or noise sources, which can yield spurious correlations and inflating false positives unless tested against an appropriate null.

Building a null distribution that preserves the slow drifts, for example, would help us understand how likely it is for the two time series to be correlated when the slow drifts are still present, and how much better the current correlation is, compared to this more conservative null. You can perform this by phase randomizing one of the two time courses N times (e.g., N=1000), which maintains the autocorrelation structure while breaking any true co-occurrence in patterns between the two time series, and compute a non-parametric p-value. I suggest using this approach in all correlation analyses between two time series.

This is an important statistical point to clarify, and the suggested analysis is valuable. The reviewer is correct that the raw fMRI and EEG time series are autocorrelated. However, because our statistical approach is a twolevel analysis, we reasoned that non-independence at the correlation-level would not invalidate the higher-level t-test. The t-test’s assumption of independence applies to the individual participants' coefficients, which are independent across participants. Thus, we believe that our initial approach is broadly appropriate, and its simplicity allows it to be easily communicated.

Nonetheless, the permutation-testing procedure that the Reviewer describes seems like an important analysis to test, given that permutation-testing is the gold standard for evaluating statistical significance, and it could guarantee that our above logic is correct. We thus computed the analysis as the reviewer described. For each participant, we phase-randomized the fMRI fluctuation amplitude time series. Specifically, we randomized the Fourier phases of the |PA–VD| series (within run), while retaining the original amplitude spectrum; inverse transforms yielded real surrogates with the same power spectrum. This was done for each participant once per permutation. Each participant’s phase-randomized data was submitted to the analysis of each oscillatory power band as originally, generating one mean correlation for each band. This was done 1,000 times.

Across the five bands, we find that the grand mean correlation is near zero (M_r = .0006) and the 97.5^th percentile critical value of the null distribution is r = ~.025; this 97.5^th percentile corresponds to the upper end of a 95% confidence interval for a band’s correlation; the threshold minimally differs across bands (.024 < rs < .026). Our original correlation coefficients for Delta (M_r = .042) and Theta (M_r = .041), which our conclusions focused on, remained significant (p ≤ .002); we can perform family-wise error-rate correction by taking the highest correlation across any band for a given permutation, and the Delta and Theta effects remain significant (p_FWEcorrected ≤ .003); previously Reviewer comment R1.4c requested that we employ family-wise error correction.

These correlations were previously reported in Table 1, and we updated the caption to note what effects remain significant when evaluated using permutation-testing and with family-wise error correction (p. 19):

“The effects for Delta, Theta, Beta, and Gamma remain significant if significance testing is instead performed using permutation-testing and with family-wise error rate correction (p_corrected < .05).”

We updated the Methods to describe the permutation-testing analysis (p. 43):

“To confirm the significance of our fMRI-EEG correlations with a non-parametric approach, we performed a group-level permutation-test. For each of 1,000 permutations, we phase-randomized the fMRI fluctuation amplitude time series. Specifically, we randomized the Fourier phases of the |PA–VD| series (within run), while retaining the original amplitude spectrum; inverse transforms yielded real surrogates with the same power spectrum. This procedure breaks the true temporal relationship between the fMRI and EEG data while preserving its structure. We then re-computed the mean Spearman correlation for each frequency band using this phase-randomized data. We evaluated significance using a family-wise error correction approach that accounts for us analyzing five oscillatory power bands. We thus create a null distribution composed of the maximum correlation value observed across all frequency bands from each permutation. Our observed correlations were then tested for significance against this distribution of maximums.”

(R3.5) Analysis choices

If I'm understanding correctly, the algorithm used to identify modules does so by assigning nodes to communities, but it does not itself restrict what edges can be formed from these modules. This makes me wonder whether the decision to focus only on connections between adjacent modules, rather than considering the full connectivity, was an analytic choice by the authors. If so, could you clarify the rationale? In particular, what justifies assuming that the gradient of PE states should be captured by edges formed only between nearby modules (as shown in Figure 2E and Figure 4), rather than by the full connectivity matrix? If this restriction is instead a by-product of the algorithm, please explain why this outcome is appropriate for detecting a global signature of PE states in both task and rest.

We discuss this matter in our response to comment R2.(4).

When assessing the correspondence across task-fMRI and rs-fMRI in section 2.2.2, why was the pattern during task calculated from selecting a pair of bilateral ROIs (resulting in a group of eight ROIs), and the resting state pattern calculated from posterior-anterior/ventral-dorsal fluctuation modules? Doesn't it make more sense to align the two measures? For example, calculating task effects on these same modules during task and rest?

We thank the reviewer for this question, as it highlights a point in our methods that we could have explained more clearly. The reviewer is correct that the two measures must be aligned, and we can confirm that they were indeed perfectly matched.

For the analysis in Section 2.2.2, both the task and resting-state measures were calculated on the exact same anatomical substrate for each comparison. The analysis iteratively selected a symmetrical subset of eight ROIs from our larger four quadrants. For each of these 3,432 iterations, we computed the task-fMRI PE effect (the Connectivity Direction × PE interaction) and the resting-state fluctuation amplitude (E[|PA – VD|]) using the identical set of eight ROIs. The goal of this analysis was precisely to test if the fine-grained anatomical pattern of these effects correlated within an individual across the task and rest states. We will revise the text in Section 2.2.2 to make this direct alignment of the two measures more explicit.

Recommendations for authors:

Reviewer #1 (Recommendations for authors):

(R1.3) Several prior studies have described co-activation or connectivity "templates" that spontaneously alternate during rest and task states, and are linked to behavioral variability. While they are interpreted differently in terms of cognitive function (e.g., in terms of sustained attention: Monica Rosenberg; alertness: Catie Chang), the relationship between these previously reported templates and those identified in the current study warrants discussion. Are the current templates spatially compatible with prior findings while offering new functional interpretations beyond those already proposed in the literature? Or do they represent spatially novel patterns?

Thank you for this suggestion. Broadly, we do not mean to propose spatially novel patterns but rather focus on how these are repurposed for PE processing. In the Discussion, we link our identified connectivity states to established networks (e.g., the FPCN). We updated this paragraph to mention that these patterns are largely not spatially novel (p. 20):

“The connectivity patterns put forth are, for the most part, not spatially novel and instead overlap heavily with prior functional and anatomical findings.”

Regarding the specific networks covered in the prior work by Rosenberg and Chang that the reviewer seems to be referring to, [7,8] this research has emphasized networks anchored heavily in sensorimotor, subcortical– cerebellar, and medial frontal circuits, and so mostly do not overlap with the connectivity effects we put forth.

(R1.4) Additional points:

(R1.4a) I do not think that the logic for taking the absolute difference of fMRI connectivity is convincing. What happens if the sign of the difference is maintained ?

Thank you for pointing out this area that requires clarification. Our analysis targets the amplitude of the fluctuation between brain states, not the direction. We define high fluctuation amplitude as moments when the brain is strongly in either the PA state (PA > VD) or the VD state (VD > PA). The absolute difference |PA – VD| correctly quantifies this intensity, whereas a signed difference would conflate these two distinct high-amplitude moments. Our simulation study (Supplemental Materials, Section 5) provides the theoretical validation for this logic, showing how this absolute difference measure in slow fMRI data can track the amplitude of a fast underlying neural oscillator.

When the analysis is tested in terms of the signed difference, as suggested by the Reviewer, the association between the fMRI data and EEG power is insignificant for each power band (ps_uncorrected ≥ .47). We updated Supplemental Materials 6 to include these results. Previously, this section included the fluctuation amplitude (fMRI) × EEG power results while controlling for: (i) the signed difference between posterior-anterior and ventral-dorsal connectivity, (ii) the sum of posterior-anterior and ventral-dorsal connectivity, and (iii) the absolute value of the sum of posterior-anterior and ventral-dorsal connectivity. For completeness, we also now report the correlation between each EEG power band and each of those other three measures (SM, p. 9)

“We additionally tested the relationship between each of those three measures and the five EEG oscillation bands. Across the 15 tests, there were no associations (ps_uncorrected  ≥ .04); one uncorrected p-value was at p = .044, although this was expected given that there were 15 tests. Thus, the association between EEG oscillations and the fMRI measure is specific to the absolute difference (i.e., amplitude) measure.”

(R1.4b) Reasoning of focus on frontal and theta band is weak, and described as "typical" (line 359) based on a single study.

Sorry about this. There is a rich literature on the link between frontal theta and prediction error,[3,9–11] and we updated the Introduction to include more references to this work (p. 18): “The analysis was first done using power averaged across frontal electrodes, as these are the typical focus of PE research on oscillations.[3,9–11]”

We have also updated the Methods to cite more studies that motivate our electrode choice (p. 41): “The analyses first targeted five midline frontal electrodes (F3, F1, Fz, F2, F4; BioSemi64 layout), given that this frontal row is typically the focus of executive-function PE research on oscillations.[9–11]”

(R1.4c) No correction appears to have been applied for the association between EEG power and fMRI connectivity. Given that 100 frequency bins were collapsed into 5 canonical bands, a correction for 5 comparisons seems appropriate. Notably, the strongest effects in the delta and theta bands (particularly at fronto-central electrodes) may still survive correction, but this should be explicitly tested and reported.

Thanks for this suggestion. We updated the Table 1 caption to mention what results survive family-wise error rate correction – as the reviewer suggests, the Delta/Theta effects would survive Bonferroni correction for five tests, although per a later comment suggesting that we evaluate statistical significance with a permutationtesting approach (comment R3.4), we instead report family-wise error correction based on that. The revised caption is as follows (p. 19):

“The effects for Delta, Theta, Beta, and Gamma remain significant if significance testing is instead performed using permutation-testing and with family-wise error rate correction (p_corrected < .05).”

(R1.4d) Line 135. Not sure I understand what you mean by "moods". What is the overall point here?

The overall argument is that the fluctuations occur rapidly rather than slowly. By slow “moods” we refer to how a participant could enter a high anxiety state of >10 seconds, linked to high PE fluctuations, and then shift into a low anxiety state, linked to low PE fluctuations. We argue that this is not occurring. Regardless, we recognize that referring to lengths of time as short as 10 seconds or so is not a typical use of the word “mood” and is potentially ambiguous, so we have omitted this statement, which was originally on page 6: “Identifying subsecond fluctuations would broaden the relevance of the present results, as they rule out that the PE states derive from various moods.”

(R1.4e) Line 100. "Few prior PE studies have targeted PE, contrasting the hundreds that have targeted BOLD". I don't understand this sentence. It's presumably about connectivity vs activity?

Yes, sorry about this typo. The reviewer is correct, and that sentence was meant to mention connectivity. We corrected (p. 5): “Few prior PE studies have targeted connectivity, contrasting the hundreds that have targeted BOLD.”

(R1.4f) Line 373: "0-0.5Hz" in the caption is probably "0-50Hz".

Yes, this was another typo, thank you. We have corrected it (p. 19): “… every 0.5 Hz interval from 0-50 Hz.”

Reviewer #2 (Recommendations for authors):

(R2.6) (Page 3) When referring to the "limited" hypothesis of local PE, please clarify in what sense is it limited. That statement is unclear.

Thank you for pointing out this text, which we now see is ambiguous. We originally use "limited" to refer to the hypothesis's constrained scope – namely, that PE is relevant to various low-level operations (e.g., sensory processing or rewards) but the minimization of PE does not guide more abstract cognitive processes. We edited this part of the Introduction to be clearer (p. 3)

“It is generally agreed that the brain uses PE mechanisms at neuronal or regional levels,[15,16] and this idea has been useful in various low-level functional domains, including early vision [15] and dopaminergic reward processing.[17] Some theorists have further argued that PE propagates through perceptual pathways and can elicit downstream cognitive processes to minimize PE.”

(R2.7) (Page 5) "Few prior PE have targeted PE"... this statement appears contradictory. Please clarify.

Sorry about this typo, which we have corrected (p. 5):

“Few prior PE studies have targeted connectivity, contrasting the hundreds that have targeted BOLD.”

(R2.8) What happened to the data of the medium PE condition in Study 1A?

The medium PE condition data were not excluded. We modeled the effect of prediction error on connectivity using a linear regression across the three conditions, coding them as a continuous variable (Low = -1, Medium = 0, High = +1). This approach allowed us to identify brain connections that showed a linear increase or decrease in strength as a function of increasing PE. This linear contrast is a more specific and powerful way to isolate PErelated effects than a High vs. Low contrast. We updated the Results slightly to make this clearer (p. 8-9):

“In the fMRI data, we compared the three PE conditions’ beta-series functional connectivity, aiming to identify network-level signatures of PE processing, from low to high. […] For the modularity analysis, we first defined a connectome matrix of beta values, wherein each edge’s value was the slope of a regression predicting that edge’s strength from PE (coded as Low = -1, Medium = 0, High = +1; Figure 2A).”

(R2.9) (Page 15) The point about how the dots in 6H follow those in 6J better than those in 6I is a little subjective - can the authors provide an objective measure?

Thank you for pointing out this issue. The visual comparison using Figure 6 was not meant as a formal analysis but rather to provide intuition. However, as the reviewer describes, this is difficult to convey. Our formal analysis is provided in Supplemental Materials 5, where we report correlation coefficients between a very large number of simulated fMRI data points and EEG data points corresponding to different frequencies. We updated this part of the Results to convey this (p. 16-17):

“Notice how the dots in Figure 6H follow the dots in Figure 6J (3 Hz) better than the dots in Figure 6I (0.5 Hz) or Figure 6K (10 Hz); this visual comparison is intended for illustrative purposes only, and quantitative analyses are provided in Supplemental Materials 5.”

References

(1) Zalesky, A., Fornito, A. & Bullmore, E. T. Network-based statistic: identifying differences in brain networks. Neuroimage 53, 1197–1207 (2010)

(2) Strijkstra, A. M., Beersma, D. G., Drayer, B., Halbesma, N. & Daan, S. Subjective sleepiness correlates negatively with global alpha (8–12 Hz) and positively with central frontal theta (4–8 Hz) frequencies in the human resting awake electroencephalogram. Neuroscience letters 340, 17–20 (2003).

(3) Cavanagh, J. F. & Frank, M. J. Frontal theta as a mechanism for cognitive control. Trends in cognitive sciences 18, 414–421 (2014).

(4) Grech, R. et al. Review on solving the inverse problem in EEG source analysis. Journal of neuroengineering and rehabilitation 5, 25 (2008)

(5) Palva, J. M. et al. Ghost interactions in MEG/EEG source space: A note of caution on inter-areal coupling measures. Neuroimage 173, 632–643 (2018).

(6) Koles, Z. J. Trends in EEG source localization. Electroencephalography and clinical Neurophysiology 106, 127–137 (1998).

(7) Rosenberg, M. D. et al. A neuromarker of sustained attention from whole-brain functional connectivity. Nature neuroscience 19, 165–171 (2016).

(8) Goodale, S. E. et al. fMRI-based detection of alertness predicts behavioral response variability. elife 10, e62376 (2021).

(9) Cavanagh, J. F. Cortical delta activity reflects reward prediction error and related behavioral adjustments, but at different times. NeuroImage 110, 205–216 (2015)

(10) Hoy, C. W., Steiner, S. C. & Knight, R. T. Single-trial modeling separates multiple overlapping prediction errors during reward processing in human EEG. Communications Biology 4, 910 (2021).

(11) Neo, P. S.-H., Shadli, S. M., McNaughton, N. & Sellbom, M. Midfrontal theta reactivity to conflict and error are linked to externalizing and internalizing respectively. Personality neuroscience 7, e8 (2024).

(12) Friston, K. J. The free-energy principle: a unified brain theory? Nature reviews neuroscience 11, 127–138 (2010)

(13) Feldman, H. & Friston, K. J. Attention, uncertainty, and free-energy. Frontiers in human neuroscience 4, 215 (2010).

(14) Friston, K. J. et al. Active inference and epistemic value. Cognitive neuroscience 6, 187–214 (2015).

(15) Rao, R. P. & Ballard, D. H. Predictive coding in the visual cortex: a functional interpretation of some extraclassical receptive-field effects. Nature neuroscience 2, 79–87 (1999)

(16) Walsh, K. S., McGovern, D. P., Clark, A. & O’Connell, R. G. Evaluating the neurophysiological evidence for predictive processing as a model of perception. Annals of the new York Academy of Sciences 1464, 242– 268 (2020)

(17) Niv, Y. & Schoenbaum, G. Dialogues on prediction errors. Trends in cognitive sciences 12, 265–272 (2008).

(18) Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal statistical society: series B (Methodological) 57, 289–300 (1995).

Author response:

The following is the authors’ response to the previous reviews

eLife Assessment

This valuable study combines a computational language model, i.e., HM-LSTM, and temporal response function (TRF) modeling to quantify the neural encoding of hierarchical linguistic information in speech, and addresses how hearing impairment affects neural encoding of speech. The analysis has been significantly improved during the revision but remain somewhat incomplete - The TRF analysis should be more clearly described and controlled. The study is of potential interest to audiologists and researchers who are interested in the neural encoding of speech.

We thank the editors for the updated assessment. In the revised manuscript, we have added a more detailed description of the TRF analysis on p. of the revised manuscript. We have also updated Figure 1 to better visualize the analyses pipeline. Additionally, we have included a supplementary video to illustrate the architecture of the HM-LSTM model, the ridge regression methods using the model-derived features, and mTRF analysis using the acoustic envelop and the binary rate models.

Public Reviews:

Reviewer #1 (Public review):

About R squared in the plots:

The authors have used a z-scored R squared in the main ridge regression plots. While this may be interpretable, it seems non-standard and overly complicated. The authors could use a simple Pearson r to be most direct and informative (and in line with similar work, including Goldstein et al. 2022 which they mentioned). This way the sign of the relationships is preserved.

We did not use Pearson’s r as in Goldstein et al. (2022) because our analysis did not involve a train-test split, which was a key aspect of their approach. Specifically, Goldstein et al. (2022) divided their data into training and testing sets, trained a ridge regression model on the training set, and then used the trained model to predict neural responses on the test set. They calculated Pearson’s r to assess the correlation between the predicted and observed neural responses, making the correlation coefficient (r) their primary measure of model performance. In contrast, our analysis focused on computing the model fitting performance (R²) of the ridge regression model for each sensor and time point for each subject. At the group level, we conducted one-sample t-tests with spatiotemporal cluster-based correction on the R² values to identify sensors and time windows where R² values were significantly greater than baseline. We established the baseline by normalizing the R² values using Fisher z-transformation across sensors within each subject. We have added this explanation on p.13 of the revised manuscript.

About the new TRF analysis:

The new TRF analysis is a necessary addition and much appreciated. However, it is missing the results for the acoustic regressors, which should be there analogous to the HM-LSTM ridge analysis. The authors should also specify which software they have utilized to conduct the new TRF analysis. It also seems that the linguistic predictors/regressors have been newly constructed in a way more consistent with previous literature (instead of using the HM-LSTM features); these specifics should also be included in the manuscript (did it come from Montreal Forced Aligner, etc.?). Now that the original HM-LSTM can be compared to a more standard TRF analysis, it is apparent that the results are similar.

We used the Python package Eelbrain (https://eelbrain.readthedocs.io/en/r0.39/auto_examples/temporal-response-functions/trf_intro.html) to conduct the multivariate temporal response function (mTRF) analyses. As we previously explained in our response to R3, we did not apply mTRF to the acoustic features due to the high dimensionality of the input. Specifically, our acoustic representation consists of a 130-dimensional vector sampled every 10 ms throughout the speech stimuli (comprising a 129-dimensional spectrogram and a 1dimensional amplitude envelope). This led to interpreting the 130-dimensional TRF estimation difficult to interpret. A similar constraint applied to the hidden-layer activations from our HMLSTM model for the five linguistic features. After dimensionality reduction via PCA, each still resulted in 150-dimensional vectors. To address this, we instead used binary predictors marking the offset of each linguistic unit (phoneme, syllable, word, phrase, sentence). Since our speech stimuli were computer-synthesized, the phoneme and syllable boundaries were automatically generated. The word boundaries were manually annotated by a native Mandarin as in Li et al. (2022). The phrase boundaries were automatically annotated by the Stanford parser and manually checked by a native Mandarin speaker. These rate models are represented as five distinct binary time series, each aligned with the timing of the corresponding linguistic unit, making them well-suited for mTRF analysis. Although the TRF results from the 1-dimensional rate predictors and the ridge regression results from the high-dimensional HM-LSTM-derived features are similar, they encode different things: The rate regressors only encode the timing of linguistic unit boundaries, while the model-derived features encode the representational content of the linguistic input. Therefore, we do not consider the mTRF analyses to be analogous to the ridge regression analyses. Rather, these results complement each other and both provide informative results into the neural tracking of linguistic structures at different levels for the attended and unattended speech.

Since the TRF result for the continuous acoustic features also concerns R2, we have added an mTRF analysis where we fitted the one-dimensional speech envelope to the EEG. We extracted the envelope at 10 ms intervals for both attended and unattended speech and computed mTRFs independently for each subject and sensor using a basis of 50 ms Hamming windows spanning –100 ms to 300 ms relative to envelope onset. The results showed that in hearing-impaired participants, attended speech elicited a significant cluster in the bilateral temporal regions from 270 to 300 ms post-onset (t = 2.40, p = 0.01, Cohen’s d = 0.63). Unattended speech elicited an early cluster in right temporal and occipital regions from –100 ms to –80 ms (t = 3.07, p = 0.001, d = 0.83). Normal-hearing participants showed significant envelope tracking in the left temporal region at 280–300 ms after envelope onset (t = 2.37, p = 0.037, d = 0.48), with no significant cluster for unattended speech. These results further suggest that hearing-impaired listeners may have difficulty suppressing unattended streams. We have added the new TRF results for envelope to Figure S3 and the “mTRF results for attended and unattended speech” on p.7 and the “mTRF analysis” in Material and Methods of the revised manuscript.

The authors' wording about this suggests that these new regressors have a nonzero sample at each linguistic event's offset, not onset. This should also be clarified. As the authors know, the onset would be more standard, and using the offset has implications for understanding the timing of the TRFs, as a phoneme has a different duration than a word, which has a different duration from a sentence, etc.

In our rate‐model mTRF analyses, we initially labelled linguistic boundaries as “offsets” because our ridge‐regression with HM-LSTM features was aligned to sentence offsets rather than onsets. However, since each offset coincides with the next unit’s onset—and our regressors simply mark these transition points as 1—the “offset” and “onset” models yield identical mTRFs. To avoid confusion, we have relabeled “offset” as “boundary” in Figure S2.

As discussed in our prior responses, this design was based on the structure of our input to the HM-LSTM model, where each input consists of a pair of sentences encoded in phonemes, such as “t a_1 n əŋ_2 f ei_1 <sep> zh ə_4 sh iii_4 f ei_1 j ii_1” (“It can fly <sep> This is an airplane”). The two sentences are separated by a special <sep> token, and the model’s objective is to determine whether the second sentence follows the first, similar to a next-sentence prediction task. Since the model processes both sentences in full before making a prediction, the neural activations of interest should correspond to the point at which the entire sentence has been processed by humans. To enable a fair comparison between the model’s internal representations and brain responses, we aligned our neural analyses with the sentence offsets, capturing the time window after the sentence has been fully perceived by the participant. Thus, we extracted epochs from -100 to +300 ms relative to each sentence offset, consistent with our model-informed design.

We understand that phonemes, syllables, words, phrases, and sentences differ in their durations. However, the five hidden activity vectors extracted from the model are designed to capture the representations of these five linguistic levels across the entire sentence. Specifically, for a sentence pair such as “It can fly <sep> This is an airplane,” the first 2048-dimensional vector represents all the phonemes in the two sentences (“t a_1 n əŋ_2 f ei_1 <sep> zh ə_4 sh iii_4 f ei_1 j ii_1”), the second vector captures all the syllables (“ta_1 nəŋ_2 fei_1 <sep> zhə_4 shiii_4 fei_1jii_1”), the third vector represents all the words, the fourth vector captures the phrases, and the fifth vector represents the sentence-level meaning. In our dataset, input pairs consist of adjacent sentences from the stimuli (e.g., Sentence 1 and Sentence 2, Sentence 2 and Sentence 3, and so on), and for each pair, the model generates five 2048-dimensional vectors, each corresponding to a specific linguistic level. To identify the neural correlates of these model-derived features—each intended to represent the full linguistic level across a complete sentence—we focused on the EEG signal surrounding the completion of the second sentence rather than on incremental processing. Accordingly, we extracted epochs from -100 ms to +300 ms relative to the offset of the second sentence and performed ridge regression analyses using the five model features (reduced to 150 dimensions via PCA) at every 50 ms across the epoch. We have added this clarification on p.12 of the revised manuscript.

About offsets:

TRFs can still be interpretable using the offset timings though; however, the main original analysis seems to be utilizing the offset times in a different, more confusing way. The authors still seem to be saying that only the peri-offset time of the EEG was analyzed at all, meaning the vast majority of the EEG trial durations do not factor into the main HM-LSTM response results whatsoever. The way the authors describe this does not seem to be present in any other literature, including the papers that they cite. Therefore, much more clarification on this issue is needed. If the authors mean that the regressors are simply time-locked to the EEG by aligning their offsets (rather than their onsets, because they have varying onsets or some such experimental design complexity), then this would be fine. But it does not seem to be what the authors want to say. This may be a miscommunication about the methods, or the authors may have actually only analyzed a small portion of the data. Either way, this should be clarified to be able to be interpretable.

We hope that our response in RE4, along with the supplementary video, has helped clarify this issue. We acknowledge that prior studies have not used EEG data surrounding sentence offsets to examine neural responses at the phoneme or syllable levels. However, this is largely due to a lack of model that represent all linguistic levels across an entire sentence. There is abundant work comparing model predictors with neural data time-locked to offsets because they mark the point at which participants has already processed the relevant information (Brennan, 2016; Brennan et al., 2016; Gwilliams et al., 2024, 2025). Similarly, in our model– brain alignment study, our goal is to identify neural correlates for each model-derived feature. If we correlate model activity with EEG data aligned to sentence onsets, we would be examining linguistic representations at all levels (from phoneme to sentence) of the whole sentence at the time when participants have not heard the sentence yet. Although this limits our analysis to a subset of the data (143 sentences × 400 ms windows × 4 conditions), it targets the exact moment when full-sentence representations emerge against background speech, allowing us to examine each model-derived feature onto its neural signature. We have added this clarification on p.12 of the revised manuscript.

Reviewer #2 (Public review):

This study presents a valuable finding on the neural encoding of speech in listeners with normal hearing and hearing impairment, uncovering marked differences in how attention to different levels of speech information is allocated, especially when having to selectively attend to one speaker while ignoring an irrelevant speaker. The results overall support the claims of the authors, although a more explicit behavioural task to demonstrate successful attention allocation would have strengthened the study. Importantly, the use of more "temporally continuous" analysis frameworks could have provided a better methodology to assess the entire time course of neural activity during speech listening. Despite these limitations, this interesting work will be useful to the hearing impairment and speech processing research community. The study compares speech-in-quiet vs. multi-talker scenarios, allowing to assess within-participant the impact that the addition of a competing talker has on the neural tracking of speech. Moreover, the inclusion of a population with hearing loss is useful to disentangle the effects of attention orienting and hearing ability. The diagnosis of high-frequency hearing loss was done as part of the experimental procedure by professional audiologists, leading to a high control of the main contrast of interest for the experiment. Sample size was big, allowing to draw meaningful comparisons between the two populations.

We thank you very much for your appreciation of our research and we have now added a more description of the mTRF analyses on p.13-14 of the revised manuscript.

An HM-LSTM model was employed to jointly extract speech features spanning from the stimulus acoustics to word-level and phrase-level information, represented by embeddings extracted at successive layers of the model. The model was specifically expanded to include lower level acoustic and phonetic information, reaching a good representation of all intermediate levels of speech. Despite conveniently extracting all features jointly, the HMLSTM model processes linguistic input sentence-by-sentence, and therefore only allows to assess the corresponding EEG data at sentence offset. If I understood correctly, while the sentence information extracted with the HM-LSTM reflects the entire sentence - in terms of its acoustic, phonetic and more abstract linguistic features - it only gives a condensed final representation of the sentence. As such, feature extraction with the HM-LSTM is not compatible with a continuous temporal mapping on the EEG signal, and this is the main reason behind the authors' decision to fit a regression at nine separate time points surrounding sentence offsets.

Yes, you are correct. As explained in RE4, the model generates five hidden-layer activity vectors, each intended to represent all the phonemes, syllables, words, phrases within the entire sentence (“a condensed final representation”). This is the primary reason we extract EEG data surrounding the sentence offsets—this time point reflects when the full sentence has been processed by the human brain. We assume that even at this stage, residual neural responses corresponding to each linguistic level are still present and can be meaningfully analyzed.

While valid and previously used in the literature, this methodology, in the particular context of this experiment, might be obscuring important attentional effects impacted by hearing-loss. By fitting a regression only around sentence-final speech representations, the method might be overlooking the more "online" speech processing dynamics, and only assessing the permanence of information at different speech levels at sentence offset. In other words, the acoustic attentional bias between Attended and Unattended speech might exist even in hearing-impaired participants but, due to a lower encoding or permanence of acoustic information in this population, it might only emerge when using methodologies with a higher temporal resolution, such as Temporal Response Functions (TRFs). If a univariate TRF fit simply on the continuous speech envelope did not show any attentional bias (different trial lengths should not be a problem for fitting TRFs), I would be entirely convinced of the result. For now, I am unsure on how to interpret this finding.

We agree and we have added the mTRF results using the rate models for the 5 linguistic levels in the prior revision. The rate model aligns with the boundaries of each linguistic unit at each level. As explained in RE3, the rate regressors encode the timing of linguistic unit boundaries, while the model-derived features encode the representational content of the linguistic input. The mTRF results showed similar patterns to those observed using features from our HM-LSTM model with ridge regression (see Figure S2). These results complement each other and both provide informative results into the neural tracking of linguistic structures at different levels for the attended and unattended speech.

We have also added TRF results fitting the envelope of attended and unattended speech at every 10 ms to the whole 10-minute EEG data at every 10 ms. Our results showed that in hearing-impaired participants, attended speech elicited a significant cluster in the bilateral temporal regions from 270 to 300 ms post-onset (t = 2.40, p = 0.01, Cohen’s d = 0.63). Unattended speech elicited an early cluster in right temporal and occipital regions from –100 ms to –80 ms (t = 3.07, p = 0.001, d = 0.83). Normal-hearing participants showed significant envelope tracking in the left temporal region at 280–300 ms after envelope onset (t = 2.37, p = 0.037, d = 0.48), with no significant cluster for unattended speech. These results further suggest that hearing-impaired listeners may have difficulty suppressing unattended streams. We have added the new TRF results for envelope to Figure S3 and the “mTRF results for attended and unattended speech” on p.7 and the “mTRF analysis” in Material and Methods of the revised manuscript.

Despite my doubts on the appropriateness of condensed speech representations and singlepoint regression for acoustic features in particular, the current methodology allows the authors to explore their research questions, and the results support their conclusions. This work presents an interesting finding on the limits of attentional bias in a cocktail-party scenario, suggesting that fundamentally different neural attentional filters are employed by listeners with highfrequency hearing loss, even in terms of the tracking of speech acoustics. Moreover, the rich dataset collected by the authors is a great contribution to open science and will offer opportunities for re-analysis.

We sincerely thank you again for your encouraging comments regarding the impact of our study.

Reviewer #3 (Public review):

Summary:

The authors aimed to investigate how the brain processes different linguistic units (from phonemes to sentences) in challenging listening conditions, such as multi-talker environments, and how this processing differs between individuals with normal hearing and those with hearing impairments. Using a hierarchical language model and EEG data, they sought to understand the neural underpinnings of speech comprehension at various temporal scales and identify specific challenges that hearing-impaired listeners face in noisy settings.

Strengths:

Overall, the combination of computational modeling, detailed EEG analysis, and comprehensive experimental design thoroughly investigates the neural mechanisms underlying speech comprehension in complex auditory environments. The use of a hierarchical language model (HM-LSTM) offers a data-driven approach to dissect and analyze linguistic information at multiple temporal scales (phoneme, syllable, word, phrase, and sentence). This model allows for a comprehensive neural encoding examination of how different levels of linguistic processing are represented in the brain. The study includes both single-talker and multi-talker conditions, as well as participants with normal hearing and those with hearing impairments. This design provides a robust framework for comparing neural processing across different listening scenarios and groups.

Weaknesses:

The analyses heavily rely on one specific computational model, which limits the robustness of the findings. The use of a single DNN-based hierarchical model to represent linguistic information, while innovative, may not capture the full range of neural coding present in different populations. A low-accuracy regression model-fit does not necessarily indicate the absence of neural coding for a specific type of information. The DNN model represents information in a manner constrained by its architecture and training objectives, which might fit one population better than another without proving the non-existence of such information in the other group. It is also not entirely clear if the DNN model used in this study effectively serves the authors' goal of capturing different linguistic information at various layers. More quantitative metrics on acoustic/linguistic-related downstream tasks, such as speaker identification and phoneme/syllable/word recognition based on these intermediate layers, can better characterize the capacity of the DNN model.

We agree that, before aligning model representations with neural data, it is essential to confirm that the model encodes linguistic information at multiple hierarchical levels. This is the purpose of our validation analysis: We evaluated the model’s representations across five layers using a test set of 20 four-syllable sentences in which every syllable shares the same vowel—e.g., “mā ma mà mǎ” (mother scolds horse), “shū shu shǔ shù” (uncle counts numbers; see Table S1). We hypothesized that the activity in the phoneme and syllable layer would be more similar than other layers for same-vowel sentences. The results confirmed our hypothesis: Hidden-layer activity for same-vowel sentences exhibited much more similar distributions at the phoneme and syllable levels compared to those at the word, phrase and sentence levels Figure 3C displays the scatter plot of the model activity at the five linguistic levels for each of the 20 4-syllable sentences, post dimension reduction using multidimensional scaling (MDS). We used color-coding to represent the activity of five hidden layers after dimensionality reduction. Each dot on the plot corresponds to one test sentence. Only phonemes are labeled because each syllable in our test sentences contains the same vowels (see Table S1).The plot reveals that model representations at the phoneme and syllable levels are more dispersed for each sentence, while representations at the higher linguistic levels—word, phrase, and sentence—are more centralized. Additionally, similar phonemes tend to cluster together across the phoneme and syllable layers, indicating that the model captures a greater amount of information at these levels when the phonemes within the sentences are similar.

Apart from the DNN model, we also included the rate models which simply mark 1 at each unit boundaries across the 5 levels. We performed mTRF analyses with these rate models and found similar patterns to our ridge‐regression results with the DNN: (see Figure S2). This provides further evidence that the model reliably captures information across all five hierarchical levels.

Since EEG measures underlying neural activity in near real-time, it is expected that lower-level acoustic information, which is relatively transient, such as phonemes and syllables, would be distributed throughout the time course of the entire sentence. It is not evident if this limited time window effectively captures the neural responses to the entire sentence, especially for lower-level linguistic features. A more comprehensive analysis covering the entire time course of the sentence, or at least a longer temporal window, would provide a clearer understanding of how different linguistic units are processed over time.

We agree that lower-level linguistic features may be distributed throughout the whole sentence, however, using the entire sentence duration was not feasible, as the sentences in the stimuli vary in length, making statistical analysis challenging. Additionally, since the stimuli consist of continuous speech, extending the time window would risk including linguistic units from subsequent sentences. This would introduce ambiguity as to whether the EEG responses correspond to the current or the following sentence. Additionally, our model activity represents a “condensed final representation” at the five linguistic levels for the whole sentence, rather than incrementally during the sentence. We think the -100 to 300 ms time window relative to each sentence offset targets the exact moment when full-sentence representations are comprehended and a “condensed final representation” for the whole sentence across five linguistic level have been formed in the brain. We have added this clarification on p.13 of the revised manuscript.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Here are some specifics and clarifications of my public review:

Initially I was interpreting the R squared as a continuous measure of predicted EEG relative to actual EEG, based on an encoding model, but this does not appear to be correct. Thank you for pointing out that the y axis is z-scored R squared in your main ridge regression plots. However, I am not sure why/how you chose to represent this that way. It seems to me that a simple Pearson r would be most informative here (and in line with similar work, including Goldstein et al. 2022 that you mentioned). That way you preserve the sign of the relationships between the regressors and the EEG. With R squared, we have a different interpretation, which is maybe also ok, but I also don't see the point of z-scoring R squared. Another possibility is that when you say "z-transformed" you are referring to the Fisher transformation; is that the case? In the plots you say "normalized", so that sounds like a z-score, but this needs to be clarified; as I say, a simple Pearson r would probably be best.

We did not use Pearson’s r, as in Goldstein et al. (2022), because our analysis did not involve a train-test split, which was central to their approach. In their study, the data were divided into training and testing sets, and a ridge regression model was trained on the training set. They then used the trained model to predict neural responses on the held-out test set, and calculated Pearson’s r to assess the correlation between the predicted and observed neural responses. As a result, their final metric of model performance was the correlation coefficient (r). In contrast, our analysis is more aligned with standard temporal response function (TRF) approaches. We did not perform a train-test split; instead, we computed the model fitting performance (R²) of the ridge regression model at each sensor and time point for each subject. At the group level, we conducted one-sample t-tests with spatiotemporal cluster-based correction on the R² values to determine which sensors and time windows showed significantly greater R² values than baseline. To establish a baseline, we z-scored the R² values across sensors and time points, effectively centering the distribution around zero. This normalization allowed us to interpret deviations from the mean R² as meaningful increases in model performance and provided a suitable baseline for the statistical tests. We have added this clarification on p.13 of the revised manuscript.

Thank you for doing the TRF analysis, but where are the acoustic TRFs, analogous to the acoustic results for your HM-LSTM ridge analyses? And what tools did you use to do the TRF analysis? If it is something like the mTRF MATLAB toolbox, then it is also using ridge regression, as you have already done in your original analysis, correct? If so, then it is pretty much the same as your original analysis, just with more dense timepoints, correct? This is what I meant by referring to TRFs originally, because what you have basically done originally was to make a 9-point TRF (and then the plots and analyses are contrasts of pairs of those), with lags between -100 and 300 ms relative to the temporal alignment between the regressors and the EEG, I think (more on this below).

Also with the new TRF analysis, you say that the regressors/predictors had "a value of 1 at each unit boundary offset". So this means you re-made these predictors to be discrete as I and reviewer 3 were mentioning before (rather than using the HM-LSTM model layer(s)), and also, that you put each phoneme/word/etc. marker at its offset, rather than its onset? I'm also confused as to why you would do this rather than the onset, but I suppose it doesn't change the interpretation very much, just that the TRFs are slid over by a small amount.

We used the Python package Eelbrain (https://eelbrain.readthedocs.io/en/r0.39/auto_examples/temporal-response-functions/trf_intro.html) to conduct the multivariate temporal response function (mTRF) analyses. As we previously explained in our response to Reviewer 3, we did not apply mTRF to the acoustic features due to the high dimensionality of the input. Specifically, our acoustic representation consists of a 130-dimensional vector sampled every 10 ms throughout the speech stimuli (comprising a 129-dimensional spectrogram and a 1-dimensional amplitude envelope). This renders the 130 TRF weights to the acoustic features uninterpretable. However, we have now added TRF results from the 1- dimension envelope to the attended and unattended speech at every 10 ms.

A similar constraint applied to the hidden-layer activations from our HM-LSTM model for the five linguistic features. After dimensionality reduction via PCA, each still resulted in 150-dimensional vectors, further preventing their use in mTRF analyses. To address this, we instead used binary predictors marking the offset of each linguistic unit (phoneme, syllable, word, phrase, sentence). These rate models are represented as five distinct binary time series, each aligned with the timing of the corresponding linguistic unit, making them well-suited for mTRF analysis. It is important to note that these rate predictors differ from the HM-LSTMderived features: They encode only the timing of linguistic unit boundaries, not the content or representational structure of the linguistic input. Therefore, we do not consider the mTRF analyses to be equivalent to the ridge regression analyses based on HM-LSTM features

For onset vs. offset, as explained RE4, we labelled them “offsets” because our ridge‐regression with HM-LSTM features was aligned to sentence offsets rather than onsets (see RE4 and RE15 below for the rationale of using sentence offset). However, since each unit offset coincides with the next unit’s onset—and the rate model simply mark these transition points as 1—the “offset” and “onset” models yield identical mTRFs. To avoid confusion, we have relabeled “offset” as “boundary” in Figure S2.

I'm still confused about offsets generally. Does this maybe mean that the EEG, and each predictor, are all aligned by aligning their endpoints, which are usually/always the ends of sentences? So e.g. all the phoneme activity in the phoneme regressor actually corresponds to those phonemes of the stimuli in the EEG time, but those regressors and EEG do not have a common starting time (one trial to the next maybe?), so they have to be aligned with their ends instead?

We chose to use sentence offsets rather than onsets based on the structure of our input to the HM-LSTM model, where each input consists of a pair of sentences encoded in phonemes, such as “t a_1 n əŋ_2 f ei_1 <sep> zh ə_4 sh iii_4 f ei_1 j ii_1” (“It can fly <sep> This is an airplane”). The two sentences are separated by a special <sep> token, and the model’s objective is to determine whether the second sentence follows the first, similar to a next-sentence prediction task. Since the model processes both sentences in full before making a prediction, the neural activations of interest should correspond to the point at which the entire sentence has been processed. To enable a fair comparison between the model’s internal representations and brain responses, we aligned our neural analyses with the sentence offsets, capturing the time window after the sentence has been fully perceived by the participant. Thus, we extracted epochs from -100 to +300 ms relative to each sentence offset, consistent with our modelinformed design. If we align model activity with EEG data aligned to sentence onsets, we would be examining linguistic representations at all levels (from phoneme to sentence) of the whole sentence at the time when participants have not heard the sentence yet. By contrast, aligning to sentence offsets ensures that participants have constructed a full-sentence representation.

We understand that it is a bit confusing why the regressor of each level is not aligned to their own offsets in the data. The hidden-layer activations of the HM-LSTM model corresponding to the five linguistic levels (phoneme, syllable, word, phrase, sentence) are consistently 150-dimensional vectors after PCA reduction. As a result, for each input sentence pair, the model produces five distinct hidden-layer activations, each capturing the representational content associated with one linguistic level for the whole sentence. We believe our -100 to 300 ms time window relative to sentence offset reflects a meaningful period during which the brain integrates and comprehends information across multiple linguistic levels.

Being "time-locked to the offset of each sentence at nine latencies" is not something I can really find in any of the references that you mentioned, regarding the offset aspect of this method. Can you point me more specifically to what you are trying to reference with that, or further explain? You said that "predicting EEG signals around the offset of each sentence" is "a method commonly employed in the literature", but the example you gave of Goldstein 2022 is using onsets of words, which is indeed much more in line with what I would expect (not offsets of sentences).

You are correct that Goldstein (2022) aligned model predictions to onsets rather than offsets; however, many studies in the literature also align model predictions with unit offsets. typically because they mark the point at which participants has already processed the relevant information (Brennan, 2016; Brennan et al., 2016; Gwilliams et al., 2024, 2025). Similarly, in our study, we aim to identify neural correlates for each model-derived feature. If we correlate model activity with EEG data aligned to sentence onsets, we would be examining linguistic representations at all levels (from phoneme to sentence) of the whole sentence at the time when participants have not heard the sentence yet. By contrast, aligning to sentence offsets ensures that participants have constructed a full-sentence representation. Although this limits our analysis to a subset of the data (143 sentences × 400 ms windows × 4 conditions), it targets the exact moment when full-sentence representations emerge against background speech, allowing us to examine each model-derived feature onto its neural signature. We have added this clarification on p.12 of the revised manuscript.

This new sentence does not make sense to me: "The regressors are aligned to sentence offsets because all our regressors are taken from the hidden layer of our HM-LSTM model, which generates vector representations corresponding to the five linguistic levels of the entire sentence".

Thank you for the suggestion. We hope our responses in RE4, 15 and 16, along with our supplementary video have now clarified the issue. We have deleted the sentence and provided a more detailed explanation on p.12 of the revised manuscript: The regressors are aligned to sentence offsets because our goal is to identify neural correlates for each model-derived feature of a whole sentence. If we align model activity with EEG data time-locked to sentence onsets, we would be finding neural responses to linguistic levels (from phoneme to sentence) of the whole sentence at the time when participants have not processed the sentence yet. By contrast, aligning to sentence offsets ensures that participants have constructed a full-sentence representation. Although this limits our analysis to a subset of the data (143 sentences × 2 sections × 400 ms windows), it targets the exact moment when full-sentence representations emerge against background speech, allowing us to examine each model-derived feature onto its neural signature. We understand that phonemes, syllables, words, phrases, and sentences differ in their durations. However, the five hidden activity vectors extracted from the model are designed to capture the representations of these five linguistic levels across the entire sentence Specifically, for a sentence pair such as “It can fly <sep> This is an airplane,” the first 2048dimensional vector represents all the phonemes in the two sentences (“t a_1 n əŋ_2 f ei_1 <sep> zh ə_4 sh iii_4 f ei_1 j ii_1”), the second vector captures all the syllables (“ta_1 nəŋ_2 fei_1 <sep> zhə_4 shiii_4 fei_1jii_1”), the third vector represents all the words, the fourth vector captures the phrases, and the fifth vector represents the sentence-level meaning. In our dataset, input pairs consist of adjacent sentences from the stimuli (e.g., Sentence 1 and Sentence 2, Sentence 2 and Sentence 3, and so on), and for each pair, the model generates five 2048dimensional vectors, each corresponding to a specific linguistic level. To identify the neural correlates of these model-derived features—each intended to represent the full linguistic level across a complete sentence—we focused on the EEG signal surrounding the completion of the second sentence rather than on incremental processing. Accordingly, we extracted epochs from -100 ms to +300 ms relative to the offset of the second sentence and performed ridge regression analyses using the five model features (reduced to 150 dimensions via PCA) at every 50 ms across the epoch.

More on the issue of sentence offsets: In response to reviewer 3's question about -100 - 300 ms around sentence offset, you said "Using the entire sentence duration was not feasible, as the sentences in the stimuli vary in length, making statistical analysis challenging. Additionally, since the stimuli consist of continuous speech, extending the time window would risk including linguistic units from subsequent sentence." This does not make sense to me, so can you elaborate? It sounds like you are actually saying that you only analyzed 400 ms of each trial, but that cannot be what you mean.

Yes, we analyzed only the 400 ms window surrounding each sentence offset. Although this represents just a subset of our data (143 sentences × 400 ms × 4 conditions), it precisely captures when full-sentence representations emerge against background speech. Because our model produces a single, condensed representation for each linguistic level over the entire sentence—rather than incrementally—we think it is more appropriate to align to the period surrounding sentence offsets. Additionally, extending the window (e.g. to 2 seconds) would risk overlapping adjacent sentences, since sentence lengths vary. Our focus is on the exact period when integrated, level-specific information for each sentence has formed in the brain, and our results already demonstrate different response patterns to different linguistic levels for the two listener groups within this interval. We have added this clarification on p.13 of the revised manuscript.

