DOI: 10.1016/j.isci.2025.114450

Resource: (Proteintech Cat# 66240-1-Ig, RRID:AB_2881629)

Curator: @scibot

SciCrunch record: RRID:AB_2881629

What is this?

Creo que hace falta más hincapié en el aspecto regresivo y hacen falta más que comillas para desarticular la potencia de la palabra "modernización" que, no por nada, es la elegida para presentar esta reforma. Sugiero no usar la palabra modernización a menos que este acompañada por nuestra propia definición que podría ser, simplemente, "retroceso" laboral.

Al mostrar estos números, entiendo que lo que están suponiendo es que cada año se van a despedir casi 1 millón de trabajadores con antigüedad. De manera similar, en el siguiente punto, suponen que cada año se van a desvincular a 1 millón y medio de trabajadores en periodo de prueba. Por supuesto que en los casos en los que haya despidos significa una transferencia de ingresos y un ataque grave al ingreso, y aun si no los hubiera, la amenaza de que podría haberlos es un golpe a la capacidad organizativa de los trabajadores. Pero si están haciendo lo que creo que están haciendo, no sería así como debería calcularse.

Para que se den una idea, un informe retomado en esta nota calcula 200.000 despidos en los primeros 5 meses de la era Milei https://www.ambito.com/economia/advierten-que-hubo-casi-200000-despidos-los-primeros-5-meses-la-era-javier-milei-n6036930

Ya es malo de por si, pero los números que ustedes presentan exceden esto por muchísimo. Quizás me equivoco, pero me parece que están tomando los totales de trabajadores que pertenecen a ciertas categorías, cuando en estos casos tendrían que tomar números de despidos reales y proyectarlos para 2025.

Quizás si incluyesen un apartado metodológico explicando de dónde sacan esos números se podría entender mejor

Es interesante esta generalidad desde los bloques (objetos, tipos y relaciones, que se juntan en un grafo). Los Dumems en Cardumem son otra forma de generalización desde el hipertexto programable (gracias al scripting en YueScript) y los metadatos personalizables que permiten las tablas de Lua.

Sin embargo, para disminuir la complejidad y aumentar la practicidad, en Cardumem no apuntamos a tecnologías de la llamada web 3.0, sino que usamos las buenas y confliables web 2.0 con algo de retrofuturismo en los sistemas hipermedia.

Author response:

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

eLife Assessment

This Review Article explores the intricate relationship between humans and Mycobacterium tuberculosis (Mtb), providing an additional perspective on TB disease. Specifically, this review focuses on the utilization of systems-level approaches to study TB, while highlighting challenges in the frameworks used to identify the relevant immunologic signals that may explain the clinical spectrum of disease. The work could be further enhanced by better defining key terms that anchor the review, such as "unified mechanism" and "immunological route." This review will be of interest to immunologists as well as those interested in evolution and host-pathogen interactions.

We thank the editors for reviewing our article and for the primarily positive comments. We accept that better definition and terminology will improve the clarity of the message, and so have changed the wording as suggested above in the revised manuscript.

Public Reviews:

Reviewer #1 (Public review):

Summary:

This is an interesting and useful review highlighting the complex pathways through which pulmonary colonisation or infection with Mycobacterium tuberculosis (Mtb) may progress to develop symptomatic disease and transmit the pathogen. I found the section on immune correlates associated with individuals who have clearly been exposed to and reacted to Mtb but did not develop latent infections particularly valuable. However, several aspects would benefit from clarification.

Strengths:

The main strengths lie in the arguments presented for a multiplicity of immune pathways to TB disease.

Weaknesses:

The main weaknesses lie in clarity, particularly in the precise meanings of the three figures.

We accept this point, and have completely changed figure 2, and have expanded the legends for figure 1 and 3 to maximise clarity.

I accept that there is a 'goldilocks zone' that underpins the majority of TB cases we see and predominantly reflects different patterns of immune response, but the analogies used need to be more clearly thought through.

We are glad the reviewer agrees with the fundamental argument of different patterns of immunity, and have revised the manuscript throughout where we feel the analogies could be clarified.