In your mTRF analysis, you are now saying that the discrete predictors have "a value of 1" at each of the "boundary offsets", and those TRFs look very similar to your original plots. It sounds to me like you should not be referring to time zero in your original ridge analysis as "sentence offset". If what you mean is that sentence offset time is merely how you aligned the regressors and EEG in time, then your time zero still has a standard, typical TRF interpretation. It is just the point in time, or lag, at which the regressor(s) and EEG are aligned. So activity before zero is "predictive" and activity after zero is "reactive", to think of it crudely. So also in the text, when you say things like "50-150 ms after the sentence offsets", I think this is not really what you mean. I think you are referring to the lags of 50 - 150 ms, relative to the alignment of the regressor and the EEG.

Thank you very much for the explanation. We agree that, in our ridge‐regression time course, pre zero lags index “predictive” processing and post-zero lags index “reactive” processing. Unlike TRF analysis, we applied ridge regression to our high-dimensional model features at nine discrete lags around the sentence offset. At each lag, we tested whether the regression score exceeded a baseline defined as the mean regression score across all lags. For example, finding a significantly higher regression score between 50 and 150 ms suggests that our regressor reliably predicted EEG activity in that time window. So here time zero refers to the precise moment of the sentence offset—not the the alignment of the regressor and the EEG.

I look forward to discussing how much of my interpretation here makes sense or doesn't, both with the authors and reviewers.

Thank you very much for these very constructive feedback and we hope that we have addressed all your questions.

DOI: 10.1038/s41589-025-01951-y

Resource: (RRID:CVCL_0062)

Curator: @evieth

SciCrunch record: RRID:CVCL_0062

What is this?

DOI: 10.1038/s41589-025-01951-y

Resource: (RRID:CVCL_0006)

Curator: @scibot

SciCrunch record: RRID:CVCL_0006

What is this?

DOI: 10.1038/s41589-025-01951-y

Resource: (BCRC Cat# 60005, RRID:CVCL_0030)

Curator: @scibot

SciCrunch record: RRID:CVCL_0030

What is this?

DOI: 10.1038/s41589-025-01951-y

Resource: (Cell Signaling Technology Cat# 38066, RRID:AB_2827657)

Curator: @scibot

SciCrunch record: RRID:AB_2827657

What is this?

DOI: 10.1038/s41589-025-01951-y

Resource: (UC Davis/NIH NeuroMab Facility Cat# 75-455, RRID:AB_2651167)

Curator: @scibot

SciCrunch record: RRID:AB_2651167

What is this?

DOI: 10.1038/s41589-025-01951-y

Resource: (Cell Signaling Technology Cat# 5483, RRID:AB_10693472)

Curator: @scibot

SciCrunch record: RRID:AB_10693472

What is this?

DOI: 10.1038/s41589-025-01951-y

Resource: (LI-COR Biosciences Cat# 926-68070, RRID:AB_10956588)

Curator: @scibot

SciCrunch record: RRID:AB_10956588

What is this?

DOI: 10.1038/s41589-025-01951-y

Resource: (Cell Signaling Technology Cat# 4302, RRID:AB_1904036)

Curator: @scibot

SciCrunch record: RRID:AB_1904036

What is this?

DOI: 10.1038/s41589-025-01951-y

Resource: (Cell Signaling Technology Cat# 12741, RRID:AB_2617131)

Curator: @scibot

SciCrunch record: RRID:AB_2617131

What is this?

DOI: 10.1038/s41589-025-01951-y

Resource: (JCRB Cat# IFO50038, RRID:CVCL_0007)

Curator: @scibot

SciCrunch record: RRID:CVCL_0007

What is this?

DOI: 10.1038/s41589-025-01951-y

Resource: (RRID:CVCL_0063)

Curator: @scibot

SciCrunch record: RRID:CVCL_0063

What is this?

DOI: 10.1038/s41589-025-01951-y

Resource: (Cell Signaling Technology Cat# 14793, RRID:AB_2572291)

Curator: @scibot

SciCrunch record: RRID:AB_2572291

What is this?

DOI: 10.1038/s41589-025-01951-y

Resource: (Sigma-Aldrich Cat# T6199, RRID:AB_477583)

Curator: @scibot

SciCrunch record: RRID:AB_477583

What is this?

DOI: 10.1038/s41589-025-01951-y

Resource: (Cell Signaling Technology Cat# 3724, RRID:AB_1549585)

Curator: @scibot

SciCrunch record: RRID:AB_1549585

What is this?

DOI: 10.1038/s41589-025-01951-y

Resource: (Cell Signaling Technology Cat# 13647, RRID:AB_2732796)

Curator: @scibot

SciCrunch record: RRID:AB_2732796

What is this?

DOI: 10.1038/s41589-025-01951-y

Resource: (Cell Signaling Technology Cat# 29047, RRID:AB_2773013)

Curator: @scibot

SciCrunch record: RRID:AB_2773013

What is this?

DOI: 10.1016/j.xcrm.2025.102546

Resource: (Cell Signaling Technology Cat# 8946, RRID:AB_10891805)

Curator: @scibot

SciCrunch record: RRID:AB_10891805

What is this?

DOI: 10.1007/s00018-025-06014-y

Resource: (Ximbio Cat# 152135, RRID:CVCL_W186)

Curator: @scibot

SciCrunch record: RRID:CVCL_W186

What is this?

Me gusta un montón la noción de mutualidad. Expresa mejor ese "ser con otros" que la palabra colaboración donde está intrínseca la cuestión de la tarea y el fin.

Esta parte, junto con la descripción del compost como lugar de encuentro multiespecie, me interpela. Creo que los jardines nos sirven para conectar desde otras formas de relacionamiento, desde los intereses y las ideas de la/s otra/s persona/s. Este diálogo desde lo divergente permite escapar a la automatización de la conversación, de las frases hechas y la fórmula de construcción de la IA.

Destaco solamente esto para señalar las posibilidades de uno de esos juegos de palabras que nos gustan tanto, y pensar los jardines digitales como un proceso que va desde la computadora a la compostadora. Entendiendo que la computación es algo más estrictamente cuantitativo y la compostadora trataría de aportar matices cualitativos al ecosistema digital

El cuidado de lo digital desde el descuido, lo que nace fuera de la observación meditada y funcionalista.

Será bonito ver cuándo se escriba el post sobre la importancia del compost en Asturias y esa mención sea un enlace a esa nueva entrada. Ganas de leerlo.

Que poderoso. Que por un momento las normas de la vida no existen solo este amor.

No entiendo perfectamente qué está pasando, pero se ve como hay alguien que se está colando para encontrar Sor Juana. ¿A mejor un amor secreto? Está hablando de amor y a mejor tenía de ser secreto por ser religiosa.

Hipótesis: las cosas de la noche están fuera del normal. Me recuerdo a unas de las obras de Lorca en que los pensamientos y deseos están iluminados en la noche. Noche oscura del alma se ve como un poema que abre los ojos de los problemas de día con las acciones de la noche.

No quiero ser muy rígido, pero digo que hay una gran diferencia en lo que está escrito durante su tiempo en prisión y después porque la emoción está más cruda en el momento. Sin embargo, con todo de esta época, información va lento y casi nunca van a tener los datos específicos. Con esto, es interesante ver si realimente hay una diferencia en su literatura durante y después de prisión.

Author response:

The following is the authors’ response to the original reviews

eLife Assessment

This is a valuable polymer model that provides insight into the origin of macromolecular mixed and demixed states within transcription clusters. The well-performed and clearly presented simulations will be of interest to those studying gene expression in the context of chromatin. While the study is generally solid, it could benefit from a more direct comparison with existing experimental data sets as well as further discussion of the limits of the underlying model assumptions.

We thank the editors for their overall positive assessment. In response to the Referees’ comments, we have addressed all technical points, including a more detailed explanation of the methodology used to extract gene transcription from our simulations and its analogy with real gene transcription. Regarding the potential comparison with experimental data and our mixing–demixing transition, we have added new sections discussing the current state of the art in relevant experiments. We also clarify the present limitations that prevent direct comparisons, which we hope can be overcome with future experiments using the emerging techniques.

Reviewer #1 (Public Review):

This manuscript discusses from a theory point of view the mechanisms underlying the formation of specialized or mixed factories. To investigate this, a chromatin polymer model was developed to mimic the chromatin binding-unbinding dynamics of various complexes of transcription factors (TFs).

The model revealed that both specialized (i.e., demixed) and mixed clusters can emerge spontaneously, with the type of cluster formed primarily determined by cluster size. Non-specific interactions between chromatin and proteins were identified as the main factor promoting mixing, with these interactions becoming increasingly significant as clusters grow larger.

These findings, observed in both simple polymer models and more realistic representations of human chromosomes, reconcile previously conflicting experimental results. Additionally, the introduction of different types of TFs was shown to strongly influence the emergence of transcriptional networks, offering a framework to study transcriptional changes resulting from gene editing or naturally occurring mutations.

Overall I think this is an interesting paper discussing a valuable model of how chromosome 3D organisation is linked to transcription. I would only advise the authors to polish and shorten their text to better highlight their key findings and make it more accessible to the reader.

We thank the Referee for carefully reading our manuscript and recognizing its scientific value. As suggested, we tried to better highlight our key findings and make the text more accessible while addressing also the comments from the other Referees.

Reviewer #2 (Public Review):

Summary:

With this report, I suggest what are in my opinion crucial additions to the otherwise very interesting and credible research manuscript ”Cluster size determines morphology of transcription factories in human cells”.

Strengths:

The manuscript in itself is technically sound, the chosen simulation methods are completely appropriate the figures are well-prepared, the text is mostly well-written spare a few typos. The conclusions are valid and would represent a valuable conceptual contribution to the field of clustering, 3D genome organization and gene regulation related to transcription factories, which continues to be an area of most active investigation.

Weaknesses:

However, I find that the connection to concrete biological data is weak. This holds especially given that the data that are needed to critically assess the applicability of the derived cross-over with factory size is, in fact, available for analysis, and the suggested experiments in the Discussion section are actually done and their results can be exploited. In my judgement, unless these additional analysis are added to a level that crucial predictions on TF demixing and transcriptional bursting upon TU clustering can be tested, the paper is more fitted for a theoretical biophysics venue than for a biology journal such as eLife.

We thank the Reviewer for their positive assessment of the soundness of our work and its contribution to the field. We have added a paragraph to the Conclusions highlighting the current state of experimental techniques and outlining near-term experiments that could be extended to test our predictions. We also emphasise that our analysis builds on state-of-the-art polymer models of chromatin and on quantitative experimental datasets, which we used both to build the model construction and to validate its outcomes (gene activity). We hope this strengthened link to experiment will catalyse further studies in the field.

Major points:

(1) My first point concerns terminology.The Merriam-Webster dictionary describes morphology as the study of structure and form. In my understanding, none of the analyses carried out in this study actually address the form or spatial structuring of transcription factories. I see no aspects of shape, only size. Unless the authors want to assess actual shapes of clusters, I would recommend to instead talk about only their size/extent. The title is, by the same argument, in my opinion misleading as to the content of this study.

We agree with the Referee that the title could be misleading. In our study we characterized clusters size, that is a morphological descriptor, and cluster composition that isn’t morphology per se but used in the community in a broader sense. Nevertheless to strength the message we have changed the title in: “Cluster size determines internal structure of transcription factories in human cells”

(2) Another major conceptual point is the choice of how a single TF:pol particle in the model relates to actual macromolecules that undergo clustering in the cell. What about the fact that even single TF factories still contain numerous canonical transcription factors, many of which are also known to undergo phase separation? Mediator, CDK9, Pol II just to name a few. This alone already represents phase separation under the involvement of different species, which must undergo mixing. This is conceptually blurred with the concept of gene-specific transcription factors that are recruited into clusters/condensates due to sequencespecific or chromatin-epigenetic-specific affinities. Also, the fact that even in a canonical gene with a ”small” transcription factory there are numerous clustering factors takes even the smallest factories into a regime of several tens of clustering macromolecules. It is unclear to me how this reality of clustering and factory formation in the biological cell relates to the cross-over that occurs at approximately n=10 particles in the simulations presented in this paper.

This is a good point. However in our case we can either look at clustering transcription factors or transcription units. In an experimental situation, transcription units could be “coloured”, or assigned different types, by looking at different cell types, so that they can be classified as housekeeping, or cell-type independent, or cell-type specific. This is similar to how DHS can be clustered. In this way the mixing or demixing state can be identified by looking at the type of transcription unit, removing any ambiguity due to the fact that the same protein may participate in different TF complexes..

(3) The paper falls critically short in referencing and exploiting for analysis existing literature and published data both on 3D genome organization as well as the process of cluster formation in relation to genomic elements. In terms of relevant literature, most of the relevant body of work from the following areas has not been included:

(i) mechanisms of how the clustering of Pol II, canonical TFs, and specific TFs is aided by sequence elements and specific chromatin states

(ii) mechanisms of TF selectivity for specific condensates and target genomic elements

(iii) most crucially, existing highly relevant datasets that connect 3D multi-point contacts with transcription factor identity and transcriptional activity, which would allow the authors to directly test their hypotheses by analysis of existing data

Here, especially the data under point (iii) are essential. The SPRITE method (cited but not further exploited by the authors), even in its initial form of publication, would have offered a data set to critically test the mixing vs. demixing hypothesis put forward by the authors. Specifically, the SPRITE method offers ordered data on k-mers of associated genomic elements. These can be mapped against the main TFs that associate with these genomic elements, thereby giving an account of the mixed / demixed state of these k-mer associations. Even a simple analysis sorting these associations by the number of associated genomic elements might reveal a demixing transition with increasing association size k. However, a newer version of the SPRITE method already exists, which combines the k-mer association of genomic elements with the whole transcriptome assessment of RNAs associated with a particular DNA k-mer association. This can even directly test the hypotheses the authors put forward regarding cluster size, transcriptional activation, correlation between different transcription units’ activation etc.

To continue, the Genome Architecture Mapping (GAM) method from Ana Pombo’s group has also yielded data sets that connect the long-range contacts between gene-regulatory elements to the TF motifs involved in these motifs, and even provides ready-made analyses that assess how mixed or demixed the TF composition at different interaction hubs is. I do not see why this work and data set is not even acknowledged? I also strongly suggest to analyze, or if they are already sufficiently analyzed, discuss these data in the light of 3D interaction hub size (number of interacting elements) and TF motif composition of the involved genomic elements.

Further, a preprint from the Alistair Boettiger and Kevin Wang labs from May 2024 also provides direct, single-cell imaging data of all super-enhancers, combined with transcription detection, assessing even directly the role of number of super-enhancers in spatial proximity as a determinant of transcriptional state. This data set and findings should be discussed, not in vague terms but in detailed terms of what parts of the authors’ predictions match or do not match these data.

For these data sets, an analysis in terms of the authors’ key predictions must be carried out (unless the underlying papers already provide such final analysis results). In answering this comment, what matters to me is not that the authors follow my suggestions to the letter. Rather, I would want to see that the wealth of available biological data and knowledge that connects to their predictions is used to their full potential in terms of rejecting, confirming, refining, or putting into real biological context the model predictions made in this study.

References for point (iii):

- RNA promotes the formation of spatial compartments in the nucleus https://www.cell.com/cell/fulltext/S0092-8674(21)01230-7?dgcid=raven_jbs_etoc_email

- Complex multi-enhancer contacts captured by genome architecture mapping https://www.nature.com/articles/nature21411

- Cell-type specialization is encoded by specific chromatin topologies https://www.nature.com/articles/s41586-021-04081-2

- Super-enhancer interactomes from single cells link clustering and transcription https://www.biorxiv.org/content/10.1101/2024.05.08.593251v1.full

For point (i) and point (ii), the authors should go through the relevant literature on Pol II and TF clustering, how this connects to genomic features that support the cluster formation, and also the recent literature on TF specificity. On the last point, TF specificity, especially the groups of Ben Sabari and Mustafa Mirx have presented astonishing results, that seem highly relevant to the Discussion of this manuscript.

We appreciate the Reviewer’s insightful suggestion that a comparison between our simulation results and experimental data would strengthen the robustness of our model. In response, we have thoroughly revised the literature on multi-way chromatin contacts, with particular attention to SPRITE and GAM techniques. However, we found that the currently available experimental datasets lack sufficient statistical power to provide a definitive test of our simulation predictions, as detailed below.

As noted by the Reviewer, SPRITE experiments offer valuable information on the composition of highorder chromatin clusters (k-mers) that involve multiple genomic loci. A closer examination of the SPRITE data (e.g., Supplementary Material from Ref. [1]) reveals that the majority of reported statistics correspond to 3-mers (three-way contacts), while data on larger clusters (e.g., 8-mers, 9-mers, or greater) are sparse. This limitation hinders our ability to test the demixing-mixing transition predicted in our simulations, which occurs for cluster sizes exceeding 10.

Moreover, the composition of the k-mers identified by SPRITE predominantly involves genomic regions encoding functional RNAs—such as ITS1 and ITS2 (involved in rRNA synthesis) and U3 (encoding small nucleolar RNA)—which largely correspond to housekeeping genes. Conversely, there is little to no data available for protein-coding genes. This restricts direct comparison to our simulations, where the demixing-mixing transition depends critically on the interplay between housekeeping and tissue-specific genes.

Similarly, while GAM experiments are capable of detecting multi-way chromatin contacts, the currently available datasets primarily report three-way interactions [2,3].

In summary, due to the limited statistical data on higher-order chromatin clusters [4], a quantitative comparison between our simulation results and experimental observations is not currently feasible. Nevertheless, we have now briefly discussed the experimental techniques for detecting multi-way interactions in the revised manuscript to reflect the current state of the field, mentioning most of the references that the Reviewer suggested.

(4) Another conceptual point that is a critical omission is the clarification that there are, in fact, known large vs. small transcription factories, or transcriptional clusters, which are specific to stem cells and ”stressed cells”. This distinction was initially established by Ibrahim Cisse’s lab (Science 2018) in mouse Embryonic Stem Cells, and also is seen in two other cases in differentiated cells in response to serum stimulus and in early embryonic development:

- Mediator and RNA polymerase II clusters associate in transcription-dependent condensates https://www.science.org/doi/10.1126/science.aar4199

- Nuclear actin regulates inducible transcription by enhancing RNA polymerase II clustering https://www.science.org/doi/10.1126/sciadv.aay6515

- RNA polymerase II clusters form in line with surface condensation on regulatory chromatin https://www.embopress.org/doi/full/10.15252/msb.202110272

- If ”morphology” should indeed be discussed, the last paper is a good starting point, especially in combination with this additional paper: Chromatin expansion microscopy reveals nanoscale organization of transcription and chromatin https://www.science.org/doi/10.1126/science.ade5308

We thank the Reviewer for pointing out the discussion about small and large clusters observed in stressed cells. Our study aims to provide a broader mechanistic explanation on the formation of TF mixed and demixed clusters depending on their size. However, to avoid to generate confusion between our terminology and the classification that is already used for transcription factories in stem and stressed cells, we have now added some comments and references in the revised text.

(5) The statement scripts are available upon request is insufficient by current FAIR standards and seems to be non-compliant with eLife requirements. At a minimum, all, and I mean all, scripts that are needed to produce the simulation outcomes and figures in the paper, must be deposited as a publicly accessible Supplement with the article. Better would be if they would be structured and sufficiently documented and then deposited in external repositories that are appropriate for the sharing of such program code and models.

We fully agree with the Reviewer. We have now included in the main text a link to an external repository containing all the codes required to reproduce and analyze the simulations.

Recommendations for the authors:

Minor and technical points

(6) Red, green, and yellow (mix of green and red) is a particularly bad choice of color code, seeing that red-green blindness is the most common color blindness. I recommend to change the color code.

We appreciate the Reviewer’s thoughtful comment regarding color accessibility. We fully agree that red–green combinations can pose challenges for color-blind readers. In our figures, however, we chose the red–green–yellow color scheme deliberately because it provides strong contrast and intuitive representation for different TF/TU types. To ensure accessibility, we optimized brightness and saturation within red-green schemes and we carefully verified that the chosen hues are distinguishable under the most common forms of color vision deficiency, i.e. trichromatic color blindness, using color-blindness simulation tools (e.g., Coblis).

How is the dispersing effect of transcriptional activation and ongoing transcription accounted for or expected to affect the model outcome? This affects both transcriptional clusters (they tend to disintegrate upon transcriptional activation) as well as the large scale organization, where dispersal by transcription is also known.

We thank the Reviewer for this very insightful question. The current versions of both our toy model and the more complex HiP-HoP model do not incorporate the effects of RNA Polymerase elongation. Our primary goal was to develop a minimalisitc framework that focuses on investigating TF clusters formation and their composition. Nevertheless, we find that this straightforward approach provides a good agreement between simulations and Hi-C and GRO-seq experiments, lending confidence to the reliability of our results concerning TF cluster composition.

We fully agree, however, that the effects of transcription elongation are an interesting topic for further exploration. For example, modeling RNA Polymerases as active motors that continually drive the system out of equilibrium could influence the chromatin polymer conformation and the structure of TF clusters. Additionally, investigating how interactions between RNA molecules and nuclear proteins, such as SAF-A, might lead to significant changes in 3D chromatin organization and, consequently, transcription [5], is also in intriguing prospect. Although we do not believe that the main findings of our study, particularly regarding cluster composition and mixed-demixed transition, would be impacted by transcription elongation effects, we recognize the importance of this aspect. As such, we have now included some comments in the Conclusions section of the revised manuscript.

“and make the reasonable assumption that a TU bead is transcribed if it lies within 2.25 diameters (2.25σ) of a complex of the same colour; then, the transcriptional activity of each TU is given by the fraction of time that the TU and a TF:pol lie close together.” How is that justified? I do not see how this is reasonable or not, if you make that statement you must back it up.

As pointed out by the Referee, we consider a TU to be active if at least one TF is within a distance 2.25σ from that TU. This threshold is a slightly larger than the TU-TF interaction cutoff distance, r_c \= 1.8σ between TFs and TUs. The rationale for this choice is to ensure that, in the presence of a TU cluster surrounded by TFs, TUs that are not directly in contact with a TF are still considered active. Nonetheless, we find that using slightly different thresholds, such as 1.8σ or 1.1σ, leads to comparable results, as shown in Fig. S11, demonstrating the robustness of our analysis.

Clearly, close proximity in 1D genomic space favours formation of similarly-coloured clusters. This is not surprising, it is what you built the model to do. Should not be presented as a new insight, but rather as a check that the model does what is expected.

We believed that this sentence already conveyed that the formation of single-color clusters driven by 1D genomic proximity is not a surprising outcome. However, we have now slightly rephrased it to better emphasize that this is not a novel insight.

That said, we would like to highlight that while 1D genomic proximity facilitates the formation of clusters of the same color, the unmixed-to-mixed transition in cluster composition is not easily predictable solely from the TU color pattern. Furthermore, in simulations of real chromosomes, where TU patterns are dictated by epigenetic marks, the complexity of these patterns makes it challenging—if not impossible—to predict cluster composition based solely on the input data of our model.

“…how closely transcriptional activities of different TUs correlate…” Please briefly state over what variable the correlation is carried out, is it cross correlation of transcription activity time courses over time? Would be nice to state here directly in the main text to make it easier for the reader.

We have now included a brief description in the revised manuscript explaining how the transcriptional correlations were evaluated and how the correlation matrix was constructed.

“The second concerns how expression quantitative trait loci (eQTLs) work. Current models see them doing so post-transcriptionally in highly-convoluted ways [11, 55], but we have argued that any TU can act as an eQTL directly at the transcriptional level [11].” This text does not actually explain what eQTLs do. I think it should, in concise words.

We agree with the Referee’s suggestion. We have revised the sentence accordingly and now provide a clear explanation of eQTLs upon their first mention. The revised paragraph now reads as follows:

“The second concerns how expression quantitative trait loci (eQTLs)—genomic regions that are statistically associated with variation in gene expression levels—function. While current models often attribute their effects to post-transcriptional regulation through complex mechanisms [6,7], we have previously argued that any transcriptional unit (TU) can act as an eQTL by directly influencing gene expression at the transcriptional level [7]. Here, we observe individual TUs up-regulating or down-regulating the activity of others TUs – hallmark behaviors of eQTLs that can give rise to genetic effects such as “transgressive segregation” [8]. This phenomenon refers to cases in which alleles exhibit significantly higher or lower expression of a target gene, and can be, for instance, caused by the creation of a non-parental allele with a specific combination of QTLs with opposing effects on the target gene.”

“In the string with 4 mutations, a yellow cluster is never seen; instead, different red clusters appear and disappear (Fig. 2Eii)…” How should it be seen? You mutated away most of the yellow beads. I think the kymograph is more informative about the general model dynamics, not the effects of mutations. Might be more appropriate to place a kymograph in Figure 1.

We agree with the Referee that the kymograph is the most appropriate graphical representation for capturing the effects of mutations. Panel 2E already refers to the standard case shown in Figure 1. We have now clarified this both in the caption and in the main text. In addition, we have rephrased the sentence—which was indeed misleading—as follows:

“From the activity profiles in Fig. 2C, we can observe that as the number of mutations increases, the yellow cluster is replaced by a red cluster, with the remaining yellow TUs in the region being expelled (Fig. 2B(ii)). This behavior is reflected in the dynamics, as seen by comparing panels E(i) and E(ii): in the string with four mutations, transcription of the yellow TUs is inhibited in the affected region, while prominent red stripes—corresponding to active, transcribing clusters—emerge (Fig. 2E(ii)).” We hope that the comparison is now immediately clear to the reader.

“…but this block fragments in the string with 4 mutations…” I don’t know or cannot see what is meant by ”fragmentation” in the correlation matrix.

With the sentence “this block fragments in the string with 4 mutations” we mean that the majority of the solid red pixels within the black box become light-red or white once the mutations are applied. We have now added a clarification of this point in the revised manuscript.

“Fig. 3D shows the difference in correlation between the case with reduced yellow TFs and the case displayed in Fig. 1E.” Can you just place two halves of the different matrices to be compared into the same panel? Similar to Fig. S5. Will be much easier to compare.

We thank the Referee for this suggestion. We tried to implement this modification, and report the modified figure below (Author response image 1). As we can see, in the new figure it is difficult to spot the details we refer to in the main text, therefore we prefer to keep the original version of the figure.

Author response image 1.

Heatmap comparing activity correlations of TUs in the random string under normal conditions (top half) and with reduced yellow-TF concentration (bottom half).

What is the omnigenic model? It is not introduced.

We thank the Reviewer for highlighting this important point. The omnigenic model, first introduced by Boyle et al in Ref. [6], was proposed to explain how complex traits, including disease risk, are influenced by a vast number of genes. Accordingly to this model, the genetic basis of a trait is not limited to a small set of core genes whose expression is directly related to the trait, but also includes peripheral genes. The latter, although not directly involved in controlling the trait, can influence the expression of core genes through gene regulatory networks, thereby contributing to the overall genetic influence on the trait. We have now added a few lines in the revised manuscript to explain this point.

“Additionally, blue off-diagonal blocks indicate repeating negative correlations that reflect the period of the 6-pattern.” How does that look in a kymograph? Does this mean the 6 clusters of same color steal the TFs from the other clusters when they form?

The intuition of the Referee is indeed correct. The finite number of TFs leads to competition among TUs of the same colour, resulting in anticorrelation:when a group of six nearby TUs of a given colour is active, other, more distant TUs of the same colour are not transcribing due to the lack of available TFs. As the Referee suggested,this phenomenon is visible in the kymograph showing TU activity. In Author response image 2, it can be observed that typically there is a single TU cluster for each of the three colours (yellow, green, and red). These clusters can be long-lived (e.g., the yellow cluster at the center of the kymograph) or may destroy during the simulation (e.g., the red cluster at the top of the kymograph, which dissolves at t ∼ 600 × 10⁵ τ_B). In the latter case, TFs of the corresponding colour are released into the system and can bind to a different location, forming a new cluster (as seen with the red cluster forming at the bottom of the kymograph for t > 600 × 10⁵ τ_B). This point is further discussed at the point 2.30 of this Reply where additional graphical material is provided.

Author response image 2.

Kymograph showing the TU activity during a typical run in the 6-pattern case. Each row reports the transcriptional state of a TU during one simulation. Black pixels correspond to inactive TUs, red (yellow, green) pixels correspond to active red (yellow, green) TUs.

“Conversely, negative correlations connect distant TUs, as found in the single-color model…” But at the most distal range, the negative correlation is lost again! Why leave this out? Your correlation curves show the same , equilibration towards no correlation at very long ranges.

As highlighted in Figure 5Ai, long-range negative correlations (grey segments) predominantly connect distant TUs of the same colour. This is quantified in Figure 5Bi: restricting to same-colour TUs shows that at large genomic separations the correlation is almost entirely negative, with small fluctuations at distances just below 3000 kbp where sampling is sparse; we therefore avoid further interpretation of this regime.

“These results illustrate how the sequence of TUs on a string can strikingly affect formation of mixed clusters; they also provide an explanation of why activities of human TUs within genomic regions of hundreds of kbp are positively correlated [60].” This is a very nice insight.

We thank the Reviewer for the very supportive comment.

“To quantify the extent to which TFs of different colours share clusters, we introduce a demixing coefficient, θ_dem (defined in Fig. 1).” This is not defined in Fig. 1 or anywhere else here in the main text.

We thank the Referee for pointing this out. For a given cluster, the demixing coefficient is defined as

where n is the number of colors, i indexes each color present in the model, and x_i,max the largest fraction of TFs of the same i-th color in a single TF cluster.

The demixing coefficient is defined in the Methods section; therefore, we have replaced defined in Fig. 1 with see Methods for definition.

“Mixing is facilitated by the presence of weakly-binding beads, as replacing them with non-interacting ones increases demixing and reduces long-range negative correlations (Figure S3). Therefore, the sequence of strong and weak binding sites along strings determines the degree of mixing, and the types of small-world network that emerge. If eQTLs also act transcriptionally in the way we suggest [11], we predict that down-regulating eQTLs will lie further away from their targets than up-regulating ones.” Going into these side topics and minke points here is super distracting and waters down the message. Maybe first deal with the main conclusions on mixed vs demixed clusters in dependence on the strong and specific binding site patterns, before dealing with other additional points like the role of weak binding sites.

Thank you for the suggestion. We now changed the paragraph to highlight the main results. The new paragraph is as follows. “These results on activity correlation and TF cluster composition suggest that, if eQTLs act transcriptionally as expected [7], down-regulating eQTLs are likely to be located further from their target genes than up-regulating ones. In addition, it is important to note that mixing is promoted by the presence of weakly binding beads; replacing these with non-interacting ones leads to increased demixing and a reduction in long-range negative correlations (Figure S3). More generally, our findings indicate that the presence of multiple TF colors offers an effective mechanism to enrich and fine-tune transcriptional regulation.”

“…provides a powerful pathway to enrich and modulate transcriptional regulation.” Before going into the possible meaning and implications of the results, please discuss the results themselves first.

See previous point.

Figure 5B. Does activation typically coincide with spatial compaction of the binding sites into a small space or within the confines of a condensate? My guess would be that colocalization of the other color in a small space is what leads to the mixing effect?

As the Reviewer correctly noted, the activity of a given TU is indeed influenced by the presence of nearby TUs of the same color, since their proximity facilitates the recruitment of additional TFs and enhances the overall transcriptional activity. In this context, the mixing effect is certainly affected by the 1D arrangement of TUs along the chromatin fiber. As emphasized in the revised manuscript, when domains of same-color TUs are present (as in the 6-pattern string), the degree of demixing is greater compared to the case where TUs of different colors alternate and large domains are absent (as in the 1-pattern string). This difference in the demixing parameter as a function of the 1D TU arrangement is clearly visible in Fig. S2B.

“…euchromatic regions blue, and heterochromatic ones grey.” Please also explain what these color monomers mean in terms of non specific interactions with the TFs.

Generally, in our simulation approach we assume euchromatin regions to be more open and accessible to transcription factors, whereas heterochromatin corresponds to more compacted chromatin segments [9]. To reflect this, we introduce weak, non-specific interactions between euchromatin and TFs, while heterochromatin interacts with TFs only thorugh steric effects. To clarify this point, we have now slightly revised the caption of Fig.6.

“More quantitatively, Spearman’s rank correlation coefficient is 3.66 10⁻¹, which compares with 3.24 10⁻¹ obtained previously using a single-colour model [11].” This comparison does not tell me whether the improvement in model performance justifies an additional model component. There are other, likelihood based approaches to assess whether a model fits better in a relevant extent by adding a free model parameter. Can these be used for a more conclusive comparison? Besides, a correlation of 0.36 does not seem so good?

We understand the Reviewer’s concern that the observed increase in the activity correlation may not appear to provide strong evidence for the improvement of the newly introduced model. However, within the context of polymer models developed to study realistic gene transcription and chromatin organization, this type of correlation analysis is a widely accepted approach for model validation. Experimental data commonly used for such validation include Hi-C maps, FISH experiments, and GRO-seq data [10,11]. The first two are typically employed to assess how accurately the model reproduces the 3D folding of chromatin; a comparison between experimental and simulated Hi-C maps is provided in the Supplementary Information (Fig. S5), showing a Pearson correlation of 0.7. GRO-seq or RNA-seq data, on the other hand, are used to evaluate the model’s ability to predict gene transcription levels. To date, the highest correlation for transcriptional activity data has been achieved by the HiP-HoP model at a resolution of 1 kbp [10], reporting a Spearman correlation of 0.6. Therefore, the correlation obtained with our 2-color model represents a good level of agreement when compared with the more complex HiP-HoP model. In this context, the observed increase in correlation—from 0.324 to 0.366—can be regarded as a modest yet meaningful improvement.

“…consequently, use of an additional color provides a statisticallysignificant improvement (p-value < 10⁻⁶, 2-sided t-test).” I do not follow this argument. Given enough simulation repeats, any improvement, no matter how small, will lead to statistically significant improvements.

We agree that this sentence could be misleading. We have now rephrased it in a clearer manner specifying that each of the two correlation values is statistically significant alone, while before we were wrongly referring to the significance of the improvement.

“Additionally, simulated contact maps show a fair agreement with Hi-C data (Figure S5), with a Pearson correlation r ∼ 0.7 (p-value < 10⁻⁶, 2-sided t-test).” Nice!

We thank the Reviewer for the positive comment.

“Because we do not include heterochromatin-binding proteins, we should not however expect a very accurate reproduction of Hi-C maps: we stress that here instead we are interested in active chromatin, transcription and structure only as far as it is linked to transcription.” Then why do you not limit your correlation assessment to only these regions to show that these are very well captured by your model?

We thank the Reviewer for this insightful comment. Indeed, we could have restricted our investigation to active chromatin regions, as done in our previous works [11,12]. However, our intention in this section of the manuscript was to clarify that the current model is relatively simple and therefore not expected to achieve a very high level of agreement between experimental and simulated Hi-C maps. Another important limitation of the two color model described in the section is the absence of active loop extrusion mediated by SMC proteins, which is known to play a central role in establishing TADs boundaries. Consequently, even if our analysis were limited to active chromatin regions, the agreement with experimental Hi-C maps would still remain lower than that obtained with more comprehensive models, such as HiP-HoP, that we use later in the last section of the paper. We have now added a comment in the revised manuscript explicitly noting the lack of active loop extrusion in our 2-color model.

“We also measure the average value of the demixing coefficient, θ_dem (Materials and Methods). If θ_dem = 1, this means that a cluster contains only TFs of one colour and so is fully demixed; if θ_dem = 0, the cluster contains a mixture of TFs of all colors in equal number, and so is maximally mixed.” Repetitive.

We have now rephrased the sentence in a more concise way.

“…notably, this is similar to the average number of productivelytranscribing pols seen experimentally in a transcription factory [6].” That seems a bit fast and loose. The number of Polymerases can differ depending on state, type of factory, gene etc. and vary between anything from to a few hundreds of Polymerase complexes depending on definition of factory, and what is counted as active. Also, one would think that polymerases only make up a small part of the overall protein pool that constitutes a condensate, so it is unclear whether this is a pertinent estimate.

Here we refer to the average size of what is normally referred to as a PolII factory, not a generic nuclear condensate. These are the clusters which arise in our simulations. These structures emerge through microphase separation and have been well characterised, for instance see [13] for a recent review. For these structures while there is a distribution the average is well defined and corresponds to a size of about 100 nm, which is very much in line with the size of the clusters we observe, both in terms of 3D diameter and number of participating proteins. Because of the size, the number of active complexes which can contribute cannot be significantly more than ∼ 10. These estimates are, we note, very much in line with super-resolution measurements of SAF-A clusters [14], which are associated with active transcription and hence it is reasonable to assume they colocalise with RNA and polymerase clusters.

“Conversely, activities of similar TUs lying far from each other on the genetic map are often weakly negatively correlated, as the formation of one cluster sequesters some TFs to reduce the number available to bind elsewhere.” This point is interesting, and I strongly suspect that this indeed happening. But I don’t think it was shown in the analysis of the simulation results in sufficient clarity. We need direct assessment of this sequestration, currently it’s only indirectly inferred.

Indeed, this is the mechanism underlying the emergence of negative long-range correlations among TU activity values. As the Reviewer correctly pointed out, the competition for a finite number of TFs was only indirectly inferred in the original manuscript. To address this, we have now included a new figure explicitly illustrating this effect. In Fig. S12, we show the kymograph of active TUs (left panel), as in Fig. 2E(i) of the main text, alongside a new kymograph depicting the number of green TFs within a sphere of radius 10σ centered on each green TU (right panel). For simplicity, we focus here only on green TUs and TFs. It can be observed that, during the initial part of the simulation, green TFs are localized near genomic position ∼ 2000(right panel), where green TUs are transcriptionally active (left panel). Toward the end of the simulation, TUs near genomic position ∼ 500 become active, coinciding with the relocation of TFs to this region and the depletion of the previous one.

In the definition for the demixing coefficient (equation 1), what does the index i stand for?

Here i is an index denoting each of the colors present in the model. We have now specified the meaning of i after Eq. 1.

Reviewer 3 (Public Review):

In this work, the authors present a chromatin polymer model with some specific pattern of transcription units (TUs) and diffusing TFs; they simulate the model and study TFclustering, mixing, gene expression activity, and their correlations. First, the authors designed a toy polymer with colored beads of a random type, placed periodically (every 30 beads, or 90kb). These colored beads are considered a transcription unit (TU). Same-colored TUs attract with each other mediated by similarly colored diffusing beads considered as TFs. This led to clustering (condensation of beads) and correlated (or anti-correlation) ”gene expression” patterns. Beyond the toy model, when authors introduce TUs in a specific pattern, it leads to emergence of specialized and mixed cluster of different TFs. Human chromatin models with realistic distribution of TUs also lead to the mixing of TFs when cluster size is large.

Strengths.

This is a valuable polymer model for chromatin with a specific pattern of TUs and diffusing TF-like beads. Simulation of the model tests many interesting ideas. The simulation study is convincing and the results provide solid evidence showing the emergence of mixed and demixed TF clusters within the assumptions of the model.

Weaknesses.

Weakness of the work: The model has many assumptions. Some of the assumptions are a bit too simplistic. Concerns about the work are detailed below:

We thank the Referee for this overall positive evaluation.

We thank the Referee for this important observation. The way we The authors assume that when the diffusing beads (TFs) are near a TU, the gene expression starts. However, mammalian gene expression requires activation by enhancer-promoter looping and other related events. It is not a simple diffusion-limited event. Since many of the conclusions are derived from expression activity, will the results be affected by the lack of looping details?

We do not need to assume promoter-enhancer contact, this emerges naturally through the bridging-induced phase separation and indeed is a key strength of our model. Even though looping is not assumed as key to transcriptional initiation, in practice the vast majority of events in which a TF is near a TU are associated with the presence of a cluster where regulatory elements are looped. So transcription in our case is associated with the bridging-induced phase separation, and there is no lack of looping, looping is naturally associated with transcription, and this is an emergent property of the model (not an assumption), which is an important feature of our model. Accordingly, both contact maps and transcriptional activity are well predicted by our model, both in the version described here and in the more sophisticated single-colour HiP-HoP model [10] (an important ingredient of which is the bridging-induced phase separation).

Authors neglect protein-protein interactions. Without proteinprotein interactions, condensate formation in natural systems is unlikely to happen.

We thank the Reviewer for pointing out the absence of protein-protein interactions in our simulations. While we acknowledge this limitation, we would like to emphasize that experimental studies have not observed nuclear proteins forming condensates at physiological concentrations in the absence of DNA or chromatin. For example, studies such as Ryu et al. [15] and Shakya et al. [16] show that protein-protein interactions alone are insufficient to drive condensate formation in vivo. Instead, the presence of a substrate, such as DNA or chromatin, is essential to favor and stabilize the formation of protein clusters.

In our simulations, we propose that protein liquid-liquid phase separation (LLPS) is driven by the presence of both strong and weak attractions between multivalent protein complexes and the chromatin filament. As stated in our manuscript, the mechanism leading to protein cluster formation is the bridging induced attraction. This mechanism involves a positive feedback loop, where protein binding to chromatin induces a local increase in chromatin density, which then attracts more proteins, further promoting cluster formation.

While we acknowledge that adding protein-protein interactions could be incorporated into our simulations, we believe this would need to be a weak interaction to remain consistent with experimental data. Additionally, incorporating such interactions would not alter the conclusions of our study.

What is described in this paper is a generic phenomenon; many kinds of multivalent chromatin-binding proteins can form condensates/clusters as described here. For example, if we replace different color TUs with different histone modifications and different TFs with Hp1, PRC1/2, etc, the results would remain the same, wouldn’t they? What is specific about transcription factor or transcription here in this model? What is the logic of considering 3kb chromatin as having a size of 30 nm? See Kadam et al. (Nature Communications 2023). Also, DNA paint experimental measurement of 5kb chromatin is greater than 100 nm (see work by Boettiger et al.).

We thank the Reviewer for this important observation, which we now address. To begin, we consider the toy model introduced in the first part of the manuscript, where TUs are randomly positioned rather than derived from epigenetic data. As the Reviewer points out, in this simplified context, our results reflect a generic phenomenon: the composition of clusters depends primarily on their size, independent of the specific types of proteins involved. However, the main goal of our work is to gain insights into apparently contradictory experimental findings, which show that some transcription factories consist of a single type of transcription factors, while other contain multiple types. This led us to focus on TF clusters and their role in transcriptional regulation and co-regulation of distant genes. Therefore, in the second part of the manuscript, we use DNase I hypersensitive site (DHS) data to position TUs based on predicted TF binding sites, providing a more biological framework. In both the toy model and the more realistic HiP-HoP model, we observe a size-dependent transition in cluster composition. However, we refrain from generalizing these results to clusters composed of other protein complexes, such as HP1 and PRC, as their binding is governed by distinct epigenetic marks (e.g. H3K927me3 and H3K27me3), which exhibit different genomic distributions compared to DHS marks.

Finally, the mapping of 3kb to 30nm is an estimate which does not significantly impact our conclusions. The relationship between genomic distance (in kbp) and spatial distance (in nm) is highly dependent on the degree of chromatin compaction, which can vary across cell types and genomic context. As such, providing an exact conversion is challenging [17]. For example, in a previous work based on the HiP-HoP model [12] we compared simulated and experimental FISH measurements and found that 1kbp typically corresponds to 15 − 20nm, implying that 3kbp could span 60nm. Nevertheless, we emphasize that varying this conversion factor does not affect the core results or conclusions of our study. We have now included a clarification in the revised SI to highlight this point.

Recommendations for the authors:

Other points.

Figure 1(D) caption says 2.25σ = 1.6 nanometer. Is this a typo? Sigma is 30nm.

Yes, it was. As 1σ ∼ 30nm, we have 2.25σ = 2.25 · 30 nm = 67.2 nm ∼ 6.7 × 10⁻⁸m. We have now corrected the caption.

Page 6, column 2nd, 3rd para, it is written that θ_dem (”defined in Fig.1”). There is no θ_dem defined in Fig.1, is there? I can see it defined in Methods but not in Fig. 1.

Correct, we replaced (defined in Fig.1) with (see Methods for definition).

Page 6, column 2, 4th para: what does “correlations overlap and correlations diverge mean”?

With reference to the plots from Fig. 5B, correlation overlap and diverge simply refers to the fact that same-colour (red curves) and different-colour (blue curves) correlation trends may or may not overlap on each other. We have now clarified this point.

What is the precise definition of correlation in Fig 5B (Y-axis)?

In Fig.5B, correlation means Pearson correlation. We have now specified this point in the revised text and in the caption of Fig.5.

References

(1) S. A. Quinodoz, J. W. Jachowicz, P. Bhat, N. Ollikainen, A. K. Banerjee, I. N. Goronzy, M. R. Blanco, P. Chovanec, A. Chow, Y. Markaki et al., “Rna promotes the formation of spatial compartments in the nucleus,” Cell, vol. 184, no. 23, pp. 5775–5790, 2021.

(2) R. A. Beagrie, A. Scialdone, M. Schueler, D. C. Kraemer, M. Chotalia, S. Q. Xie, M. Barbieri, I. de Santiago, L.-M. Lavitas, M. R. Branco et al., “Complex multi-enhancer contacts captured by genome architecture mapping,” Nature, vol. 543, no. 7646, pp. 519–524, 2017.

(3) R. A. Beagrie, C. J. Thieme, C. Annunziatella, C. Baugher, Y. Zhang, M. Schueler, A. Kukalev, R. Kempfer, A. M. Chiariello, S. Bianco et al., “Multiplex-gam: genome-wide identification of chromatin contacts yields insights overlooked by hi-c,” Nature Methods, vol. 20, no. 7, pp. 1037–1047, 2023.

(4) L. Liu, B. Zhang, and C. Hyeon, “Extracting multi-way chromatin contacts from hi-c data,” PLOS Computational Biology, vol. 17, no. 12, p. e1009669, 2021.

(5) R.-S. Nozawa, L. Boteva, D. C. Soares, C. Naughton, A. R. Dun, A. Buckle, B. Ramsahoye, P. C. Bruton, R. S. Saleeb, M. Arnedo et al., “Saf-a regulates interphase chromosome structure through oligomerization with chromatin-associated rnas,” Cell, vol. 169, no. 7, pp. 1214–1227, 2017.

(6) E. A. Boyle, Y. I. Li, and J. K. Pritchard, “An expanded view of complex traits: from polygenic to omnigenic,” Cell, vol. 169, no. 7, pp. 1177–1186, 2017.

(7) C. Brackley, N. Gilbert, D. Michieletto, A. Papantonis, M. Pereira, P. Cook, and D. Marenduzzo, “Complex small-world regulatory networks emerge from the 3d organisation of the human genome,” Nat. Commun., vol. 12, no. 1, pp. 1–14, 2021.

(8) R. B. Brem and L. Kruglyak, “The landscape of genetic complexity across 5,700 gene expression traits in yeast,” Proceedings of the National Academy of Sciences, vol. 102, no. 5, pp. 1572– 1577, 2005.

(9) M. Chiang, C. A. Brackley, D. Marenduzzo, and N. Gilbert, “Predicting genome organisation and function with mechanistic modelling,” Trends in Genetics, vol. 38, no. 4, pp. 364–378, 2022.

(10) M. Chiang, C. A. Brackley, C. Naughton, R.-S. Nozawa, C. Battaglia, D. Marenduzzo, and N. Gilbert, “Genome-wide chromosome architecture prediction reveals biophysical principles underlying gene structure,” Cell Genomics, vol. 4, no. 12, 2024.

(11) A. Buckle, C. A. Brackley, S. Boyle, D. Marenduzzo, and N. Gilbert, “Polymer simulations of heteromorphic chromatin predict the 3d folding of complex genomic loci,” Mol. Cell, vol. 72, no. 4, pp. 786–797, 2018.

(12) G. Forte, A. Buckle, S. Boyle, D. Marenduzzo, N. Gilbert, and C. A. Brackley, “Transcription modulates chromatin dynamics and locus configuration sampling,” Nature Structural & Molecular Biology, vol. 30, no. 9, pp. 1275–1285, 2023.

(13) P. R. Cook and D. Marenduzzo, “Transcription-driven genome organization: a model for chromosome structure and the regulation of gene expression tested through simulations,” Nucleic acids research, vol. 46, no. 19, pp. 9895–9906, 2018.

(14) M. Marenda, D. Michieletto, R. Czapiewski, J. Stocks, S. M. Winterbourne, J. Miles, O. C. Flemming, E. Lazarova, M. Chiang, S. Aitken et al., “Nuclear rna forms an interconnected network of transcription-dependent and tunable microgels,” BioRxiv, pp. 2024–06, 2024.

(15) J.-K. Ryu, C. Bouchoux, H. W. Liu, E. Kim, M. Minamino, R. de Groot, A. J. Katan, A. Bonato, D. Marenduzzo, D. Michieletto et al., “Bridging-induced phase separation induced by cohesin smc protein complexes,” Science advances, vol. 7, no. 7, p. eabe5905, 2021.

(16) A. Shakya, S. Park, N. Rana, and J. T. King, “Liquid-liquid phase separation of histone proteins in cells: role in chromatin organization,” Biophysical journal, vol. 118, no. 3, pp. 753–764, 2020.

(17) A.-M. Florescu, P. Therizols, and A. Rosa, “Large scale chromosome folding is stable against local changes in chromatin structure,” PLoS computational biology, vol. 12, no. 6, p. e1004987, 2016.

Revisaría la redacción de este párrafo. Me parece que se repite muchas veces una o dos ideas y que se podrían presentar de manera más concisa.

En general, creo que hace falta citas en este párrafo.

Sobre esta frase en particular, ¿hay alguna evaluación desde Pregrado sobre la implementación de las adecuaciones curriculares?

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public review):

In this manuscript, Aghabi et al. present a comprehensive characterization of ZFT, a metal transporter located at the plasma membrane of the eukaryotic parasite Toxoplasma gondii. The authors provide convincing evidence that ZFT plays a crucial role in parasite fitness, as demonstrated by the generation of a conditional knockdown mutant cell line, which exhibits a marked impact on mitochondrial respiration, a process dependent on several iron-containing proteins. Consistent with previous reports, the authors also show that disruption of mitochondrial metabolism leads to conversion into the persistent bradyzoite stage. The study then employed advanced techniques, such as inductively coupled plasma-mass spectrometry (ICP-MS) and X-ray fluorescence microscopy (XFM), to demonstrate that ZFT depletion results in reduced parasite-associated metals, particularly iron and zinc. Additionally, the authors show that ZFT expression is modulated by the availability of these metals, although defects in the transporter could not be compensated for by exogenous addition of iron or zinc. 

While the manuscript does not directly investigate the transport function of ZFT through biochemical assays, the authors indirectly support the notion that ZFT can transport zinc by demonstrating its ability to compensate for a lack of zinc transport in a yeast heterologous system. Furthermore, phenotypic analyses suggest defects in iron availability, particularly with regard to Fe-S mitochondrial proteins and mitochondrial function. Overall, the manuscript provides a solid, well-rounded argument for ZFT's role in metal transport, using a combination of complementary approaches. Although direct biochemical evidence for the transporter's substrate specificity and transport activity is lacking, the converging evidence, including changes in metal concentrations upon ZFT depletion, yeast complementation data, and phenotypic changes linked to iron deficiency, presents a convincing case. Some aspects of the results may appear somewhat unbalanced, particularly since iron transport could not be confirmed through heterologous complementation, while zinc-related phenotypes in the parasites have not been thoroughly explored (which is challenging given the limited number of zinc-dependent proteins characterized in Toxoplasma). Nevertheless, given that metal acquisition remains largely uncharacterized in Toxoplasma, this manuscript provides an important first step in identifying a metal transporter in these parasites, and the data presented are generally convincing and insightful. 

We thank the reviewer for their assessment and would like to highlight that we now add direct biochemical characterisation in the new Figure 8, supporting our hypothesis and confirming iron transport by this protein.

Reviewer #2 (Public review): 

Summary: 

The intracellular pathogen Toxoplasma gondii scavenges metal ions such as iron and zinc to support its replication; however, mechanistic studies of iron and zinc uptake are limited. This study investigates the function of a putative iron and zinc transporter, ZFT. In this paper, the authors provide evidence that ZFT mediates iron and zinc uptake by examining the regulation of ZFT expression by iron and zinc levels, the impact of altered ZFT expression on iron sensitivity, and the effects of ZFT depletion on intracellular iron and zinc levels in the parasite. The effects of ZFT depletion on parasite growth are also investigated, showing the importance of ZFT function for the parasite. 

Strengths: 

A key strength of the study is the use of multiple complementary approaches to demonstrate that ZFT is involved in iron and zinc uptake. Additionally, the authors build on their finding that loss of ZFT impairs parasite growth by showing that ZFT depletion induces stage conversion and leads to defects in both the apicoplast and mitochondrion. 

Weaknesses: 

(1) Excess zinc was shown not to alter ZFT expression, but a cation chelator (TPEN) did lead to decreased expression. While TPEN is often used to reduce zinc levels, does it have any effect on iron levels? Could the reduction in ZFT after TPEN treatment be due to a reduction in the level of iron or another cation?

WE thank the reviewers for this comment, we agree that TPEN is a fairly unspecific cation chelator so to determine if its effects are due to removal of zinc or other cations we treated with TPEN and either zinc or iron. Co-incubation of TPEN and zinc prevented ZFT depletion, while TPEN+FAC had no effect compared to TPEN alone (new Figure 6h and i), strongly suggesting the effects on ZFT abundance are linked to zinc and not just iron.  

(2) ZFT expression was found to be dynamic depending on the size of the vacuole, based on mean fluorescence intensity measurements. Looking at protein levels by Western blot at different times during infection would strengthen this finding. 

We show here that ZFT expression is highly dynamic, depending both the iron status of the host cell and the number of parasites/vacuole. However, validating this finding by western would be complex due to the highly unsynchronised nature of parasite replication and the large number (5x10⁶ - 1x10⁷cells) of parasites required to visualise ZFT. Further, we show that ZFT is apparently internalised prior to degradation. For this reason, we have not attempted to validate this finding by western blotting at this time.

(3) ZFT localization remained at the parasite periphery under low iron conditions. However, in the images shown in Figure S1c, larger vacuoles (containing 4-8 parasites) are shown for the untreated conditions, and single parasite-containing vacuoles are shown for the low iron condition. As ZFT localization is predominantly at the basal end of the parasite in larger PV and at the parasite periphery for smaller vacuoles, it would be better to compare vacuoles of similar size between the untreated and low-iron conditions.

The reviewer brings up a good point, the concentration of iron chelator that we used here does not enable parasite replication, making an assessment of changes in localisation challenging. To address this, have new data using a much lower concentration of chelator (20 mM), which is still expected to impact the parasites (Hanna et al, 2025), but allows for replication. In this low iron environment, ZFT localisation remained significantly more peripheral (Fig. S1d,e), supporting our hypothesis that ZFT localisation is iron dependent, independent of vacuolar stage.

Reviewer #3 (Public review): 

Summary:

Aghabi et al set out to characterize a T. gondii transmembrane protein with a ZIP domain, termed ZFT. The authors investigate the consequences of ZFT downregulation and overexpression for parasite fitness. Downregulation of ZFT causes defects in the parasite's endosymbiotic organelles, the apicoplast and the mitochondrion. Specifically, lack of ZFT causes a decrease in mitochondrial respiration, consistent with its role as an iron transporter. This impact on the mitochondria appears to trigger partial differentiation to bradyzoites. The authors furthermore demonstrate that expression of TgZFT can rescue a yeast mutant lacking its zinc transporter and perform an array of direct metal ion measurements, including X-ray fluorescence microscopy and inductively coupled mass spectrometry (ICP-MS). These reveal reduced metal ions in parasites depleted in ZFT. Overall, the data by Aghabi et al. reveal that ZFT is a major metal ion transporter in T. gondii, importing iron and zinc for diverse essential processes. 

Strengths:

This study's strength lies in the thorough characterization of the transporter. The authors combine a number of techniques to measure the impact of ZFT depletion, ranging from the direct measurement of metal ions to determining the consequences for the parasite's metabolism (mitochondrial respiration), as well as performing a yeast mutant complementation. This work is very thorough and clearly presented, leaving little doubt about this protein's function. 

Weaknesses:

This study offers no major novel insights into the biology of T. gondii. The transporter was already annotated as a zinc transporter (ToxoDB), was deemed essential (PMID: 27594426), and localized to the plasma membrane (PMID: 33053376). This study mostly confirms and validates these previous datasets. The authors identify three other proteins with a ZIT domain. Particularly, the role of TGME49_225530 is intriguing, as it is likely fitness-conferring (score: -2.8, PMID: 27594426) and has no subcellular localization assigned. Characterizing this protein as well, revealing its localization, and identifying if and how these transporters coordinate metal ion transport would have been worthwhile. 

We agree that the work presented here validates the previous datasets, and if that was all we had done, we agree that the biological insights would be limited. However, we have gone significantly beyond the predictions, demonstrating dynamic localisation changes, iron-mediated regulation, the lack of substrate-based complementation and validating transport activity of both zinc and iron. Although in silico predictions and screens can be informative, it remains important to validate biological functions experimentally. While we agree that characterisation of TGME49_225530 (as well as the other two annotated ZIP proteins) would be interesting, and will certainly form part of our future plans, it is significantly beyond the scope of the presented manuscript.

Another weakness is the data related to the impact of ZFT downregulation on the apicoplast in Figure 4. The authors show that downregulation of ZFT causes an increase in elongated apicoplasts (Figure 4d). The subsequent panels seem to show that the parasites present a dramatic growth defect at that time point. This growth arrest can directly explain the elongated apicoplast, but does not allow any conclusion about an impact on the organelle. In any case, an assessment of 'delayed death' as presented in Figure 4c seems futile, since the many other processes affected by zinc and iron depletion likely cause a rapid death, masking any potential delayed death.

To address this point, we agree that given the importance of iron and zinc to the parasite that we cannot differentiate the death of the parasite due to apicoplast defects from death from other causes and we have modified the discussion to reflect this, as below.

“However, given the delayed phenotype typically seen upon apicoplast disruption, we cannot determine if this is a direct effect of ZFT, or a downstream consequence of metal depletion”

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Specific Comments: 

(1) The background on the typical sequence features that would identify Toxoplasma ZIP homologues should be expanded and clarified. While these proteins are likely quite divergent and may lack many conserved features, the manuscript currently does not provide enough detail to assess how similar (or different) TgZIPs are from well-characterized family members. Additionally, the justification for focusing on TGGT1_261720 (ZFT) over TGGT1_225530, as stated in the first paragraph of the results section, seems unclear. There is no predictive data supporting a potential plasma membrane localization for TGGT1_225530 (yet this cannot be excluded), and TGGT1_225530 appears to have more canonical metal-binding motifs. I believe that the fact that only TGGT1_261720 is iron-regulated should be sufficient justification for its selection, and this point could be emphasized more clearly. Furthermore, the discussion mentions a leucine residue that may be associated with broad substrate specificity, but this is not addressed in the initial comparative sequence analysis. These residues and the HK motif are not actually addressed in the Gyimesi et al. reference currently mentioned; thus this could be clarified and updated with references (such as PMID: 31914589) that provide more recent insights into key residues involved in metal selectivity in ZIP transporters.

We thank you for this comment, to address these points:

We agree that the iron-mediated regulation is sufficient for our focus on ZFT and have clarified the text to reflect this, as described above.

We have also updated the references as suggested, our apologies for this oversight.

We have further expanded the discussion, especially with reference to our new results using heterologous expression in oocytes (please see above).

(2) Figure 1D, Figure 2A, C, H, Figure 3D, Figure 6F, H, corresponding text and paragraph 2 of the Discussion: It seems that most of the "non-specific bands" annotated in Figure 1D, which are lower molecular weight products, are not present in the parental cell line, suggesting they may not be non-specific after all. These bands also vary depending on the cell line (e.g., promoter used, see Figures 2H and 3D) or experimental conditions (e.g., iron excess or depletion). Given the dynamic localization of ZFT during intracellular development, it may be worth exploring whether these lower molecular weight bands represent degraded forms of TgZFT, possibly corresponding to the basally-clustered signal observed by immunofluorescence, with only the full-length protein associating with the plasma membrane. This possibility should be investigated or at least discussed further.

While the lower bands are not present in the parental, we do see them in other HA-tagged lines, especially when the expression of the tagged protein is low, seen below (Author response image 1). We don’t currently have an explanation for these, but we can confirm that they do not change in abundance in parallel with the full length protein, supporting our hypothesis that these bands are an artefact of the anti-HA antibody in our system. Although ZFT is clearly degraded (e.g. Fig. 1g), we currently do not believe these bands are ZFT c-terminal degradation products.

Author response image 1.

Western blot of ZFT-3HA_zft and another HA-tagged unrelated cytosolic protein, demonstrating that the lower bands are most likely nonspecific.

(3) It is unfortunate that ZFT could not complement a yeast iron transporter mutant cell line, as this would have provided a strong argument for ZFT's role in iron transport. The manuscript does not provide much detail about the Δfet2/3 yeast mutant line. Fet3 is the ferroxidase subunit, while Ftr1 is the permease subunit of the high-affinity iron transport complex in yeast. Fet2, however, appears to be Saccharomyces cerevisiae's VPS41 homolog. Therefore, is Δfet2/3 the most appropriate mutant to use, or would another mutant line (e.g., ΔFtr1) be a better choice? Additionally, while Figure 7 suggests a decrease in metal uptake upon ZFT depletion, it would be useful to test whether overexpression of ZFT leads to enhanced metal incorporation, perhaps using a FerroOrange assay. 

We thank the reviewer for their comments, which we have answered below:

The Δfet2/3 yeast mutant was a typo and has been corrected, or apologies, we did use the  Δfet3/4 mutant line, based on previous successful experiments involving plant metal transporters (e.g  (DiDonato et al., 2004)).

Unfortunately, we were unable to perform the FerroOrange assay in the overexpression line as this line is endogenously fluorescent in the same channel as FerroOrange.

However, as detailed above we have now added significant new data, confirming our hypothesis that ZFT is an iron/zinc transporter through heterologous expression in Xenopus oocytes in the new figure 8. This provides direct evidence of transport of iron, and evidence that zinc can inhibit this transport, consistent with our hypothesis.  

(4) The annotation of the blot in Figure 2H suggests that overexpressed ZFT-TY can only be detected in the absence of heat denaturation. However, this is not addressed in the text. Does heat denaturation also affect the detection of ZFT-3HA or the lower molecular weight products? This should be clarified in the manuscript. 

Interestingly, ZFT is detectable after boiling at 95° C for 5 minutes when expressed at endogenous (or near endogenous) levels in the ZFT-3HA_sag1 and ZFT-3HA_zft tagged parasite lines. However, overexpression of ZFT leads to a loss of detection via western blot when boiled, although the protein is detectable without heat denaturation.

A possible explanation for this is that overexpression of protein may cause ZFT to miss-fold, making the protein more prone to aggregation following boiling, rendering the protein insoluble and unable to enter the gel. Moreover, heat aggregation can sometimes mask the epitope tags on the protein that is required for the antibody to be recognised, possibly explaining by ZFT is undetectable when overexpressed and exposed to boiling conditions, as has previously been observed for other transmembrane proteins (e.g. (Tsuji, 2020)).

We have clarified this in the results section, although we do not have a full explanation for this, we consider it important to share for others who may be looking at expression of these proteins.

(5) Figure 3G: It might be helpful to include an uncropped gel profile to allow readers to visualize that the main product does indeed correspond to a potential dimeric form in the native PAGE. 

This has now been added in Figure S3e, thank you for this suggestion.

(6) The investigation of the impact of ZFT depletion on the apicoplast could be improved. The authors suggest that ZFT knockdown inhibits apicoplast replication based on a modest increase in elongated organelles, but the term "delayed death" is not appropriate in that case, as it is typically linked to a loss of the organelle. This is not observed here and is also illustrated by the unchanged CPN60 processing profile. So, clearly, there seems to be no strong morphological effect on the apicoplast early on after ZFT depletion. On the other hand, the authors dismiss any impact on TgPDH-E2 lipoylation (which is iron-dependent) based on the fact that the lipoylated form of the protein is still detected by Western blot. However, closer inspection of the blot in Figure 4B suggests that the intensity of the annotated TgPDH-E2 signal is reduced compared to the -ATc condition (although there might be differences in protein loading, as indicated by the control) or even with the mitochondrial 2-oxoglutarate dehydrogenase-E2, whose lipoylation is presumably iron-independent (see PMID: 16778769). This experiment should be repeated, and the results quantified properly in case something was missed, and the duration of depletion conditions perhaps extended further. Of note, it would also be worthwhile to revisit size estimations, as the displayed profiles seem inconsistent with the typical sizes of lipoylated proteins detected with the anti-lipoyl antibody (e.g., ~100 kDa for PDH-E2, ~60 kDa for branched-chain 2-oxo acid dehydrogenase, and ~40 kDa 2-oxoglutarate dehydrogenase).

We thank the reviewer for this comment. We agree that there is no strong defect on the apicoplast in the first lytic cycle and we have modified the language to remove reference to delayed death, as given the magnitude of changes associated with loss of iron and zinc, we cannot be certain about the role of the apicoplast.

Based on this suggestion, we have now quantified the levels of lipoylation of PDH-E2, BDCK-E2 and OGDH-E2 and now include this in Figure S4b, c, d. Supporting our other results, we do not see a significant change in PDH-E2 lipolyation upon ZFT knockdown. However, although OGDH-E2 lipoylation is unchanged (Figure S4c) interestingly we do see a significant increase in BDCK-E2 lipoylation (Figure S4d). This process is not expected to be directly iron related, as mitochondrial lipoylation is through scavenging rather than synthesis however, speaks to the larger mitochondrial disruption that we see. We now consider this further in the discussion.

For the sizes, we thank the reviewer for bringing this up, our apologies this was due to an error in the annotation, and we have now corrected this in the figure.

(7) In the third paragraph of the discussion, the authors mention the inability to complement ZFT loss by adding exogenous metals. One argument is the potential lack of metal access to the parasitophorous vacuole (PV). Although largely unexplored, this point could be expanded further in the discussion, as the issue of metal transport to the parasite involves not only the parasite plasma membrane but also the PV membrane. Additionally, the authors mention the absence of functional redundancy in transporters, but it would be helpful to discuss potential stage-specific or differential expression of other ZIP candidates. Transcriptomic data available on Toxodb.org could provide useful insights into this, and experimental approaches, such as RT-PCR, could be used to assess the expression of these candidates in the absence of ZFT. 

On the issue of metals crossing the PV membrane, we agree that while we do not currently know mechanisms of metal transport within the infected host cell, we do have experimental confirmation that the concentration and form of the metals that we are using can impact the parasites. We show that metal treatment inhibits parasites growth (e.g. Figure 3k-n, Figure 6a-d) and we can detect the increased metals through our experiments using FerroOrange and FluroZine (Figure 7a, c). In these experiments, parasites were treated intracellularly and so we can confirm that, regardless of the mechanism, iron and zinc can reach the parasite. While entry of metals across the PV is an intriguing question, it is beyond the scope of the present work which focuses on the role of the selected transporter.

We agree that a more detailed discussion of the other ZIP transporters is warranted. We have extended this section of the discussion although for now, we cannot determine the role of the other ZIP transporters in Toxoplasma.

(8) In the discussion, the authors mention that « Inhibition of respiration has previously been linked to bradyzoite conversion ». To strengthen their point, the authors could mention that mitochondrial Fe-S mutants, as well as mutants affecting mitochondrial translation or the mitochondrial electron transport chain, also initiate bradyzoite conversion (PMID: 34793583). This would reinforce the connection between mitochondrial dysfunction and stage conversion. 

This is an excellent point and we have added this to the discussion as follows:

“Inhibition of mitochondrial Fe-S biogenesis or mitochondrial respiration have both previously been linked to bradyzoite conversion (Pamukcu et al., 2021; Tomavo and Boothroyd, 1995), however we do not yet know the signalling factors linking iron, zinc or mitochondrial function to bradyzoite differentiation”.

(9) As a general comment on manuscript formatting, providing page and line numbers would significantly improve the manuscript's readability and allow reviewers to more easily reference specific sections. This would help address the minor issues of typos (e.g., multiple occurrences of "promotor"). I suggest a careful read-through to correct these issues. 

We thank the reviewer for this comment and in the resubmitted version we have corrected these issues. 

Reviewer #2 (Recommendations for the authors): 

(1) In the alignment (Figure 1a), the BPZIP sequence is from which organism (genus, species)? It would be helpful to include this information in the figure legend.

Apologies for this oversight, this figure and section have been reworked and the species name (Bordetella bronchiseptica) added.

(2) In reference to Figure 1a, the authors state, "Interestingly, all parasite ZIP-domain proteins examined have a HK motif at the M2 metal binding". I was wondering if by "all" the authors mean Toxoplasma and Plasmodium falciparum (shown in Figure 1a) or did the authors also look at other apicomplexan parasites such as Cryptosporidium or Neospora? Is this a general feature of apicomplexan parasites? 

We looked at this, and the HK motif in the M2 binding site is conserved in Neospora Cryptosporidium, and even the digenic gregarine Porospora cf. gigantea. However, in the more distantly related Chromera we find a HH motif at the same position. This suggests that the HK motif is present in the Apicomplexa, but not conserved in the free-living Alveolata. Although we cannot speculate on the role of this motif currently, its role in metal import in Apicomplexa does deserve future scrutiny. To reflect this finding we have modified Figure 1a and the text.

(3) In Figure 1e, to better visualize the ZFT-3HA staining at the basal pole, it would be better to omit the DAPI staining from the merged image. It is difficult to see the ZFT staining in the image of the large vacuole.

We have removed the DAPI from this image to improve clarity.

(4) Based on the "delayed-death" phenotype of the apicoplast, it is not surprising that no defects were observed in CPN60 processing or protein lipoylation. Have the authors considered measuring these phenotypes after a further round of growth (as was done for visualizing apicoplast morphology)? 

We agree that changes in apicoplast function are often only seen in the second round of replication. However, here we wanted to check if ZFT depletion led to immediate changes in function of the organelle, which was not the case. It is highly likely that after the second round, we would see significant defects in the apicoplast function, however given the immediate importance of iron and zinc to many processes within the parasite, we believe that these experiments would be complicated to interpret.

(5) Depleting ZFT led to a reduction in expression levels for the mitochondrial Fe-S protein SDHB but not for a cytosolic Fe-S protein. Is it expected that less intracellular iron (via depleted ZFT) would differentially affect mitochondrial versus cytosolic Fe-S proteins? 

Previous studies (e.g., Maclean et al., 2024; Renaud et al., 2025) have shown that upon direct inhibition of the cytosolic Fe-S pathway, ABCE1 is fairly stable and levels can persist for 2-3 days post treatment. However, our recent work has shown that rapid and acute depletion of iron directly (though treatment with a chelator) can lead to ABCE1 levels decreasing within 24h (Hanna et al., 2025). In the case of ZFT knockdown, due to the more gradual reduction in iron levels seen (e.g. Figure 7j) we believe the parasites are prioritising key Fe-S pathways (e.g. essential proteostasis through ABCE1), probably while remodelling metabolism (as seen in our Seahorse assays). However, there are many proteins expected to be directly impacted by iron and zinc restriction that these parasites experience, and different protein classes are expected to behave differently in these conditions.

Reviewer #3 (Recommendations for the authors): 

(1) Is the effect on the plaque size between T7S4-ZFT (-aTc) in regular and 'high iron' conditions significant? The authors show convincingly that the plaque size is smaller due to the swapped promoter and the resulting overexpression of ZFT. But is the effect aggravated in high iron? This would be expected if excess iron were the problem.

The plaque sizes are significantly smaller in the T7S4-ZFT line under high iron compared to the untreated condition, and compared to the parental untreated line. However, if we normalise plaque size to untreated conditions for both lines, there is not a significant change in plaque size in high iron between the parental and T7S4-ZFT. This is possibly due to the concentration of iron used (200 mM), which may not be optimal to see this effect, or the time taken for plaque assays (6-7 days), which may allow the excess iron to be stored by the host cells, changing the effective concentration of parasite exposure.

(2) I struggle to understand the intracellular growth assay in Figure 5b. Here, T7S4-ZFT parasites show 25 % of vacuoles with more than 8 parasites (labelled 8+). But such large vacuoles are not observed in the parental strain. It appears as if the inducible strain grows faster even though it was earlier shown to have a fitness defect (see Figure 3j). Can you please clarify?

This is a result of rapid growth of the parental line, some vacuoles in this line lysed and initiated a new round of replication at this time point while we saw no evidence at any timepoint that ZFT-depleted parasites were able to lyse the host cell. However, the initial (24-48h post ATc addition) replication rate of the ZFT KD remains similar to the parental. In this panel, we wanted to emphasize that the major phenotype we see upon ZFT depletion is vacuole disorganisation, which we believe is linked to the start of differentiation into bradyzoites.

(3) Did the authors perform an IFA in addition to the Western blot to localize the 2nd Ty-tagged ZFT copy? It seems important to validate that the protein correctly localizes to the plasma membrane. 

We have done so and now include these data in Figure S2b. Overexpression of ZFT-Ty localises to internal structures (probably vesicles) with some signal at the periphery, however, this limited expression at the periphery is sufficient to mediate the phenotypes that we see.

(4) First sentence of the abstract and introduction: The authors speak of metabolism and cellular respiration as though they are two different processes. Is respiration not part of metabolism? 

This is an excellent point, we wanted to distinguish mitochondrial respiration  from general cellular metabolism, but this was not clear. We have now changed this in the introduction to the below:

“Iron, and other transition metals such as zinc, manganese and copper, are essential nutrients for almost all life, playing vital roles in biological processes such as DNA replication, translation, and metabolic processes including mitochondrial respiration (Teh et al., 2024)”

(5) 2nd paragraph of the introduction: toxoplasmosis is written capitalized but should be lower case.

This has been corrected.

(6) Figure 4j legend: change 'shits parasites to a more quiescent stage' to 'shifts parasites'.

This has been corrected, our apologies.

(7) Please correct the following sentence: 'These data demonstrate ZFT depletion leads to the expression of the bradyzoite-specific markers BAG1 and DBL.' DBL is not expressed by the parasite. It is a lectin that binds to the sugars in the cyst wall.

We have now modified this in the text. The sentence now reads: “These data show that ZFT depletion leads to the expression of the bradyzoite marker BAG1 and the production of the cyst wall, as detected by DBL”.

(8) In the section on yeast complementation with TgZFT, the authors write: 'Based on this success, we also attempted to complement...'. Please consider changing 'Success' to something more neutral.

We have modified the text to now read: “Based on these results, we also attempted to complement”…

(9) In the discussion, the authors write: 'We see a delayed phenotype on the apicoplast, suggesting that metal import is also required in this organelle, although no apicoplast metal transporters have yet been identified.' Please consider the study Plasmodium falciparum ZIP1 Is a Zinc-Selective Transporter with Stage-Dependent Targeting to the Apicoplast and Plasma Membrane in Erythrocytic Parasites (PMID: (38163252).

We thank the reviewer for the note and have modified the text to include this and the reference. Please see below:

“Iron is known to be required in the apicoplast (Renaud et al., 2022), zinc also may be required, as the fitness-conferring Plasmodium zinc transporter ZIP1 is transiently localised to the apicoplast (Shrivastava et al., 2024), although the functional relevance of this localisation has not yet been established”.

(10) The authors write: 'Iron is known to be required in the apicoplast (Renaud et al., 2022), although a potential role for zinc in this organelle has not yet been established.' The role for zinc in the apicoplast may not have been shown formally, but surely among its hundreds of proteins, and those involved in replication and transcription, there are some that depend on zinc...?

Yes, we agree it would make sense, however multiple searches using ToxoDB and the datasets from Chen et al (2025) were unable to find any apicoplast-localised proteins with zinc-binding domains. We cannot exclude that zinc is in the apicoplast, and the results from Plasmodium (Shrivastava et al., 2024) may suggest that is, however currently we do not have any evidence for its role within this organelle.

References

DiDonato, R.J., Roberts, L.A., Sanderson, T., Eisley, R.B., Walker, E.L., 2004. Arabidopsis Yellow Stripe-Like2 (YSL2): a metal-regulated gene encoding a plasma membrane transporter of nicotianamine-metal complexes. Plant J 39, 403–414. https://doi.org/10.1111/j.1365-313X.2004.02128.x

Hanna, J.C., Shikha, S., Sloan, M.A., Harding, C.R., 2025. Global translational and metabolic remodelling during iron deprivation in Toxoplasma gondii. https://doi.org/10.1101/2025.08.11.669662

Maclean, A.E., Sloan, M.A., Renaud, E.A., Argyle, B.E., Lewis, W.H., Ovciarikova, J., Demolombe, V., Waller, R.F., Besteiro, S., Sheiner, L., 2024. The Toxoplasma gondii mitochondrial transporter ABCB7L is essential for the biogenesis of cytosolic and nuclear iron-sulfur cluster proteins and cytosolic translation. mBio 15, e00872-24. https://doi.org/10.1128/mbio.00872-24

Pamukcu, S., Cerutti, A., Bordat, Y., Hem, S., Rofidal, V., Besteiro, S., 2021. Differential contribution of two organelles of endosymbiotic origin to iron-sulfur cluster synthesis and overall fitness in Toxoplasma. PLoS Pathog 17, e1010096. https://doi.org/10.1371/journal.ppat.1010096

Renaud, E.A., Maupin, A.J.M., Berry, L., Bals, J., Bordat, Y., Demolombe, V., Rofidal, V., Vignols, F., Besteiro, S., 2025. The HCF101 protein is an important component of the cytosolic iron–sulfur synthesis pathway in Toxoplasma gondii. PLoS Biol 23, e3003028. https://doi.org/10.1371/journal.pbio.3003028

Shrivastava, D., Jha, A., Kabrambam, R., Vishwakarma, J., Mitra, K., Ramachandran, R., Habib, S., 2024. Plasmodium falciparum ZIP1 Is a Zinc-Selective Transporter with Stage-Dependent Targeting to the Apicoplast and Plasma Membrane in Erythrocytic Parasites. ACS Infect. Dis. 10, 155–169. https://doi.org/10.1021/acsinfecdis.3c00426

Teh, M.R., Armitage, A.E., Drakesmith, H., 2024. Why cells need iron: a compendium of iron utilisation. Trends in Endocrinology & Metabolism 35, 1026–1049. https://doi.org/10.1016/j.tem.2024.04.015 Tomavo, S., Boothroyd, J.C., 1995. Interconnection between organellar functions, development and drug resistance in the protozoan parasite, Toxoplasma gondii. International Journal for Parasitology 25, 1293–1299. https://doi.org/10.1016/0020-7519(95)00066-B.

Briefing : Renouer avec l'Autorité à l'École avec Jean-Pierre Bellon

Source : Extraits de "Instant Canopé : renouer avec l'autorité à l'école avec Jean Pierre Bellon"

Date : Journée nationale de lutte contre le harcèlement scolaire (veille) 2024

Intervenants :

• Sophie Courau : Directrice d'ESF sciences humaines, éditrice des ouvrages de Jean-Pierre Bellon.

• Jean-Pierre Bellon : Professeur de philosophie, pionnier de la lutte contre le harcèlement scolaire en France, auteur de "Renouer avec l'autorité à l'école".

• Public : Professionnels de l'éducation (enseignants, chefs d'établissement, CPE, directeurs d'école).

Synthèse :

Cette discussion avec Jean-Pierre Bellon met en lumière les liens étroits entre la crise de l'autorité à l'école, le harcèlement scolaire et le chahut en classe.

Bellon, fort de son expérience d'enseignant et de son travail sur le harcèlement, propose dix mesures concrètes pour restaurer un climat scolaire apaisé, insistant sur la nécessité d'une autorité bienveillante alliant courtoisie et fermeté.

Il critique le manque de formation des enseignants sur la gestion des incivilités et des classes difficiles, le flou entourant la définition et la hiérarchisation des infractions scolaires, et l'inefficacité perçue des sanctions actuelles, notamment dans le premier degré.

Des propositions sont faites pour repenser les punitions, les sanctions (notamment l'exclusion temporaire), l'utilisation des téléphones portables, l'architecture des établissements et les relations entre l'école et les familles, en plaidant pour des protocoles clairs, une approche collective et une verticalité institutionnelle renforcée.

Thèmes principaux et Idées clés :

Lien entre Manque d'Autorité, Harcèlement et Chahut : Bellon établit un lien direct entre la défaillance de l'autorité et les phénomènes de harcèlement et de chahut.

Les élèves victimes de harcèlement témoignent que la situation est "le pire" dans les classes des professeurs en difficulté.

Le chahut, tout comme le harcèlement, est un phénomène de groupe, rendant les sanctions individuelles inefficaces et potentiellement contre-productives en provoquant la coalition du groupe.

Le manque d'autorité se manifeste par le chahut, dont la réalité dans les classes françaises est confirmée par des enquêtes internationales comme PISA. PISA 2022 révèle qu'un lycéen sur deux considère qu'il y a trop de bruit en classe pour entendre le professeur, une situation qualifiée d'"injustice scolaire gigantesque".

Citation : "Le lien est direct moi le lien que j'ai vu d'abord entre le le harcèlement et et les classes dites dit difficile il était le suivant tous les élèves victime de brimade tous les élèves victimes de harcèlement faisaient tous le même constat c'est que c'était dans la classe du professeur en difficulté lui-même que la situation était le pire."

Citation : "le harcèlement comme le Chahu sont deux symptômes d'une défaillance de l'autorité tout de même."

Citation : "PISA 2022 nous apprend qu'un lycéen sur de considère qu'il y a trop de bruit dans sa classe au point qu'il n'entend pas ce que dit le professeur imaginez l'injustice que cela représente."

La Crise de l'Autorité à l'École, Symptôme d'une Crise Sociétale :

Bellon reconnaît que l'école n'échappe pas à une crise de l'autorité plus vaste qui touche la société.

Cependant, il estime que l'école est un lieu où il est possible "d'essayer de faire quelque chose" pour rétablir l'autorité, en raison du contact quotidien et prolongé (de 8h à 17h) avec les jeunes en formation.

Propositions pour Rétablir l'Autorité :

Allier Courtoisie et Fermeté : S'inspirant d'Hannah Arendt, l'autorité se situe entre la force/contrainte et la persuasion/négociation.

Il s'agit de donner des injonctions claires et fermes, tout en maintenant la courtoisie.

L'objectif est de ne laisser à l'élève récalcitrant que le choix du refus d'obtempérer, qui peut alors être traité formellement.

Citation : "cette alliance entre courtoisie et fermeté ne laisse à l'élève contrevenant si jeose dire qu'une seule porte de sortie c'est le refus d'obtempéré."

Citation : "je pense qu'il convient absolument de rétablir au sein de des établissements scolaires des règles de courtoisie de civilité."

Nécessité de la Formation des Enseignants : Il existe un "scandale" concernant le manque de formation des enseignants à la gestion des comportements difficiles (élèves arrogants, insultants, menaçants). Contrairement à d'autres métiers en contact avec le public, les enseignants ne disposent pas de protocoles de réaction.

Citation : "le défaut de formation des enseignants il est quand même criant c'est un purure scandale."

Citation : "on nous a pas formé à un protocole sur comment je réagis lorsque j'ai face à moi un élève arrogant un élève insultant un élève menaçant et cetera."

Définir et Hiérarchiser les Infractions Scolaires : Il n'existe pas de liste claire et hiérarchisée des incidents scolaires non délictuels.

Bellon a tenté de le faire (56 incidents listés) pour permettre aux enseignants de savoir à quoi s'attendre et de faire la distinction entre les incidents mineurs et graves.

Le manque de hiérarchie conduit à des sanctions disproportionnées (ex: lettre d'excuse pour une insulte grave).

Citation : "on s'était jamais penché sur qu'est-ce que c'est qu'un incident scolaire j'ai essayé de le faire j'ai essayer rédiger la liste des incidents scolaires c'est-à-dire tout ce qui ne devrait pas se faire se produire dans une classe dans un établissement dans une école et cetera je suis arrivé à 56 inincid."

Citation : "Avouez quand même que oublier son livre et insulter un professeur c'est pas tout à fait de même nature."

Tolérance Zéro et Signalement Systématique : Toutes les infractions, même mineures, doivent être systématiquement signalées.

Cela permettrait d'informer l'opinion, les parents et les élèves, et de s'accorder sur une "échelle" de gravité et de sanctions appropriées ("un barème").

Citation : "non pas forcément mais elles doivent être systématiquement signalé il faut en laisser passer aucune faut une tolérance zéro à cet égard il faut un signalement systématique de toutes les infractions."

Réformer le Système de Sanctions (Punitions vs Sanctions) :

La distinction actuelle entre punitions (données par tout personnel) et sanctions (données par le chef d'établissement) est jugée désordonnée et inefficace.

Les enseignants sont mis en difficulté en devant gérer seuls les punitions.

Bellon suggère de supprimer le pouvoir discrétionnaire de punition des enseignants.

Citation : "ce désordre de la distinction entre les punitions et les sanctions c'est quelque chose qui a fait son temps qui n'a plus de sens."

Citation : "je suggère qu'on leur retire ce pouvoir discrétionnaire dont ils ne savent pas quoi faire d'ailleurs et qu' est met à risque parce que l'occasion de la sanction en classe c'est un risque authentique pour les professeurs."

Confier la Proposition de Sanctions à une Commission : Il est proposé de créer une commission (composée de différents professionnels de l'établissement) qui examinerait tous les incidents signalés et ferait une proposition de sanction au chef d'établissement.

Cela permettrait une plus grande cohérence et déchargerait l'enseignant.

Adapter la Sanction dans le Primaire : La situation dans le premier degré est décrite comme un "désordre absolu" en matière de sanctions, avec un manque criant de "vie scolaire" et de lieux dédiés pour gérer les élèves perturbateurs. La solitude des professeurs des écoles est soulignée.

Repenser l'Exclusion Temporaire (Exclusion Internée) : L'exclusion sèche est paradoxale et inefficace, surtout pour les élèves en difficulté.

L'exclusion devrait être "internée", c'est-à-dire que l'élève reste dans l'établissement mais avec des contraintes (horaires décalés, lieu spécifique, travail différent).

Il s'agit d'une pratique héritée des Bénédictins (règle de Saint-Benoît). La sanction doit être "frustrante" (enlever quelque chose) et "signifiante" (verbalisée).

Citation : "renvoyer un garçon ou une fille chez lui ça peut pas marcher ben c'est bien précisément le contraire qu'il faut faire."

Citation : "l'idée de que l'exclusion soit internée c'est-à-dire que l'élève a l'obligation devenenir en cours mais il pourra peut-être pas faire exactement les mêmes choses."

Citation : "pour une sanction pour qu'elle soit éducative faut qu'elle soit dite faut qu'elle soit mise en mot faut qu'elle soit verbalisée... il faut qu'aussi la sanction elle soit frustrante."

Gérer l'Utilisation du Téléphone Portable : Il n'y a pas de "ligne claire" en France sur ce sujet.

Bellon préconise une règle simple et ferme : téléphone éteint dans le sac au fond de la pièce, comme lors d'un examen.

Toute utilisation entraîne un signalement et une sanction. Le téléphone en classe représente un risque pour les enseignants.

L'exemple d'un incident où une enseignante est insultée après avoir demandé à un élève de ranger son téléphone illustre cette difficulté.

Citation : "sur la question du téléphone portable il faut quand même avoir une ligne claire en France on n'a pas de ligne clair."

Citation : "franchement les téléphones portables ils n'ont rien à faire à l'école."

Aménager l'Espace et le Temps Scolaire (Architecture des Établissements) : L'architecture actuelle des établissements peut être propice aux problèmes d'autorité (ex: salle des profs inadaptée, cours de récréation regroupant tous les élèves).

Bellon suggère de repenser les espaces pour les adapter aux besoins (bureaux pour les enseignants, plusieurs cours de récréation, lieux dédiés pour les sanctions).

Ce point est considéré comme un projet à plus long terme ("le lycée Hannah Arendt").

Citation : "tous les architectes scolaires d'ailleurs j'observent que les architectes scolaires c'est pas toujours ils interrogent pas toujours les professeurs pour c'est quand même énorme ça quand on construit une maison en général on s'intéresse à aux habitants là non pas trop."

Améliorer les Relations École-Familles : Les relations entre l'école et les parents sont souvent tendues, avec des directeurs d'école et enseignants confrontés à des incivilités et des contestations (44% des directeurs d'école insultés selon une étude).

Bellon recommande l'établissement de protocoles pour accueillir les familles et gérer les situations difficiles.

Il souligne que les professionnels du primaire sont plus exposés en l'absence de sas d'accueil.

Citation : "vous pointez les mauvaises relations qu'entretiennent trop souvent les parents avec l'institution scolaire et vous citez notamment une étude de George futinos selon laquelle 44 % des directeurs d'école ont déclaré avoir été insultés par des parents d'élèves."

Citation : "il faut vraiment là encore avoir un protocole pour réagir face aux incivilités."

Principes de la Sanction Éducative :

S'inspirant d'Éric Prerat, Bellon insiste sur trois aspects d'une sanction éducative :

• Signifiante : Elle doit être dite, verbalisée, prononcée avec gravité et solennité, en distinguant l'infraction de la personne.

• Frustrante : Elle doit enlever quelque chose à l'élève (un droit, un avantage, la participation à une activité).

• Réparatrice : Elle doit inclure une dimension de réparation, en lien avec le préjudice causé à la vie collective ou aux personnes (excuses, réparation matérielle).

Citation : "Eric prerat dit que pour une sanction pour qu'elle soit éducative faut qu'elle soit dite faut qu'elle soit mise en mot faut qu'elle soit verbalisée."

Citation : "il faut qu'aussi la sanction elle soit frustrante faut qu'on m'enlève quelque chose."

Citation : "il faut que la sanction elle est une dimension réparatrice."

Nécessité d'une Approche Collective et Institutionnelle :

Les directeurs d'école, en particulier, manquent de pouvoir hiérarchique et de protection institutionnelle face aux contestations des parents.

L'idée de rattacher les écoles aux collèges (proposée par un ancien ministre) aurait pu offrir une direction institutionnelle et une vie scolaire dans le primaire.

La contestation des parents est également vue comme un symptôme d'une forte inquiétude quant à l'avenir de leurs enfants, qui se manifeste par une contestation systématique de la moindre sanction.

Il est crucial qu'il y ait une "verticalité" et une "protection systématique" de la part de l'institution (rectorat, dasen, chefs d'établissement) pour soutenir les enseignants face aux contestations et aux pressions.

Les failles institutionnelles peuvent être exploitées par des "adversaires de l'école".

Citation : "il faudrait renforcer le pouvoir hiérarchique des directeurs du Prim."

Citation : "il faut une verticalité il faut qu'il y ait des choses qui ne se négocient pas."

Certaines règles devraient être non négociables : contenu des enseignements, règles de civilité/courtoisie, respect absolu des personnes, tenue correcte.

Les enseignants sont des modèles pour les élèves et doivent également faire preuve d'élégance et de distinction.

Le travail de rétablissement de l'autorité et de gestion des difficultés doit être "construit collectivement" au sein des établissements, en s'inspirant des erreurs et des bonnes pratiques.

Le dispositif SCORE (inspiré de la préoccupation partagée) pour les classes difficiles est présenté comme un exemple d'approche collective et axée sur la recherche de solutions par les élèves eux-mêmes.

Points d'action suggérés par Jean-Pierre Bellon :

Développer des formations spécifiques pour les enseignants sur la gestion des comportements difficiles et l'application de protocoles.

Établir une liste claire et hiérarchisée des infractions scolaires.

Mettre en place un système de signalement systématique de toutes les infractions.

Supprimer le pouvoir discrétionnaire de punition des enseignants et le confier, ainsi que la proposition de sanction, à une commission dédiée.

Repenser et adapter les sanctions dans le premier degré, notamment en explorant l'idée d'espaces et d'organisation permettant des sanctions frustrantes (ex: récréation décalée).

Généraliser l'exclusion temporaire "internée". Instaurer une règle claire et ferme concernant l'utilisation des téléphones portables en classe.

Engager une réflexion à long terme sur l'architecture des établissements pour mieux gérer l'espace et le temps scolaire.

Développer des protocoles d'accueil et de gestion des relations avec les familles, notamment dans le primaire, et renforcer le soutien institutionnel aux professionnels de terrain.

Construire collectivement au sein des établissements des chartes ou des règles non négociables concernant le comportement, le respect et la tenue.

Utiliser des dispositifs collectifs comme SCORE pour gérer les classes difficiles.

Conclusion :

Jean-Pierre Bellon propose une approche globale pour faire face à la crise de l'autorité à l'école, en identifiant les liens entre ce phénomène, le harcèlement et le chahut.

Ses propositions visent à professionnaliser la gestion des incivilités et des conflits, à clarifier les règles et les conséquences des manquements, à repenser les sanctions pour les rendre plus éducatives, et à renforcer la protection et le soutien institutionnel des professionnels de l'éducation.

L'accent est mis sur la nécessité d'une action collective au sein des établissements et d'une réaffirmation de la verticalité institutionnelle pour faire face aux contestations et garantir un climat scolaire apaisé.

Note d'information : Éco-délégués, le pouvoir d'agir

Résumé Exécutif

Cette note d'information synthétise les perspectives et les analyses issues du podcast "Éco-délégués : donnons-leur le pouvoir d'agir".

Le document met en lumière le rôle complexe des éco-délégués, les attentes élevées placées en eux — qualifiés de "héros ordinaires" — et les multiples facettes de leur engagement.

Les motivations des élèves sont profondes, allant du désir d'agir pour la planète, souvent nourri par une certaine éco-anxiété, à un sentiment de responsabilité et à l'influence de leur entourage.

L'analyse révèle que l'efficacité du dispositif repose de manière critique sur l'accompagnement des adultes. Un écueil majeur, l'« adultisme », où les projets sont imposés par les adultes, doit être évité au profit d'une approche qui laisse les élèves proposer, construire et piloter leurs propres initiatives.