Reviewer #2 (Public review):

Summary:

This is a thought-provoking perspective by Reichmann et al, outlining supportive evidence that Mycobacterium tuberculosis co-evolved with its host Homo Sapiens to both increase susceptibility to infection and reduce rates of fatal disease through decreased virulence. TB is an ancient disease where two modes of virulence are likely to have evolved through different stages of human evolution: one before the Neolithic Demographic Transition, where humans lived in sparse hunter-gatherer communities, which likely selected for prolonged Mtb infection with reduced virulence to allow for transmission across sparse populations. Conversely, following the agricultural and industrial revolutions, Mtb virulence is likely to have evolved to attack a higher number of susceptible individuals. These different disease modalities highlight the central idea that there are different immunological routes to TB disease, which converge on a disease phenotype characterized by high bacterial load and destruction of the extracellular matrix. The writing is very clear and provides a lot of supportive evidence from population studies and the recent clinical trials of novel TB vaccines, like M72 and H56. However, there are areas to support the thesis that have been described only in broad strokes, including the impact of host and Mtb genetic heterogeneity on this selection, and the alternative model that there are likely different TB diseases (as opposed to different routes to the same disease), as described by several groups advancing the concept of heterogeneous TB endotypes. I expand on specific points below.

Strengths:

The idea that Mtb evolved to both increase transmission (and possible commensalism with humans) with low rates of reactivation is intriguing. The heterogeneous TB phenotypes in the collaborative cross model (PMID: 35112666) support this idea, where some genetic backgrounds can tolerate a high bacterial load with minimal pathology, while others show signs of pathogenesis with low bacterial loads. This supports the idea that the underlying host state, driven by a number of factors like genetics and nutrition, is likely to explain whether someone will co-exist with Mtb without pathology, or progress to disease. I particularly enjoyed the discussion of the protective advantages provided by Mtb infection, which may have rewired the human immune system to provide protection against heterologous pathogens- this is supported by recent studies showing that Mtb infection provides moderate protection against SARS-CoV-2 (PMID: 35325013, and 37720210), and may have applied to other viruses that are likely to have played a more significant role in the past in the natural selection of Homo Sapiens.

We thank the reviewer for their positive comments, and also for pointing out work that we have overlooked citing previously. We now discuss and cite the work above as suggested

Modeling from Marcel Behr and colleagues (PMID: 31649096) indeed suggests that there are at least TB clinical phenotypes that likely mirror the two distinct phases of Mtb co-evolution with humans. Most of the TB disease progression occurs rapidly (within 1-2 years of exposure), and the rest are slow cases of reactivation over time. I enjoyed the discussion of the difference between the types of immune hits needed to progress to disease in the two scenarios, where you may need severe immune hits for rapid progression, a phenotype that likely evolved after the Neolithic transition to larger human populations. On the other hand, a series of milder immune events leading to reactivation after a long period of asymptomatic infection likely mirrors slow progression in the hunter-gatherer communities, to allow for prolonged transmission in scarce populations. Perhaps a clearer analysis of these models would be helpful for the reader.

We agree that we did not present these concepts in as much detail as we should, and so we now discuss this more on lines 81 – 83 and 184 - 187)

Weaknesses:

The discussion of genetic heterogeneity is limited and only discusses evidence from MSMD studies. Genetics is an important angle to consider in the co-evolution of Mtb and humans. There is a large body of literature on both host and Mtb genetic associations with TB disease. The very fact that host variants in one population do not necessarily cross-validate across populations is evidence in support of population-specific adaptations. Specific Mtb lineages are likely to have co-evolved with distinct human populations. A key reference is missing (PMID: 23995134), which shows that different lineages co-evolved with human migrations. Also, meta-analyses of human GWAS studies to define variants associated with TB are very relevant to the topic of co-evolution (e.g., PMID: 38224499). eQTL studies can also highlight genetic variants associated with regulating key immune genes involved in the response to TB. The authors do mention that Mtb itself is relatively clonal with ~2K SNPs marking Mtb variation, much of which has likely evolved under the selection pressure of modern antibiotics. However, some of this limited universe of variants can still explain co-adaptations between distinct Mtb lineages and different human populations, as shown recently in the co-evolution of lineage 2 with a variant common in Peruvians (PMID: 39613754).

We thank the reviewer for these comments and agree we failed to cite and discuss the work from Sebastian Gagneux’s group on co-migration, which we now discuss. We include a new paragraph discussing co-evolution as suggested on lines 145 – 155 and 218 -220 , citing the work proposed, which we agree enhances the arguments about co-evolution.