Le rôle du référent est de trouver un équilibre délicat entre l'écoute, le soutien logistique et l'impulsion, afin de transformer les idées en actions concrètes.

Les projets menés varient considérablement, des éco-gestes classiques (tri des déchets) à des transformations ambitieuses de l'établissement (végétalisation de la cour, création de zones de bien-être), s'étendant au-delà de l'écologie pour englober l'ensemble des Objectifs de Développement Durable (ODD), comme l'égalité filles-garçons.

Cependant, de nombreux obstacles freinent leur action : la lenteur administrative ("le temps des adultes"), les contraintes financières, le manque de reconnaissance par les pairs et l'absence d'un temps institutionnalisé pour leurs activités.

La dynamique dépend fortement de la gouvernance de l'établissement, décrite comme des "montagnes russes".

Malgré ces défis, l'engagement en tant qu'éco-délégué est profondément formateur. Il développe la confiance en soi, le sens de la citoyenneté et le "pouvoir d'agir" des élèves.

Ce dispositif transforme également les adultes impliqués, modifiant leur regard sur les élèves et leurs propres pratiques pédagogiques, et a le potentiel de catalyser un changement positif à l'échelle de l'établissement et du territoire.

--------------------------------------------------------------------------------

Analyse Détaillée

1. Portrait de l'Éco-délégué : Motivations et Identité

Les Motivations de l'Engagement

L'engagement des élèves en tant qu'éco-délégués est mû par un ensemble de motivations profondes, identifiées par la chercheuse Eveline Bois :

• Agir pour la planète : La motivation principale est la volonté de "sauver le monde", de le changer à leur échelle.

Les élèves expriment une conscience aiguë de la dégradation de l'environnement et de l'urgence climatique, ce qui peut générer de l'éco-anxiété. L'action devient alors un moyen de la combattre.

• Sentiment de responsabilité : Les jeunes se sentent responsables de l'avenir et perçoivent l'établissement scolaire comme une bonne échelle pour commencer à agir.

• Influences externes : La famille et les amis jouent un rôle significatif. Des élèves s'engagent pour suivre l'exemple de parents impliqués dans des associations (ex: "zéro déchet") ou pour partager une expérience avec leurs camarades.

• Utilité et participation : Comme l'exprime Laur, éco-déléguée depuis quatre ans, le désir de "se rendre utile à la vie au collège" et de s'investir est un moteur important.

Méthodes de Recrutement et Profils

Le mode de désignation des éco-délégués influence la dynamique du groupe. Sandrine Aoussour, enseignante référente, a opté dans son collège pour un système basé sur le volontariat, ouvert tout au long de l'année. Ce choix vise à :

• Garantir d'avoir des élèves "réellement motivés".

• Éviter la compétition inhérente à une élection.

• Créer un "noyau vraiment d'élèves motivés" tout en permettant une flexibilité (possibilité de rejoindre ou de quitter le groupe).

2. Le Rôle Crucial de l'Accompagnement Adulte

L'Écueil de l'« Adultisme »

Eveline Bois met en garde contre l'« adultisme », une tendance des adultes à concevoir des dispositifs pour les élèves en minimisant leur capacité à faire des choix et des propositions.

Le témoignage du professeur Raphaël Grass est emblématique : il a commencé par apporter lui-même les projets avant de réaliser qu'ils ne correspondaient pas aux attentes des élèves et de leur "donner la parole".

• Conséquence : Un décalage se crée entre les désirs des élèves (sauver le monde) et les actions qu'on leur propose (installer un cendrier devant le lycée).

• Solution : L'accompagnement doit évoluer pour faire confiance aux élèves et leur laisser l'initiative.

Le Référent : un Équilibriste entre Soutien et Autonomie

Le rôle de l'enseignant ou du CPE référent est central et complexe. Il ne s'agit pas de diriger mais de faciliter.

• Partir des préoccupations des élèves : Sandrine Aoussour insiste sur l'importance de partir des idées des élèves (créer un potager, un "coin zen") et d'aider à les concrétiser en trouvant des solutions (budgets participatifs, partenariats).

• Proposer sans imposer : L'adulte peut aussi être force de proposition (installation d'une ruche via une fondation), mais ces propositions sont soumises aux élèves.

• Besoin d'adultes "entreprenants" : Du point de vue de l'éco-déléguée Laur, les élèves attendent des adultes qu'ils soient encore plus proactifs pour les aider à réaliser leurs projets les plus ambitieux, comme la végétalisation de la cour.

Les témoignages d'anciens éco-délégués de lycée confirment ce besoin d'un équilibre : ils préconisent une "instance autonome avec une certaine flexibilité", où ils peuvent travailler seuls pour libérer la parole, tout en bénéficiant de l'accompagnement des adultes pour les aspects logistiques et financiers.

3. Des Éco-gestes à la Transformation Durable

Le Spectre des Actions

Les projets menés par les éco-délégués couvrent un large éventail, de l'action symbolique à la transformation structurelle de l'établissement.

| Type d'Action | Exemples Concrets du Podcast | | --- | --- | | Éco-gestes classiques | Tri des bouchons, du papier, lutte contre le gaspillage alimentaire, ramassage des poubelles de tri. | | Amélioration de l'environnement scolaire | Installation d'un "coin zen", de plantes dans les classes, d'un apiscope, d'hôtels à insectes. | | Projets ambitieux et structurels | Création d'un potager, d'une zone de biodiversité, projet de végétalisation de la cour de récréation. | | Sensibilisation et citoyenneté | Campagnes de sensibilisation dans les classes, organisation d'une "manif au collège pour le climat". | | Actions sociales (ODD) | Recherche de sponsors pour un distributeur de protections périodiques, aménagement de la cour pour une meilleure égalité filles-garçons, collecte pour le Secours populaire. |

Dépasser la "Vitrine Verte"

Eveline Bois souligne le risque que les projets ne soient qu'une "vitrine verte". Le passage à une transformation durable dépend de plusieurs facteurs :

1. La qualité de l'accompagnement : Un bon accompagnement permet de dépasser les actions de surface pour s'attaquer à des projets de plus grande ampleur.

2. L'élargissement des thématiques : L'engagement va au-delà de l'écologie stricte pour inclure les 17 ODD, comme le bien-être animal ou l'égalité des genres.

Sandrine Aoussour cite l'exemple d'un projet de réaménagement de la cour initié par les filles pour contrer l'occupation de l'espace par les garçons.

3. Le frottement au réel : Les projets ambitieux confrontent les élèves aux réalités du monde adulte : recherche de financements (devis, sponsors), complexité des règles (mobilier urbain), et temporalité administrative.

4. Obstacles et Limites à l'Action

L'engagement des éco-délégués se heurte à des difficultés systémiques et culturelles.

• Le Temps et l'Argent :

◦ Le "temps des adultes" : Les élèves découvrent la lenteur des processus de décision et de mise en œuvre, ce qui peut être une source de frustration.   

◦ Le financement : La recherche de fonds est un obstacle majeur. Les élèves réalisent que les projets ont un coût élevé (ex : "un banc ça coûte extrêmement cher").   

◦ L'emploi du temps : Il n'y a pas de temps institutionnel dédié. Les réunions ont lieu sur la pause méridienne, après les cours ou, plus rarement, sur le temps de classe, ce qui pose des questions d'organisation et d'équité.

• Les Freins Institutionnels et Sociaux :

◦ La gouvernance : Le soutien de la direction est crucial mais fluctuant. Sandrine Aoussour parle de "montagnes russes" selon les équipes de direction en place.  

◦ Le manque de reconnaissance : Les éco-délégués peuvent souffrir d'un manque de reconnaissance de la part de leurs camarades ("vous servez à quoi, il y a déjà les délégués").  

◦ La valorisation : La question de la valorisation de leur engagement (par exemple, sur le dossier scolaire) reste à creuser pour éviter le désengagement.

5. Le "Pouvoir d'Agir" : Impacts et Bénéfices

Malgré les obstacles, le dispositif, lorsqu'il fonctionne bien, a un impact profondément positif sur tous les acteurs.

• Pour les élèves :

◦ Développement personnel : Gain de confiance en soi, joie de partager et de réaliser des projets collectifs.  

◦ Développement de compétences : Prise de parole en public, gestion de projet, argumentation, etc.    ◦ Développement citoyen : Le dispositif est un apprentissage concret de la citoyenneté. Certains élèves poursuivent leur engagement en dehors du collège (ex: au Secours populaire).  

◦ Sentiment d'empowerment : "Les jeunes interrogés qui se sentent libres et à qui on fait confiance entretiennent un fort sentiment du pouvoir agir" (Eveline Bois).

• Pour les enseignants et l'établissement :

◦ Épanouissement professionnel : Les référents parlent de "joie" et de "contact privilégié" avec les élèves.  

◦ Transformation des pratiques : L'engagement en tant que référent modifie le regard des adultes sur le potentiel des élèves et peut transformer leurs pratiques de classe.   

◦ Dynamique collective : Un projet réussi peut rayonner et impliquer toute la communauté éducative (gestionnaires, direction, agents), devenant un véritable projet d'établissement.

Concepts Clés et Inspirations

• De la responsabilité de surface à la responsabilité intégrale : Eveline Bois cite la chercheuse Luce Sauvé pour distinguer deux approches de l'écocitoyenneté :

1. Responsabilité de surface : Limitée aux "bons gestes" et à une vision normative (écocivisme).

2. Responsabilité intégrale : Implique une "réflexion critique, un pouvoir d'agir et la participation à la vie démocratique".

L'objectif est de tendre vers cette seconde approche.

• Le bonheur comme projet collectif : Sandrine Aoussour s'inspire d'un rapport de l'UNESCO pour souligner que le bonheur à l'école est un projet communautaire.

La "joie de se découvrir capable d'être au service d'un collectif" est un levier d'apprentissage puissant.

• L'empouvoirement : Ce néologisme résume l'objectif final du dispositif : donner réellement du pouvoir aux élèves et aux enseignants pour qu'ils deviennent les moteurs du changement.

Droits de l'enfant : Transformer l'École de l'intérieur

Synthèse exécutive

Ce document de synthèse analyse les stratégies et les impacts de l'intégration des droits de l'enfant au cœur du fonctionnement de l'école.

Basée sur les témoignages d'experts et de praticiens, l'analyse révèle que si la France a ratifié la Convention Internationale des Droits de l'Enfant (CIDE) depuis 1990, son application reste inégale, notamment en ce qui concerne le droit à l'expression et à la participation des élèves.

L'approche préconisée dépasse le simple enseignement théorique des droits pour les incarner dans la posture des adultes, les relations interpersonnelles et l'organisation même de l'établissement.

Le programme "École amie des droits de l'enfant" de l'UNICEF sert de modèle central, illustrant une démarche qui vise un changement de culture profond et durable.

Cette méthode s'appuie sur un diagnostic participatif, l'implication de toute la communauté éducative (enseignants, élèves, personnels, parents) et l'utilisation d'outils concrets comme la "marche exploratoire" pour évaluer l'environnement scolaire du point de vue de l'enfant.

Les bénéfices identifiés sont significatifs : amélioration notable du climat scolaire, renforcement du respect de soi et des autres, et développement précoce des compétences citoyennes.

Les données issues d'expériences internationales démontrent une augmentation du sentiment de sécurité et de l'écoute perçue par les élèves, ainsi que de leur capacité à influencer les décisions qui les concernent.

Cependant, la mise en œuvre se heurte à des défis majeurs, tels que la prévalence de l' "adultisme" – la tendance des adultes à décider à la place des enfants – et la perception d'une surcharge de travail pour les enseignants.

La clé du succès réside dans un engagement sur le temps long, considérant ces programmes non comme une initiative ponctuelle mais comme un investissement fondamental pour former des citoyens actifs et responsables.

État des lieux des droits de l'enfant dans le système éducatif français

La Convention Internationale des Droits de l'Enfant (CIDE) : Un cadre juridique sous-appliqué

La CIDE, adoptée par les Nations Unies en 1989 et ratifiée par la France en 1990, constitue le socle juridique des droits de l'enfant.

Ce texte de 54 articles protège les individus de 0 à 18 ans et couvre l'ensemble de leurs droits fondamentaux.

Cependant, selon Valérie Becket, professeure en sciences de l'éducation, l'application de cette convention en France est "inégale selon les domaines".

La France n'est pas considérée comme un "bon élève", particulièrement sur les enjeux d'expression et de participation.

Des enquêtes comparatives à l'échelle européenne montrent que, malgré l'existence de dispositifs comme les conseils d'école ou les conseils d'enfants, un décalage persiste entre les droits permis et le ressenti réel des enfants, plaçant parfois la France en bas du classement.

Julie Zarlot, de l'UNICEF France, précise que si la France est exemplaire dans certains domaines comme le droit global à la santé ou à l'éducation, des manques subsistent pour certains enfants qui n'ont pas un accès suffisant à l'école, à la santé ou à la protection.

La perception des droits à l'école

L'environnement scolaire présente des tensions inhérentes à l'application des droits de l'enfant. Richard Côtier, directeur d'école, souligne que "l'organisation prend le pas sur le respect de chacun".

La focalisation sur les objectifs d'apprentissage peut parfois occulter la nécessité de garantir les droits fondamentaux des élèves.

• L'équilibre Droits/Devoirs : Une réaction fréquente des adultes (enseignants, parents) à l'évocation des droits de l'enfant est la question des devoirs.

La réponse apportée est que le droit de l'un implique le devoir pour l'autre de le respecter. "Le devoir, c'est le devoir de respecter les droits de tous, y compris les siens propres et ceux des autres."

• Écart de perception : Les diagnostics menés en amont des projets révèlent souvent un décalage entre la perception de l'école par les élèves, qui la vivent de l'intérieur, et celle de leurs parents, qui sont à l'extérieur.

Cette différence justifie la nécessité de recueillir le point de vue de toutes les parties prenantes.

Le programme "École amie des droits de l'enfant" : Une approche transformative

Philosophie et approche pédagogique

Le programme de l'UNICEF est présenté comme une démarche de prévention positive.

Plutôt que de se concentrer sur la lutte contre des problèmes (comme le harcèlement) par une approche "par la négative", il vise à "motiver tout le monde pour faire en sorte que les droits de tous soient respectés".

L'approche pédagogique de l'UNICEF, qualifiée d'"approche par les droits", repose sur trois piliers :

1. Apprendre sur les droits : Acquérir la connaissance de la CIDE.

2. Apprendre par les droits : Expérimenter les droits dans la pratique quotidienne, via la posture de l'enseignant et le fonctionnement de l'école.

3. Apprendre pour les droits : Devenir capable de défendre ses propres droits et ceux des autres.

L'objectif est un "changement de comportement" et un "renforcement des capacités" des adultes comme des enfants. Il ne s'agit pas simplement d'un apport de connaissances, mais d'une transformation profonde du fonctionnement de l'école.

Mise en œuvre concrète à l'école L. Martine

L'école dirigée par Richard Côtier, engagée dans le programme depuis un an et demi, illustre cette mise en œuvre.

• Comité de Pilotage (Copil) : Un comité a été créé pour piloter le projet, rebaptisé "Conseil de vie citoyenne" pour préparer les élèves au collège.

Sa particularité est d'inclure un large éventail d'acteurs : élèves, enseignants, AVS, personnel d'entretien, animateurs du périscolaire.

Ce lieu permet de "penser tous avec nos regards différents le fonctionnement de l'école du point de vue des droits".

• La "Marche Exploratoire" : Cet outil concret consiste à parcourir l'école en se posant des questions spécifiques sous l'angle d'un droit (ex: la sécurité).

Les élèves et adultes observent et analysent des lieux précis pour déterminer s'ils s'y sentent en sécurité, si les adultes sont perçus comme un secours potentiel, etc.

Cette démarche permet d'objectiver le diagnostic initial en se basant sur la perception et le vécu de l'enfant.

• Une approche modeste et progressive : La première phase a consisté à assurer une formation sur les droits de l'enfant dans toutes les classes et à mettre en place les structures participatives.

L'accent est mis sur la modestie des objectifs annuels pour assurer leur réalisation concrète et maintenir la confiance dans le processus.

Le temps long comme condition du succès

Richard Côtier insiste sur le fait que la transformation d'une culture scolaire est un processus long.

Il considère la durée de trois ans du programme UNICEF comme "juste la piqûre, juste le vaccin". Selon lui, il faudra "peut-être encore 5, 10 ans derrière" pour qu'un établissement puisse affirmer avoir durablement intégré cette culture.

Le but est de créer une dynamique pérenne où la communauté éducative constate un changement profond et irréversible dans son fonctionnement.

Impacts, défis et généralisation

Impacts mesurables sur le climat scolaire et les élèves

L'expérience du Royaume-Uni, où le programme existe depuis plus de dix ans dans 4500 écoles, fournit des données quantitatives sur son impact.

| Indicateur d'impact (Évaluation au Royaume-Uni) | Chiffres clés | | --- | --- | | Amélioration du respect de soi et des autres | 93 % des enfants | | Augmentation du sentiment de sécurité à l'école | \+ 5 % | | Augmentation du sentiment d'être écouté à l'école | \+ 5 % | | Augmentation de la capacité à influencer les décisions | \+ 14 % des enfants | | Augmentation du sentiment d'être respecté par les adultes/pairs | \+ 11 % | | Connaissance supérieure de leurs droits | \+ 37 % des enfants |

Au-delà des chiffres, l'impact qualitatif est la formation de futurs citoyens qui ne sont pas "relativement passifs", mais qui ont expérimenté que leur parole peut avoir un effet sur le monde qui les entoure et que le changement nécessite un engagement collectif.

Surmonter les freins et les obstacles

• L' "Adultisme" et la peur de la contestation : Valérie Becket identifie un frein majeur dans la tendance des adultes à voir les "risques" (désordre, contestation) de la participation des élèves plutôt que les "bénéfices" à long terme.

Cette posture, qui consiste à décider "à la place de l'enfant", peut priver ce dernier d'expériences nécessaires à son développement.

• La charge de travail des enseignants : La crainte que ces programmes représentent une "couche" supplémentaire de travail est une objection fréquente. Julie Zarlot répond que les outils pédagogiques de l'UNICEF sont conçus pour être directement liés aux programmes scolaires, permettant aux enseignants de "piocher" dans différentes disciplines pour illustrer les droits "sans en avoir l'air".

Au-delà du primaire : Application au collège et au lycée

Valérie Becket note que l'enseignement secondaire dispose déjà de nombreuses structures de participation (Conseil de la Vie Collégienne, Conseil de la Vie Lycéenne, délégués). Cependant, leur existence ne garantit pas une meilleure application des droits ni une meilleure écoute des élèves.

Elle suggère que des outils comme la "marche exploratoire" seraient très pertinents pour les adolescents afin d'analyser leur vécu de l'établissement.

Surtout, elle insiste sur la nécessité de créer des "passerelles" entre le primaire et le secondaire pour assurer une continuité. Sans cela, un élève habitué à participer et à être écouté risque de subir un choc ("patatra") en arrivant dans un environnement où il "ne peut plus rien dire".

Citations clés

Valérie Becket, sur le droit le plus important à travailler à l'école : "Le droit d'avoir un point de vue."

Richard Côtier, sur la nécessité d'un engagement sur le long terme : "Le programme UNICEF par exemple il est prévu sur 3 ans et moi je pense que 3 ans c'est juste la piqûre, c'est juste le vaccin.

Ce n'est pas le temps qu'il va falloir pour construire un système où vraiment on aura pris en compte ce phénomène là."

Julie Zarlot, sur l'intégration des droits dans le quotidien : "On peut parler des droits de l'enfant et les rendre quotidien, effectif, presque sans en avoir l'air."

Richard Côtier, sur le risque de ne pas favoriser la participation : "Le risque de faire grandir des élèves qui sont pas dans la participation [...] ça veut dire que on fait des enfants qui sont relativement passifs, qui laissent prendre les autres des initiatives parce que finalement on leur demande pas leur avis."

Santé Mentale à l'École : État des Lieux, Enjeux et Stratégies

Résumé Exécutif

Ce document de synthèse analyse l'état critique de la santé mentale des élèves dans le système éducatif français, en s'appuyant sur les témoignages d'experts de terrain.

Il met en lumière une crise croissante, caractérisée par une augmentation des troubles dépressifs, des addictions et des tentatives de suicide chez les jeunes.

Face à ce phénomène, l'institution scolaire, bien que consciente de l'enjeu, peine à déployer une réponse à la hauteur, confrontée à un manque de ressources humaines (médecins, infirmières scolaires) et à un déficit de formation généralisée des personnels.

Dans ce contexte, les enseignants se retrouvent en première ligne, agissant comme des "sentinelles" essentielles mais souvent démunies.

Deux stratégies d'action complémentaires émergent :

d'une part, la structuration de l'intervention par des formations certifiées en premiers secours en santé mentale, comme le protocole "AÉRÉ" de PSSM France, qui vise à donner aux adultes un cadre d'action sécurisé.

D'autre part, le développement de projets pédagogiques holistiques, à l'image de l'initiative "On se bouge", qui intègrent le bien-être et le "vivre ensemble" au cœur des apprentissages, améliorant ainsi la qualité de vie des élèves et des équipes.

La conclusion est claire : une approche combinant la formation des adultes, la création d'un environnement scolaire bienveillant et l'implication des jeunes eux-mêmes est indispensable pour transformer l'école en un lieu promoteur de santé mentale.

--------------------------------------------------------------------------------

I. Le Constat : Une Crise de Santé Mentale Croissante chez les Jeunes

A. L'Ampleur du Phénomène

La question de la santé mentale des jeunes n'est plus un sujet tabou et s'impose avec une urgence inédite, au point d'être désignée "grande cause nationale pour 2025".

Les professionnels de l'éducation dressent un constat alarmant, corroboré par de multiples études.

• Une souffrance inédite : Damien Duran, IAPR Établissement et Vie Scolaire, témoigne : « Je travaille dans l'éducation nationale depuis 1979 [...] et je n'ai jamais vu autant de jeunes en souffrance dans les établissements scolaires : de jeunes dépressifs, ayant des troubles du comportement, des addictions diverses et beaucoup de tentatives de suicide. »

• Une visibilité accrue : Anaïs Mangin, professeure d'EPS, observe que les élèves « vont de plus en plus mal et le montrent corporellement ou même via les réseaux sociaux ».

• Une problématique précoce : La crise ne se limite pas aux adolescents.

Les phénomènes de harcèlement et les troubles du comportement sont de plus en plus présents et "massifiés" dès le premier degré (école maternelle et élémentaire).

B. Les Facteurs Aggravants Identifiés

Plusieurs facteurs sociétaux et relationnels sont identifiés comme contribuant à la détérioration de la santé mentale des élèves.

• L'impact des réseaux sociaux : La frontière entre la sphère scolaire et la sphère privée s'est estompée, privant les jeunes de moments de répit.

Selon Anaïs Mangin, « les jeunes n'ont pas de pause en fait ».

• La défiance envers les adultes : Un point jugé fondamental par Damien Duran est la « défiance croissante à l'égard des adultes ».

Cette perte de confiance constitue « le terreau de l'agressivité » et une marque d'inquiétude face à l'avenir.

II. La Réponse Institutionnelle : Entre Prise de Conscience et Manque de Moyens

L'Éducation Nationale reconnaît l'ampleur du défi, mais sa capacité d'action reste limitée par des contraintes structurelles et un manque de préparation historique.

A. Une Institution en Difficulté

Selon Damien Duran, la réponse institutionnelle est actuellement « faible par rapport à l'ampleur du phénomène ». Plusieurs points de friction sont soulignés :

• Pénurie de personnel qualifié : L'institution fait face à un manque criant de médecins scolaires, difficiles à recruter, et à un nombre insuffisant d'infirmières, qui ne sont pas présentes dans tous les établissements.

• Déficit de formation : La majorité des personnels n'a pas été formée pour aborder ces problématiques.

Des plans de formation se déploient progressivement, sur le modèle du programme Phare contre le harcèlement, mais cela « prend du temps ».

B. Le Rôle Central mais Complexe des Enseignants

Les enseignants sont au cœur du dispositif de repérage et de premier soutien, un rôle qu'ils assument avec engagement malgré les difficultés.

• Des "sentinelles" en première ligne : Les professeurs d'EPS, comme Anaïs Mangin, se perçoivent comme des "sentinelles" capables de détecter un mal-être par l'expression corporelle des élèves, souvent avant leurs collègues.

• Le défi de l'intégration : La principale difficulté pour les enseignants est de trouver le temps d'intégrer la prévention et le soutien en santé mentale aux exigences de leurs programmes scolaires.

• Un engagement massif : Malgré ces obstacles, Damien Duran souligne l'implication remarquable des enseignants, qui sont « massivement présents » dans les formations sur le harcèlement et la santé mentale, y compris hors temps scolaire.

III. Stratégies d'Action : Formation et Projets de Terrain

Pour répondre à cette crise, deux approches complémentaires se dessinent : la formation structurée des personnels et la mise en place de projets pédagogiques innovants.

A. Le Secourisme en Santé Mentale : Structurer l'Intervention

L'objectif est de permettre à chaque adulte d'intervenir "à bon escient", en évitant les maladresses qui peuvent aggraver une situation.

• Le Protocole PSSM France ("AÉRÉ") : Issu d'une méthode australienne, ce programme de formation de deux jours propose un protocole d'intervention en quatre étapes pour la prise en charge d'une personne en difficulté.

◦ Approcher la personne, évaluer et assister en cas de crise.  

◦ Écouter activement et sans jugement.    ◦ Réconforter et informer sur les aides existantes.  

◦ Aller vers des professionnels (médecin, psychologue, etc.).

• Le Protocole du Ministère (DGESCO) : Ce protocole est décrit comme plus "organisationnel". Il vise à identifier et cartographier les ressources et partenaires disponibles dans et hors de l'établissement pour orienter les équipes.

Les deux démarches sont vues comme parfaitement complémentaires.

B. L'Exemple du Projet "On se bouge" : Une Approche Holistique

Le projet mené par Anaïs Mangin au collège Croix de Metz est un exemple concret d'une école qui prend soin de ses élèves en intégrant le bien-être aux apprentissages.

| Caractéristiques du Projet "On se bouge" | Description | | --- | --- | | Concept de base | "Apprendre autrement" en associant l'EPS à d'autres disciplines (histoire-géo, maths, physique) lors de sorties hebdomadaires. | | Objectifs atteints | Renforcer le "vivre ensemble" et créer un fort sentiment d'appartenance (à la classe, à une petite équipe). | | Actions sur la santé mentale | Création de six ateliers sportifs sur l'estime de soi, la confiance et la gestion des émotions, en partenariat avec le Centre Médico-Psychologique (CMP) local. | | Impact | Amélioration notable du bien-être des élèves, mais aussi des enseignants qui travaillent en équipe et partagent la charge de travail. |

C. L'Importance des Gestes Quotidiens et de la Formation des Jeunes

Au-delà des programmes structurés, l'amélioration de la santé mentale passe par des actions simples et par l'implication directe des élèves.

• Le pouvoir des micro-interactions : Anaïs Mangin insiste sur l'impact de gestes simples comme dire "bonjour", "bonne journée" ou "as-tu bien dormi ?", qui peuvent amorcer une relation de confiance et changer le déroulement de la journée d'un élève.

• Former les jeunes : Damien Duran plaide pour la formation des jeunes eux-mêmes au secourisme en santé mentale.

Son anecdote d'une élève de 6e en pleurs, inaccessible pour lui (l'adulte) mais accompagnée par une camarade, illustre que les pairs sont souvent les mieux placés pour apporter un premier soutien.

IV. Perspectives et Recommandations

A. Changer de Paradigme : De "Climat Scolaire" à "Qualité de Vie au Travail"

Damien Duran propose une évolution sémantique et conceptuelle :

« Je pense qu'on devrait s'intéresser à la qualité de vie au travail des élèves et [...] faire le parallèle avec la qualité de vie au travail des personnels. »

Cette approche positionne le bien-être comme une condition structurelle et non comme une simple "ambiance", reconnaissant que la souffrance des personnels et celle des élèves sont interconnectées.

B. Les Bénéfices de la Formation pour les Personnels

La formation en secourisme en santé mentale offre des avantages concrets et profonds, tant sur le plan professionnel que personnel.

• Réduction de l'anxiété et gain de compétence : Elle permet de se sentir "moins inquiet" face à une situation de crise et de savoir comment réagir.

• Un changement de posture : Damien Duran raconte comment sa formation lui a permis de "switcher" de la panique à l'action lors d'une tentative de suicide de sa voisine, en appliquant un raisonnement structuré.

• Une aide pour soi et pour les autres : La formation change le regard sur autrui, renforce l'empathie et donne des outils pour mieux accompagner ses collègues et ses proches.

C. Ressources Clés

Les intervenants ont partagé plusieurs ressources pour approfondir le sujet et passer à l'action :

1. Le Cartable des compétences psychosociales : Une plateforme proposant des outils et des activités pratiques (jeux, exercices de 10 à 30 minutes) pour les enseignants, utilisables en classe pour travailler sur la gestion des conflits ou d'autres compétences.

2. Le site de PSSM France (pssmfrance.fr) : Pour s'informer sur les modules de formation en premiers secours en santé mentale, accessibles aux personnels ou à titre privé.

3. Le Protocole Santé Mentale des Élèves (DGESCO) : Document officiel ("Du repérage à la prise en charge") qui doit être disponible et renseigné dans chaque établissement, recensant les partenaires locaux et la marche à suivre en cas de crise.

Dossier de Synthèse : Le Jeu Libre comme Outil Pédagogique Essentiel à l'École

Synthèse Exécutive

Ce document synthétise les perspectives d'experts sur le rôle fondamental du jeu libre dans le développement de l'enfant et sa mise en œuvre en milieu scolaire.

L'analyse révèle que le jeu libre est une activité essentielle, souvent mal comprise et sous-évaluée, qui participe directement à la construction de soi, au développement de la pensée et à l'acquisition de compétences transversales.

Les points critiques à retenir sont les suivants :

1. Nature du Jeu Libre : Le jeu, par définition, est libre. Il est caractérisé par la décision du joueur, l'établissement d'un cadre de "second degré" (distinct de la réalité), l'absence de conséquences réelles ("frivolité"), une organisation interne et une incertitude quant à son issue.

Il se distingue radicalement des "jeux" éducatifs structurés qui sont en réalité des formes de travail déguisé avec des objectifs et des attentes externes.

2. Rôle de l'Enseignant : La posture professionnelle requise est celle d'un observateur disponible et d'un architecte du cadre ludique, et non celle d'un intervenant directif.

L'enseignant doit agir sur l'environnement (aménagement de l'espace, choix des objets, règles de fonctionnement) pour permettre au jeu d'advenir, plutôt que de diriger l'activité des enfants.

3. Bénéfices Pédagogiques : Bien que 80% des processus à l'œuvre dans le jeu soient invisibles, ses bénéfices sont profonds.

Le jeu libre favorise la concentration, la socialisation, la créativité, la libération de la parole et l'expérimentation sans crainte de l'échec.

Il constitue un espace d'expression émotionnelle crucial et un terrain d'observation privilégié pour déceler les besoins des élèves, notamment ceux à besoins éducatifs particuliers.

4. Mise en Pratique : L'instauration du jeu libre en classe repose sur un aménagement réfléchi : des espaces bien définis, des objets de qualité et réalistes en quantité raisonnable, et une préparation soignée qui valorise l'activité.

Le rangement devient un acte pédagogique à part entière.

5. Portée Universelle : Le besoin et les bienfaits du jeu libre ne se limitent pas à l'école maternelle.

Il est tout aussi pertinent et nécessaire à l'école élémentaire, offrant un espace d'exploration et de complexification des apprentissages adapté à chaque âge.

En conclusion, redonner sa place au jeu libre à l'école n'est ni un luxe ni une perte de temps, mais une stratégie pédagogique fondamentale qui, en faisant confiance à l'enfant, permet l'émergence d'apprentissages informels profonds et la construction d'un rapport positif à l'école.

--------------------------------------------------------------------------------

1. Définir le Jeu Libre : Une Affaire Sérieuse

Le concept de "jeu libre" souffre d'une mécompréhension fondamentale, souvent perçu à tort comme une simple récréation.

Les experts soulignent qu'il s'agit d'une activité essentielle au développement de l'être humain.

1.1. La Nature Intrinsèque du Jeu

Selon Nadège Aberbuche, ludo-pédagogue, le terme "jeu libre" est un pléonasme.

S'appuyant sur les travaux du sociologue Gilles Brougère, elle affirme que le jeu, par définition, est libre.

Il n'est pas une simple activité, mais un cadre spécifique que le joueur décide de créer et d'habiter.

Citation clé : "On rate l'essentiel [...] c'est-à-dire que le jeu participe à la construction de soi, à la construction de la pensée, donc ça n'est pas que pour se distraire et s'amuser, c'est absolument essentiel au développement de l'être humain." - Nadège Aberbuche

1.2. Les Cinq Caractéristiques du Jeu (selon Gilles Brougère)

Pour clarifier ce qu'est le jeu, cinq caractéristiques principales sont identifiées :

1. Le Second Degré : Le jeu n'est pas la réalité. Le joueur adhère à un cadre fictionnel, une réalité alternative, pour la durée du jeu.

2. La Prise de Décision : Le jeu n'existe que par les décisions des joueurs.

Ils décident de tout : entrer dans le jeu, en définir les contours, et même en sortir à tout moment.

3. La Frivolité : Ce qui se passe dans le jeu n'a pas de conséquences directes sur la réalité du joueur.

Cette caractéristique est cruciale car elle autorise l'exploration, le tâtonnement, l'erreur et l'invention sans pression ni enjeu réel.

4. Les Mécanismes d'Organisation : Tout jeu, même le plus simple, est structuré. Les joueurs définissent des règles, des rôles, des scénarios et des limites.

5. L'Incertitude : L'issue du jeu n'est jamais connue à l'avance, ce qui en fait son "sel" et motive les joueurs à recommencer.

2. La Confusion Fondamentale : Jeu contre Travail Déguisé

Un obstacle majeur à la mise en place du jeu libre à l'école est la confusion entre le jeu authentique et les activités d'apprentissage ludifiées.

2.1. L'instrumentalisation du Jeu

Cécile Beautier Richard, enseignante en toute petite section, observe que de nombreux enseignants utilisent le mot "jeu" pour désigner des activités avec des objectifs pédagogiques précis (travailler les couleurs, les mathématiques) et parfois même une évaluation.

• Le point de vue de l'enseignant : Il s'agit d'un "travail" visant l'acquisition de compétences définies dans les programmes.

• Le point de vue de l'enfant : L'enfant, qui sait intuitivement ce qu'est jouer, peut se sentir "trompé" lorsque l'activité annoncée comme un jeu se révèle être un exercice scolaire.

Ce sentiment peut conduire à un désinvestissement de l'enfant vis-à-vis du jeu lui-même.

2.2. La Nécessité de la Clarté

Les intervenantes s'accordent sur l'importance d'être clair avec les enfants.

Il n'y a pas de honte à proposer des "ateliers" ou du "travail", car les enfants ont un désir naturel d'apprendre.

La distinction sémantique et conceptuelle est essentielle pour préserver l'intégrité et la puissance du jeu libre.

3. L'Importance Capitale du Jeu Libre pour le Développement de l'Enfant

Le jeu libre est un espace-temps où se déroulent des apprentissages informels, invisibles mais cruciaux.

3.1. Un Laboratoire Cognitif et Émotionnel

Cécile Beautier Richard illustre ce point avec l'exemple d'un élève de 3 ans manipulant des aimants pendant 15 minutes en totale concentration.

Citation clé : "80 % du jeu de l'enfant [...] n'est pas visible en fait à l'œil nu. [...] je ne sais pas ce qui se passait dans sa tête [...] visiblement il a l'air de se passer 1000 connexions à la seconde dans son cerveau et c'est super." - Cécile Beautier Richard

Ce temps, qui peut sembler improductif, est en réalité un moment de construction intense de la pensée, de la représentation spatiale et d'autres compétences non-identifiables sur le moment.

3.2. Un Espace d'Expression et de Transformation

• Libération de la parole : En grande section, des enfants peu locuteurs dans un cadre formel se mettent à parler abondamment lorsqu'ils jouent librement, distribuant les rôles et créant des scénarios complexes.

• Expression des émotions : Le jeu permet de "faire semblant" et d'exprimer des émotions ou des pulsions (colère, agressivité) de manière symbolique et sans conséquence.

Nadège Aberbuche insiste sur le fait que jouer à la bagarre ou à la guerre est un exutoire nécessaire qui, en étant autorisé dans le "faux", peut prévenir des passages à l'acte dans le "vrai".

Il est crucial de ne pas confondre ces jeux symboliques avec des activités dangereuses (comme le "jeu du foulard") qui ne sont pas des jeux.

• Inclusion : Le jeu libre est particulièrement bénéfique pour les élèves à besoins éducatifs particuliers.

Comme le souligne Cédric Guerro, directeur du Centre national de formation au métier du jeu et du jouet, le jeu "accepte l'autre tel qu'il est", sans les exigences parfois écrasantes des situations d'apprentissage formelles.

4. La Posture Professionnelle de l'Enseignant : De l'Intervention à l'Observation

Le succès du jeu libre dépend entièrement de la posture de l'adulte.

4.1. La Métaphore du "Culbuto"

Cédric Guerro propose la métaphore du "culbuto" (jouet qui revient toujours à sa base) pour décrire la posture de l'enseignant.

Sa position de base doit être celle de l'observateur disponible.

Toute intervention doit être une réponse à une observation et à l'interprétation d'un besoin, et non une action par défaut.

4.2. Agir sur le Cadre, pas sur l'Enfant

L'enseignant doit se concentrer sur la création et le maintien d'un cadre propice au jeu. Ce cadre comprend :

• L'aménagement de l'espace.

• Le choix et la disposition des objets.

• Les règles de fonctionnement claires (distinction entre "faire semblant" et "faire pour de vrai").

En agissant sur ce cadre, l'enseignant influence indirectement et positivement le comportement des enfants, leur permettant de développer leur jeu en autonomie et en sécurité.

4.3. Un Nouveau Regard sur l'Élève

L'observation du jeu libre permet de découvrir les élèves sous un autre jour, de voir émerger des compétences (concentration, socialisation, leadership) insoupçonnées dans un cadre scolaire classique.

L'enseignant voit alors "l'enfant plus que l'élève".

5. Mise en Pratique en Classe : Aménager un Environnement Propice

La mise en place du jeu libre n'est pas une improvisation mais le résultat d'un travail pédagogique rigoureux en amont.

5.1. L'Aménagement de l'Espace

Cécile Beautier Richard donne plusieurs conseils concrets :

• Se mettre à hauteur d'enfant pour concevoir les espaces.

• Définir clairement les zones de jeu (par exemple avec des morceaux de lino de couleurs différentes).

• Ne pas surcharger les espaces.

5.2. Le Choix des Objets

• Privilégier la qualité à la quantité. Des objets réalistes, fonctionnels et en bon état sont essentiels.

Une poêle doit être à la taille des aliments factices, une poupée ne doit pas être cassée.

• Organiser de manière logique et accessible. Éviter d'empiler les puzzles ; les objets doivent être facilement préhensibles.

5.3. Le Rangement comme Acte Pédagogique

Le temps passé à ranger et à préparer l'espace de jeu après le départ des enfants (30-40 minutes par jour pour Cécile Beautier Richard) est fondamental.

• Cela valorise l'activité aux yeux de l'enfant.

• Cela donne envie de jouer le lendemain.

• Cela constitue la préparation de la séance, au même titre que la préparation d'un atelier dirigé.

Citation clé : "Le jeu libre c'est pas ce qu'on fait quand les enfants ils ont plus rien à faire [...] Non, il faut le considérer comme un atelier à part entière." - Cécile Beautier Richard

6. Au-delà de la Maternelle : Le Jeu Libre pour Tous les Âges

L'importance du jeu libre ne s'arrête pas aux portes de l'école élémentaire. Nadège Aberbuche qualifie ce combat de "même combat" pour tous les niveaux.

• À la ludothèque "Les enfants du jeu", des classes jusqu'au CM2 sont accueillies.

• Les élèves plus âgés se réapproprient des espaces de jeu de "petits" (bacs à sable, etc.), mais pour y mener des expérimentations plus complexes, adaptées à leur développement cognitif. Il ne s'agit pas d'une régression.

• C'est une occasion rare pour les enseignants du primaire de voir leurs élèves jouer, une activité qui a largement disparu des cours de récréation, souvent au profit de tensions et de violences.

--------------------------------------------------------------------------------

7. Recommandations et Ressources

Les expertes proposent des ressources pour les enseignants souhaitant se lancer ou approfondir leur pratique du jeu libre.

| Type | Titre | Auteur / Source | Description | Recommandé par | | --- | --- | --- | --- | --- | | Livre (Théorique) | Jouer/Apprendre | Gilles Brougère | Une référence pour comprendre la distinction et les passerelles entre éducation formelle et informelle. | Cécile Beautier Richard | | Documents Pédagogiques | Jouer et apprendre | Eduscol | Des documents très bien faits (cadrage général, volets par type de jeu, vidéos) pour se lancer. | Cécile Beautier Richard | | Livre (Psychologie) | Libre pour apprendre | Peter Grey | Un ouvrage d'un psychologue américain qui requestionne la notion d'apprentissage, avec un chapitre important sur le jeu. | Nadège Aberbuche | | Film | Permis de jouer | \- | Un film tourné à la ludothèque "Les enfants du jeu", centré sur le jeu symbolique des enfants d'âge élémentaire, avec des témoignages d'enseignants. | Nadège Aberbuche |

Le Conseil de Classe Participatif : Analyse d'une Initiative Pédagogique

Résumé Exécutif

Ce document de synthèse analyse le projet de "conseil de classe participatif" mis en œuvre par Émilie Roger, professeure de SVT au collège de la Largue.

Né du constat de l'inefficacité et du manque d'engagement suscités par les conseils de classe traditionnels, ce dispositif vise à transformer cette instance en un outil pédagogique centré sur l'élève.

En s'appuyant sur les sciences cognitives et la métacognition, le projet prépare les élèves de 6ème à auto-évaluer leurs compétences, à formuler un bilan personnel et à définir des objectifs de progression.

Les principaux résultats montrent un engagement accru des élèves, qui développent une conscience juste de leurs points forts et de leurs difficultés.

Le format, bien que lourd sur le plan organisationnel, génère des moments d'échange d'une grande richesse, valorisant l'élève et renforçant le dialogue pédagogique.

Les défis majeurs résident dans la logistique complexe, qui limite son déploiement à un seul niveau, et dans la nécessité d'un suivi régulier pour ancrer les objectifs fixés.

L'implication des parents, expérimentée ponctuellement, est identifiée comme un levier majeur pour décupler l'impact du dispositif.

1. Contexte et Genèse du Projet

L'initiative du conseil de classe participatif a été développée en réponse à une double insatisfaction concernant le format traditionnel de cette instance.

Le Constat d'Inefficacité

Émilie Roger, en tant que professeure de SVT participant à de nombreux conseils de classe, a identifié plusieurs limites au format classique :

• Rôle passif des enseignants : Hormis le professeur principal, les autres enseignants assistent principalement à une "lecture d'appréciation globale" avec très peu d'échanges pédagogiques de fond.

• Absence de focus sur les compétences : Les discussions sont rarement centrées sur les compétences de l'élève et les moyens de les améliorer.

• Manque d'impact sur l'élève : Le déclencheur du projet fut la révélation qu'un élève n'avait même pas lu les conseils formulés sur son bulletin scolaire.

L'Objectif de Transformation

Face à ce constat, l'objectif était clair : "comment finalement transformer un conseil de classe classique en quelque chose qui pourrait être utile à l'élève où l'élève pourrait s'engager dans son évaluation de son parcours et pouvoir s'asseoir dessus pour progresser".

Le projet vise à rendre l'élève acteur de son évaluation et de sa progression.

2. Le Dispositif du Conseil de Classe Participatif

Le projet se décompose en une phase de préparation rigoureuse et un déroulement spécifique, repensé pour maximiser l'interaction individuelle.

La Phase de Préparation

Avant chaque conseil, trois à quatre séances sont organisées, généralement sur les heures de "devoir fait" ou de "vie de classe", pour préparer les élèves.

Cette préparation inclut :

1. Introduction aux Compétences : Explication de ce qu'est une compétence, comment elle est évaluée et comment atteindre les meilleurs niveaux de maîtrise.

2. Auto-positionnement : L'élève est invité à se positionner sur les différentes compétences évaluées.

3. Construction du Bilan : Les élèves apprennent à construire leur propre bilan, en identifiant leurs points forts et les points à améliorer pour la période suivante.

Le Déroulement Concret

La session du conseil de classe participatif dure au total entre 1h30 et 1h45.

• Session Plénière (15 minutes) : Un bilan global est présenté par les élèves délégués, puis par la professeure principale.

• Ateliers par Pôles : La classe est ensuite divisée en deux équipes équilibrées (ex: "pôle scientifique" et "pôle français").

• Entretiens Individuels (7 minutes par élève) : Chaque élève présente son bilan personnel aux enseignants du pôle.

Pour une classe de 30, chaque pôle gère environ 15 élèves.

3. Fondements Pédagogiques et Approche Cognitive

Le projet est explicitement ancré dans les apports des sciences cognitives, visant à doter l'élève d'une meilleure compréhension de ses propres mécanismes d'apprentissage.

• Formation en Neuroéducation : L'initiatrice du projet a obtenu un diplôme en neuroéducation et s'est formée auprès de l'association "Apprendre et former avec les sciences cognitives".

• Éducation au fonctionnement du cerveau : L'objectif est de former l'élève sur son propre cerveau : comment il apprend, mémorise et maintient son attention.

• Développement de la Métacognition : L'approche consiste à amener l'élève à réfléchir sur ses propres processus d'apprentissage.

Il est encouragé à s'auto-évaluer face à une tâche ("Est-ce que c'est facile, difficile ?"), et si elle est difficile, à identifier les stratégies à mettre en place ("Quelle aide tu pourrais demander pour justement atteindre tes objectifs ?").

4. Résultats, Impacts et Témoignages

Le dispositif a produit des effets significatifs sur l'engagement, la lucidité et la confiance des élèves.

L'Engagement et la Prise de Conscience

Le principal bénéfice observé est une prise de conscience par les élèves de leurs propres difficultés et de leurs capacités à progresser.

• Impact émotionnel sur l'enseignante : Émilie Roger témoigne être systématiquement impressionnée, au point d'avoir "envie de pleurer", en voyant "les plus timides qui osent parler, qui osent dire leur fragilité".

• Transformation des élèves "difficiles" : Même les élèves souvent perçus comme perturbateurs parviennent à verbaliser leurs difficultés (ex: le bavardage), ce qui est considéré comme une victoire pédagogique majeure.

Le fait qu'ils "s'expriment" sur leurs défis est vu comme "magnifique".

La Justesse de l'Auto-évaluation

Il est noté que les élèves font preuve d'une grande lucidité. Il y a rarement une différence entre leur auto-évaluation et les appréciations des enseignants sur le bulletin.

Témoignages d'Élèves

Les extraits de dialogues illustrent la capacité des élèves à analyser leur parcours et à se projeter.

| Thème | Citation de l'élève | Contexte / Analyse | | --- | --- | --- | | Effort et Motivation | "Ce qu'il faut savoir c'est qu'il aime pas l'école en fait. \[...\] Il fait d'énormes efforts pour réussir sans avoir forcément la motivation derrière ça." | Un élève exprime son manque d'intérêt pour certains sujets, tout en fournissant un travail important. | | Identification des Difficultés | "J'ai plus de difficultés à mémoriser l'histoire-géo \[...\] J'ai du mal à redire ce que j'ai appris." | L'élève distingue un problème de mémorisation d'un problème de compréhension. | | Fierté et Résilience | "Je suis fière d'avoir réussi à m'organiser pour réviser pour les contrôles, de ne pas avoir baissé les bras alors que c'est difficile pour moi." | Une élève met en avant sa capacité d'organisation et sa persévérance face à la difficulté. | | Définition d'Objectifs | "Mon objectif pour l'année prochaine serait de rester plus concentré. \[...\] Me mettre pas avec des personnes que j'aime bien forcément à côté." | Un élève identifie le bavardage comme sa difficulté et propose une stratégie concrète pour y remédier. | | Stratégies d'Entraide | "Quand eux \[les copains\] ils t'aident, est-ce que tu arrives mieux à comprendre ? - Oui un petit peu." | Une élève reconnaît que travailler avec ses pairs l'aide à mieux comprendre les exercices de mathématiques. |

5. Défis, Limites et Perspectives

Malgré son succès pédagogique, le dispositif fait face à des obstacles importants qui freinent son expansion.

Les Contraintes Organisationnelles

La "plus grosse difficulté" est d'ordre logistique.

• Gestion du temps : Le format se déroule sur des créneaux de cours (généralement 15h-17h), ce qui oblige à "libérer des classes" et à réorganiser les emplois du temps des enseignants et des élèves.

• Limitation au niveau 6ème : En raison de cette complexité, le projet est actuellement cantonné aux classes de 6ème. L'équipe pédagogique souhaiterait l'étendre au niveau 3ème, où il serait pertinent pour l'orientation, mais cela n'est pas réalisable pour le moment.

La Question du Suivi Post-Conseil

"L'après est plus difficile" et reste un point en cours d'amélioration.

L'oubli étant "biologique", il est nécessaire de rappeler régulièrement aux élèves leurs objectifs et de les interroger sur les moyens qu'ils mettent en œuvre pour les atteindre, afin d'ancrer durablement la progression.

Le Potentiel de l'Implication Parentale

Une expérience a été menée il y a deux ans en faisant venir les parents pour qu'ils écoutent le bilan de leur enfant et échangent avec l'équipe.

Cette formule est décrite comme "le top du top", car elle combine l'engagement de l'enfant et l'écoute du parent dans une démarche de "valorisation de l'élève".

L'Adoption par l'Équipe Enseignante

Le projet est activement soutenu et mis en place par trois professeurs principaux, qui seront quatre l'année prochaine.

D'autres collègues sont plus réticents, non pas pour des raisons pédagogiques, mais principalement à cause du "beaucoup de temps" que l'organisation requiert.

Synthèse sur l'Éducation à la Citoyenneté Numérique : S'appuyer sur les Pratiques des Jeunes

Résumé Exécutif

Ce document de synthèse analyse les perspectives et stratégies d'éducation à la citoyenneté numérique, basées sur les interventions d'experts en sociologie, en éducation au numérique et d'un praticien en milieu scolaire.

L'idée centrale est un changement de paradigme : passer d'une approche "riscocentrée", focalisée sur la protection et l'interdiction, à une posture d'accompagnement qui s'appuie sur les pratiques réelles et les centres d'intérêt des jeunes.

Les intervenants soulignent que les jeunes utilisent le numérique pour des raisons profondes liées à la construction identitaire, à la régulation du stress et à la recherche de réponses que les adultes ne fournissent pas toujours.

Pour être efficaces, les éducateurs doivent adopter une posture d'empathie, de légitimation des cultures numériques des jeunes et de co-construction des savoirs.

L'objectif final est de développer leur réflexivité, leur esprit critique et leur pouvoir d'agir, en les aidant à comprendre les mécanismes des plateformes, leurs droits, leurs devoirs et le potentiel émancipateur du numérique, plutôt que de se limiter à une posture de méfiance.

--------------------------------------------------------------------------------

1. Redéfinir la Citoyenneté Numérique au-delà des Risques

Le point de départ de la discussion est le constat que la notion de citoyenneté numérique est souvent perçue par les adultes à travers le prisme de l'inquiétude et de la protection.

Les intervenants s'accordent sur la nécessité d'élargir cette vision.

• Une Définition Élargie : La définition du Conseil de l'Europe est citée comme un modèle, incluant des dimensions positives telles que l'inclusion, la créativité, l'empathie et la participation active.

• Faire "avec eux" plutôt que "pour eux" : Il y a une prise de conscience croissante de l'importance d'impliquer les jeunes dans la construction de leur citoyenneté numérique.

• Un Vocabulaire Inadapté : Selon Nicolas Bourgeon, professeur documentaliste, le terme "citoyenneté numérique" est un jargon institutionnel qui ne résonne pas chez les élèves.

L'approche efficace consiste à "utiliser leur mot à eux".

Priorités pour l'Éducation au Numérique

Chaque intervenant définit une priorité pour l'éducation à la citoyenneté numérique :

| Intervenant | Organisation | Priorité | Citation Clé | | --- | --- | --- | --- | | Axel Dein | Directrice, Internet sans crainte | Comprendre | "Comprendre l'espace numérique dans lequel on évolue, comprendre les services qu'on utilise, comprendre les algorithmes pour être un utilisateur éclairé." | | Jocelyn Lachance | Sociologue, Crédat | Valoriser | "Ce qu'on oublie souvent, c'est que la plupart des jeunes se comportent quand même bien à l'heure du numérique et la question c'est en tant qu'adulte qu'est-ce qu'on est capable de valoriser les bonnes pratiques." | | Nicolas Bourgeon | Professeur Documentaliste | S'adapter | "Ce sont des mots qui appartiennent au vocabulaire plutôt institutionnel et l'approche que j'essaie d'avoir bah d'utiliser leur mot à eux." |

2. Changer le Regard des Adultes sur les Pratiques Numériques des Jeunes

Une critique fondamentale adressée à l'approche actuelle est le regard que les adultes portent sur les usages numériques des jeunes, souvent teinté de méconnaissance et de fantasmes.

Le Regard "Riscocentré" et ses Limites

Jocelyn Lachance identifie que l'intérêt des adultes pour les pratiques des jeunes est souvent "riscocentré", se concentrant sur les aspects délétères.

Cette focalisation a plusieurs conséquences négatives :

• Elle occulte les bénéfices : Les jeunes utilisent le numérique pour des raisons essentielles à leur développement : construction de l'identité, gestion de questions existentielles, socialisation.

• Elle crée un décalage : Les jeunes ont l'impression que les adultes "passent à côté de ce qui est l'essentiel pour eux", à savoir le sens et les avantages qu'ils trouvent en ligne.

La Solitude des Jeunes et l'Indisponibilité des Adultes

Un thème récurrent est le sentiment de solitude des jeunes face au numérique.

• Manque d'accompagnement : Selon Axel Dein, les jeunes "sont extrêmement seuls" et "n'identifient pas les adultes autour d'eux comme des personnes qui sont susceptibles de les accompagner".

• Le numérique comme palliatif : Jocelyn Lachance confirme que les jeunes vont chercher en ligne ce qu'ils ne trouvent pas auprès des adultes.

Une recherche sur l'usage de l'IA par les jeunes montre qu'ils s'en servent pour obtenir "une réponse structurée et rassurante" lorsqu'ils perçoivent les adultes comme indisponibles ou que le sujet est délicat (sexualité, mort).

La Question de l'Interdiction

L'interdiction est une pratique éducative structurante, mais son application au numérique soulève des questions complexes.

Jocelyn Lachance met en garde contre une approche simpliste :

1. Le Sens : Les adultes doivent s'interroger sur leurs motivations réelles derrière une interdiction.

2. L'Efficacité et le Déplacement : Interdire l'accès à un espace peut pousser les jeunes vers un autre espace potentiellement moins sécurisé.

3. La Perte de Bénéfices : L'interdiction peut supprimer des pratiques bénéfiques pour les jeunes, comme la régulation du stress.

L'exemple d'un lycée québécois interdisant les smartphones est parlant : les élèves ont révélé qu'ils utilisaient leur téléphone pour écouter de la musique et s'isoler afin de gérer leur stress avant les examens.

3. De la Prévention à l'Accompagnement : S'appuyer sur les Pratiques Réelles

La deuxième partie de la discussion se concentre sur les méthodes pour passer d'une posture de simple prévention à un véritable accompagnement, en partant des usages concrets des jeunes.

La Co-construction et l'Immersion

Internet sans crainte, dirigé par Axel Dein, développe des ressources (serious games, scénarios interactifs) en impliquant directement les jeunes.

• Le rôle des panels de jeunes : Ils sont essentiels pour assurer la justesse et l'authenticité des ressources.

Les jeunes poussent souvent les scénarios à être plus intenses pour refléter la réalité ("Mais là c'est trop tiède ce qu'on vit c'est plus intense c'est plus dur que ça.").

• Susciter l'esprit critique : L'objectif n'est pas de donner des leçons de manière descendante, mais de "les amener à prendre du recul, à se questionner".

Ces séances collectives permettent une "autorégulation" bienveillante entre pairs.

Partir des Centres d'Intérêt des Élèves

Nicolas Bourgeon mène un projet avec des élèves de 6ème sur les influenceurs, un sujet qui les passionne. La démarche est la suivante :

1. Point de départ : Les élèves choisissent un influenceur qu'ils apprécient.

2. Analyse guidée : Ils décryptent le modèle économique (économie de l'attention, monétisation), les partenariats commerciaux (encadrés par la loi de 2023) et les techniques pour capter l'audience.

3. Prise de conscience : Ce travail leur fait réaliser que lorsqu'ils consultent du contenu, "ils créent de la valeur". Les élèves identifient facilement les circuits financiers (produits, boutiques, microdons).

4. La Citoyenneté Numérique en Action : Vers l'Émancipation

La dernière partie explore les moyens de donner aux jeunes un réel pouvoir d'agir (empowerment) et de développer leur réflexivité.

L'Expérience "Digital Practice Awareness" (DPA)

Une recherche menée par Mélina Solari Landa auprès de lycéens offre des enseignements clés :

• La primauté du désir : "Le désir est le meilleur moteur de l'usage des adolescents."

Le besoin de socialiser et les émotions l'emportent sur une évaluation rationnelle des risques, même lorsque les jeunes sont informés de l'utilisation de leurs données.

• Difficulté avec la temporalité et la distance : Les jeunes ont du mal à percevoir comment leurs actions en ligne actuelles peuvent avoir des conséquences à long terme ou affecter des personnes à une échelle globale.

• L'inefficacité des approches prescriptives : Les logiques restrictives ne permettent pas de développer la réflexivité.

Développer la Réflexivité et la Confiance

• L'objectif de réflexivité : Pour Jocelyn Lachance, le but est d'amener les jeunes à réfléchir à ce qu'ils vivent et ressentent avant qu'une situation problématique ne survienne.

Un "carnet de déconnexion" accompagné est plus efficace qu'un simple défi.

• Le risque de briser la confiance : Une approche trop axée sur les risques peut être contre-productive.

Une jeune fille, après avoir reçu de la prévention, n'a pas osé parler à ses parents d'une expérience sur une application de rencontre par peur de se faire gronder.

• Légitimer leur culture : Pour Nicolas Bourgeon, établir la confiance passe par la reconnaissance de la légitimité de la "culture geek" des élèves.

Éduquer aux Droits, aux Devoirs et au Pouvoir d'Agir

Axel Dein insiste sur la nécessité de former les jeunes à la compréhension de leurs droits et devoirs en ligne, car leur première activité numérique est souvent sociale.

Internet sans crainte a développé une mallette pédagogique qui aborde trois axes :

1. Comprendre ses droits et devoirs.

2. Comprendre le rapport à l'autre en ligne (limites public/privé, liberté d'expression).

3. Comment le numérique donne le pouvoir d'agir.

S'inspirer des Codes Numériques

Jocelyn Lachance suggère de s'inspirer des raisons du succès des YouTubeurs et Twitchers auprès des jeunes. Ces créateurs donnent le sentiment de créer un espace sécurisant où :

• Les jeunes sentent que les discussions partent d'eux ("on peut poser les vraies questions").

• Leur parole est comprise et valorisée.

• Leur culture n'est pas "délégitimée".

L'enjeu pour l'adulte est de s'interroger sur sa propre posture :

"Est-ce que moi je suis personnellement dans une posture qui délégitime les pratiques numériques et qui fait un espèce de mouvement de répulsion par rapport aux jeunes ?"

Améliorer l'Engagement des Élèves en Collège et Lycée : Synthèse des Stratégies de Hassan Nassiri

Résumé Exécutif

Ce document synthétise les stratégies et les réflexions partagées par Hassan Nassiri, professeur et formateur, pour améliorer l'engagement des élèves dans le second degré.

L'approche préconisée repose sur quatre leviers d'action fondamentaux :

Ritualiser les cours pour créer un cadre sécurisant,

Varier les supports et les modalités pour maintenir l'attention,

Donner des responsabilités pour impliquer les élèves, et

Valoriser la progression plutôt que la seule performance.

Pour les élèves les plus réticents, la méthode consiste à identifier les causes de leur décrochage et à proposer des "entrées progressives" via des micro-tâches pour créer des premiers succès.

La création d'une dynamique de classe collective, à travers des projets interdisciplinaires et une charte de classe co-construite, est également essentielle.

La posture de l'enseignant est déterminante : elle doit incarner une alchimie entre exigence et bienveillance, en établissant un cadre clair tout en offrant des encouragements constants.

La gestion de l'erreur doit être dédramatisée, celle-ci étant présentée comme une étape nécessaire à l'apprentissage.

Enfin, il est crucial de ne pas rester isolé et de s'appuyer sur l'équipe pédagogique (collègues, CPE, direction) pour gérer les situations complexes et assurer une cohérence éducative.

1. Introduction et Contexte

Hassan Nassiri, professeur en établissement à mi-temps et formateur pour le Réseau Canopé et l'inspection académique, aborde la question centrale de l'engagement des élèves en collège et lycée.

Fort de son expérience, notamment en lycée professionnel, il partage des gestes professionnels concrets et des retours de terrain destinés à aider les enseignants, particulièrement les débutants, à "ne laisser personne sur le bord de la route".

La problématique principale est de savoir comment mettre en activité tous les élèves, y compris ceux qui semblent les moins coopératifs, afin de créer et de pérenniser une dynamique de classe positive tout au long de l'année.

Ses conseils s'appliquent aussi bien aux classes dédoublées (12-15 élèves) qu'aux classes à effectif plus lourd (24-30 élèves et plus).

2. Fondements Théoriques et Pédagogiques

Pour nourrir sa réflexion, Hassan Nassiri s'appuie sur plusieurs références clés qui soulignent l'importance de la pédagogie et de l'organisation dans la gestion de classe :

• François Dubet ("Les lycéens") : Cet ouvrage analyse finement le rapport des élèves au travail scolaire, montrant la grande variété des profils et l'influence de leur histoire personnelle sur leur engagement.

• Philippe Merieu ("La pédagogie différenciée") : Nassiri retient de Merieu l'idée fondamentale d'adapter les dispositifs pédagogiques pour que chaque élève trouve sa place et que personne ne se sente exclu.

• Ressources Eduscol et Réseau Canopé : Ces ressources rappellent un principe essentiel : "la gestion de classe, ce n'est pas que de la discipline, c'est avant tout de l'organisation et de la pédagogie".

3. Les Quatre Levier Fondamentaux de l'Engagement

Hassan Nassiri identifie quatre leviers concrets pour transformer ces idées en actions en classe.

a. Ritualiser

Instaurer des rituels en début et en fin de cours permet de créer un cadre rassurant pour les élèves, notamment les plus effacés.

• Début de séance : Commencer par une "question flash" ou un "mot d'actualité".

• Fin de séance : Terminer par un rapide tour de table pour synthétiser ce qui doit être retenu.

b. Varier

Pour éviter la routine et l'ennui, il est crucial de varier les supports et les modalités de travail.

• Alternance des supports : Combiner des supports écrits traditionnels ("la bonne vieille méthode du papier") avec des outils numériques (quiz, etc.). Hassan Nassiri insiste sur l'importance de faire écrire les élèves, estimant qu'ils "n'écrivent pas assez".

• Alternance des activités : Le but est de casser la routine durant l'heure de cours pour capter l'attention.

• Travaux de groupe : Cette modalité est jugée "très intéressante" pour responsabiliser les élèves et impliquer ceux qui sont plus effacés ou timides.

c. Donner (des responsabilités)

Attribuer des rôles spécifiques aux élèves, notamment dans le cadre des travaux de groupe, modifie radicalement leur implication.

• Exemples de rôles : Gardien du temps, rapporteur, responsable du matériel.

• Impact : "Quand tes élèves se sentent utiles, leur implication change." Cela fonctionne particulièrement bien pour les élèves timides.

• Pédagogie de projet : Mettre les élèves en projet les rend "vraiment acteurs de leur formation".

Des exemples concrets incluent la création d'une mini-entreprise ou l'organisation de mobilités internationales (Erasmus).

d. Valoriser

Ce levier est jugé "très, très important". Il s'agit de valoriser la progression des élèves et pas uniquement leur performance finale.

• Signal fort : Féliciter un élève en difficulté non seulement pour une bonne réponse, mais aussi pour une démarche claire ou une progression par rapport à la séance précédente.

• Message transmis : "Cela montre que l'effort compte autant que le résultat."

• Impact sur l'élève : L'encouragement régulier et la reconnaissance de l'effort boostent l'élève et renforcent sa confiance.

4. Stratégies pour les Élèves Réticents et en Retrait

Face aux élèves qui résistent à l'engagement, Hassan Nassiri propose une approche ciblée.

• Identifier la cause : Il faut se demander ce qui motive le refus (peur de l'échec, rejet de l'école, historique personnel). "Il y a toujours une explication."

• Proposer des "entrées progressives" : Commencer par une "micro-tâche" simple, par exemple en binôme, puis augmenter progressivement la difficulté.

L'objectif est de "créer un premier succès, même petit", pour encourager l'élève.

• Accompagnement personnalisé : Profiter des moments en demi-groupe pour s'approcher physiquement des élèves les plus timides, s'asseoir à côté d'eux pour les rassurer et les accompagner de manière individualisée.

• Valoriser la participation : L'élève doit sentir qu'il a le droit à l'erreur. Il faut l'encourager pour sa tentative, même s'il se trompe.

Lui confier une mission simple, comme expliquer une réponse au tableau, le rend visible et le valorise.

5. La Force du Collectif : Créer une Dynamique de Classe

Au-delà des actions individuelles, il est primordial de construire une culture de classe collective.

• Charte de classe : Élaborer une charte avec les élèves sur les valeurs et les attitudes à adopter.

Cette démarche, bien que chronophage, les rend "complètement acteurs de leur apprentissage".

• Projets communs : Lancer des projets interdisciplinaires permet de travailler avec d'autres collègues, de ne pas rester seul, et de montrer aux élèves les liens entre les disciplines.

Cela crée du lien tant pour les élèves que pour les enseignants.

6. Thèmes Spécifiques Abordés (Session Q&A)

| Thème | Stratégies et Conseils | | --- | --- | | Gestion des binômes | Il n'y a pas de "formule miracle" (faibles ensemble vs. mixité). L'enseignant connaît ses élèves et doit adapter la composition. Hassan Nassiri privilégie les groupes par affinité et insiste : "il ne faut jamais imposer les binômes", sauf cas exceptionnel. La supervision de l'enseignant est clé. | | Gestion de l'erreur | L'erreur doit être dédramatisée et valorisée comme un moteur d'apprentissage. Il faut affirmer aux élèves : "vous avez le droit de vous tromper. L'erreur n'est pas négative, justement l'erreur permet d'avancer". L'erreur peut aussi provenir d'un manque de clarté dans les consignes de l'enseignant. | | Engagement inter-matières | Pour contrer la tendance des élèves à négliger les matières à faible coefficient, il faut leur expliquer, en s'appuyant sur le référentiel du baccalauréat, que "toutes les matières comptent". Ce discours doit être porté par toute l'équipe pédagogique pour être efficace. | | Usage du numérique | L'alternance papier/numérique est essentielle, car le "tout numérique" peut lasser les élèves. Pour les outils comme Kahoot en collège (sans smartphone autorisé), l'enseignant doit fixer un cadre et des règles très claires avant de lancer l'activité et sanctionner si elles ne sont pas respectées pour garantir sa crédibilité. | | Gestion des classes agitées | Face à une classe très remuante (26 élèves et plus) : ne pas rester seul. S'appuyer sur l'équipe (collègues, CPE, direction), identifier les meneurs, les interroger en individuel pour comprendre leur comportement, et utiliser des outils comme le plan de classe. | | Intelligence Artificielle (IA) | Il est préconisé d'adopter une approche proactive : plutôt que d'interdire, il faut accompagner les élèves. Cela passe par une séance dédiée pour leur apprendre à "rédiger un prompt" et à utiliser l'IA de manière "raisonnée", en comprenant les réponses générées. La fixation d'un cadre par l'enseignant est impérative. |

7. La Posture Enseignante : Clé de Voûte de l'Engagement

La réussite de ces stratégies repose fondamentalement sur la posture de l'enseignant.

• L'alchimie de l'exigence et de la bienveillance : C'est le principe central. Il faut donner un cadre clair et être exigeant, mais toujours accompagné d'encouragements constants et d'une écoute bienveillante.

• Accessibilité et crédibilité : Soyez accessible et tenez parole. "Quand vous dites quelque chose, bah faites-le", car ne pas le faire fait perdre toute crédibilité.

La gentillesse ne doit pas être perçue comme de la faiblesse, mais comme une partie d'un cadre respecté.

• S'appuyer sur l'équipe : Il est essentiel de collaborer avec les collègues expérimentés et surtout avec le ou la CPE, qui a une connaissance fine des élèves et peut apporter une aide précieuse sur l'aspect psychologique.

• La liberté pédagogique : Hassan Nassiri conclut en rappelant que la "fameuse liberté pédagogique" est un atout précieux qui permet aux enseignants de mettre en place ces stratégies et de donner tout son sens à leur métier.

Micro-violences et Micro-attentions en Milieu Éducatif : Analyse et Perspectives

Synthèse Exécutive

Ce document de synthèse analyse les concepts de micro-violences et de micro-attentions en milieu éducatif, en s'appuyant sur l'expertise de Laurent Muller, maître de conférences en sciences de l'éducation, et de Lucie Perrin, inspectrice de l'Éducation nationale (faisant fonction d'IEN).

Les micro-violences sont définies comme des gestes, paroles, attitudes ou oublis quotidiens, souvent banalisés et passant sous les radars, qui dégradent la personne à petit feu.

Elles ne sont pas seulement interpersonnelles mais aussi institutionnelles, découlant d'une logique qui privilégie les intérêts de l'institution sur ceux des usagers.

L'impact de ces "presque-riens" est considérable car ils heurtent des besoins psychiques fondamentaux et universels (autonomie, appartenance, compétence), particulièrement chez des élèves en pleine construction identitaire.

La prise de conscience par les enseignants est un processus complexe, souvent freiné par un sentiment de jugement ou de culpabilité, qui peut mener au déni.

Les facteurs systémiques, tels que la culture de conformité à l'autorité (l'état agentique de Milgram), la gestion du temps collectif au détriment du temps individuel, et la reproduction sociale par des enseignants "survivants" du système scolaire, entretiennent ces pratiques.

En contrepoint, les micro-attentions — un sourire, un mot bienveillant, une écoute active — sont présentées comme des outils puissants pour prévenir et restaurer le lien éducatif.

Des stratégies concrètes sont proposées, comme la Communication Non-Violente, la création d'espaces de parole pour les élèves et la nécessité pour les enseignants de prendre soin de leurs propres besoins avec le soutien de l'institution.

La transformation des pratiques passe par une posture d'humilité, une analyse réflexive et une volonté de "perdre du temps" pour en gagner sur le plan des apprentissages et du bien-être.

--------------------------------------------------------------------------------

1. Définition et Impact des Micro-violences Éducatives

1.1. Nature et Caractéristiques des Micro-violences

Les micro-violences sont décrites comme des "presque-riens qui ne sont pas des riens". Il s'agit de violences banalisées, normalisées et souvent invisibles, qui prennent la forme de :

• Paroles : Remarques blessantes, humour humiliant, expressions toutes faites. Exemples cités : "Hélène, ne te leurre pas, tu ne feras jamais de science", "c'est pas grave, c'était pour rire".

• Attitudes : Regards qui éteignent, souffles exaspérés, postures de supériorité.

• Gestes : Classer les copies par ordre de notes.

• Oublis et silences : Ne pas dire bonjour, ignorer un élève, créer des silences qui excluent.

Selon Laurent Muller, ces actes dégradent la personne "à petit feu" et ne doivent pas être confondus avec la notion de "micro-agression", qui est plus subjective.

L'objectivité de la micro-violence réside dans sa capacité à heurter des besoins psychiques universels.

1.2. La Double Dimension : Interpersonnelle et Institutionnelle

Les micro-violences ne se limitent pas aux interactions entre enseignants et élèves.

Elles possèdent une dimension institutionnelle profonde.

• Violence institutionnelle : Laurent Muller, citant Eliane Corbet, la définit comme le fait de "privilégier l'intérêt de l'institution sur l'intérêt des usagers".

• Logique biopolitique : Au sens de Michel Foucault, il s'agit d'une "gestion des flux de population qui sert à normaliser les corps et les pensées".

Les enseignants et les directions peuvent eux-mêmes être victimes de cette logique systémique.

Cette double dimension explique pourquoi les enseignants peuvent être à la fois auteurs et victimes de micro-violences, pris dans des logiques qui les dépassent.

1.3. L'Impact sur les Élèves : Le Heurt des Besoins Psychiques

L'impact puissant des micro-violences, même subtiles, s'explique par deux facteurs principaux :

1. L'âge des élèves : Ils sont en pleine construction identitaire, ce qui les rend particulièrement vulnérables.

2. Le heurt des besoins psychiques : Considérés comme des "nutriments psychiques", leur non-satisfaction produit une dégradation de l'état psychique.

Laurent Muller s'appuie sur les travaux de Deci et Ryan pour identifier trois besoins fondamentaux et universels :

| Besoin Psychique | Description | Conséquence du Heurt | | --- | --- | --- | | Autonomie | Besoin de se sentir à l'origine de ses propres actions. | Sentiment d'aliénation, perte de motivation intrinsèque. | | Appartenance | Besoin de se sentir respecté, reconnu, accueilli, en lien. | Isolement, qui est un facteur majeur de morbidité. | | Compétence | Besoin de se sentir efficace et capable d'agir sur son environnement. | Sentiment d'échec, dévalorisation, décrochage. |

Lucie Perrin confirme que partir des besoins de l'élève est essentiel pour créer les conditions favorables à l'apprentissage.

2. La Prise de Conscience : Un Processus Délicat

2.1. Réactions des Enseignants et Obstacles

Lors des formations, Lucie Perrin observe que les enseignants sont souvent "étonnés" et "bouche bée" face à la liste des violences pédagogiques ordinaires (recensées par Christophe Marcellier), car "ils se reconnaissent".

Cette reconnaissance peut entraîner deux réactions problématiques :

• Le sentiment d'être jugé : Les enseignants peuvent se sentir accusés, ce qui entrave la réflexion.

• La culpabilisation : Laurent Muller avertit que la culpabilité "risque de conduire au déni" et de renforcer les mécanismes de défense.

L'objectif n'est pas de culpabiliser mais de responsabiliser, c'est-à-dire de "reprendre des marges de liberté" pour éviter d'entretenir le cycle de la violence.

2.2. Le Rôle du Langage et de l'Humour

Des automatismes de langage, analysés par Hannah Arendt dans le contexte du cas Eichmann, fonctionnent comme des "mécanismes de défense" qui invisibilisent la souffrance de l'autre et autorisent à "faire mal pour faire faire".

| Type d'Expression | Exemples | Fonction | | --- | --- | --- | | Anticipation positive | "C'est pour ton bien", "Tu me remercieras plus tard" | Justifier une action douloureuse par un bénéfice futur. | | Version accusatoire | "C'est à moi que ça fait mal" | Inverser la culpabilité. | | Fatalisme | "C'est la vie", "On n'a pas le choix" | Se déresponsabiliser en invoquant une force supérieure. | | Minimisation | "On n'en est pas mort", "Moi aussi, je suis passé par là" | Nier l'impact du ressenti de l'autre. | | Exagération/Ironie | "C'est bon, t'exagères", "Mon pauvre chou, tu fais ta princesse" | Ridiculiser l'émotion de l'autre. | | Verdict de facilité | "Allez-y, c'est facile" (ajouté par Lucie Perrin) | Créer une pression et un sentiment d'incompétence chez l'élève en difficulté. |

L'humour est un vecteur particulièrement puissant, car il permet de "détruire l'autre en l'accusant de manquer d'humour s'il ne rigole pas à l'humiliation qu'il est en train de subir".

2.3. Stratégies de Conscientisation

Pour prendre conscience de ces gestes sans se filmer, plusieurs pistes sont évoquées :

• Reconnaître l'écart entre intention et action : Accepter que de bonnes intentions ne garantissent pas des pratiques bienveillantes.

• L'analyse réflexive : Se remémorer les micro-violences subies et celles que l'on a pu commettre.

• Inviter des collègues en classe : Obtenir un regard extérieur sur ses pratiques.

• Donner la parole aux élèves : Leur permettre d'exprimer leur ressenti, comme l'a expérimenté Laurent Muller.

3. Les Facteurs Systémiques d'Entretien des Micro-violences

3.1. Conformisme et Soumission à l'Autorité

Laurent Muller s'appuie sur les travaux de Stanley Milgram sur la "conversion à l'état agentique" pour expliquer une tendance au conformisme dans l'Éducation nationale.

Dans cet état, un individu ne se sent plus à l'origine de son action et devient un "agent d'exécution" d'une volonté extérieure jugée légitime.

Cela conduit à une "culture de la reproduction des attitudes".

Ce phénomène est renforcé par le fait que les enseignants sont des "survivants du système scolaire" et donc porteurs d'un "biais particulier" qui les incline à reproduire les normes qui ont assuré leur propre succès.

3.2. L'Influence de la Forme Scolaire

La structure même de l'école ("forme scolaire") est un terreau fertile pour les micro-violences.

• La gestion du temps : La priorité donnée au temps collectif (finir les programmes) sur le temps propre de chaque élève est une source majeure de micro-violence.

Comme le dit Rousseau cité par L. Muller, le paradoxe de l'éducation est de "savoir en perdre [du temps]".

• La taille des classes : Une classe de 30 ou 35 élèves rend la prise en compte des besoins individuels extrêmement difficile, favorisant une approche normalisatrice.

• L'espace : Lucie Perrin évoque la posture de l'enseignant "systématiquement debout face à ses élèves" comme un geste sécurisant pour lui, mais qui peut instaurer une distance.

Le contexte de l'enseignement spécialisé (SEGPA), avec des effectifs réduits, montre a contrario que lorsque les conditions le permettent, la création de lien et l'attention aux besoins individuels deviennent prioritaires.

4. Stratégies de Transformation : Les Micro-attentions

4.1. Le Pouvoir des Micro-attentions

Face aux micro-violences, les micro-attentions sont les "véritables petits moteurs du lien".

Elles préviennent et peuvent restaurer la relation.

• Exemples : "Je t'écoute", "Tu as raison de dire ça", un bonjour et un sourire à l'accueil, une main sur l'épaule, un mot sympathique.

• L'importance de l'accueil : Pour Lucie Perrin, tout se joue dans les premières minutes.

Un "bonjour" et un "sourire" peuvent "instaurer un climat de confiance et mettre les élèves dans de bonnes conditions".

4.2. Outils et Postures

Plusieurs approches sont proposées pour cultiver une pédagogie de la micro-attention :

• La Communication Non-Violente (CNV) : Développée par Marshall Rosenberg, elle propose un processus pour clarifier les pratiques langagières violentes.

Laurent Muller précise que ce n'est pas une "solution mécanique" ou "miraculeuse" et qu'elle doit être "irriguée par une culture éthique de l'attention".

• Donner du temps et la parole aux élèves : Consacrer 10 minutes en début de cours pour demander aux élèves comment ils vont n'est pas du temps perdu, mais un investissement qui facilite les apprentissages en créant un climat de bien-être.

• La posture d'humilité : Lucie Perrin insiste sur la nécessité d'être prudent et humble, de reconnaître que l'on a pu soi-même commettre des erreurs, et de contextualiser les réactions des enseignants, qui font face à des adolescents aux vécus parfois complexes.

4.3. Restaurer la Relation et Soutenir les Enseignants

Lorsqu'une micro-violence a été commise, il est possible d'agir.

• Restaurer, non réparer : Laurent Muller préfère le terme "restaurer" ou "raccommoder" à "réparer", car il s'agit du vivant et non d'un mécanisme.

• La reconnaissance et les excuses : Le processus de restauration commence par "la reconnaissance explicite de ce qui a été fait" et le fait de "présenter simplement ses excuses".

C'est en mettant des mots (M-O-T-S) que l'on peut soigner les maux (M-A-U-X).

• Le soutien institutionnel : Pour que les enseignants puissent prodiguer des micro-attentions, il est crucial que "l'institution puisse également soutenir les enseignants".

La bienveillance doit commencer par soi-même : les enseignants doivent pouvoir prendre soin de leurs propres besoins pour pouvoir s'occuper de ceux de leurs élèves.

5. Inspirations et Références Clés

Pour approfondir la réflexion et l'action, les intervenants proposent les pistes suivantes :

• Laurent Muller :

◦ La psychologie humaniste : Les travaux de Carl Rogers et Marshall Rosenberg (fondateur de la CNV).  

◦ L'écoute des élèves : "Ils ont tout à nous apprendre par rapport à cette question-là."

• Lucie Perrin :

◦ Les travaux de Rebecca Shankland : Spécialiste du bien-être à l'école.  

◦ La qualité du temps passé à l'école : Reconnaître que les élèves voient parfois plus leurs enseignants que leur famille, et que ce temps doit être de qualité, empreint de bienveillance.

Développer les Compétences Psychosociales à l'École : Synthèse de la Table Ronde "Osons la Communication NonViolente"

Résumé Exécutif

Ce document de synthèse analyse les points clés de la table ronde organisée par le Réseau Canopé autour de l'ouvrage "Développer les compétences psychosociales à l'école - Osons la Communication NonViolente".

Confrontée à un contexte sociétal anxiogène et violent, l'école doit opérer une révolution éducative en intégrant pleinement les compétences psychosociales (CPS) et socio-émotionnelles.

Loin d'être une simple opinion, cette approche repose sur des fondements scientifiques solides issus des neurosciences, qui démontrent le lien direct entre la sécurité affective, l'empathie et le développement cérébral optimal pour l'apprentissage.

La Communication NonViolente (CNV) est présentée comme un levier majeur de cette transformation. Elle offre des outils concrets pour réguler les émotions, gérer les conflits et changer fondamentalement la posture des adultes.

Il ne s'agit pas de renoncer à l'autorité, mais de la redéfinir comme une auctoritas inspirante, basée sur la confiance et le respect, plutôt que sur un pouvoir coercitif.

Le point de départ de ce changement réside dans la formation des adultes de la communauté éducative (enseignants, personnels de direction, administratifs), qui, par leur propre exemplarité et leur capacité à l'auto-empathie, deviennent des modèles pour les élèves.

La systématisation de formations volontaires, inter-catégorielles et leur intégration dans la formation initiale sont identifiées comme des conditions essentielles pour répondre à l'urgence actuelle tout en construisant une vision éducative durable.

--------------------------------------------------------------------------------

1. Contexte et Urgence : L'École Face aux Défis Sociétaux

La discussion s'est ouverte sur le constat d'une pression croissante sur le système scolaire, qui doit naviguer entre des crises multiples et la nécessité de maintenir un climat apaisé.

• Le Reflet des Tensions Mondiales : Christophe Kéréro, Recteur de Paris, souligne que l'école, en tant que "reflet de la société", subit de plein fouet les agressions et la violence d'un contexte international et sociétal "extrêmement complexe".

Les élèves, réceptacles de ces tensions (géostratégiques, climatiques), vivent dans un environnement anxiogène qui impacte leur construction en tant qu'individus et citoyens.

• L'Impératif d'un Climat Scolaire Apaisé : Face à ce constat, l'institution est sommée de garantir la sérénité dans les établissements.

Cela s'inscrit dans un cadre plus large de lutte contre des phénomènes comme le harcèlement, mais ne peut faire abstraction des "fractures" qui traversent la société.

• La Double Temporalité : Un défi majeur réside dans la gestion d'une double temporalité. D'une part, une "société très impatiente" demande des résultats rapides face à l'urgence.

D'autre part, le développement des compétences psychosociales est un "travail sur le temps long", s'étalant sur "une voire deux générations". L'enjeu pour l'Éducation nationale est donc de "gérer à la fois l'urgence et le travail sur le temps long".

2. Le Lien Indissociable entre Émotions et Apprentissages : Fondements Scientifiques

L'intégration des émotions à l'école, souvent perçue comme une perturbation, est en réalité un prérequis fondamental pour l'apprentissage, soutenu par des décennies de recherche scientifique.

• Les Neurosciences Affectives et Sociales comme Base : Catherine Gueguen, pédiatre, insiste sur le fait que l'importance des compétences émotionnelles et sociales "ne sont pas des opinions ni des croyances, c'est fondé sur des recherches scientifiques".

Ces recherches montrent que l'empathie favorise le développement global du cerveau, à la fois intellectuel et affectif.

• Le Rôle Crucial de l'Empathie dans le Développement Cérébral : Des études précises sont citées pour étayer ce propos :

◦ Une étude hollandaise a montré que chez les enfants de 7 ans dont les parents sont empathiques, "toute la substance grise du cerveau se développe" avec un "épaississement du cortex préfrontal".  

◦ La bienveillance et l'empathie développent le cortex orbitofrontal, siège de fonctions humaines essentielles : l'empathie, la gestion des émotions, la capacité à faire des choix et le sens éthique et moral.  

◦ À l'inverse, les punitions et humiliations (physiques ou verbales) entravent le développement de ce cortex.

• L'Adulte comme Modèle par Imprégnation : La formation doit prioritairement concerner les adultes de la communauté éducative, car "les enseignants sont des modèles très puissants pour les enfants". Une fois les enseignants formés, les enfants "vont imiter par imprégnation".

3. La Communication NonViolente (CNV) comme Levier de Transformation

La CNV est présentée comme une approche pratique et profonde pour mettre en œuvre le développement des compétences socio-émotionnelles.

• Un Changement de Regard : des Comportements aux Besoins : Catherine Schmid-Gherardi explique que le principe fondamental de la CNV est de comprendre que "toute parole et tout comportement sert à nourrir un besoin".

Cette prise de conscience permet de "beaucoup moins prendre les paroles et les comportements contre nous" et de voir derrière une maladresse une tentative de prendre soin de soi.

• La Régulation Émotionnelle pour Libérer l'Espace d'Apprentissage : Lorsque l'enfant est submergé par une émotion, "ça prend toute la place et [...] il n'y a pas d'espace pour que les apprentissages se fassent".

Accueillir et nommer l'émotion (la sienne ou celle de l'élève) permet de la libérer et de rendre "l'espace à nouveau ouvert pour les apprentissages". La CNV lie l'émotion à un besoin, ce qui permet à l'enfant de devenir "proactif" et autonome.

• La Gestion des Conflits et la Désescalade : La CNV transforme la gestion des conflits en déplaçant le focus de la recherche du "coupable" ("qui a commencé ?") vers l'écoute empathique de deux individus en souffrance.

En accueillant les émotions et besoins de chacun, on "désamorce les tensions" et on amène les élèves à "trouver des solutions par eux-mêmes".

4. Redéfinir l'Autorité : De la Puissance sur l'Autre à l'« Auctoritas »

La table ronde a unanimement rejeté l'idée que l'empathie serait incompatible avec l'autorité, proposant au contraire une vision plus mature et efficace de celle-ci.

• Compatibilité entre Empathie et Cadre : Catherine Gueguen précise que l'empathie "n'a strictement rien à voir avec le laxisme".

L'adulte doit transmettre des valeurs, savoir dire non et rappeler ce qui est "permis ou interdit", mais il peut le faire "en comprenant les émotions et les besoins de l'enfant et sans l'humilier".

• Équilibre entre Verticalité et Horizontalité : Patrice Noy, enseignant, témoigne de la recherche d'un équilibre entre la "verticalité" (être garant du cadre et transmettre un savoir) et une "horizontalité" pour créer "une relation à l'élève qui lui permet un épanouissement de l'autonomie".

• L'Enseignant comme Figure d'Inspiration : Véronique Gaspard distingue le "pouvoir sur l'autre" de l'autorité que les élèves "accordent" à un adulte inspirant.

Elle cite une autrice : "partout où il y a violence, il y a perte d'autorité". L'enjeu est de faire en sorte que les adultes deviennent inspirants et donnent envie aux jeunes de grandir à leur contact.

5. La Posture de l'Adulte : Point de Départ du Changement

Le succès de l'intégration des CPS et de la CNV dépend avant tout du travail que les adultes font sur eux-mêmes.

• L'Auto-Empathie et la Responsabilité Émotionnelle : Patrice Noy explique que la CNV lui a permis de prendre conscience que "mes émotions m'appartenaient" et de ne pas "en faire porter la responsabilité aux autres".

La formation offre "ce temps entre la colère qui peut monter [...] et de voir d'où ça vient", permettant d'éviter de pénaliser l'acte pédagogique. Il souligne également l'importance de savoir s'excuser ("être désolé") après avoir "débordé".

• L'Impact de la Formation sur le Bien-Être des Enseignants : Catherine Gueguen rapporte que les études montrent que lorsque les enseignants sont formés, "ils vont beaucoup mieux, ils se sentent plus compétents [...] et ensuite ça prévient le burnout".

• L'Exemplarité au Sein de Toute la Communauté Éducative :

Catherine Piel, ancienne personnel de direction, insiste : "si on veut que les enseignants le fassent vivre à leurs élèves, ça me semble indispensable que les personnels de direction le fassent vivre aussi à leur équipe".

François Moutapa ajoute qu'il y a une "urgence à ce que l'ensemble des adultes de la communauté éducative, particulièrement les personnels administratifs, soit exposé et sensibilisé".

L'impact est systémique : l'amélioration des relations entre adultes a un effet direct sur le climat scolaire et le comportement des élèves.

6. Stratégies de Déploiement : La Formation comme Clé de Voûte

Le déploiement de ces compétences passe par une stratégie de formation réfléchie, systémique et ambitieuse.

• Une Approche Volontaire et non Injonctive : Plusieurs intervenants, dont Véronique Gaspard, insistent sur le fait que la formation doit partir d'un "espace choisi" et non d'une injonction. Ces approches étant "bouleversantes", il est crucial que les personnes soient prêtes à s'engager dans ce processus.

• L'Importance des Formations Inter-Catégorielles : Le format privilégié est celui qui rassemble différents corps de métier d'un même établissement (enseignants, direction, administratifs, etc.).

Patrice Noy et Catherine Schmid-Gherardi soulignent que cela crée "une entité qui partage des expériences en local" et une "qualité de relation" qui transforme la dynamique d'équipe.

• Vers une Intégration dans la Formation Initiale (INSPÉ) : Un consensus fort émerge sur la nécessité d'intégrer ces compétences "dès la formation initiale".

Les enseignants formés plus tardivement déplorent unanimement de ne pas avoir eu ces outils plus tôt, ce qui leur aurait permis de "gagner du temps" et d'adopter directement une posture plus constructive.

• L'Écosystème de Formation : La table ronde met en lumière les initiatives et collaborations existantes pour déployer ces formations sur le territoire.

| Organisation/Programme | Rôle et Initiatives | | --- | --- | | Réseau Canopé | Publication de l'ouvrage de référence, organisation de conférences et d'ateliers sur la plateforme Canotech et dans les ateliers Canopé (Paris, Alençon, Arras). | | Académie de Paris (Labs'Orbonne, EFC) | Partenariat fort avec Canopé pour construire des formations d'ampleur, professionnaliser les acteurs et créer un pôle de formateurs experts. | | Santé publique France | Mandaté pour le déploiement national des CPS, avec un travail de recensement et de formation par région et par académie. | | Déclic CNV | Association visant à rendre chaque académie autonome avec un pôle de formateurs qualifiés en CNV pour répondre aux demandes des personnels. |

¡Hola! Bienvenido. ¿Qué quieres comer hoy?Quiero una arepa con queso, por favor.¿La quieres con un poco de salsa picante o sin salsa?Sin salsa, gracias. Yo: Muy bien. ¿Quieres algo de beber también? Compañero: Sí, un refresco, por favor. Perfecto. Son cinco dólares en total. Compañero:Aquí tiene. Gracias. ¡Gracias a ti! Que disfrutes tu comida.

Ahora yo voy a comprar algo del puesto de salchipapas de mi compañero: Hola, quiero una porción de salchi papas, por favor. ¿Con salsa de ajo o ketchup? Con salsa de ajo, gracias. ¿Quieres algo de beber? Sí, un jugo de naranja, por favor. Muy bien. Son seis dólares en total. Aquí tiene. Gracias. ¡Gracias! Que disfrutes.

Los puestos de comida callejera rápida que vemos en las imágenes son diferentes a la comida rápida de mi pueblo. En mi pueblo, la comida rápida suele ser hamburguesas, pizzas o papas fritas, mientras que en los puestos hispanos venden arepas, empanadas o salchipapas.

Este tipo de comida callejera lo como cuando quiero algo rápido o diferente, como en la calle o en festivales. También como más comida callejera en días feriados o en fiestas locales, porque hay más puestos y es parte de la celebración.

En los próximos días voy a comer patatas en papas fritas y en puré de patatas. Voy a comer arroz en arroz con verduras. Voy a comer tomates en ensalada y en salsa de pasta. Voy a comer lechuga en una ensalada y voy a comer manzanas como merienda o en un postre.

En el mapa hay muchas comidas interesantes. Me gustan las patatas, el arroz, las manzanas, las naranjas y el maíz porque los como muy a menudo y tienen buen sabor. No me gustan mucho las cebollas crudas ni el azúcar en exceso, porque el sabor es muy fuerte o demasiado dulce para mí.

Las fresas son de Europa. El maíz es de América, especialmente de México. Los tomates son de América, de la región de México y Perú. Las manzanas son de Asia, especialmente de Asia Central. Las naranjas son de Asia, principalmente de China. Las cebollas son de Asia. El arroz es de Asia. La lechuga es de la región del Mediterráneo. El azúcar es de India, donde se cultivaba la caña de azúcar. Las patatas son de América, especialmente de Perú.

Sí, quiero servir este plato en la fiesta porque el gazpacho es un plato tradicional de España y es refrescante. Creo que a mis compañeros les va a gustar y es una buena forma de compartir la cultura española.

En la comida tradicional de mi pueblo no hay un plato exactamente igual al gazpacho, pero sí tenemos sopas frías o platos con tomate que se comen en verano. Son un poco parecidos, pero el gazpacho es único por su sabor y su forma de preparación.

Sí, me gustan casi todos los ingredientes de la receta, como los tomates, el pepino, el pimiento verde y el aceite de oliva. El ajo crudo es un poco fuerte para mí, pero creo que en general sí me va a gustar ese plato, especialmente en un día caluroso.

El ingrediente principal de mi receta de gazpacho es el tomate. Sí, puedo encontrar tomates fácilmente en el supermercado de mi barrio, especialmente en la sección de frutas y verduras.

summary: brief, consise

La Métacognition : Stratégies pour des Apprentissages Réussis

Résumé Exécutif

Ce document de synthèse analyse les stratégies pédagogiques fondées sur la métacognition pour favoriser la réussite de tous les élèves.

La métacognition est définie comme l'ensemble des processus par lesquels un individu régule ses propres activités cognitives, devenant ainsi le "pilote de sa cognition".

Elle se décline en deux facettes principales : la métacognition explicite, qui est la connaissance consciente de ses propres processus d'apprentissage ("apprendre à apprendre"), et la métacognition implicite, qui repose sur les sentiments et la motivation intrinsèque.

Face aux constats partagés de difficultés d'attention, d'oubli des savoirs et d'un manque de motivation chez les élèves, l'enseignement direct des stratégies métacognitives apparaît comme un levier puissant.

Les approches concrètes incluent l'explication du fonctionnement du cerveau, la gestion de l'attention, la régulation de la mémorisation et le développement de la flexibilité cognitive pour résister aux automatismes.

Un point central est la relation entre succès et motivation. Plutôt que de postuler que la motivation précède la réussite, les expériences de terrain suggèrent que c'est la réussite qui engendre la motivation et l'envie d'apprendre.

En mettant les élèves en situation de succès, en leur proposant des tâches accessibles et en clarifiant les objectifs d'apprentissage, on crée un cercle vertueux d'engagement.

Cette démarche ne constitue pas une révolution, mais une évolution des pratiques professionnelles vers un enseignement plus ciblé ("moins mais mieux") et un outil efficace pour lutter contre les inégalités scolaires.

--------------------------------------------------------------------------------

1. Fondements de la Métacognition

La métacognition est présentée comme une méthode pédagogique efficace, s'appuyant sur la recherche, pour prévenir les difficultés scolaires et favoriser la réussite de tous les élèves.

1.1. Définition et Capacités Clés

La métacognition englobe l'ensemble des processus par lesquels un individu régule son apprentissage.

Selon Frédéric Guy, chargé de mission au Cézanne, cela inclut les capacités à :

• Réguler son attention

• Choisir de s'informer

• Planifier et résoudre un problème

• Repérer et corriger ses propres erreurs

Ces processus permettent de prédire la faisabilité d'une tâche et d'évaluer ses propres performances. Ils reposent sur quatre capacités fondamentales :

1. Fixer des buts et identifier les actions nécessaires pour les atteindre.

2. Détecter et identifier les erreurs pour y remédier.

3. Évaluer ses résultats et ses conclusions.

4. Réviser les stratégies utilisées.

1.2. Les Deux Facettes de la Métacognition

Il est essentiel de distinguer deux aspects complémentaires de la métacognition :

| Type de Métacognition | Description | Caractéristiques | | --- | --- | --- | | Explicite (ou Déclarative) | L'approche classique de la "cognition sur la cognition". C'est la capacité de l'élève à verbaliser ses stratégies et ses connaissances sur l'apprentissage. | • Consciente et conceptuelle.<br>• Repose sur des méta-représentations (ex: "pour apprendre, je dois faire cela").<br>• Concerne les perceptions sur les tâches ("c'est difficile") ou sur soi ("je suis bon en maths"). | | Implicite | Une régulation qui se fait sur la base de sentiments dédiés à l'apprentissage.

Elle est liée à la motivation et à l'évaluation intuitive de l'effort à fournir. | • Basée sur des sentiments et des intuitions.<br>• Moins consciente, plus automatique.<br>• Influence directement la motivation et l'engagement. |

2. Pistes Pédagogiques pour la Métacognition Explicite

L'objectif est de donner aux élèves les outils pour devenir autonomes dans leur apprentissage.

La citation clé de Marie Bridenne, Conseillère Pédagogique, résume cette ambition :

« Développer ses compétences métacognitives, c’est devenir pilote de sa cognition. »

2.1. Comprendre le Fonctionnement du Cerveau

Pour que les élèves puissent réguler leur cognition, il faut d'abord qu'ils en comprennent les mécanismes de base.

• Action : Parler du cerveau en classe, à tous les niveaux, et questionner les élèves sur leurs représentations ("A-t-on tous le même cerveau ?", "Comment fonctionne-t-il ?").

• Outils : Utilisation de ressources pédagogiques comme les ouvrages Découvrir le cerveau à l'école (Canopé), _Kididoc :

Explore ton cerveau_, ou C'est (pas) moi, c'est mon cerveau !.

2.2. Gérer et Adapter son Attention

L'attention est une ressource limitée qui doit être maîtrisée.

• Action : Mettre en place des programmes attentionnels pour faire découvrir aux élèves ce qu'est l'attention, ses limites, et comment la maîtriser de façon autonome (équilibre attentionnel, retour au calme).

• Outils : Programmes structurés comme ATOLE (Apprendre l'ATtention à l'écOLE) pour les cycles 2 et 3, et ADOLE pour le collège et le lycée.

2.3. Réguler les Processus de Mémorisation

La mémorisation efficace repose sur trois piliers : comprendre, se questionner, répéter.

• Action : Mettre en place des routines et des outils pour structurer la mémorisation et la révision.

• Outils :

◦ Fiches mémo pour synthétiser les savoirs.  

◦ Cartes quiz rédigées par les élèves pour s'auto-interroger.  

◦ Boîtes de Leitner pour organiser la répétition espacée des notions.  

◦ Calendrier de reprises expansées pour planifier les révisions.

2.4. Résister aux Automatismes et Être Flexible

Apprendre, c'est acquérir des automatismes, mais c'est aussi savoir y résister pour progresser.

• Action : Entraîner les élèves à inhiber leurs réflexes pour développer de nouvelles stratégies, un regard critique et une plus grande tolérance à l'erreur.

• Exemples :

◦ Comprendre que la lettre "O" ne produit pas systématiquement le son [o].    ◦ Changer de procédure en calcul mental (ex: pour ajouter 9, ajouter 10 puis retirer 1).

3. Motivation et Métacognition Implicite : Le Cercle Vertueux de la Réussite

La motivation est indispensable à l'engagement dans les tâches. Les sources soulèvent une question fondamentale :

« Faut-il être motivé pour vouloir apprendre et réussir ? Ou faut-il réussir pour vouloir apprendre et se motiver ? » La réponse apportée par l'expérience de terrain est que la réussite est le principal moteur de la motivation.

3.1. Les Levier pour Vouloir Apprendre

Pour susciter l'envie, il est crucial de créer les conditions de la réussite et du plaisir d'apprendre.

• Mettre les élèves en réussite : Les buts de performance peuvent avoir des effets délétères en cas d'échec. Il faut donc concevoir des tâches que les élèves considèrent comme accessibles.

• Développer des projets motivants : Lier les apprentissages à des projets concrets et stimulants (rallyes mathématiques, balades lexicales, projet CNR "J'y arrive !").

• S'appuyer sur les 4 piliers de la motivation :

◦ Intérêt : Le plaisir pris à réaliser la tâche.  

◦ Importance : La valeur accordée à la tâche.  

◦ Effort : La perception du coût en énergie.   

◦ Succès : Le sentiment de compétence et la réussite effective.

3.2. Les Levier pour Pouvoir Apprendre

Donner aux élèves la capacité d'apprendre passe par la clarification du cadre et des objectifs.

• Clarifier les objectifs d'apprentissage : Différencier l'objectif réel de la consigne.

L'élève doit comprendre ce qu'il est en train d'apprendre (ex : non pas "colorier une carte", mais "apprendre à réaliser une carte en respectant un code de couleurs").

• Structurer le temps et les activités : Utiliser un "Menu du jour" pour rendre les objectifs de la journée visibles et explicites.

• Verbaliser les apprentissages : Instaurer un "Journal des apprentissages" où l'élève note ce qu'il a compris ("J'ai compris que...").

Cela aide à la prise de conscience et à l'appropriation des savoirs.

4. Mise en Œuvre Stratégique

L'intégration de la métacognition dans les pratiques pédagogiques doit être pensée de manière systémique et progressive.

4.1. Exemple d'une Dynamique de Circonscription (2022-2025)

| Année | Actions Clés | Objectifs | | --- | --- | --- | | 2022-2023 | • Conférences "Talents du cerveau".<br>• Séminaire sur les neuromythes et la flexibilité. | Développement d’une culture commune autour de la métacognition. | | 2023-2024 | • Diffusion auprès des équipes (conseils de maîtres).<br>• Ateliers pratiques (F. Guilleray).<br>• Séminaire sur les pratiques évaluatives. | Acculturation des enseignants et déploiement des outils. | | 2024-2025 | • Conseil-École-Collège sur les compétences attentionnelles et mémorielles.<br>• Projet CNR "J'y arrive" (accompagné par JF Chesné).<br>• Accompagnement des enseignants débutants. | Ancrage des pratiques et suivi des effets sur les élèves. |

4.2. Une Évolution des Pratiques Professionnelles

L'approche métacognitive n'est « pas une révolution mais une évolution des gestes professionnels ».

Elle invite à une rationalisation des pratiques sous le principe « MOINS MAIS MIEUX », en se concentrant sur les stratégies qui ont le plus d'impact.

Conclusion

Enseigner les connaissances et les stratégies métacognitives est un levier puissant pour lutter contre les inégalités éducatives et favoriser la réussite scolaire de TOUS les élèves. En leur donnant les clés pour comprendre et réguler leur propre fonctionnement cognitif, l'école leur permet de passer d'un statut d'apprenant passif à celui d'acteur autonome et conscient de ses apprentissages. Cette démarche outille les élèves pour qu'ils puissent, tout au long de leur vie, apprendre de manière plus efficace et plus sereine.

la otra parte de la implementación de una lista enlazada, consiste en construir esa colección de nodos cada uno vinculado, para ello se crea la clase ListaEnlazada con referencia al primer nodo, es decir a la cabeza, que contiene el primer ítem de la lista. A su vez este nodo contiene la referencia al siguiente nodo y asi sucesivamente.

Author response:

General Statements

In this manuscript we characterize an exquisitely reproducible model of iPSC differentiation into neuroepithelial cells, use it to mechanistically study cell shape changes and planar cell polarity signaling activation during this transition, then apply it to identify patient-specific cell deficiencies in both forward and reverse genetic screens as a power tool for patient-stratification in personalized medicine. To our knowledge, we provide the first evidence of a human pathogenic mutation directly impairing apical constriction: an evolutionarily conserved behavior of epithelial cells which is the subject of intense research. 

We are very pleased with the balanced and rigorous reviews generated through Review Commons, which we have already used to improve our manuscript. Reviewer 1 highlights that our study “is significant not only for verifying the cell behaviors necessary for neural tube closure in a human iPSC model, but also for establishing a robust assay for the functional testing of NTD-associated sequence variants.” Reviewer 2 agrees that “results are solid and convincing, the data are quantitative, and the manuscript is well written”, and that our “derivation of patient lines from amniotic fluid and execution of experiments before birth is a remarkable demonstration that points toward precision-medicine applications, while motivating rescue strategies and additional clean genetic models.” Reviewer 3 is “enthusiastic about this work and believe it represents a significant step forward in the effort to establish precision medicine approaches for diagnoses of the patient-specific causative cellular defects underlying human neural tube closure defects.” 

Below, we have replied to each of the reviewers’ comments.

Description of the planned revisions

R2.2. Lines 156-166. The authors claim that changes in gene expression precede morphological changes. I am not convinced this is supported by their data. Fig. 1g (epithelial thickness) and Fig. 1k (PAX6 expression) seem to have similar dynamics. The authors can perform a cross-correlation between the two plots to see which Δt gives maximum correlation. If Δt < 0, then it would suggest that gene expression precedes morphology, as they claim. Fig. 1j shows that NANOG drops before the morphological changes, but loss of NANOG is not specific to neural differentiation and therefore should not be related to the observed morphological changes.

We are happy to do this analysis fully in revision. Our initial analysis performing crosscorrelation between apical area and CDH2 protein in one line shows the highest crosscorrelation at Δt = -1, suggesting neuroepithelial CDH2 increases before apical area decreases. In contrast, the same analysis comparing apical area versus PAX6 shows Δt = 0, suggesting concurrence. This analysis will be expanded to include the other markers we quantified and the manuscript text amended accordingly. We are keen to undertake additional experiments to test whether these cells swap their key cadherins – CDH1 and CDH2 - before they begin to undergo morphological changes (see the response to Reviewer 3’s minor comment 1 immediately below).

R3.1(Minor) There seems to be a critical window at day 5 of the differentiation protocol, both in terms of cell morphology and the marker panel presented in Figure 1i. Do the authors have any data spanning the hours from day 5 to 6? If not, I don't think they need to generate any, but do I think this is a very interesting window worthy of further discussion for a couple of reasons. First, several studies of mouse neural tube closure have shown that various aspects of cell remodeling are temporally separable. For example, between Grego-Bessa et al 2016 and Brooks et al 2020 we can infer that apicobasal elongation rapidly increases starting at E8.5, whereas apical surface area reduction and constriction are apparent somewhat earlier at E8.0. I think it would be interesting to see if this separability is conserved in humans. Second, is there a sense of how the temporal correlation between the pluripotent and early neural fate marker data presented here corroborate or contradict the emerging set of temporally resolved RNA seq data sets of mouse development at equivalent early neural stages?

Cell shape analysis between days 5 and 6 has now been added (see the response to point 2.1 below). As the reviewer predicted, this is a transition point when apical area begins to decrease and apicobasal elongation begins to increase.

We also thank the reviewer for this prompt to more closely compare our data to the previous mouse publications, which we have added to the discussion. The Grego-Bessa 2016 paper appears to show an increase in thickness between E7.75 and E8.5, but these are not statistically compared. Previous studies showed rapid apicobasal elongation during the period of neural fold elevation, when neuroepithelial cells apically constrict. This has now been added to the discussion: 

Discussion: “In mice, neuroepithelial apicobasal thickness is spatially-patterned, with shorter cells at the midline under the influence of SHH signalling[14,77,78]. Apicobasal thickness of the cranial neural folds increases from ~25 µm at E7.75 to ~50 µm at E8.5[79]: closely paralleling the elongation between days 2 and 8 of differentiation in our protocol. The rate of thickening is non-uniform, with the greatest increase occurring during elevation of the neural folds[80], paralleled in our model by the rapid increase in thickness between days 4-6 as apical areas decrease. Elevation requires neuroepithelial apical constriction and these cells’ apical area also decreases between E7.75 and E8.5 in mice[79], but we and others have recently shown that this reduction is both region and sex-specific[14,81]. Specifically, apical constriction occurs in the lateral (future dorsal) neuroepithelium: this corresponds with the identity of the cells generated by the dual SMAD inhibition model we use[56]. More recently, Brooks et al[82] showed that the rapid reduction in apical area from E8-E8.5 is associated with cadherin switching from CDH1 (E-cadherin) to CDH2 (N-cadherin). This is also directly paralleled in our human system, which shows low-level co-expression of CDH1 and CDH2 at day 4 of differentiation, immediately before apical area shrinks and apicobasal thickness increases.”

Prompted by the in vivo data in Brooks et al (2025)[82], we are keen to further explore the timing of CDH1/CDH2 switching versus apical constriction with new experimental data in revisions.

R3.2(Minor) 2) Can the authors elaborate a bit more on what is known regarding apicobasal thickening and pseudo-stratification and how their work fits into the current understanding in the discussion? This is a very interesting and less well studied mechanism critical to closure, which their model is well suited to directly address. I am thinking mainly of the Grego-Bessa at al., 2016 work on PTEN, though interestingly the work of Ohmura et al., 2012 on the NUAK kinases also shows reduced tissue thickening (and apical constriction) and I am sure I have missed others. Given that the authors identify MED24 as a likely candidate for the lack of apicobasal thickening in one of their patient derived lines, is there any evidence that it interacts with any of the known players?

We have now added further discussion on the mechanisms by which the neuroepithelium undergoes apicobasal elongation. Nuclear compaction is likely to be necessary to allow pseudostratification and apicobasal elongation. The reviewer’s comment has led us to realise that diminished chromatin compaction is a potential outcome of MED24 down-regulation in our GOSB2 patient-derived line. Figure 4D suggests the nuclei of our MED24 deficient patientderived line are less compacted than control equivalents and we propose to quantify nuclear volume in more detail to explore this possibility.

Additionally, we have already expanded our discussion as suggested by the reviewer:

Discussion: “Mechanistic separability of apical constriction and apicobasal elongation is consistent with biomechanical modelling of Xenopus neural tube closure showing that both are independently required for tissue bending[61]. Nonetheless, neuroepithelial apical constriction and apicobasal elongation are co-regulated in mouse models: for example, deletion of Nuak1/2[83], Cfl1[84], and Pten[79] all produce shorter neuroepithelium with larger apical areas. Neuroepithelial cells of the GOSB2 line described here, which has partial loss of MED24, similarly produces a thinner neuroepithelium with larger apical areas. Although apical areas were not analysed in mouse models of Med24 deletion, these embryos also have shorter and non-pseudostratified neuroepithelium.

Our GOSB2 line – which retains readily detectable MED24 protein – is clearly less severe than the mouse global knockout, and the clinical features of the patient from which this line was derived are milder than the phenotype of Med24 knockout embryos[68]. Mouse embryos lacking one of Med24’s interaction partners in the mediator complex, Med1, also have thinner neuroepithelium and diminished neuronal differentiation but successfully close their neural tube[85]. As general regulators of polymerase activity, MED proteins have the potential to alter the timing or level of expression of many other genes, including those already known to influence pseudostratification or apicobasal elongation. MED depletion also causes redistribution of cohesion complexes[86] which may impact chromatin compaction, reducing nuclear volume during differentiation.”

R3.3(Minor) 3) Is there any indication that Vangl2 is weakly or locally planar polarized in this system? Figure 2F seems to suggest not, but Supplementary Figure 5 does show at least more supracellular cable like structures that may have some polarity. I ask because polarization seems to be one of the properties that differs along the anteroposterior axis of the neural plate, and I wonder if this offers some insight into the position along the axis that this system most closely models?

VANGL2 does not appear to be planar polarised in this system. This is similar to the mouse spinal neuroepithelium, in which apical VANGL2 is homogenous but F-actin is planar polarised (Galea et al Disease Models and Mechanisms 2018). We do observe local supracellular cablelike enrichments of F-actin in the apical surface of iPSC-derived neuroepithelial cells:

Author response image 1.

Preliminary identification of apical supracellular cables suggestive of local polarity. Top: F-actin staining shown in inverted grey LUT highlighting enrichment along directionally-polarised cell borders (blue arrows). Bottom: Staining orientation (blue ~ X axis, red ~ Y axis) based on OrientationJ analysis illustrating localised organisation of F-actin enrichment.

We propose to compare the length of F-actin cables and coherency of their orientation at the start and end of neuroepithelial differentiation, and in wild-type versus VANGL2mutant epithelia.

Description of the revisions that have already been incorporated in the transferred manuscript

Reviewer #1:

Major points

(1) It is mentioned throughout the manuscript that 3 plates were evaluated per line. I believe these are independently differentiated plates. This detail is critical concerning rigor and reproducibility. This should be clearly stated in the Methods section and in the first description of the experimental system in the Results section for Figure 1.

These experimental details have now been clarified. Unless otherwise stated, all findings were confirmed in three independently differentiated plates from the same line or at least one differentiation from each of three lines. 

Methods: Unless otherwise stated, for each iPSC line three independently differentiated plates were generated and analysed, with each plate representing a separate differentiation experiment performed on different days.

(2) For the patient-specific lines - how many lines were derived per patient?

This has now been clarified in the methods. Microfluidic reprogramming of a small number of amniocytes produces one line per patient representing a pool of clones. Subcloning from individual cells would not be possible within the timeframe of a pregnancy. 

Methods: For patient-specific iPSC lines, one independent iPSC line was obtained per patient following microfluidic mmRNA reprogramming.

(3) Was the Vangl2 variant introduced by prime editing? Base editing? The details of the methods are sparse.

We have now expanded these details:

Methods: “VANGL2 knock-in lines were generated using CRSIPR-Cas9 homology directed repair editing by Synthego (SO-9291367-1). The guide sequence was AUGAGCGAAGGGUGCGCAAG and the donor sequence was CAATGAGTACTACTATGAGGAGGCTGAGCATGAGCGAAGGGTGTGCAAGAGGAGGGCCAGGTGGGTCCCTGGGGGAGAAGAGGAGAG.

Sequence modification was confirmed by Sanger sequencing before delivery of the modified clones, and Sanger sequencing was repeated after expansion of the lines (Supplementary Figure 5) as well as SNP arrays (Illumina iScan, not shown) confirming genomic stability.”

Author response image 2.

Snapshot of Illumina iScan SNP array showing absence of chromosomal duplications or deletions in the CRISPR-modified VANGL2-knockin lines or their congenic control.

(4) Suggested text changes.

Some additional suggestions for improvement.

The abstract could be more clearly written to effectively convey the study's importance. Here are some suggestions

Line 26: Insert "apicobasal" before "elongation" - the way it is written, I initially interpreted it as anterior-posterior elongation.

Line 29: Please specify that the lines refer to 3 different established parent iPSC lines with distinct origins and established using different reprogramming methods, plus 2 control patient-derived lines. - The reproducibility of the cell behaviors is impressive, but this is not captured in the abstract.

Line 32: add that this mutation was introduced by CRISPR-Cas9 base/prime editing.

The last sentence of the abstract states that the study only links apical constriction to human NTDs, but also reveals that neural differentiation and apical-basal elongation were found. The introduction could also use some editing.

Line 71: insert "that pulls actin filaments together" after "power strokes" Line 73: "apically localized," do you mean "mediolaterally" or "radially"?

Line 75: Can you specify that PCP components promote "mediolaterally orientated" apical constriction Lines 127: Specify that NE functions include apical basal elongation and neurodifferentiation are disrupted in patient-derived models

All have now been corrected.

Reviewer #2:

Major comments:

(1) Figure 1. The authors use F-actin to segment cell areas. Perhaps this could be done more accurately with ZO-1, as F-actin cables can cross the surface of a single cell. In any case, the authors need to show a measure of segmentation precision: segmented image vs. raw image plus a nuclear marker (DAPI, H2B-GFP), so we can check that the number of segmented cells matches the number of nuclei.

We used ZO-1 to quantify apical areas of the VANGL2-konckin lines in Figure 3. Segmentation of neuroepithelial apical areas based on F-actin staining is commonplace in the field (e.g. in the Brooks et al 2022 paper cited by another reviewer), and is generally robust because the cell junctions are much brighter than any apical fibres not associated with the apical cortex. However, we accept that at earlier stages of differentiation there may be more apical fibres when cells are cuboidal. We have therefore repeated our analysis of apical area using ZO-1 staining as suggested, analysing a more temporally-detailed time course in one iPSC line. This new analysis confirms our finding of lack of apical area change between days 2-4 of differentiation, then progressive reduction of apical area between days 4-8, further validating our system. Including nuclear images is not helpful because of the high nuclear index of pseudostratified epithelia (e.g. see Supplementary Figure 7) which means that nuclei overlap along the apicobasal axis. Individual nuclei cannot be related to their apical surface in projected images.

(3) Figure 2d. The laser ablation experiment in the presence of ROCK inhibitor is clear, as I can easily see the cell outlines before and after the experiment. In the absence of ROCK inhibitor, the cell edges are blurry, and I am not convinced the outline that the authors drew is really the cell boundary. Perhaps the authors can try to ablate a larger cell patch so that the change in area is more defined.

The outlines on these images are not intended to show cell boundaries, but rather link landmarks visible at both timepoints to calculate cluster (not cell) change in area. This is as previously shown in Galea et al Nat Commun 2021 and Butler et al J Cell Sci 2019. We have now amended the visualisation of retraction to make representation of differences between conditions more intuitive. 

(4) Figure 2d. Do the cells become thicker after recoil?

This is unlikely because the ablated surface remains in the focal plane. Unfortunately, we are unable to image perpendicularly to the direction of ablation to test whether their apical surface moves in Z even by a very small amount. This has now been clarified in the results:

Results: “The ablated surface remained within the focal plane after ablation, indicating minimal movement along the apical-basal axis.”

(6) Lines 403-415. The authors report poor neural induction and neuronal differentiation in GOSB2. As far as I understand, this phenotype does not represent the in vivo situation. Thus, it is not clear to what extent the in vitro 2D model describes the human patient.

The GOSB2 iPSC line we describe does represent the in vivo situation in Med24 knockout mouse embryos, but is clearly less severe because we are still able to detect MED24 protein expressed in this line. We do not have detailed clinical data of the patient from which this line was obtained to determine whether their neurological development is normal. However, it is well established that some individuals who have spina bifida also have abnormalities in supratentorial brain development. It is therefore likely that abnormalities in neuron differentiation/maturation are concomitant with spina bifida. Our findings in the GOSB2 line complement earlier studies which also identified deficiencies in the ability of patient-derived lines to form neurons, but were unable to functionally assess neuroepithelial cell behaviours we studied. This has now been clarified in the discussion:

Discussion: “Neuroepithelial cells of the GOSB2 line described here, which has partial loss of MED24, similarly produces a thinner neuroepithelium with larger apical areas. Although apical areas were not analysed in mouse models of Med24 deletion, these embryos also have shorter and non-pseudostratified neuroepithelium. 

Our GOSB2 line – which retains readily detectable MED24 protein – is clearly less severe than the mouse global knockout, and the clinical features of the patient from which this line was derived are milder than the phenotype of Med24 knockout embryos[68].

Mouse embryos lacking one of Med24’s interaction partners in the mediator complex, Med1, also have thinner neuroepithelium and diminished neuronal differentiation but successfully close their neural tube[85].”

(7) The experimental feat to derive cell lines from amniotic fluid and to perform experiments before birth is, in my view, heroic. However, I do not feel I learned much from the in vitro assays. There are many genetic changes that may cause the in vivo phenotype in the patient. The authors focus on MED24, but there is not enough convincing evidence that this is the key gene. I would like to suggest overexpression of MED24 as a rescue experiment, but I am not sure this is a single-gene phenotype. In addition, the fact that one patient line does not differentiate properly leads me to think that the patient lines do not strengthen the manuscript, and that perhaps additional clean mutations might contribute more.

We appreciate the reviewer’s praise of our personalised medicine approach and fully agree that neural tube defects are rarely monogenic. The patient lines we studied were not intended to provide mechanistic insight, but rather to demonstrate the future applicability of our approach to patient care. Our vision is that every patient referred for fetal surgery of spina bifida will have amniocytes (collected as part of routine cystocentesis required before surgery) reprogrammed and differentiated into neuroepithelial cells, then neural progenitors, to help stratify their postnatal care. One could also picture these cells becoming an autologous source for future cellbased therapies if they pass our reproducible analysis pipeline as functional quality control. This has now been clarified in the discussion:

Discussion: “The multi-genic nature of neural tube defect susceptibility, compounded by uncontrolled environmental risk factors (including maternal age and parity[102]), mean that patient-derived iPSC models are unlikely to provide mechanistic insight. They do provide personalised disease models which we anticipate will enable functional validation of genetic diagnoses for patients and their parents’ recurrence risk in future pregnancies, and may eventually stratify patients’ postnatal care. We also envision this model will enable quality control of patient-derived cells intended for future autologous cell replacement therapies, as is being developed in post-natal spinal cord injury[103]. Thus, the highly reproducible modelling platform we evaluate – which is robust to differences in iPSC reprogramming method, sex and ethnicity – represents a valuable tool for future mechanistic insights and personalised disease modelling applications.”

Significance:

In addition, the model was unsuccessful in one of the two patient-derived lines, which limits generalizability and weakens claims of patient-specific predictive value.

We disagree with the reviewer that “the model was unsuccessful in one of the two patientderived lines”. The GOSB1 line demonstrated deficiency of neuron differentiation independently of neuroepithelial biomechanical function, whereas the GOSB2 line showed earlier failure of neuroepithelial function. We also do not, at this stage, make patient-specific predictive claims: this will require longer-term matching of cell model findings with patient phenotypes over the next 5-10 years.  

Reviewer #3:

Major comments

(1) One of my few concerns with this work is that the relative constriction of the apical surface with respect to the basal surface is not directly quantified for any of the experiments. This worry is slightly compounded by the 3D reconstructions Figure 1h, and the observation that overall cell volume is reduced and cell height increased simultaneously to area loss. Additionally, the net impact of apical constriction in tissues in vivo is to create local or global curvature change, but all the images in the paper suggest that the differentiated neural tissues are an uncurved monolayer even missing local buckles. I understand that these cells are grown on flat adherent surfaces limiting global curvature change, but is there evidence of localized buckling in the monolayer? While I believe-along with the authors-that their phenotypes are likely failures in apical constriction, I think they should work to strengthen this conclusion. I think the easiest way (and hopefully using data they already have) would be to directly compare apical area to basal area on a cell wise basis for some number of cells. Given the heterogeneity of cells, perhaps 30-50 cells per condition/line/mutant would be good? I am open to other approaches; this just seems like it may not require additional experiments.

As the reviewer observes, our cultures cannot bend because they are adhered on a rigid surface. The apical and basal lengths of the cultures will therefore necessarily be roughly equal in length. Some inwards bending of the epithelium is expected at the edges of the dish, but these cannot be imaged. The live imaging we show in Figure 2 illustrates that, just as happens in vivo, apical constriction is asynchronous. This means not all cells will have ‘bottle’ shapes in the same culture. We now illustrate the evolution of these shapes in more detail in Supplementary Figure 1.

Additionally, the reviewer’s comment motivated us to investigate local buckles in the apical surface of our cultures when their apical surfaces are dilated by ROCK inhibition. We hypothesised that the very straight apical surface in normal cultures is achieved by a balance of apical cell size and tension with pressure differences at the cell-liquid interface. Consistent with our expectation, the apical surface of ROCK-inhibited cultures becomes wrinkled (Supplementary figure 4). The VANGL2-KI lines do not develop this tortuous apical surface (as shown in Figure 3), which is to be expected given their modification is present throughout differentiation unlike the acute dilation caused by ROCK inhibition.

This new data complements our visualisation of apical constriction in live imaging, apical accumulation of phospho-myosin, and quantification of ROCK-dependent apical tension as independent lines of evidence that our cultures undergo apical constriction. 

(2) Another slight experimental concern I have regards the difference in laser ablation experiments detailed in Figure 3h-i from those of Figure 2d-e. It seems like WT recoil values in 3h-I are more variable and of a lower average than the earlier experiments and given that it appears significance is reached mainly by impact of the lower values, can the authors explain if this variability is expected to be due to heterogeneity in the tissue, i.e. some areas have higher local tension? If so, would that correspond with more local apical constriction?

There is no significant difference in recoil between the control lines in Figures 2 and 3, albeit the data in Figure 3 is more variable (necessitating more replicates: none were excluded). We also showed laser ablation recoil data in Supplementary Figure 10, in which we did identify a graphing error (now corrected, also no significant difference in recoil from the other control groups as shown in Author response image 3).

Author response image 3.

Recoil following laser ablation is not significantly different between different experiments. X axis labels indicate the figure panel each set of ablation data is shown in. Points represent an independent differentiation dish.

(4)(Minor) I think some of the commentary on the strengths and limitations of the model found in the Results section should be collated and moved to the discussion in a single paragraph. For example, this could also briefly touch on/compare to some of the other models utilizing hiPSCs (These are mentioned briefly in the intro, but this comparison could be elaborated on a bit after seeing all the great data in this work).

These changes have now been made:

Discussion: “Some of these limitations, potentially including inclusion of environmental risk factors, can be addressed by using alternative iPSC-derived models[93,94]. For example, if patients have suspected causative mutations in genes specific to the surface (non-neural) ectoderm, such as GRHL2/3, 3D models described by Karzbrun et al[49] or Huang et al[95] may be informative. Characterisation of surface ectoderm behaviours in those models is currently lacking. These models are particularly useful for high-throughput screens of induced mutations[95], but their reproducibility between cell lines, necessary to compare patient samples to non-congenic controls, remains to be validated. Spinal cell identities can be generated in human spinal cord organoids, although these have highly variable morphologies[96,97]. As such, each iPSC model presents limitations and opportunities, to which this study contributes a reductionist and highly reproducible system in which to quantitatively compare multiple neuroepithelial functions.”

(5) While the authors are generally good about labeling figures by the day post smad inhibition, in some figures it is not clear either from the images or the legend text. I believe this includes supplemental figures 2,5,6,8, and 10 (apologies if I simply missed it in one or more of them)

These have now been added.

(6) The legend for Figure 2 refers to a panel that is not present and the remaining panel descriptions are off by a letter. I'm guessing this is a versioning error as the text itself seems largely correct, but it may be good to check for any other similar errors that snuck in

This has now been corrected.

(7) The cell outlines in Figure 3d are a bit hard to see both in print and on the screen, perhaps increase the displayed intensity?

This has now been corrected.

Description of analyses that authors prefer not to carry out

R2.5. Figure 3. The authors mention their previous study in which they show that Vangl2 is not cell-autonomously required for neural closure. It will be interesting to study whether this also the case in the present human model by using mosaic cultures.

The reviewer is correct that this is one of the exciting potential future applications of our model, which will first require us to generate stable fluorescently-tagged lines (to identify those cells which lack VANGL2). We will also need to extensively analyze controls to validate that mixing fluo-tagged and untagged lines does not alter the homogeneity of differentiation, or apical constriction, independently of VANGL2 deletion. As such, the reviewer is suggesting an altogether new project which carries considerable risk and will require us to secure dedicated funding to undertake.

R3.8(Minor) The authors show a fascinating piece of data in Supplementary Figure 1, demonstrating that nuclear volume is halved by day 8. Do they have any indication if the DNA content remains constant (e.g., integrated DAPI density)? I suppose it must, and this is a minor point in the grand scheme, but this represents a significant nuclear remodeling and may impact the overall DNA accessibility.

We agree with the reviewer that the reduction in nuclear volume is important data both because it informs understanding of the reduction in total cell volume, and because it suggests active chromatin compaction during differentiation. Unfortunately, the thicker epithelium and superimposition of nuclei in the differentiated condition means the laser light path is substantially different, making direct comparisons of intensity uninterpretable. Additionally, the apical-most nuclei will mostly be in G2/M phase due to interkinetic nuclear migration. As such, the comparison of DAPI integrated density between epithelial morphologies would not be informative (Author response image 4).

Author response image 4.

Lateral views of DAPI-stained nuclei on Days 2 and 8 of differentiation. Note the rapid loss of staining intensity below the apical pseudo-row of nuclei on Day 8. This intensity change is likely due to the apical nuclei being in G2/M phase and therefore having more DNA, and rapid loss of 405nm wavelength signal at depth.

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

Learn more at Review Commons

Reply to the reviewers

We were very pleased to see the very positive evaluation of our work by all 3 reviewers and appreciate their constructive comments and suggestions. We have now addressed all reviewers’ comments by making changes and clarifications to the manuscript.

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

In the present manuscript, the authors present an in-depth study on the effect of a heat-shock response on the ability of yeast to regain viability after quiescence when their ability to respire is inhibited. They nicely demonstrate that these effects correlate with the measured diffusion coefficients, providing deeper insight into the (at least partially) responsible environmental stress response and the molecular players involved. This work is an important contribution to the growing (or resurging) field of the physical properties of the cell.

We thank this reviewer for this very positive evaluation.

My two main comments are the following:

• The authors determine the diffusion coefficients from the MSD, as well as further analyze them all the way up to the confinement size. As far as I can judge from the manuscript, these analyses are for 2D systems and were initially developed for processes on membranes. How does this change for 3D systems? I understand that for a straightforward qualitative comparison of apparent MSD, this assumption is acceptable, but it may deviate more strongly with the additional analyses the authors present.

This is indeed an important point, and the reviewer is correct that the trajectories are analyzed in 2D (x,y) while the cytoplasm is a 3D environment. We fully agree that this requires careful interpretation, particularly for metrics beyond the short-lag diffusion coefficient.

First, for the diffusion coefficient, it is well established that for isotropic 3D motion the movements in all three dimensions are independent of each other and the projected 2D MSD satisfies:

= 4*D*τ

Thus, estimating from the short-lag slope of the 2D MSD yields the correct diffusivity of the underlying 3D process (up to standard experimental corrections such as localization error and motion blur). This approach is therefore widely used in cytoplasmic SPT and GEM studies, including in yeast, and is not restricted to membrane diffusion [1, 2].

Regarding confinement-related metrics derived from longer time lags, we agree that these were originally developed and most rigorously interpreted for 2D systems. In our study, these quantities are intentionally used as effective in-plane (x,y) descriptors of particle motion rather than as a full reconstruction of a 3D confinement geometry. Mapping a 2D MSD plateau to an absolute 3D confinement size depends on assumptions about geometry and isotropy and cannot be done uniquely without full 3D tracking. Nevertheless, MSD-based analyses have been successfully extended to explicitly model and quantify 3D confined diffusion in previous studies, provided that full 3D trajectories or well-defined confinement geometries are available. [2, 3]

[1] Gómez-García, P.A., Portillo-Ledesma, S., Neguembor, M.V., Pesaresi, M., Oweis, W., Rohrlich, T., Wieser, S., Meshorer, E., Schlick, T., Cosma, M.P., Lakadamyali, M., 2021. Mesoscale Modeling and Single-Nucleosome Tracking Reveal Remodeling of Clutch Folding and Dynamics in Stem Cell Differentiation. Cell Rep. 34. https://doi.org/10.1016/j.celrep.2020.108614

[2] Delarue, M., Brittingham, G.P., Pfeffer, S., Surovtsev, I. V., Pinglay, S., Kennedy, K.J., Schaffer, M., Gutierrez, J.I., Sang, D., Poterewicz, G., Chung, J.K., Plitzko, J.M., Groves, J.T., Jacobs-Wagner, C., Engel, B.D., Holt, L.J., 2018. mTORC1 Controls Phase Separation and the Biophysical Properties of the Cytoplasm by Tuning Crowding. Cell 174, 338-349.e20.

[3] Lerner, J., Gómez-García, P.A., McCarthy, R.L., Liu, Z., Lakadamyali, M., Zaret, K.S., 2020. Two-parameter single-molecule analysis for measurement of chromatin mobility. STAR Protoc 1.

Importantly, we do not assume perfect isotropy of the yeast cytoplasm. Local anisotropies are expected due to organelles, crowding heterogeneity, and cell geometry. However, the system is sufficiently close to isotropic at the length and time scales probed that the extracted confinement radius is highly reproducible across independent biological replicates. In our experiments, we observe consistent radius of confinements across three replicates, indicating that any bias introduced by partial anisotropy or projection into 2D is systematic and small.

Based on the observed reproducibility and the finite depth of field of our measurements (~100 nm), we estimate that potential errors in the absolute values of confinement-related parameters arising from 2D projection and incomplete isotropy are on the order of We have now clarified this point explicitly in the Methods section, emphasizing that confinement parameters are effective 2D measures, that the cytoplasm is not assumed to be perfectly isotropic, and that the conclusions rely on consistent, comparative measurements obtained under identical imaging and analysis conditions. The updated Methods paragraph is as follows:

[…] Trajectory analysis: Radius of Confinement

The radius of confinement was obtained only for the subgroup of confined trajectories. It quantifies the degree of confinement by estimating the radius of the 2D area explored by the particle in the imaging plane, which serves as a proxy measurement for the 3D volume that it explores. It was measured by fitting a circle-confined diffusion model to the TE-MSD (ensemble of all trajectories) (Wieser and Schütz, 2008).

TE-MSD = R^2 * (1 - exp(-4*D*t_lag/R^2)) + O

where R is the radius of confinement and D is the diffusion coefficient at short timescales. O is an offset value that comes from the localization precision limit inherent to localization-based microscopy methods.

Trajectories were analyzed in the imaging plane (x,y), and confinement metrics were therefore derived from 2D MSDs. Although particles diffuse in a three-dimensional cytoplasmic environment, projection onto 2D does not bias estimation of the short-lag diffusion coefficient for isotropic motion, since the projected MSD follows ⟨Δr_xy²(τ)⟩ = 4Dτ. However, confinement-related parameters derived from longer lag times should be interpreted as effective in-plane descriptors of mobility rather than as a direct reconstruction of a full 3D confinement geometry. Mapping a 2D MSD plateau to an absolute 3D confinement size would require explicit assumptions about geometry or full 3D tracking. Our conclusions rely on comparative analyses performed under identical imaging and analysis conditions, and the extracted confinement radii were highly reproducible across biological replicates, indicating that any bias introduced by 2D projection or moderate anisotropy is systematic and does not affect the validity of the relative differences reported.

• The authors show data in the supporting information where the GEMs provide larger foci after stress with longer imaging times. Could the authors provide the images of the shorter imaging times that they use? That seems a more equal comparison than Figure C. It is also unclear to me why fixed cells are used in Figure C, as well as the meaning of the x-axis. In line with this, can the authors exclude that GEMs dimerize/oligomerize after stress, and therefore display a lower diffusion coefficient?

We are happy to include the images acquired at a shorter time interval and have done so (Fig S2A). We apologize for insufficiently explaining the GEM intensity experiment shown in Figure S2C. The fixation was done to immobilize the GEMs, since they are rapidly diffusing in live cell imaging and the diffusion speed relative to camera exposure time will impact the brightness (any movement of a particle during exposure causes the signal on the detector to become “blurred” and reduces the intensity per pixel). Hence, GEM brightness does not solely reflect the monomer or potential aggregate/multimer state, but is also affected by diffusion speed and exposure time: faster moving GEMs will generally appear dimmer than slower moving ones, since the signal detection during the acquisition time is reduced by the particle movement. Another effect is that, since GEMs are moving in live cell imaging, they have a probability of spatially overlapping, enhancing the signal levels of the single detected spots.

We have quantified the brightness distribution in the different conditions to detect aggregation or multimerization of GEMs, which we expect to be visible as a shoulder on the Gaussian curve. The x-axis shows the intensity which we have determined for each trajectory. We chose to assess GEM intensity in the frame with the highest intensity, and to take the “Total” intensity, meaning we sum up the intensity of the pixels within the Point Spread Function (PSF) of each localization in that frame.

To clarify these points, we have extended the description of this experiment in the Results and Methods sections:

Results:

[...] Additional evidence for this comes from the observation that imaging GEMs at a lower frame rate (i.e., longer exposure time of 100 ms) showed a uniformly diffuse signal in SCD, whereas distinct foci appeared under starvation conditions (Figures S2A and S2B). This might suggest that GEMs aggregate in starvation. However, imaging GEMs at a faster frame rate (used for SPT, 30 ms exposure time) shows GEMs freely diffusing in all conditions (Figure S2A). Furthermore, analyzing GEM particle intensities in fixed cells, to eliminate motion blur-induced intensity attenuation, showed uniform GEM brightness distributions in all conditions (Figure S2C). Rather than aggregates, the bright foci thus represent immobile, single GEM particles that are confined and appear brighter during long exposure times due to their confinement in low-diffusive compartments. [...]

Methods:

[...] Trajectory analysis: Track Total Intensity

To assess GEM brightness, we determined the intensity of each trajectory in fixed cells. Cell fixation eliminates the motion blur-induced intensity attenuation, which would otherwise confound the GEM brightness depending on the movement speed and confinement. For each individual particle trajectory, the frame with the highest signal intensity of the localized particle was determined and the sum of the pixel intensities of the particle in that frame was calculated as the “Track Total Intensity”. In fixed cells, the GEM intensities were comparable in all conditions (Figure S2C). All GEM intensity histograms show a single, bell-shaped distribution of intensities with no indication of several GEM particles aggregating into brighter foci. [...]

Other comments: - For the precision of the language, the authors equate ribosome content with macromolecular crowding, with the diffusion of the GEMs throughout, and this becomes more conflated in the discussion, where it is compared to viscosity and macromolecular crowding effects, e.g., translation. Is it macromolecular crowding, mesoscale crowding, nano-rheology, or ribosome crowding? What is measured precisely?

We agree that careful and consistent nomenclature is important and thank the reviewer for bringing this point to our attention. We believe our manuscript maintains the proper distinctions of the terms diffusion, crowding and viscosity. We refer to what we study with the GEM single-particle tracking consistently as “(cytoplasmic) diffusion”. In Figure 2, we add “crowding” as an additional term since we observe a change in ribosome concentration and we affect the cytoplasmic crowdedness with a hyperosmotic shock. Our in-depth analysis of the confined and unconfined trajectory diffusion suggested that the cytoplasm is not simply globally affected by crowding or viscosity, but contains regions or compartments that trap GEM. Apart from Figure 2, we do not use the term viscosity or crowding, and we only return to “crowding” in the Discussion, either in reference to the aforementioned experiments from Figure 2 (ribosome concentration, hyperosmotic shock) or when discussing studies from cited works.

However, we did not use the term “macromolecular crowding” consistently and simplified it to “crowding” in a few instances. To be more precise, we now specify “macromolecular crowding” instead of “crowding” wherever applicable; namely in the text referring to Figure 2, where we specifically assess macromolecular crowding.

• In the EM images, the ribosomes seem smaller after starvation. Is that correct, and how should we interpret this? Is this due to an increased number of monosomes?

This is an important point, and it indeed appears that in SCD some ribosomes are close together, potentially as polysomes. In SC, the ribosomes appear more distinctly separated from each other, which would be expected due to the polysome collapse that occurs in starvation. However, the apparent size of individual ribosomes is identical in both conditions. Unfortunately, the resolution is not good enough to accurately measure the sizes of the ribosomes and clearly determine their monomer/polysome state.

• The authors refer to recent work on how biochemical reactions, such as translation, are determined by the cytoplasm. There is some older work on this, see for example in bacteria https://doi.org/10.1073/pnas.1310377110, and also in vitro here DOI: 10.1021/acssynbio.0c00330

We thank this reviewer for pointing out these publications and have included them in this group of citations.

• On the section of correlating diffusion and survival outcomes (bottom page 12), it is mentioned that the lowered diffusion could enhance aggregation. However, literature indicates that the opposite is true in buffer; lower diffusion reduces aggregation (also nucleation is inversely proportional to the viscosity).

This is a valuable point and we have happily expanded on it in the Discussion section. It is true that chemical assays have demonstrated that higher viscosity and slower diffusion decrease nucleation and aggregate formation. However, in vitro studies that alter diffusion through crowding changes have revealed a complex relation between crowding and aggregation propensity. The basic idea is that the excluded volume effect decreases aggregation by stabilization of the more compact, folded state. But the opposite effect, precluded protein folding, has also been ascribed to the excluded volume effect. As of now, studies with different crowders (dextran, ficoll, PEG, etc.) demonstrated increased or reduced protein aggregation upon crowding [1, 2, 3, 4]. The variable effect on aggregation seems to be not only based on the protein that is studied, but also the properties of the crowder (charges, shape, size), the interaction of the crowder with the protein, and the mixture of crowders [5].

Even though the relationship between crowding and protein aggregation is complex, we speculate that lower diffusion in our more crowded cells could cause protein aggregation, because these starvation conditions are known to induce the formation of protein fibrils and the condensation of mRNA and proteins.

[1] Uversky, V.N., M. Cooper, E., Bower, K.S., Li, J., Fink, A.L., 2002. Accelerated α-synuclein fibrillation in crowded milieu. FEBS Lett. 515, 99–103. https://doi.org/10.1016/S0014-5793(02)02446-8

[2] Munishkina, L.A., Cooper, E.M., Uversky, V.N., Fink, A.L., 2004. The effect of macromolecular crowding on protein aggregation and amyloid fibril formation. J. Mol. Recognit. 17, 456–464. https://doi.org/10.1002/jmr.699

[3] Biswas, S., Bhadra, A., Lakhera, S., Soni, M., Panuganti, V., Jain, S., Roy, I., 2021. Molecular crowding accelerates aggregation of α-synuclein by altering its folding pathway. Eur. Biophys. J. https://doi.org/10.1007/s00249-020-01486-1

[4] Mittal, S., Singh, L.R., 2014. Macromolecular crowding decelerates aggregation of a β-rich protein, bovine carbonic anhydrase: a case study. J. Biochem. 156, 273–282. https://doi.org/10.1093/jb/mvu039

[5] Kuznetsova, I.M., Zaslavsky, B.Y., Breydo, L., Turoverov, K.K., Uversky, V.N., 2015. Beyond the excluded volume effects: Mechanistic complexity of the crowded milieu. Molecules 20, 1377–1409. https://doi.org/10.3390/molecules20011377

To be more precise, we have therefore extended our Discussion section. We believe part of this additional discussion fits better in an earlier section, where we specifically discuss how the cytoplasmic properties, and specifically crowding, have been linked to filament/condensate formation. The updated paragraphs are as follows:

[...] Additional cytoplasmic rearrangements occur upon energy depletion, including filament formation or the formation of biomolecular condensates (Narayanaswamy et al., 2009; Noree et al., 2010; Petrovska et al., 2014; Prouteau et al., 2017; Riback et al., 2017; Saad et al., 2017; Marini et al., 2020; Stoddard et al., 2020; Cereghetti et al., 2021) highlighting a broader reorganization of the cytoplasm that could further affect the diffusion of macromolecules. In turn, the amount of crowding might also influence the propensity to form condensates and filaments (Heidenreich et al., 2020). Interestingly, in vitro studies have demonstrated a complex, dual effect of crowding on protein fibrillation and aggregation, in suppressing or accelerating it (Uversky et al., 2002; Munishkina et al., 2004; Mittal and Singh, 2014; Biswas et al., 2021). This appears to be dependent not only on the protein of study, but the properties of the crowder (size, charge, shape) and the specific mixture of crowders (Kuznetsova et al., 2015). [...]

[...] By contrast, extremely low diffusion, as seen in the absence of respiration in glucose starvation, might irreversibly impair cellular functions due to limited movement of proteins and RNA in and out of certain compartments, cellular territories and condensates. Such a model is supported by our analysis of how lower diffusion is the result of confined spaces becoming more prevalent, creating compartments that can trap macromolecules. As previously mentioned, increased crowding and reorganization of the cytoplasm have been linked to condensation and fibril formation of proteins, and, in certain in vitro contexts, accelerated aggregation. This state of crowding-induced low diffusion might therefore enhance protein aggregation or preclude the refolding of damaged proteins, which could disrupt proteostasis and lead to toxic aggregates that are a hallmark of the aging process (López-Otín et al., 2013). Together, these effects on proteins, RNA and other macromolecules likely lead to loss of cell fitness and irreversible arrest of the cells, preventing their reentry into the cell division cycle. [...]

Reviewer #1 (Significance (Required)):

General assessment: Strengths: It is a comprehensive study that provides a wealth of information and insight into the intricacies of a field that has received considerable attention, and its views are evolving rapidly. Weaknesses: It may suffer from some overinterpretation of diffusion data. Advance: The significant advance is that the molecular response pathway and precise molecular players are connected to the biophysical response of cells to starvation/quiescence. The dependence of diffusion on starvation has received considerable attention (Jacobs-Wagner, Cell, 2014; the current authors in eLife, 2016; and more recent investigations by Holt, Delarue, and others). Still, the authors take the next step and demonstrate how quiescence, and particularly how the history of a cell affects it, correlates strongly with the diffusion. As far as I can tell, this is new. As mentioned, the molecular insights into the pathways are exceptionally strong from my perspective. From personal experience, this work is also very important for researchers outside of the field from a practical standpoint: Do your measurements change when you stress cells by walking to a microscope? And even if you incubate them there, your measurement outcome will change. In my experience, this is a crucial point, and the cell's history is often overlooked. Audience: Broad -- biophysicists, molecular biologists, cell biologists, biotechnologists. My field of expertise: Biophysics.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

This manuscript addresses an important and longstanding question in the field: how eukaryotic cells remodel themselves to enter and survive quiescence, particularly under nutrient limitation and energy stress. The authors combine tools from biophysics, proteomics, stress signaling, and functional genomics to propose that stress-induced cytoplasmic reorganization, rather than ATP availability per se, is critical for long-term survival when respiration is impaired. The topic is timely, the experiments are generally well executed, and the initial phenomenology is compelling. The paper begins with a set of clear and convincing figures that establish an interesting and biologically important phenotype: when cells are shifted into glucose starvation, they can survive long term only if respiration is functional. Blocking respiration with Antimycin A (AntA) severely compromises viability. One straightforward hypothesis is that this defect simply reflects a failure to generate sufficient ATP. The authors, however, show that a 30-minute heat shock (HS) before glucose withdrawal in the presence of AntA largely rescues survival, even though cellular ATP levels remain critically low. In parallel, they use very well-executed GEM single-particle tracking experiments to demonstrate that cytoplasmic particle mobility decreases markedly in glucose-starved, respiration-deficient cells, and that this diffusion defect is also rescued by the pre-HS, again without restoring ATP. Together, these initial figures strongly support the idea that stress-induced remodeling of the cytoplasm, rather than ATP levels per se, is a key determinant of whether cells can enter and maintain a viable quiescent state. The authors then propose that this protective effect of HS is mediated by induction of the environmental stress response (ESR) and by resulting changes in protein expression. To test whether new protein synthesis is required, they pre-treat cells with cycloheximide during the HS and recovery period. This treatment largely, although not completely, abrogates the beneficial effect of HS on survival and diffusion in AntA-treated, glucose-starved cells. This is a strong experiment and supports the idea that HS-induced synthesis of specific proteins is important for protection, while also hinting that some cycloheximide-insensitive or pre-existing components may contribute. To identify the relevant proteins, the authors turn to global proteomic analysis, comparing multiple conditions: glucose starvation (SC), heat shock followed by glucose starvation (HS SC), glucose starvation plus AntA (SC + AntA), and heat shock followed by glucose starvation plus AntA (HS SC + AntA), each at 1 and 20 hours. This is where, in my view, the story becomes significantly harder to follow. The text for Figure 3 relies almost entirely on GO term enrichment, with very little description of individual proteins or even basic quantitative summaries of the dataset. For example, the authors never clearly state how many proteins were robustly quantified, nor what fraction of the proteome that represents. Without this foundational information, it is difficult to evaluate the strength and generality of their conclusions. Related to this, the GO analysis in Figure 3F reports "significant" enrichment for categories such as ribosomes or translation, yet the underlying number of proteins making up these enrichments is not shown. From the volcano plots, it appears that only a very small number of proteins change in some conditions (e.g., SC 20 h), and yet GO terms appear with extremely strong q-values. This is confusing: how can such strong enrichment occur if only a handful of proteins are changing? At minimum, the authors should provide: • the number of significantly up- or down-regulated proteins in each comparison • the number of proteins contributing to each enriched GO category • the magnitude of the changes for these proteins Because the absolute number of significantly changing proteins appears small in several conditions, the current heavy reliance on GO analysis feels unwarranted and potentially misleading. In such cases, it would likely be more informative to list all differentially abundant proteins-either in supplementary materials or in a main-text table-and briefly describe the most relevant ones, rather than relying on broad category labels. Figure 3F, in particular, needs substantially more explanation. A related issue appears in Figure 3G (and the associated text), where the authors emphasize that the proteomic response to HS + AntA and the response to long-term glucose starvation are distinct. While this conclusion is plausible, the analysis also shows a subset of proteins that are upregulated in both conditions. These overlapping proteins may, in fact, represent the core protective module that enables survival in quiescence. The authors do not discuss these proteins at all; instead, they are effectively dismissed in favor of the "distinct responses" narrative. I encourage the authors to identify and discuss these overlapping proteins explicitly. Are they chaperones, proteasome components, antioxidant enzymes, or other classical stress-response factors? Even if the global proteomes differ, the overlapping subset could be highly informative about the minimal set of proteins required to stabilize the cytoplasm and support entry into quiescence. The SATAY screen is a major strength of the paper, as it moves from correlative proteomics to functional genetic analysis. The approach appears well-controlled, but key information is missing: How many unique insertions were obtained? Was the library saturating? What was the read distribution and coverage? The authors also discuss only a small subset of the screen hits. The volcano plots show many additional genes that are not addressed. What categories do these fall into? Are they informative about pathways beyond Ras/PKA and Msn2/4? Presenting a fuller analysis would strengthen the mechanistic interpretation. The parts of the SATAY analysis that are discussed are solid. The screen implicates the Ras/PKA signaling axis and Msn2/4 in survival under HS-preconditioned, respiration-deficient starvation, and the authors validate these hits with targeted survival assays. The correspondence between genetic perturbations and changes in cytoplasmic diffusion is an intriguing connection. However, the analysis stops short of identifying the downstream effector proteins that actually produce the biophysical benefits observed. The manuscript then returns to the idea that improved cytoplasmic diffusion and reduced confinement may be essential for survival. This is an appealing hypothesis, but the evidence remains correlative. It is still unclear whether biophysical rescue is the cause of improved survival or simply a downstream marker of a properly induced stress response. What remains missing is deeper integration of the proteomics and SATAY data to identify which proteins are likely responsible for the adaptive changes in cytoplasmic organization. Overexpression of promising candidates-such as chaperones or proteostasis factors found in the overlap between HS and long-term starvation responses-could help determine whether any single protein or small group of proteins can phenocopy the HS-induced rescue. Importantly, many of the comments above are intentionally broad: the manuscript does not simply require small clarifications but would benefit from substantial expansion and deepening of the analysis. The observations are compelling, but the mechanistic chain connecting ESR activation → proteomic remodeling → cytoplasmic biophysics → survival remains insufficiently developed in the current draft. Clearer quantitative reporting, fuller presentation of the data, and more thoughtful interpretation would significantly strengthen the manuscript.

We thank reviewer 2 for this very thoughtful evaluation of our manuscript. We agree that expanding the descriptions and analysis of the presented data will improve the manuscript. Importantly, we now provide the proteomics data and the SATAY screen in an accessible format as supplementary materials. We address the individual points below.

Summary of Major Issues That Need to Be Addressed • Quantitative clarity in the proteomics o State how many proteins were quantified. o Report the numbers of significantly changing proteins in each condition. o Identify the proteins underlying each GO term and provide effect sizes.

We have now included a supplemental table containing label-free protein abundances for all 3308 reproducibly quantified proteins across all nine conditions (Supplemental Table S4). In addition, we added a sentence to the main text specifying both the number of reproducibly identified proteins and the approximate coverage of the yeast proteome.

For the comparison of protein abundances between the different stress conditions and logarithmically growing SCD cells, we now indicate the number of significantly changed proteins in the legend of Figure 3E. Furthermore, we include a heatmap of standardized protein abundances for all proteins that were significantly changed in at least one stress condition (Supplemental File S1) and provide all pairwise comparison results in the supplemental table (Supplemental Table S5). This new Supplemental File S1 replaces the previous Supplemental File S1, which had a stricter cutoff, showing all proteins with an abundance change greater than 2 standard deviations.

The information requested by the reviewer regarding GO term analysis is indeed important and was missing in the original version. We now report, for each GO term, the number of proteins in the top or bottom 10% of differentially abundant proteins and provide the corresponding effect size, calculated as the ratio of the observed to expected hits (Figure 3F).

• Over-reliance on GO analysis o Provide explicit lists of differentially expressed proteins. o Indicate whether enrichment results are meaningful given the small number of hits.

We appreciate this reviewer’s comment and agree that the presentation of the proteomic data in Figure 3 relies strongly on GO term enrichment, with limited description of individual proteins. Our primary goal for the proteomic analysis was to characterize the cellular response to stress at a global level rather than to focus on individual proteins or stress-specific details. We therefore intentionally opted for a broader, more coarse-grained analysis to not overcomplicate the manuscript and maintain accessibility for a broad readership.

That said, we agree that the underlying data should be made fully accessible. We have therefore expanded the supplemental materials to include a heatmap of all proteins that were significantly changed in at least one condition (Supplemental File S1), as well as comprehensive tables reporting protein abundances and pairwise differences across all stress conditions (Supplemental Tables S4 and S5). These additions provide direct access to the protein-level data while preserving the clarity of the main text.

With respect to GO term analysis, to avoid overinterpretation driven by small protein sets and better comparability across different conditions, we always performed the GO enrichment based on the top and bottom 10% changed proteins. This is already stated in the legend of Figure 3F and in the Methods section. We have now added the key missing parameters of the analysis to Figure 3F (see response above). Given that the analysis identifies multiple GO terms generally associated with the environmental stress response and that these terms exhibit coordinated behavior across conditions (Figure S3A), we are confident that the conclusions drawn from this analysis are robust.

• Overlooked overlapping proteins o Analyze and discuss the subset of proteins upregulated both by HS and by long-term starvation. o These may represent the core factors enabling survival.

Indeed, we agree that the overlapping proteins that are observed in our Figure 3G analysis should be presented. Perhaps surprisingly, these proteins (Hxt5, Sps19, Atg8, Aim17, Put1, Fmp45, YNL194C) have diverse functions and have so far not been implemented in the environmental stress response.

In the Results section, we now mention and briefly discuss the four that are present in both time points of the HS SC +AntA condition. We now mention all of them in the figure legend.

The modified text from the Results section is as follows:

[...] Furthermore, the proteins that are enriched in long-term starvation (SC 20 h vs. SCD) and those enriched in pre-HS respiration-deficient starvation (HS SC +AntA 1 h vs. SCD; HS SC +AntA 20 h vs. SCD) are poorly correlated and there is only a small overlap of factors that are significantly upregulated in all conditions (Figure 3G). These proteins are Aim17, Put1, Fmp45 and YNL194C. Aim17 is a mitochondrial protein of unknown function and Put1 is a mitochondrial proline dehydrogenase. Fmp45 and YNL194C are paralogous membrane proteins involved in cell wall organization. Focusing on the broad proteomic adaptation, we looked at the Gene Ontology (GO) terms of the proteomic changes across all conditions, and observed that long-term starvation (SC 20) leads to the upregulation of a few groups of proteins, mostly involved in respiratory activity and rewiring of the metabolism (Figure S3A). [...]

We greatly appreciate the suggestion to do an overexpression experiment. However, the overlapping proteins are not significant hits in the SATAY, suggesting that they are individually not required for the survival rescue although their overexpression might benefit survival.

We have therefore chosen to keep a broad perspective on the proteomics results and investigate instead the SATAY results in more detail, since they inherently contain functional relevance to survival. Overall, we feel that the overexpression of those (individually or as a group) would extend beyond the scope of our current manuscript.

• SATAY analysis needs fuller presentation o Provide insertion numbers, coverage, and basic library statistics. o Discuss additional hits beyond the Ras/PKA/Msn2/4 pathways. o Integrate SATAY results more deeply with proteomics.

We have added the insertion numbers and genome coverage percentages to the Methods section as follows:

[...] SATAY Screen: Analysis and Plotting

Sequencing detected the following total unique transposon numbers: 690’935 (A1), 558’932 (HA1), and 359’935 (HA4d) unique transposons. The transposon insertions in the different genes yielded the following genome coverages: 96.3% (A1), 94.5% (HA1) and 89.3% (HA4). For each gene [...]

We now also provide the SATAY screen data as Supplemental Table S6.

In the Results section, we mention some additional hits from the SATAY screen (ribosome biogenesis, mitochondrial respiration) but then shift our focus to the ESR genes. We now add a comment to the ribosome biogenesis genes before going to the ESR:

[...] The screen revealed several highly significant gene disruptions that promote or impair the HS-mediated rescue of respiration-deficient, glucose-starved cells (Figure 4A, Supplemental Table S6). The most significant gene hits that impair survival in 4 d HS SC +AntA when disrupted are involved in a variety of cellular processes, including ribosome biogenesis (e.g., ARX1, BUD22, RRP6), mitochondrial respiration (e.g., CBR1, COX23, ETR1), and ESR (e.g., MSN2, PSR2, YAP1). Intriguingly, the ribosome biogenesis genes being crucial for survival suggests that new ribosomes might have to be produced to ensure proper translational response during the HS. Notable among the ESR genes are MSN2 and, less significantly scored, MSN4, the master regulators of the ESR. [...]

To deepen the discussion on the lack of overlap between the SATAY screen and the proteomics, we have added a sentence highlighting that the SATAY screen detected the main regulators of the ESR, and the proteomics revealed its downstream targets involved in proteostasis and other stress proteins, and therefore these two data sets do both point to the ESR as the crucial response behind the HS-induced rescue. The modified Discussion text is as follows:

[...] Furthermore, the signaling genes that scored highly in the SATAY screen are often regulated through their activity rather than their abundance. Plausibly, their downstream target proteins are differentially expressed, whereas disrupting the regulators themselves leads to strong survival phenotypes. Similar observations have been made in other stress conditions, where fitness-relevant genes showed little overlap with genes with upregulated expression (Birrell et al., 2002; Giaever et al., 2002). Nonetheless, the SATAY screen revealed the principal regulators of the ESR while the proteomic analysis detected many of the ESR downstream targets involved in proteostasis and oxidative stress, demonstrating a functional convergence on the ESR in both data sets. [...]

• Mechanistic depth remains limited o Clarify whether cytoplasmic biophysical rescue is causal or downstream. o Test whether overexpression of candidate proteins can mimic HS-induced protection. o Expand the discussion of potential mechanisms using insights from both datasets.

Indeed, the specific mechanism(s) that govern the cytoplasmic properties in our conditions are currently not known, preventing us from manipulating the cytoplasmic properties and confirming a causal relationship. To uncover the mechanisms, extensive follow-up studies on ESR genes and/or proteins would be required, going beyond the scope of this manuscript. Furthermore, our ongoing follow-up studies are pointing towards redundancy of some potential regulation of the cytoplasmic diffusion, further complicating the analysis.

The suggested overexpression experiment is addressed in a previous comment where the overlapping proteins are mentioned.

Reviewer #2 (Significance (Required)):

This manuscript addresses a fundamental and timely question in cell biology: how eukaryotic cells remodel themselves to enter and survive quiescence, particularly under conditions of nutrient depletion and compromised energy production. Although quiescence has been studied for decades, the mechanisms that link metabolic state, stress signaling, and the physical properties of the cytoplasm remain incompletely understood. This work brings together biophysical measurements, global proteomics, and unbiased genetic screening in an ambitious effort to illuminate how cells maintain viability when respiration-and thus efficient ATP generation-is disrupted. A key conceptual contribution of this study is the demonstration that ATP levels alone do not dictate survival during starvation. Rather, the ability of cells to mount an appropriate stress response and reorganize the cytoplasm appears to be crucial. The early figures provide compelling evidence that heat shock preconditioning can rescue both viability and cytoplasmic mobility in respiration-deficient cells, even when ATP remains low. This finding is notable because it challenges the widely held assumption that energy charge is the primary determinant of successful entry into quiescence. If strengthened by deeper mechanistic analysis, this insight could reshape how the field views energy stress and cellular dormancy. The identification of the Ras/PKA-Msn2/4 axis as a key regulatory node is also significant, as it connects quiescence survival to well-established nutrient and stress signaling pathways. The integration of a genome-wide SATAY screen adds functional depth and offers the potential to uncover specific downstream effectors that remodel the cytoplasm or stabilize cellular structures during prolonged stress. Finally, the manuscript touches on a concept that is gaining traction across many subfields of biology: that the biophysical state of the cytoplasm is a regulated and physiologically meaningful parameter, not merely a passive consequence of metabolic decline. Understanding how cells tune macromolecular crowding, diffusion, and spatial organization during quiescence could have broad implications beyond yeast, including in stem cell biology, microbial dormancy, cancer cell persistence, and aging. Overall, the questions addressed are important, and the study has the potential to make a meaningful conceptual contribution. However, realizing that impact will require clearer and deeper mechanistic analysis-particularly in the proteomics and SATAY sections-to convincingly identify the specific factors and pathways that mediate the cytoplasmic remodeling underlying survival.

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

Summary. Yeast haploid cells enter quiescence during nutrient deprivation, undergoing major metabolic, transcriptional and biophysical changes. In particular, quiescent cells remodel their cytoplasm, increasing macromolecular crowding and reducing diffusion. Respiration is known to be essential for entry into quiescence and long-term survival.

In this study, the authors discovered that respiration is not intrinsically required for yeast to survive glucose-starvation-induced quiescence. In particular, they found that a short heat shock before starvation restores survival even in the absence of respiration (Antimycin A treatment), demonstrating that a stress-induced adaptation can bypass the respiratory requirement. This rescue occurs without ATP recovery and relies on de novo protein synthesis. This stress-induced adaptation also rescues quiescent-like biophysical properties of the cytoplasm (increased crowding) that are normally prevented in non-respiring cells, which are thought to be relevant for cell survival . Proteomics reveals that heat shock induces a distinct stress-response proteome enriched in proteostasis factors. A genetic screen reveals that Ras/PKA inhibition and Msn2/4 activation enable this protective reprogramming. Altogether this highlights the importance and complexity of stress adaptation to quiescence establishment.

This is an excellent paper in all aspects. I have no major points besides the data accessibility, below.

We thank this reviewer for this very positive evaluation.

Main comments. - It would be nice to have the MS data available as Excel files for the community, and uploaded to repositories such as PRIDE. Description of the MS data is a bit expedited to serve the purpose of the paper (clustering to evaluate the similarity of proteomic profiles between conditions, GO term enrichment) so having the full data available might help.

We agree that the MS data should be accessible. The label-free protein abundances for the reproducibly quantified proteins across all nine conditions (Supplemental Table S4) and the pairwise comparisons shown in Figure 3E (Supplemental Table S5) are now included as supplementary Excel files. The MS data is currently not on PRIDE but we will deposit it there upon publication of our manuscript.

• Same thing for the SATAY screen. The data is summarized in Fig 4B but I believe that the data should be provided.

We agree that the SATAY screen results should be accessible as well, and we have now included the data as Supplemental Table S6.

Minor comments and questions. -I believe that in graphs, the X axis should start at 0 to avoid confusion about the strength of the effect (eg. Fig 2B)

We thank reviewer 3 for pointing this out, and we have re-evaluated the axis limits of all plots. As suggested, we have adjusted the x-axis in Fig 2B to start at 0 to better highlight the strength of the effect. For our Radius of Confinement and %Confined Trajectories graphs, we believe adjusting the y-axis to start and end at the same values will allow better comparison across figures. However, we chose not to set those y-axes to start at 0, since our measurements lie in a range which is covered by these axes, and these plots would simply include blank space if set to start at 0.

-I found that using imaging of GEMs at low frequency to reveal cytoplasmic crowding heterogeneity very interesting. Quiescent cells are known to accumulate many "bodies" as discussed in the text, would any of those co-localize with GEM foci?

Indeed, the imaging at low frequency has revealed that fluorescently-tagged proteins might become trapped in certain regions of the cytoplasm, allowing their detection at conventional imaging frequencies. It is very likely that a similar effect occurs for other cytoplasmic “bodies”, which become visible not only through protein accumulation in a single body but also through low mobility. We have not performed any colocalization experiment with known “bodies” (such as P-bodies or stress granules). Therefore, we do not know if any stress-induced “bodies” are confined to the same spaces as GEMs. However, we would expect at best an incomplete colocalization based on the observation that glucose starvation-induced “bodies” are generally present in a higher percentage of cells than the GEM foci we observe, i.e. it is unlikely that all “bodies” overlap with a GEM focus. It might be interesting to perform such colocalization experiments in follow-up studies, but we feel that such an analysis would go beyond the current scope of this manuscript.

Reviewer #3 (Significance (Required)):

General assessment, advances in the field This is an excellent study. The key finding of this paper, ie. that heat shock can compensate for lack of respiration for entry into quiescence, challenges the current views on quiescence establishment. It describes an alternative program that contributes to cell viability upon C source depletion, with details on the proteomic changes occurring in this condition and some of the genetic basis of this pathway. The study is well designed and controlled, the conclusions are in line with the obtained results and very well discussed and placed in perspective. Experimentally, the authors combine several experimental approaches including live-cell single-particle tracking of GEM nanoparticles to quantify cytoplasmic diffusion, FIB-SEM ultrastructural imaging of the cytoplasm to measure macromolecular crowding, proteomics to map stress-induced protein changes and genome-wide SATAY transposon mutagenesis to identify genes required for survival in respiration-deficient cells. The limitations are: -we don't know how this stress program facilitates survival in the absence of restoration of ATP levels. The data suggest that protein homeostasis is involved (chaperones and proteasome up-regulated upon stress, reduced ribosomal and translation-associated proteins down-regulated in the absence of respiration) but the mechanism remains elusive. -the relationships between cytoplasmic crowding and quiescence establishment remain correlative. Yet, the authors provide another pathway to favour viability upon quiescence establishment (with HS) whose activation also displays an increased crowding and reduction of cytoplasmic movement, further consolidating this link. Both of these points are adequately discussed in the manuscript. None of these points should preclude publication of this study, in my opinion.

Audience. This study would be of interest to researchers in the field of quiescence, biophysics, proteostasis, stress response, nutrient signaling and yeast biology.

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

Learn more at Review Commons

Reply to the reviewers

We thank reviewers for the general positive feedback and insightful suggestions. Reviewers found that our study “provides a rich resource of potential E3-sensor interactions and represents a conceptual and technical advance for the field” and that our “key conclusions are convincing and interesting”. Reviewers suggested both editorial changes to improve the narrative of the manuscript and additional experiments to strengthen the conclusions of the study. We agree with both types of suggestions and decided to modify our manuscript accordingly.

Reviewer #1 (Evidence, reproducibility and clarity (Required)): 

The authors present a rational, AlphaFold-based strategy to systematically identify interactions between human nucleic acid sensors and SPRY-containing proteins. Their findings demonstrate that SPRY domains encode substrate-specific recognition patterns that govern immune responses: TRIM25-ZAP in antiviral defense and restricts LNP-encapsulated RNA, while Riplet-RIG-I for the IFNB1 production and restricts lipofection. They further dissect residue-level contributions to the ZAP-TRIM25 interface by integrating structural predictions with experimental validation. 

Specific comments.  1. The title of this manuscript appears quite broad given that this study mostly focuses on just TRIM25-ZAP and Riplet-RIG-I pairs. 

We agree that the original title was broader than the main mechanistic focus of the study. We will therefore revise the title to better reflect that the manuscript primarily dissects SPRY-domain–mediated specificity in the TRIM25-ZAP and Riplet-RIG-I interactions (identified through our AlphaFold-based screening framework), while retaining the broader screening context. Proposed new title: "SPRY domains encode ubiquitin ligase specificity for ZAP and RIG-I"

In Figure 1b, several predicted interaction scores appear inconsistent with previously reported experimental interactions. For instance, KHNYN has been experimentally validated as a TRIM25-interacting protein, yet its interaction score is notably low in your computational results. Could the authors clarify whether this discrepancy arises because the TRIM25 SPRY domain does not significantly contribute to KHNYN binding? 

We thank the reviewer for raising this point. To our knowledge, published data only support co-immunoprecipitation of TRIM25 and KHNYN in ZAP-deficient in cells (PMID: 31284899), but this does not by itself demonstrate a direct binary interaction, as the association could be mediated by other factors. Consistent with this, our AlphaFold-based screen predicts a low interaction score between KHNYN and TRIM25, suggesting that this may not be a direct protein-protein interaction. Nevertheless, we concede that our approach may have missed interactions that are governed by a small number of interacting residues. We added the following sentences on the limitation of this approach for such interactions in our discussion:

• “While our screen revealed novel interactions between SPRY domain containing proteins and innate immune sensors, it is plausible that certain interactions were missed. Interactions that rely on a small number of contacting residues or interactions that may be mediated by a third binding partner are likely to score poorly in our approach. Future optimization of our algorithm will improve the detection of such interactions.”*

In Figure 2c, the authors provide intriguing examples for shared targets by SPRY proteins with quite low homology, and distinct target profiles by nearly identical SPRY domains. However, the underlying mechanisms responsible for these observations are not discussed. 

This is an important point. At present, we cannot assign a single definitive mechanism for every example, but there are several plausible explanations consistent with our framework. First, our analysis indicates that substrate recognition is often driven by a limited subset of residues at the interaction surface, such that distinct sequences can converge on similar three-dimensional interface chemistry, while small local differences can shift binding preferences. Second, we note that although a large fraction of predicted contacting residues are within SPRY domains, other domains can also contribute to interaction and substrate recognition, which could modulate binding profiles even when SPRY sequences are near-identical. Third, the Pearson’s correlation coefficient was calculated all scores, which may include structures with low confidence scores or low interaction scores

In Figure 3e and 3f, the authors state that the Riplet-T25 SPRY chimeric protein showed enhanced AlphaFold predicted interaction with ZAP, and validated the interaction experimentally. However, the Alphafold also predicted that an increased interaction for the T25-Riplet chimera, although this mutant failed to be co-precipitated with ZAP. How do the authors reconcile this discrepancy between prediction and experimental outcome? 

The reviewer noticed an important, nuanced result in Fig. 3e. AlphaFold predicts that the TRIM25 chimera containing the Riplet SPRY domain (T25–Riplet) has a higher interaction score with ZAP than Riplet alone (Fig. 3e), yet this chimera is not recovered in ZAP co-immunoprecipitation (Fig. 3f). We reconcile this by emphasising that our framework uses an empirically benchmarked threshold: known SPRY–sensor interactions typically score >2.5, and we therefore adopted >2.5 as the cutoff for “high-confidence” candidate interactions. While the T25–Riplet chimera shows an increased score relative to Riplet, its score remains below this >2.5 cutoff in Fig. 3e (which reports interaction scores of the chimeras against ZAP). Therefore, the model is consistent with the experimental outcome: AlphaFold suggests some degree of interface compatibility, but not at a level we would classify as a robust/predictive interaction under our validated threshold. We clarified this point in the Results section to explicitly note that sub-threshold “increases” should be interpreted cautiously:

“Using the T25-RipletSPRY instead of the Riplet protein, predicted a higher interaction score despite the lack of specific pull-down between this chimera and ZAP; importantly, this interaction score is below our defined threshold (2.5), highlighting the importance of benchmarking predicted scores against known interactions.”

It is curious if the authors explain why TRIM25 consistently appears as two bands in many of the presented figures. 

We have also wondered about this observation as well. Other studies report that the double band pattern in western blots of TRIM25 (PMID: 17392790, 28060952, 21292167) and it is believed to be a product of non-degradative self-ubiquitination of TRIM25, primarily acting on the K117 residue (PMID: 21292167). We will add a brief description of this phenomenon in the figure legend.

In Figure 4b, the authors show that treatment with a proteasome inhibitor increased RIG-I ligand-induced IFNB1 expression and propose that RIG-I may undergo rapid degradation following its interaction with Riplet. However, the evidence supporting this claim is weak. The authors should demonstrate: (1) that RIG-I is indeed degraded via the proteasome, and (2) whether RIG-I undergoes K48-linked ubiquitination. Mutational analysis of putative ubiquitination sites in RIG-I would help clarify its contribution to the observed IFN responses. 

This is an important point and we are currently performing experiments addressing these questions. Specifically we will provide evidence of (1) whether RIG-I is degraded after activation using a combination of western blotting and pharmacological inhibition of the proteosome/translation machinery; (2) whether RIG-I goes K48- or K63-mediated ubiquitination by performing coIPs of RIG-I in the presence of HA-Ub wildtype or the commonly used HA-Ub K48 and K63 mutants (PMID: 15728840); and (3) whether lysine-to-arginine substitution of key residues impacts RIG-I ubiquitination/degradation.

Figure 5 c-g: why do the authors show ZAP-L, but not ZAP-S? 

Both ZAP-S and ZAP-L isoforms contain identical N-terminal domains, which is the region that interacts with TRIM25. Therefore, we assumed that the interaction between TRIM25 and ZAP-L would be similar to TRIM25-ZAP-S. However, to test this assumption, we will generate equivalent mutations in ZAP-S and perform similar co-immunoprecipitation experiments.

Reviewer #1 (Significance (Required)): 

This manuscript starts with the AlphaFold-based screening of interactions between human nucleic acid sensors and SPRY-containing proteins. However, the authors then just focused on TRIM25-ZAP and Riplet-RIG-I, whose interactions have been well demonstrated previously, although other protein interactions were not further explored. Also, the information on the evolutional aspects of TRIM25, ZAP, Riplet and RIG-I did not lead to clear conclusions. The differential contribution of TRIM25-ZAP and Riplet-RIG-I in LNP- and lipofectamine-transduced RNAs is interesting, although data shown in Fig.6 are expected from previous studies, and are not so relevant to other data in this study.  Therefore, the study is not well integrated, although pieces are interesting.  This study may attract researchers in innate 

My expertise is innate immunity and RNA biology. 

Reviewer #2 (Evidence, reproducibility and clarity (Required)): 

The paper describes the discovery of unknown E3-RNA sensor interactions from a large scale in silico prediction screen, as well as the clarification of previously described E3-sensor interactions. These findings extend previous work showing ancient relationships between nucleic acid sensors and RING E3s (e.g. PMID: 33373584), which also described the RIPLET-RIG-I pairing identified in the present screen. 

The interactions focused on are shown to have functional implications for immune signaling pathways, and stability implications for the bound sensor. The argument for the screen is that E3-target interactions are often too transient to detect biochemically. While possibly true, several of the pairings are confirmed by co-IP, with either WT E3 or a catalytically deficient E3 (known elsewhere as a 'substrate trap'). 

The key conclusions are convincing and interesting; in particular, the conserved interactions between RIPLET and RIG-I, and TRIM25 and ZAP. The hypothesis that the two E3s arose from a common ancestor is intriguing, and the use of chimeras in functional experiments suggest that the length of the coiled coil domains contributes to substrate targeting. The most interesting observation IMO is that showing that RNA vaccines can be sensed by orthogonal sensor/E3 pathways, depending on transfection method, suggesting that distinct entry routes are surveyed by different sensors. These experiments are well performed as E3 manipulation phenocopies sensor manipulation, supporting that the in silico approach will ID relevant pairings. 

Including the PAE plots for some of the key interactions would be helpful, as a lot of the interaction confidence metrics are hidden in interaction 'scores'. Fig. 1b heatmap is presented as a row max, so it is difficult to calibrate one E3 against another. The raw data from e.g. fig. 1c would be a valuable addition. This would also help orientate future studies predicting similar protein-protein interactions. 

We agree with the reviewer and we will provide the raw values for the interaction scores and PAE maps as supplementary data to be included in the final publication.

Figure 1 appears to just use the isolated SPRY domain for screening - were full-length proteins used?

The data in Figure 1 was generated using full-length proteins, but it will be interesting to test if a similar screen with SPRY domains alone can replicate the predicted interactions. We will repeat this using SPRY domain sequences.

How many copies of the FL protein were used. TRIM5 employs a low affinity, high avidity binding method; do binding patterns change when the valency of either component is altered? The Alphafold approach perhaps selects for high affinity binders? I don't expect many more experiments to be done here, but commenting on this would be useful. __ __

This is a rational consideration that we overlooked. We included in our discussion a comment on the limitation of this approach in the context of multimeric assemblies:

“Similarly, the oligomeric nature of some SPRY-containing proteins [22] is likely to impact the correct placement of these domains and, therefore, impact the predicted interaction score. Future optimization of our algorithm will improve the detection of such interactions.”

The TRIM25 -Riplet PRYSPRY swap experiments in Figure 3 are very informative and powerful. Some more detail on domain boundaries used would be helpful, including AF predictions of what these chimeras look like with respect to their natural counterparts. 

We agree with the reviewer about the need to explicitly define domain boundaries. We will include as supplemental information a comparison of the AF prediction of these chimeras in relation to the native proteins.

While AF can place confidence metrics on domain-domain interactions, SPRY containing proteins are themselves often comprised of regions of high structural confidence (e.g. many available PDBs for RINGs, coils and SPRYs) but their relative arrangement within the molecule is unpredictable. Do isolated SPRYs show any better/worse binding to targets? 

This is a good point as well, and this can be addressed by repeating the AlphaFold screen using only SPRY domain proteins rather than full-length protein (as described above).

Technically, fig. 1f does not show that TRIM58 destabilises OAS1, as there is no condition with OAS1 alone. Perhaps alter the text to reflect this or repeat with the necessary control. The direction of the text is fine, as Fig. 1g provides a striking result, but 1f needs attention. 

The reviewer raises an important consideration. To address this, we will repeat the experiment using a OAS1 alone condition, as suggested.

Fig. 2c - for clarity, please specify the meaning of the connecting lines between the bait 'hits' in the legend. What does the correlation coefficient relate to exactly? % similarity, is this across the whole molecule, or the PRYSPRY (presumably the latter would be a more useful comparison). And it is well established that single variations in SPRY variable loops can toggle binding, so this could be better referenced in the text. It would also be helpful to see e.g. dissimilar PRYSPRYs binding a common target, as PAE plots in the supplementary. Do any shared motifs occur at the variable loops between dissimilar SPRY molecules? 

We agree that this figure could be clearer. The connecting lines in Fig. 2c indicate protein-protein predictions with common sensors, i.e. connecting lines between the interaction score of ASH2L-MDA5 and the interaction score of TRIM51-MDA5. We only use % similarity of the SPRY domain alone, not the whole molecule. We have modified the figure legend to clarify this point and we include the PAE maps as supplementary information, as requested.

Fig. 2i - Bat RIG-I binds both TRIM25 and Riplet? This is in contrast to the predicted directionality in 2h? 

The reviewer astutely noted that, in Fig.2i, pulling down bat RIG-I co-immunoprecipitated with both bat Riplet and bat TRIM25, while AlphaFold predictions only suggest a Riplet-RIG-I interaction. However, while bat Riplet and bat TRIM25 express at comparable levels in the input sample, bat Riplet was far more enriched in RIG-I pulldowns than bat TRIM25. Our interpretation of this data is that, indeed, bat Riplet-RIG-I interaction is more powerful than TRIM25-Rig-I.

Fig. 3a-b, Sup Fig. 3c,d - IFNB1 transcript normalised to 3p-hRNA transfection in control NTC cells - the presentation chosen obscures the baseline IFNB1 levels in the different siRNA transfections. What is the fold induction of IFNB1 in the different cell lines? 

We will include the fold induction in each cell line as supplementary information.

Fig. 3g - RLUs of EV-A71 is only tested in TRIM25 KO cells transfected with the Riplet T25 chimera. The full panel of cDNAs (parental E3s and the inverse 25-riplet swap) should be tested in parallel to confirm the effect is specific to TRIM25 PRYSPRY. 

This is a great suggestion that will help clarify the role of different domains of TRIM25 in its antiviral activity. We are currently generating cell-line stably expressing these truncations and will perform the suggested experiment.

Fig. 4b - time point of 3p-hRNA transfection? Y-axis label suggested normalisation to NTC - incorrect label? What is the effect of bortezomib on IFNB1 mRNA in mock treated cells? 

We thank the reviewer for spotting this typo, we have known corrected the axis label. We harvested cellular mRNA 8h post-transfection. Bortezomib-treatment slightly reduced the background expression of IFNB1 mRNA, but this signal is very close to the detection limit that it is difficult to draw conclusions. Nevertheless, the addition of bortezomib did not increase IFNB1 mRNA expression in the absence of a stimulus.

Fig. 4g - these experiments would benefit from an untransfected control cell to clarify how cDNA expression impacts sensor stability. 

We agree with the reviewer and we will include an untransfected control.

There seems to be an inverse correlation between sensor degradation and signaling output - is that the summary of Fig. 4? On the one hand, sensor degradation attenuates functional output (Fig. 4b), and the E3 that degrades the sensor is required for sensor function; on the other hand, changing coil-length in the E3 disables sensor degradation (Fig. 4g) but and enhances signaling response (Fig. 3j). Do the chimeras of panel Fig. g, h influence IFNB1 expression in the assay from Fig. 3j - this experiment would marry RIG-I expression with signal output. 

This is an interesting experiment. We are in the process of generating a TRIM25-/- Riplet-/- cell line, which we will use to reconstitute with the chimeras mentioned above and perform the requested experiment.

The data is generally clear. To facilitate their interpretation and for clarity, Western blots require size markers and Co-IPs should indicate the flag-/ha-epitope tags. Would make fig. 2 i-j much clearer, particularly given apparent co-migration of IgG (heavy chain?) and riplet, and the lack of control IPs. 

We agree that contextual markings will improve the interpretation of these results. We will add size markers to the western blots in fig2 in order to improve clarity.

The figure legends could provide more detail. 

We will add additional experimental details (such as time points) to the figure legends.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

Referee #2

Evidence, reproducibility and clarity

The paper describes the discovery of unknown E3-RNA sensor interactions from a large scale in silico prediction screen, as well as the clarification of previously described E3-sensor interactions. These findings extend previous work showing ancient relationships between nucleic acid sensors and RING E3s (e.g. PMID: 33373584), which also described the RIPLET-RIG-I pairing identified in the present screen.

The interactions focused on are shown to have functional implications for immune signaling pathways, and stability implications for the bound sensor. The argument for the screen is that E3-target interactions are often too transient to detect biochemically. While possibly true, several of the pairings are confirmed by co-IP, with either WT E3 or a catalytically deficient E3 (known elsewhere as a 'substrate trap').

The key conclusions are convincing and interesting; in particular, the conserved interactions between RIPLET and RIG-I, and TRIM25 and ZAP. The hypothesis that the two E3s arose from a common ancestor is intriguing, and the use of chimeras in functional experiments suggest that the length of the coiled coil domains contributes to substrate targeting. The most interesting observation IMO is that showing that RNA vaccines can be sensed by orthogonal sensor/E3 pathways, depending on transfection method, suggesting that distinct entry routes are surveyed by different sensors. These experiments are well performed as E3 manipulation phenocopies sensor manipulation, supporting that the in silico approach will ID relevant pairings.

Including the PAE plots for some of the key interactions would be helpful, as a lot of the interaction confidence metrics are hidden in interaction 'scores'. Fig. 1b heatmap is presented as a row max, so it is difficult to calibrate one E3 against another. The raw data from e.g. fig. 1c would be a valuable addition. This would also help orientate future studies predicting similar protein-protein interactions.

Figure 1 appears to just use the isolated SPRY domain for screening - were full-length proteins used? How many copies of the FL protein were used. TRIM5 employs a low affinity, high avidity binding method; do binding patterns change when the valency of either component is altered? The Alphafold approach perhaps selects for high affinity binders? I don't expect many more experiments to be done here, but commenting on this would be useful.

The TRIM25 -Riplet PRYSPRY swap experiments in Figure 3 are very informative and powerful. Some more detail on domain boundaries used would be helpful, including AF predictions of what these chimeras look like with respect to their natural counterparts.

While AF can place confidence metrics on domain-domain interactions, SPRY containing proteins are themselves often comprised of regions of high structural confidence (e.g. many available PDBs for RINGs, coils and SPRYs) but their relative arrangement within the molecule is unpredictable. Do isolated SPRYs show any better/worse binding to targets?

Technically, fig. 1f does not show that TRIM58 destabilises OAS1, as there is no condition with OAS1 alone. Perhaps alter the text to reflect this or repeat with the necessary control. The direction of the text is fine, as Fig. 1g provides a striking result, but 1f needs attention.

Fig. 2c - for clarity, please specify the meaning of the connecting lines between the bait 'hits' in the legend. What does the correlation coefficient relate to exactly? % similarity, is this across the whole molecule, or the PRYSPRY (presumably the latter would be a more useful comparison). And it is well established that single variations in SPRY variable loops can toggle binding, so this could be better referenced in the text. It would also be helpful to see e.g. dissimilar PRYSPRYs binding a common target, as PAE plots in the supplementary. Do any shared motifs occur at the variable loops between dissimilar SPRY molecules?

Fig. 2i - Bat RIG-I binds both TRIM25 and Riplet? This is in contrast to the predicted directionality in 2h?

Fig. 3a-b, Sup Fig. 3c,d - IFNB1 transcript normalised to 3p-hRNA transfection in control NTC cells - the presentation chosen obscures the baseline IFNB1 levels in the different siRNA transfections. What is the fold induction of IFNB1 in the different cell lines?

Fig. 3g - RLUs of EV-A71 is only tested in TRIM25 KO cells transfected with the Riplet T25 chimera. The full panel of cDNAs (parental E3s and the inverse 25-riplet swap) should be tested in parallel to confirm the effect is specific to TRIM25 PRYSPRY.

Fig. 4b - time point of 3p-hRNA transfection? Y-axis label suggested normalisation to NTC - incorrect label? What is the effect of bortezomib on IFNB1 mRNA in mock treated cells?

Fig. 4g - these experiments would benefit from an untransfected control cell to clarify how cDNA expression impacts sensor stability.

There seems to be an inverse correlation between sensor degradation and signaling output - is that the summary of Fig. 4? On the one hand, sensor degradation attenuates functional output (Fig. 4b), and the E3 that degrades the sensor is required for sensor function; on the other hand, changing coil-length in the E3 disables sensor degradation (Fig. 4g) but and enhances signaling response (Fig. 3j). Do the chimeras of panel Fig. g, h influence IFNB1 expression in the assay from Fig. 3j - this experiment would marry RIG-I expression with signal output.

The data is generally clear. To facilitate their interpretation and for clarity, Western blots require size markers and Co-IPs should indicate the flag-/ha-epitope tags. Would make fig. 2 i-j much clearer, particularly given apparent co-migration of IgG (heavy chain?) and riplet, and the lack of control IPs.

The figure legends could provide more detail.

Significance

The paper provides a rich resource of potential E3-sensor interactions and represents a conceptual and technical advance for the field. The authors take a novel approach to identify these pairings. Several pairings are validated in CoIPs, and two pairings (T25-ZAP, RIPLET-RIG-I) are studied in detail. Many E3s - including the TRIM proteins which comprise the bulk of E3s studied here - are purported to regulate key nucleic acid sensors in the literature, but the large scale approach taken here provides evidence that the pairings are really quite specific. The findings also supports prior work showing that the PRYSPRY domain (here called the SPRY) is a functionally plastic module that through variable loops can bind a range of different protein substrates.

The paper will be most interesting to the innate immune field, those working on nucleic acid sensing, and those looking at innate responses to RNA vaccines.

Regulation of E3 ubiquitin ligases, viral RNA sensing

La Coopération en Classe au Service des Apprentissages et du Bien-être

Résumé Exécutif

Ce document synthétise les interventions du webinaire organisé par la Cardie de l'Académie de Paris, portant sur le développement des habiletés à coopérer.

La coopération est identifiée comme un levier fondamental pour renforcer l'engagement des élèves et améliorer le climat scolaire.

Les retours d'expérience du collège Antoine Quoisevaux, couplés à l'analyse experte de Laurent Renault, soulignent que la coopération ne doit pas être un simple "supplément d'âme", mais une modalité pédagogique structurée.

Les points clés incluent la distinction cruciale entre coopération (visant le progrès individuel par l'échange) et collaboration (visant la performance collective), l'importance de la réciprocité de l'aide pour éviter les biais de l'effet tuteur, et la nécessité de ritualiser des instances comme le conseil d'élèves pour transformer les conflits en opportunités d'apprentissage.

Bien que chronophage, cette approche favorise la motivation et le développement de compétences psychosociales essentielles.

--------------------------------------------------------------------------------

I. Retours d'Expérience : Le Projet du Collège Antoine Quoisevaux

Mis en place il y a quatre ans par Marion Saag (mathématiques) et Antoine Marteille (français), ce projet concerne des classes de 5ème dans un établissement multisecteur du 18ème arrondissement de Paris, caractérisé par une grande mixité sociale.

1. Genèse et Méthodologie

Le projet a évolué d'une pratique empirique vers une démarche étayée par la recherche et la formation (notamment les travaux de Laurent Renault et les ressources du lycée Jacques Feyder).

• Objectif : Associer des temps formels (conseils d'élèves) et informels (apprentissage coopératif en cours).

• Convaincre les élèves : La coopération n'est pas innée. Des activités "décrochées" de la didactique (ex: construire la plus haute tour de chamallows, marché de connaissances) sont organisées dès la rentrée pour apprendre à travailler en groupe.

• Métacognition : Chaque activité est suivie d'un temps de retour sur ce qui a fonctionné ou non, permettant aux élèves de s'interroger sur l'efficacité de leur travail collectif.

2. Modalités de Travail en Classe

Le travail collectif intervient généralement après une phase de réflexion individuelle ("mise en effort intellectuel"). Les enseignants font varier le tempo des séances via :

• Le binôme : Notamment pour des clôtures de séance (l'élève A explique à l'élève B ce qu'il a retenu).

• Les îlots : Groupes de quatre élèves dans des salles disposées en "L" pour faciliter la circulation.

• La classe puzzle et l'arpentage : Pour l'étude de textes.

• L'autonomie collective : Organisation spatiale spontanée pour reconstituer un récit (ex: après la projection d'un film).

--------------------------------------------------------------------------------

II. Le Conseil d'Élèves : Pilier du Climat de Classe

Le conseil d'élèves se tient tous les quinze jours. C'est un espace de parole, de régulation des conflits et de recherche collective de solutions.

1. Rôles et Responsabilités

Pour assurer un fonctionnement démocratique et serein, les rôles tournent entre les élèves :

| Rôle | Fonction | | --- | --- | | Président | Rappelle les règles et ouvre la séance de façon solennelle. | | Adjoint | Rappelle les décisions prises lors du conseil précédent. | | Secrétaire | Garde une trace écrite des échanges et des décisions. | | Distributeur de parole | Utilise un bâton de parole pour réguler les échanges. | | Protecteur de parole | Assure un cadre bienveillant et sécurisant. | | Observateur | Analyse la répartition de la parole (bilan genré, équité). |

2. Structure et Contenu du Conseil

Le conseil suit un ordre du jour ritualisé basé sur des messages écrits par les élèves :

• Remerciements et Félicitations : Valorisation de l'entraide et de l'estime de soi (ex: "Je remercie X de m'avoir expliqué les maths").

• Problèmes et Soucis : Régulation des relations entre élèves (médiation par les pairs) ou de la relation pédagogique avec les enseignants.

• Propositions : Projets de sorties, mais aussi demandes pédagogiques (ex: "Faire plus d'exposés en Histoire-Géo").

--------------------------------------------------------------------------------

III. Analyse Conceptuelle et Points de Vigilance

Laurent Renault, expert en pédagogie coopérative, apporte un éclairage théorique pour "réinterroger les évidences".

1. Coopération vs Collaboration

Il est impératif de distinguer ces deux modalités pour éviter l'exclusion des élèves les plus fragiles :

• La Coopération (visée : Progresser) : Échange de points de vue sans obligation de production immédiate (ex: le conseil d'élèves).

• La Collaboration (visée : Performer) : Répartition des tâches pour produire un résultat (ex: une affiche). Le risque est que seuls les "concepteurs" apprennent, tandis que les autres exécutent des tâches subalternes.

2. L'Effet Tuteur et la Réciprocité

L'aide entre élèves n'est pas automatiquement bénéfique pour celui qui la reçoit.

• L'aidant : Progresse toujours (mémorisation, abstraction, valorisation).

• L'aidé : Peut subir l'aide comme une illusion de compréhension et intérioriser une dépendance.

• Solution : Garantir la réciprocité de l'aide. Chaque élève doit, au cours d'une période, occuper la position d'aidant sur des compétences variées (rédaction, schéma, etc.).

3. La Posture de l'Enseignant : "Travailler à capot ouvert"

Innover, c'est accepter une part d'humilité et de déstabilisation.

• S'effacer : Dans le conseil, l'enseignant ne doit pas être moralisateur mais garant de la sécurité de la parole.

• Gérer le "bazar" initial : La coopération peut dégrader le climat scolaire à court terme car elle fait émerger des conflits latents. Ces conflits sont des matériaux d'apprentissage pour "penser ensemble".

• Considérer l'élève comme un interlocuteur valable : S'appuyer sur son ressenti et sa motivation.

--------------------------------------------------------------------------------

IV. Enjeux et Perspectives

1. Bénéfices Constatés

• Engagement : Plaisir des élèves à venir au collège et investissement accru dans les disciplines (français/mathématiques).

• Compétences psychosociales : Travail sur les trois macro-compétences définies par Santé publique France.

• Émulation : Utilisation de la motivation collective sans tomber dans la rivalité destructrice.

2. Limites et Défis

• Aspect chronophage : Nécessite un investissement important pour mener les conseils et suivre les décisions.

• Isolement de l'équipe : Difficulté à étendre le projet au-delà du binôme initial. Un tiers de l'emploi du temps est couvert, mais une cohérence d'équipe serait préférable.

• Aménagement spatial : Importance de l'ergonomie (classes flexibles, îlots en L) pour faciliter les transitions entre travail individuel et collectif.

3. Conclusion

La coopération en classe ne s'improvise pas. Elle repose sur un "tâtonnement balisé" par la recherche (Sylvain Conac, Philippe Meirieu) et une organisation rigoureuse.

L'objectif final est de passer du simple "vivre ensemble" au "penser ensemble", en respectant l'équilibre entre l'individu (le "Je") et le groupe (le "Nous").

in a sea of chaos have the capacity “A to shift the entire system.” — Ilya Prigogine =

https://bafybeigi4urr6jumopybpwxfu2i5edncg4e64c2z6dgtgm2clro7ibxmpe.ipfs.dweb.link/?filename=O%20%E2%80%94%20The%20Last%20Debt.%20When%20the%20empire%E2%80%99s%20money%20lies%2C%20its%E2%80%A6%20%EF%BD%9C%20by%20Ray%20Podder%20%EF%BD%9C%20Medium.html

Page saved with SingleFile web-indy://💻/asus/ 🧊/♖//hyperpost/~/indyweb/📓/20/26/15/o-the-last-debt

Page saved with SingleFile url: https://raypodder.medium.com/o-the-last-debt-3c12a1d998e7 saved date: Sat Dec 27 2025 21:56:44 GMT+0100 (Central European Standard Time)

https://raypodder.medium.com/o-the-last-debt-3c12a1d998e7

is just getting started.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors explore a novel concept: GPCR-mediated regulation of miRNA release via extracellular vesicles (EVs). They perform an EV miRNA cargo profiling approach to investigate how specific GPCR activations influence the selective secretion of particular miRNAs. Given that GPCRs are highly diverse and orchestrate multiple cellular pathways - either independently or collectively - to regulate gene expression and cellular functions under various conditions, it is logical to expect alterations in gene and miRNA expression within target cells.

Strengths:

The novel idea of GPCRs-mediated control of EV loading of miRNAs.

Weaknesses:

Incomplete findings failed to connect and show evidence of any physiological parameters that are directly related to the observed changes. The mechanical detail is lacking.

We appreciate the reviewer's acknowledgment of the novelty of this study. We agree with the reviewer that further mechanistic insights would strengthen the manuscript. The mechanisms by which miRNA is sorted into EVs remain poorly understood. Various factors, including RNAbinding protein, sequence motifs, and cellular location, can influence this sorting process(Garcia-Martin et al., 2022; Liu & Halushka, 2025; Villarroya-Beltri et al., 2013; Yoon et al., 2015). Ago2, a key component of the RNA-induced silencing complexes, binds to miRNA and facilitates miRNA sorting. Ago2 has been found in the EVs and can be regulated by the cellular signaling pathway.  For instance, McKenzie et al. demonstrated that KRAS-dependent activation of MEK-ERK can phosphorylate Ago2 protein, thereby regulating the sorting of specific miRNAs into EVs(McKenzie et al., 2016). In the differentiated PC12 cells, Gαq activation leads to the formation of Ago2-associated granules, which selectively sequester unique transcripts(Jackson et al., 2022). Investigating GPCR, G protein, and GPCR signaling on Ago2 expression, location, and phosphorylation states could provide valuable insights into how GPCRs regulate specific miRNAs within EVs. We have expanded these potential mechanisms and future research in the discussion section (page 16-17).

The manuscript falls short of providing a comprehensive understanding. Identifying changes in cellular and EV-associated miRNAs without elucidating their physiological significance or underlying regulatory mechanisms limits the study's impact. Without demonstrating whether these miRNA alterations have functional consequences, the findings alone are insufficient. The findings may be suitable for more specialized journals.

Thank you for the feedback. We acknowledge that validating the target genes of the top candidate miRNAs is an important next step. In response to the reviewer's concerns, we have expanded the discussion of future research in the manuscript (page 19-20). Although this initial study is primarily descriptive, it establishes a novel conceptual link between GPCR signaling and EV-mediated communication.

Furthermore, a critical analysis of the relationship between cellular miRNA levels and EV miRNA cargo is essential. Specifically, comparing the intracellular and EV-associated miRNA pools could reveal whether specific miRNAs are preferentially exported, a behavior that should be inversely related to their cellular abundance if export serves a beneficial function by reducing intracellular levels. This comparison is vital to strengthen the biological relevance of the findings and support the proposed regulatory mechanisms by GPCRs.

We appreciate the valuable suggestions from the reviewer. EV miRNA and cell miRNAs may exhibit distinct profiles as miRNAs can be selectively sorted into or excluded from EVs(Pultar et al., 2024; Teng et al., 2017; Zubkova et al., 2021). Investigating the difference between cellular miRNA levels and EV miRNA cargo would provide insight into the mechanism of miRNA sorting and the functions of miRNAs in the recipient cells. The expression of the cellular miRNAs is a highly dynamic process. To accurately compare the miRNA expression levels, profiling of EV miRNA and cellular miRNA should be conducted simultaneously. However, as an exploratory study, we were unable to measure the cellular miRNAs without conducting the entire experiment again.

Reviewer #2 (Public review):

Summary:

This study examines how activating specific G protein-coupled receptors (GPCRs) affects the microRNA (miRNA) profiles within extracellular vesicles (EVs). The authors seek to identify whether different GPCRs produce unique EV miRNA signatures and what these signatures could indicate about downstream cellular processes and pathological processes.

Methods:

(1) Used U2OS human osteosarcoma cells, which naturally express multiple GPCR types.

(2) Stimulated four distinct GPCRs (ADORA1, HRH1, FZD4, ACKR3) using selective agonists.

(3) Isolated EVs from culture media and characterized them via size exclusion chromatography, immunoblotting, and microscopy.

(4) Employed qPCR-based miRNA profiling and bioinformatics analyses (e.g., KEGG, PPI networks) to interpret expression changes.

Key Findings:

(1) No significant change in EV quantity or size following GPCR activation.

(2) Each GPCR triggered a distinct EV miRNA expression profile.

(3) miRNAs differentially expressed post-stimulation were linked to pathways involved in cancer, insulin resistance, neurodegenerative diseases, and other physiological/pathological processes.

(4) miRNAs such as miR-550a-5p, miR-502-3p, miR-137, and miR-422a emerged as major regulators following specific receptor activation.

Conclusions:

The study offers evidence that GPCR activation can regulate intercellular communication through miRNAs encapsulated within extracellular vesicles (EVs). This finding paves the way for innovative drug-targeting strategies and enhances understanding of drug side effects that are mediated via GPCR-related EV signaling.

Strengths:

(1) Innovative concept: The idea of linking GPCR signaling to EV miRNA content is novel and mechanistically important.

(2) Robust methodology: The use of multiple validation methods (biochemical, biophysical, and statistical) lends credibility to the findings.

(3) Relevance: GPCRs are major drug targets, and understanding off-target or systemic effects via EVs is highly valuable for pharmacology and medicine.

Weaknesses:

(1) Sample Size & Scope: The analysis included only four GPCRs. Expanding to more receptor types or additional cell lines would enhance the study's applicability.

We are encouraged that the reviewer recognized the novelty, methodological rigor, and significance of our work. We recognize the limitations of our current model system and emphasize the need to test additional GPCR families and cell lines in the future studies, as detailed in the discussion section (Page 19, second paragraph).

(2) Exploratory Nature: This study is primarily descriptive and computational. It lacks functional validation, such as assessing phenotypic effects in recipient cells, which is acknowledged as a future step.

We appreciate the feedback. We recognize the importance of validating the function of the top candidate miRNAs in the recipient cells, and this will be included in future studies (page 19-20).  

(3) EV heterogeneity: The authors recognize that they did not distinguish EV subpopulations, potentially confounding the origin and function of miRNAs.

Thank you for the comment. EV isolation and purification are major challenges in EV research. Current isolation techniques are often ineffective at separating vesicles produced by different biogenetic pathways. Furthermore, the lack of specific markers to differentiate EV subtypes adds to this complexity. We recognize that the presence of various subpopulations can complicate the interpretation of EV cargos. In our study, we used a combined approach of ultrafiltration followed by size-exclusion chromatography to achieve a balance between EV purity and yield. We adhere to the MISEV (Minimal Information for Studies of Extracellular Vesicles 2023) guidelines by reporting detailed isolation methods, assessing both positive and negative protein markers, and characterizing EVs by electron microscopy to confirm vesicle structure, as well as nanoparticle tracking analysis to verify particle size distribution(Welsh et al., 2024). By following these guidelines, we can ensure the quality of our study and enhance the ability to compare our findings with other studies.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

Suggestions for Future Research:

(1) Functionally validate top candidate miRNAs in recipient cells.

We acknowledge that validating the target genes of the top candidate miRNAs is a crucial next step. In response to the reviewer's concerns, we have included this in the discussion as future research in the manuscript (page 19-20).

(2) Investigate other GPCR families and repeat in primary or disease-relevant cell lines.

The inclusion of different GPCRs and cell lines is suggested as an area for further investigation in the discussion. (Page 19).

(3) Apply similar approaches in in vivo models or patient samples to assess clinical relevance.

In response to the reviewer's concerns, we have included this in the discussion as future research in the manuscript (page 19-20).

References

Garcia-Martin, R., Wang, G., Brandão, B. B., Zanotto, T. M., Shah, S., Kumar Patel, S., Schilling, B., & Kahn, C. R. (2022). MicroRNA sequence codes for small extracellular vesicle release and cellular retention. Nature, 601(7893), 446-451. https://doi.org/10.1038/s41586021-04234-3  

Jackson, L., Rennie, M., Poussaint, A., & Scarlata, S. (2022). Activation of Gαq sequesters specific transcripts into Ago2 particles. Sci Rep, 12(1), 8758. https://doi.org/10.1038/s41598022-12737-w  

Liu, X.-M., & Halushka, M. K. (2025). Beyond the Bubble: A Debate on microRNA Sorting Into Extracellular Vesicles. Laboratory Investigation, 105(2), 102206. https://doi.org/10.1016/j.labinv.2024.102206  

McKenzie, A. J., Hoshino, D., Hong, N. H., Cha, D. J., Franklin, J. L., Coffey, R. J., Patton, J. G., & Weaver, A. M. (2016). KRAS-MEK Signaling Controls Ago2 Sorting into Exosomes. Cell  Rep, 15(5), 978-987. https://doi.org/10.1016/j.celrep.2016.03.085  

Pultar, M., Oesterreicher, J., Hartmann, J., Weigl, M., Diendorfer, A., Schimek, K., Schädl, B., Heuser, T., Brandstetter, M., Grillari, J., Sykacek, P., Hackl, M., & Holnthoner, W. (2024).Analysis of extracellular vesicle microRNA profiles reveals distinct blood and lymphatic endothelial cell origins. J Extracell Biol, 3(1), e134. https://doi.org/10.1002/jex2.134  

Teng, Y., Ren, Y., Hu, X., Mu, J., Samykutty, A., Zhuang, X., Deng, Z., Kumar, A., Zhang, L., Merchant, M. L., Yan, J., Miller, D. M., & Zhang, H.-G. (2017). MVP-mediated exosomal sorting of miR-193a promotes colon cancer progression. Nature Communications, 8(1), 14448. https://doi.org/10.1038/ncomms14448  

Villarroya-Beltri, C., Gutiérrez-Vázquez, C., Sánchez-Cabo, F., Pérez-Hernández, D., Vázquez, J., Martin-Cofreces, N., Martinez-Herrera, D. J., Pascual-Montano, A., Mittelbrunn, M., & Sánchez-Madrid, F. (2013). Sumoylated hnRNPA2B1 controls the sorting of miRNAs into exosomes through binding to specific motifs. Nat Commun, 4, 2980. https://doi.org/10.1038/ncomms3980

Welsh, J. A., Goberdhan, D. C. I., O'Driscoll, L., Buzas, E. I., Blenkiron, C., Bussolati, B., Cai, H., Di Vizio, D., Driedonks, T. A. P., Erdbrügger, U., Falcon-Perez, J. M., Fu, Q. L., Hill, A. F., Lenassi, M., Lim, S. K., Mahoney, M. G., Mohanty, S., Möller, A., Nieuwland, R., . . .Witwer, K. W. (2024). Minimal information for studies of extracellular vesicles (MISEV2023): From basic to advanced approaches. J Extracell Vesicles, 13(2), e12404. https://doi.org/10.1002/jev2.12404  

Yoon, J. H., Jo, M. H., White, E. J., De, S., Hafner, M., Zucconi, B. E., Abdelmohsen, K., Martindale, J. L., Yang, X., Wood, W. H., 3rd, Shin, Y. M., Song, J. J., Tuschl, T., Becker, K. G., Wilson, G. M., Hohng, S., & Gorospe, M. (2015). AUF1 promotes let-7b loading on Argonaute 2. Genes Dev, 29(15), 1599-1604. https://doi.org/10.1101/gad.263749.115  

Zubkova, E., Evtushenko, E., Beloglazova, I., Osmak, G., Koshkin, P., Moschenko, A., Menshikov, M., & Parfyonova, Y. (2021). Analysis of MicroRNA Profile Alterations in Extracellular Vesicles From Mesenchymal Stromal Cells Overexpressing Stem Cell Factor. Front Cell Dev Biol, 9, 754025. https://doi.org/10.3389/fcell.2021.754025

I would like to clarify a point regarding the discovery of lymphatic-like vessels in the brain. The preprint appears to hint towards positioning it as the first report of this finding. However, the first published study on this subject was by Chang et al.(Ref 1 below). The authors reported on the presence and characteristics (diameters, origin, length, depth and regulation by stress) of deep brain lymphatic vessels. Their work was subsequently followed by two other independent studies (Ref 2 and 3). All studies were reviewed in a review article in 2024 (https://www.sciopen.com/article/10.26599/SAB.2024.9060001?issn=2709-1325).

Therefore, the preprint would represent a fourth report—not the first—of this discovery. It is concerning that previous foundational work has been either overlooked or cited in a misleading manner.

References 1. Chang J, Guo B, Gao Y, Li W, Tong X, Feng Y, et al. Characteristic Features of Deep Brain Lymphatic Vessels and Their Regulation by Chronic Stress. Research (Washington, DC), 2023, 6:0120.

1. Öz E. 'Game changer' method lets scientists peer into mice. Science (New York, NY), 2023, 380(6644):443.

2. Liu X-G, Hua Q, Peng T-T, Chang K-X, Deng C-G, Zhang J-N, et al. Histomorphological analysis of perfusion parameters and CNS lymphatic vessels in mice: an experimental method study. NeuroReport, 2024, 35(3).

Author response:

General Statements

We are delighted that all reviewers found our manuscript to be a technical advance by providing a much sought after method to arrest budding yeast cells in metaphase of mitosis or both meiotic metaphases. The reviewers also valued our use of this system to make new discoveries in two areas. First, we provided evidence that the spindle checkpoint is intrinsically weaker in meiosis I and showed that this is due to PP1 phosphatase. Second, we determined how the composition and phosphorylation of the kinetochore changes during meiosis, providing key insights into kinetochore function and providing a rich dataset for future studies.

The reviewers also made some extremely helpful suggestions to improve our manuscript, which we will now implement:

(1) Improvements to the discussion throughout the manuscript. The reviewers recommended that we focus our discussion on the novel findings of the manuscript and drew out some key points of interest that deserve more attention. We fully agree with this and we will address this in a revised version.

(2) We will add a new supplemental figure to help interpret the mass spectrometry data, to address Reviewer #3, point 4.

(3) We are currently performing an additional control experiment to address the minor point 1 from reviewer #3. Our experiment to confirm that SynSAC relies on endogenous checkpoint proteins was missing the cell cycle profile of cells where SynSAC was not induced for comparison. We will add this control to our full revision.

(4) In our full revision we will also include representative images of spindle morphology as requested by Reviewer #1, point 2

Description of the planned revisions

Reviewer #1 (Evidence, reproducibility and clarity):

These authors have developed a method to induce MI or MII arrest. While this was previously possible in MI, the advantage of the method presented here is that it works for MII, and chemically inducible because it is based on a system that is sensitive to the addition of ABA. Depending on when the ABA is added, they achieve a MI or MII delay. The ABA promotes dimerizing fragments of Mps1 and Spc105 that can't bind their chromosomal sites. The evidence that the MI arrest is weaker than the MII arrest is convincing and consistent with published data and indicating the SAC in MI is less robust than MII or mitosis. The authors use this system to find evidence that the weak MI arrest is associated with PP1 binding to Spc105. This is a nice use of the system.

The remainder of the paper uses the SynSAC system to isolate populations enriched for MI or MII stages and conduct proteomics. This shows a powerful use of the system but more work is needed to validate these results, particularly in normal cells.

Overall the most significant aspect of this paper is the technical achievement, which is validated by the other experiments. They have developed a system and generated some proteomics data that maybe useful to others when analyzing kinetochore composition at each division. Overall, I have only a few minor suggestions.

We appreciate the reviewers’ support of our study.

(1) In wild-type - Pds1 levels are high during M1 and A1, but low in MII. Can the authors comment on this? In line 217, what is meant by "slightly attenuated? Can the authors comment on how anaphase occurs in presence of high Pds1? There is even a low but significant level in MII.

The higher levels of Pds1 in meiosis I compared to meiosis II has been observed previously using immunofluorescence and live imaging[1–3]. Although the reasons are not completely clear, we speculate that there is insufficient time between the two divisions to re-accumulate Pds1 prior to separase re-activation.

We agree “slightly attenuated” was confusing and we have re-worded this sentence to read “Addition ABA at the time of prophase release resulted in Pds1securin stabilisation throughout the time course, consistent with delays in both metaphase I and II”.

We do not believe that either anaphase I or II occur in the presence of high Pds1. Western blotting represents the amount of Pds1 in the population of cells at a given time point. The time between meiosis I and II is very short even when treated with ABA. For example, in Figure 2B, spindle morphology counts show that the anaphase I peak is around 40% at its maxima (105 min) and around 40% of cells are in either metaphase I or metaphase II, and will be Pds1 positive. In contrast, due to the better efficiency of meiosis II, anaphase II hardly occurs at all in these conditions, since anaphase II spindles (and the second nuclear division) are observed at very low frequency (maximum 10%) from 165 minutes onwards. Instead, metaphase II spindles partially or fully breakdown, without undergoing anaphase extension. Taking Pds1 levels from the western blot and the spindle data together leads to the conclusion that at the end of the time-course, these cells are biochemically in metaphase II, but unable to maintain a robust spindle. Spindle collapse is also observed in other situations where meiotic exit fails, and potentially reflects an uncoupling of the cell cycle from the programme governing gamete differentiation[3–5]. We will explain this point in a revised version while referring to representative images that from evidence for this, as also requested by the reviewer below.

(2) The figures with data characterizing the system are mostly graphs showing time course of MI and MII. There is no cytology, which is a little surprising since the stage is determined by spindle morphology. It would help to see sample sizes (ie. In the Figure legends) and also representative images. It would also be nice to see images comparing the same stage in the SynSAC cells versus normal cells. Are there any differences in the morphology of the spindles or chromosomes when in the SynSAC system?

This is an excellent suggestion and will also help clarify the point above. We will provide images of cells at the different stages. For each timepoint, 100 cells were scored. We have already included this information in the figure legends 

(3) A possible criticism of this system could be that the SAC signal promoting arrest is not coming from the kinetochore. Are there any possible consequences of this? In vertebrate cells, the RZZ complex streams off the kinetochore. Yeast don't have RZZ but this is an example of something that is SAC dependent and happens at the kinetochore. Can the authors discuss possible limitations such as this? Does the inhibition of the APC effect the native kinetochores? This could be good or bad. A bad possibility is that the cell is behaving as if it is in MII, but the kinetochores have made their microtubule attachments and behave as if in anaphase.

In our view, the fact that SynSAC does not come from kinetochores is a major advantage as this allows the study of the kinetochore in an unperturbed state. It is also important to note that the canonical checkpoint components are all still present in the SynSAC strains, and perturbations in kinetochore-microtubule interactions would be expected to mount a kinetochore-driven checkpoint response as normal. Indeed, it would be interesting in future work to understand how disrupting kinetochore-microtubule attachments alters kinetochore composition (presumably checkpoint proteins will be recruited) and phosphorylation but this is beyond the scope of this work. In terms of the state at which we are arresting cells – this is a true metaphase because cohesion has not been lost but kinetochore-microtubule attachments have been established. This is evident from the enrichment of microtubule regulators but not checkpoint proteins in the kinetochore purifications from metaphase I and II. While this state is expected to occur only transiently in yeast, since the establishment of proper kinetochore-microtubule attachments triggers anaphase onset, the ability to capture this properly bioriented state will be extremely informative for future studies. We appreciate the reviewers’ insight in highlighting these interesting discussion points which we will include in a revised version.

Reviewer #1 (Significance):

These authors have developed a method to induce MI or MII arrest. While this was previously possible in MI, the advantage of the method presented here is it works for MII, and chemically inducible because it is based on a system that is sensitive to the addition of ABA. Depending on when the ABA is added, they achieve a MI or MII delay. The ABA promotes dimerizing fragments of Mps1 and Spc105 that can't bind their chromosomal sites. The evidence that the MI arrest is weaker than the MII arrest is convincing and consistent with published data and indicating the SAC in MI is less robust than MII or mitosis. The authors use this system to find evidence that the weak MI arrest is associated with PP1 binding to Spc105. This is a nice use of the system.

The remainder of the paper uses the SynSAC system to isolate populations enriched for MI or MII stages and conduct proteomics. This shows a powerful use of the system but more work is needed to validate these results, particularly in normal cells.

Overall the most significant aspect of this paper is the technical achievement, which is validated by the other experiments. They have developed a system and generated some proteomics data that maybe useful to others when analyzing kinetochore composition at each division.

We appreciate the reviewer’s enthusiasm for our work.

Reviewer #2 (Evidence, reproducibility and clarity):

The manuscript submitted by Koch et al. describes a novel approach to collect budding yeast cells in metaphase I or metaphase II by synthetically activating the spinde checkpoint (SAC). The arrest is transient and reversible. This synchronization strategy will be extremely useful for studying meiosis I and meiosis II, and compare the two divisions. The authors characterized this so-named syncSACapproach and could confirm previous observations that the SAC arrest is less efficient in meiosis I than in meiosis II. They found that downregulation of the SAC response through PP1 phosphatase is stronger in meiosis I than in meiosis II. The authors then went on to purify kinetochore-associated proteins from metaphase I and II extracts for proteome and phosphoproteome analysis. Their data will be of significant interest to the cell cycle community (they compared their datasets also to kinetochores purified from cells arrested in prophase I and -with SynSAC in mitosis).

I have only a couple of minor comments:

(1) I would add the Suppl Figure 1A to main Figure 1A. What is really exciting here is the arrest in metaphase II, so I don't understand why the authors characterize metaphase I in the main figure, but not metaphase II. But this is only a suggestion.

This is a good suggestion, we will do this in our full revision.

(2) Line 197, the authors state: “...SyncSACinduced a more pronounced delay in metaphase II than in metaphase I”. However, line 229 and 240 the authors talk about a "longer delay in metaphase <i compared to metaphase II"... this seems to be a mix-up.

Thank you for pointing this out, this is indeed a typo and we have corrected it.

(3) The authors describe striking differences for both protein abundance and phosphorylation for key kinetochore associated proteins. I found one very interesting protein that seems to be very abundant and phosphorylated in metaphase I but not metaphase II, namely Sgo1. Do the authors think that Sgo1 is not required in metaphase II anymore? (Top hit in suppl Fig 8D).

This is indeed an interesting observation, which we plan to investigate as part of another study in the future. Indeed, data from mouse indicates that shugoshin-dependent cohesin deprotection is already absent in meiosis II in mouse oocytes[6], though whether this is also true in yeast is not known. Furthermore, this does not rule out other functions of Sgo1 in meiosis II (for example promoting biorientation). We will include this point in the discussion.

Reviewer #2 (Significance):

The technique described here will be of great interest to the cell cycle community. Furthermore, the authors provide data sets on purified kinetochores of different meiotic stages and compare them to mitosis. This paper will thus be highly cited, for the technique, and also for the application of the technique.

Reviewer #3 (Evidence, reproducibility and clarity):

In their manuscript, Koch et al. describe a novel strategy to synchronize cells of the budding yeast Saccharomyces cerevisiae in metaphase I and metaphase II, thereby facilitating comparative analyses between these meiotic stages. This approach, termed SynSAC, adapts a method previously developed in fission yeast and human cells that enables the ectopic induction of a synthetic spindle assembly checkpoint (SAC) arrest by conditionally forcing the heterodimerization of two SAC components upon addition of the plant hormone abscisic acid (ABA). This is a valuable tool, which has the advantage that induces SAC-dependent inhibition of the anaphase promoting complex without perturbing kinetochores. Furthermore, since the same strategy and yeast strain can be also used to induce a metaphase arrest during mitosis, the methodology developed by Koch et al. enables comparative analyses between mitotic and meiotic cell divisions. To validate their strategy, the authors purified kinetochores from meiotic metaphase I and metaphase II, as well as from mitotic metaphase, and compared their protein composition and phosphorylation profiles. The results are presented clearly and in an organized manner.

We are grateful to the reviewer for their support.

Despite the relevance of both the methodology and the comparative analyses, several main issues should be addressed:

(1) In contrast to the strong metaphase arrest induced by ABA addition in mitosis (Supp. Fig. 2), the SynSAC strategy only promotes a delay in metaphase I and metaphase II as cells progress through meiosis. This delay extends the duration of both meiotic stages, but does not markedly increase the percentage of metaphase I or II cells in the population at a given timepoint of the meiotic time course (Fig. 1C). Therefore, although SynSAC broadens the time window for sample collection, it does not substantially improve differential analyses between stages compared with a standard NDT80 prophase block synchronization experiment. Could a higher ABA concentration or repeated hormone addition improve the tightness of the meiotic metaphase arrest?

For many purposes the enrichment and extended time for sample collection is sufficient, as we demonstrate here. However, as pointed out by the reviewer below, the system can be improved by use of the 4A-RASA mutations to provide a stronger arrest (see our response below). We did not experiment with higher ABA concentrations or repeated addition since the very robust arrest achieved with the 4A-RASA mutant deemed this unnecessary.

(2) Unlike the standard SynSAC strategy, introducing mutations that prevent PP1 binding to the SynSAC construct considerably extended the duration of the meiotic metaphase arrests. In particular, mutating PP1 binding sites in both the RVxF (RASA) and the SILK (4A) motifs of the Spc105(1-455)-PYL construct caused a strong metaphase I arrest that persisted until the end of the meiotic time course (Fig. 3A). This stronger and more prolonged 4A-RASA SynSAC arrest would directly address the issue raised above. It is unclear why the authors did not emphasize more this improved system. Indeed, the 4A-RASA SynSAC approach could be presented as the optimal strategy to induce a conditional metaphase arrest in budding yeast meiosis, since it not only adapts but also improves the original methods designed for fission yeast and human cells. Along the same lines, it is surprising that the authors did not exploit the stronger arrest achieved with the 4A-RASA mutant to compare kinetochore composition at meiotic metaphase I and II.

We agree that the 4A-RASA mutant is the best tool to use for the arrest and going forward this will be our approach. We collected the proteomics data and the data on the SynSAC mutant variants concurrently, so we did not know about the improved arrest at the time the proteomics experiment was done. Because very good arrest was already achieved with the unmutated SynSAC construct, we could not justify repeating the proteomics experiment which is a large amount of work using significant resources. However, we will highlight the potential of the 4A-RASA mutant more prominently in our full revision.

(3) The results shown in Supp. Fig. 4C are intriguing and merit further discussion. Mitotic growth in ABA suggest that the RASA mutation silences the SynSAC effect, yet this was not observed for the 4A or the double 4A-RASA mutants. Notably, in contrast to mitosis, the SynSAC 4A-RASA mutation leads to a more pronounced metaphase I meiotic delay (Fig. 3A). It is also noteworthy that the RVAF mutation partially restores mitotic growth in ABA. This observation supports, as previously demonstrated in human cells, that Aurora B-mediated phosphorylation of S77 within the RVSF motif is important to prevent PP1 binding to Spc105 in budding yeast as well.

We agree these are intriguing findings that highlight key differences as to the wiring of the spindle checkpoint in meiosis and mitosis and potential for future studies, however, currently we can only speculate as to the underlying cause. The effect of the RASA mutation in mitosis is unexpected and unexplained. However, the fact that the 4A-RASA mutation causes a stronger delay in meiosis I compared to mitosis can be explained by a greater prominence of PP1 phosphatase in meiosis. Indeed, our data (Figure 4A) show that the PP1 phosphatase Glc7 and its regulatory subunit Fin1 are highly enriched on kinetochores at all meiotic stages compared to mitosis.

We agree that the improved growth of the RVAF mutant is intriguing and points to a role of Aurora B-mediated phosphorylation, though previous work has not supported such a role [7].

We will include a discussion of these important points in a revised version.

(4) To demonstrate the applicability of the SynSAC approach, the authors immunoprecipitated the kinetochore protein Dsn1 from cells arrested at different meiotic or mitotic stages, and compared kinetochore composition using data independent acquisition (DIA) mass spectrometry. Quantification and comparative analyses of total and kinetochore protein levels were conducted in parallel for cells expressing either FLAG-tagged or untagged Dsn1 (Supp. Fig. 7A-B). To better detect potential changes, protein abundances were next scaled to Dsn1 levels in each sample (Supp. Fig. 7C-D). However, it is not clear why the authors did not normalize protein abundance in the immunoprecipitations from tagged samples at each stage to the corresponding untagged control, instead of performing a separate analysis. This would be particularly relevant given the high sensitivity of DIA mass spectrometry, which enabled quantification of thousands of proteins. Furthermore, the authors compared protein abundances in tagged-samples from mitotic metaphase and meiotic prophase, metaphase I and metaphase II (Supp. Fig. 7E-F). If protein amounts in each case were not normalized to the untagged controls, as inferred from the text (lines 333 to 338), the observed differences could simply reflect global changes in protein expression at different stages rather than specific differences in protein association to kinetochores.

While we agree with the reviewer that at first glance, normalising to no tag appears to be the most appropriate normalisation, in practice there is very low background signal in the no tag sample which means that any random fluctuations have a big impact on the final fold change used for normalisation. This approach therefore introduces artefacts into the data rather than improving normalisation.

To provide reassurance that our kinetochore immunoprecipitations are specific, and that the background (no tag) signal is indeed very low, we will provide a new supplemental figure showing the volcanos comparing kinetochore purifications at each stage with their corresponding no tag control.

It is also important to note that our experiment looks at relative changes of the same protein over time, which we expect to be relatively small in the whole cell lysate. We previously documented proteins that change in abundance in whole cell lysates throughout meiosis[8]. In this study, we found that relatively few proteins significantly change in abundance.

Our aim in the current study was to understand how the relative composition of the kinetochore changes and for this, we believe that a direct comparison to Dsn1, a central kinetochore protein which we immunoprecipitated is the most appropriate normalisation.

(5) Despite the large amount of potentially valuable data generated, the manuscript focuses mainly on results that reinforce previously established observations (e.g., premature SAC silencing in meiosis I by PP1, changes in kinetochore composition, etc.). The discussion would benefit from a deeper analysis of novel findings that underscore the broader significance of this study.

We strongly agree with this point and we will re-frame the discussion to focus on the novel findings, as also raised by the other reviewers.

Finally, minor concerns are:

(1) Meiotic progression in SynSAC strains lacking Mad1, Mad2 or Mad3 is severely affected (Fig. 1D and Supp. Fig. 1), making it difficult to assess whether, as the authors state, the metaphase delays depend on the canonical SAC cascade. In addition, as a general note, graphs displaying meiotic time courses could be improved for clarity (e.g., thinner data lines, addition of axis gridlines and external tick marks, etc.).

We will generate the data to include a checkpoint mutant +/- ABA for direct comparison. We will take steps to improve the clarity of presentation of the meiotic timecourse graphs, though our experience is that uncluttered graphs make it easier to compare trends.

(2) Spore viability following SynSAC induction in meiosis was used as an indicator that this experimental approach does not disrupt kinetochore function and chromosome segregation. However, this is an indirect measure. Direct monitoring of genome distribution using GFP-tagged chromosomes would have provided more robust evidence. Notably, the SynSAC mad3Δ mutant shows a slight viability defect, which might reflect chromosome segregation defects that are more pronounced in the absence of a functional SAC.

Spore viability is a much more sensitive way of analysing segregation defects that GFP-labelled chromosomes. This is because GFP labelling allows only a single chromosome to be followed. On the other hand, if any of the 16 chromosomes mis-segregate in a given meiosis this would result in one or more aneuploid spores in the tetrad, which are typically inviable. The fact that spore viability is not significantly different from wild type in this analysis indicates that there are no major chromosome segregation defects in these strains, and we therefore do not plan to do this experiment.

(3) It is surprising that, although SAC activity is proposed to be weaker in metaphase I, the levels of CPC/SAC proteins seem to be higher at this stage of meiosis than in metaphase II or mitotic metaphase (Fig. 4A-B).

We agree, this is surprising and we will point this out in the revised discussion. We speculate that the challenge in biorienting homologs which are held together by chiasmata, rather than back-to-back kinetochores results in a greater requirement for error correction in meiosis I. Interestingly, the data with the RASA mutant also point to increased PP1 activity in meiosis I, and we additionally observed increased levels of PP1 (Glc7 and Fin1) on meiotic kinetochores, consistent with the idea that cycles of error correction and silencing are elevated in meiosis I.

(4) Although a more detailed exploration of kinetochore composition or phosphorylation changes is beyond the scope of the manuscript, some key observations could have been validated experimentally (e.g., enrichment of proteins at kinetochores, phosphorylation events that were identified as specific or enriched at a certain meiotic stage, etc.).

We agree that this is beyond the scope of the current study but will form the start of future projects from our group, and hopefully others.

(5) Several typographical errors should be corrected (e.g., "Knetochores" in Fig. 4 legend, "250uM ABA" in Supp. Fig. 1 legend, etc.)

Thank you for pointing these out, they have been corrected.

Reviewer #3 (Significance):

Koch et al. describe a novel methodology, SynSAC, to synchronize budding yeast cells in metaphase I or metaphase II during meiosis, as well and in mitotic metaphase, thereby enabling differential analyses among these cell division stages. Their approach builds on prior strategies originally developed in fission yeast and human cells models to induce a synthetic spindle assembly checkpoint (SAC) arrest by conditionally forcing the heterodimerization of two SAC proteins upon addition of abscisic acid (ABA). The results from this manuscript are of special relevance for researchers studying meiosis and using Saccharomyces cerevisiae as a model. Moreover, the differential analysis of the composition and phosphorylation of kinetochores from meiotic metaphase I and metaphase II adds interest for the broader meiosis research community. Finally, regarding my expertise, I am a researcher specialized in the regulation of cell division.

Description of the revisions that have already been incorporated in the transferred manuscript

We have only corrected minor typos as detailed above.

Description of analyses that authors prefer not to carry out

The revisions we plan are detailed above. There are just two revisions we believe are either unnecessary or beyond the scope, both minor concerns of Reviewer #3. For clarity we have reproduced them, along with our justification below. In the latter case, the reviewer also acknowledged that further work in this direction is beyond the scope of the current study.

(2) Spore viability following SynSAC induction in meiosis was used as an indicator that this experimental approach does not disrupt kinetochore function and chromosome segregation. However, this is an indirect measure. Direct monitoring of genome distribution using GFP-tagged chromosomes would have provided more robust evidence. Notably, the SynSAC mad3Δ mutant shows a slight viability defect, which might reflect chromosome segregation defects that are more pronounced in the absence of a functional SAC.

Spore viability is a much more sensitive way of analysing segregation defects that GFP-labelled chromosomes. This is because GFP labelling allows only a single chromosome to be followed. On the other hand, if any of the 16 chromosomes mis-segregate in a given meiosis this would result in one or more aneuploid spores in the tetrad, which are typically inviable. The fact that spore viability is not significantly different from wild type in this analysis indicates that there are no major chromosome segregation defects in these strains, and we therefore do not plan to do this experiment.

(4) Although a more detailed exploration of kinetochore composition or phosphorylation changes is beyond the scope of the manuscript, some key observations could have been validated experimentally (e.g., enrichment of proteins at kinetochores, phosphorylation events that were identified as specific or enriched at a certain meiotic stage, etc.).

We agree that this is beyond the scope of the current study but will form the start of future projects from our group, and hopefully others.

(1) Salah, S.M., and Nasmyth, K. (2000). Destruction of the securin Pds1p occurs at the onset of anaphase during both meiotic divisions in yeast. Chromosoma 109, 27–34.

(2) Matos, J., Lipp, J.J., Bogdanova, A., Guillot, S., Okaz, E., Junqueira, M., Shevchenko, A., and Zachariae, W. (2008). Dbf4-dependent CDC7 kinase links DNA replication to the segregation of homologous chromosomes in meiosis I. Cell 135, 662–678.

(3) Marston, A.L.A.L., Lee, B.H.B.H., and Amon, A. (2003). The Cdc14 phosphatase and the FEAR network control meiotic spindle disassembly and chromosome segregation. Developmental cell 4, 711–726. https://doi.org/10.1016/S1534-5807(03)00130-8.

(4) Attner, M.A., and Amon, A. (2012). Control of the mitotic exit network during meiosis. Molecular Biology of the Cell 23, 3122–3132. https://doi.org/10.1091/mbc.E12-03-0235.

(5) Pablo-Hernando, M.E., Arnaiz-Pita, Y., Nakanishi, H., Dawson, D., del Rey, F., Neiman, A.M., and de Aldana, C.R.V. (2007). Cdc15 Is Required for Spore Morphogenesis Independently of Cdc14 in Saccharomyces cerevisiae. Genetics 177, 281–293. https://doi.org/10.1534/genetics.107.076133.

(6) El Jailani, S., Cladière, D., Nikalayevich, E., Touati, S.A., Chesnokova, V., Melmed, S., Buffin, E., and Wassmann, K. (2025). Eliminating separase inhibition reveals absence of robust cohesin protection in oocyte metaphase II. EMBO J 44, 5187–5214. https://doi.org/10.1038/s44318-025-00522-0.

(7) Rosenberg, J.S., Cross, F.R., and Funabiki, H. (2011). KNL1/Spc105 Recruits PP1 to Silence the Spindle Assembly Checkpoint. Current Biology 21, 942–947. https://doi.org/10.1016/j.cub.2011.04.011.

(8) Koch, L.B., Spanos, C., Kelly, V., Ly, T., and Marston, A.L. (2024). Rewiring of the phosphoproteome executes two meiotic divisions in budding yeast. EMBO J 43, 1351–1383. https://doi.org/10.1038/s44318-024-00059-8.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public review):

Summary:  

ZMAT3 is a p53 target gene that the Lal group and others have shown is important for p53mediated tumor suppression, and which plays a role in the control of RNA splicing. In this manuscript, Lal and colleagues perform quantitative proteomics of cells with ZMAT3 knockout and show that the enzyme hexokinase HKDC1 is the most upregulated protein. Mechanistically, the authors show that ZMAT3 does not appear to directly regulate the expression of HKDC1; rather, they show that the transcription factor c-JUN was strongly enriched in ZMAT3 pull-downs in IP-mass spec experiments, and they perform IP-western to demonstrate an interaction between c-JUN and ZMAT3. Importantly, the authors demonstrate, using ChIP-qPCR, that JUN is present at the HKDC1 gene (intron 1) in ZMAT3 WT cells and shows markedly enhanced binding in ZMAT3 KO cells. The data best fit a model whereby p53 transactivates ZMAT3, leading to decreased JUN binding to the HKDC1 promoter, and altered mitochondrial respiration.  

Strengths:

The authors use multiple orthogonal approaches to test the majority of their findings.  The authors offer a potentially new activity of ZMAT3 in tumor suppression by p53: the control of mitochondrial respiration.  

Weaknesses:

Some indication as to whether other c-JUN target genes are also regulated by ZMAT3 would improve the broad relevance of the authors' findings.