Although the examples of anti-TNF and anti-PD1 treatments are relevant as drivers of TB in limited clinical contexts, the bigger picture is that they highlight major distinct disease endotypes. These restricted examples show that TB can be driven by immune deficiency (as in the case of anti-TNF, HIV, and malnutrition) or hyperactivation (as in the case of anti-PD1 treatment), but there are still certainly many other routes leading to immune suppression or hyperactivation. Considering the idea of hyper-activation as a TB driver, the apparent higher rate of recurrence in the H56 trial referenced in the review is likely due to immune hyperactivation, especially in the context of residual bacteria in the lung. These different TB manifestations (immune suppression vs immune hyperactivation) mirror TB endotypes described by DiNardo et al (PMID: 35169026) from analysis of extensive transcriptomic data, which indicate that it's not merely different routes leading to the same final endpoint of clinical disease, but rather multiple different disease endpoints. A similar scenario is shown in the transcriptomic signatures underlying disease progression in BCG-vaccinated infants, where two distinct clusters mirrored the hyperactivation and immune suppression phenotypes (PMID: 27183822). A discussion of how to think about translating the extensive information from system biology into treatment stratification approaches, or adjunct host-directed therapies, would be helpful.

We agree with the points made and that the two publications above further enhance the paper. We have added discussion of the different disease endpoints on line 65 - 67, the evidence regarding immune herpeactivation versus suppression in the vaccination study on lines 162 - 164, and expanded on the translational implications on lines 349 – 352.

Reviewer #3 (Public review):

Summary:

This perspective article by Reichmann et al. highlights the importance of moving beyond the search for a single, unified immune mechanism to explain host-Mtb interactions. Drawing from studies in immune profiling, host and bacterial genetics, the authors emphasize inconsistencies in the literature and argue for broader, more integrative models. Overall, the article is thought-provoking and well-articulated, raising a concept that is worth further exploration in the TB field.

Strengths:

Timely and relevant in the context of the rapidly expanding multi-omics datasets that provide unprecedented insights into host-Mtb interactions.

Weaknesses (Minor):

Clarity on the notion of a "unified mechanism". It remains unclear whether prior studies explicitly proposed a single unifying immunological model. While inconsistencies in findings exist, they do not necessarily demonstrate that earlier work was uniformly "single-minded". Moreover, heterogeneity in TB has been recognized previously (PMIDs: 19855401, 28736436), which the authors could acknowledge.

We accept this point and have toned down the language, acknowledging that we are expanding on an argument that others have made, whilst focusing on the implications for the systems immunology era, and cite the previous work as suggested.

Evolutionary timeline and industrial-era framing. The evolutionary model is outdated. Ancient DNA studies place the Mtb's most recent common ancestor at ~6,000 years BP (PMIDs: 25141181; 25848958). The Industrial Revolution is cited as a driver of TB expansion, but this remains speculative without bacterial-genomics evidence and should be framed as a hypothesis. Additionally, the claim that Mtb genomes have been conserved only since the Industrial Revolution (lines 165-167) is inaccurate; conservation extends back to the MRCA (PMID: 31448322).

Our understanding is that the evolutionary timeline is not fully resolved, with conflicting evidence proposing different dates. The ancient DNA studies giving a timeline of 6,000 years seem to oppose the evidence of evidence of Mtb infection of humans in the middle east 10,000 years ago, and other estimates suggesting 70,000 years. Therefore, we have cited the work above and added a sentence highlighting that different studies propose different timelines. We would propose the industrial revolution created the ideal societal conditions for the expansion of TB, and this would seem widely accepted in the field, but have added a proviso as suggested. We did not intent to claim that Mtb genomes have been conserved since the industrial revolution, the point we were making is that despite rapid expansion within human populations, it has still remained conserved. We therefore have revised our discussion of the conservation of the Mtb genomes on lines and 72 – 74, 81 – 83 and 185 – 190.

Trained immunity and TB infection. The treatment of trained immunity is incomplete. While BCG vaccination is known to induce trained immunity (ref 59), revaccination does not provide sustained protection (ref 8), and importantly, Mtb infection itself can also impart trained immunity (PMID: 33125891). Including these nuances would strengthen the discussion.

We have refined this section. We did cite PMID: 33125891 in the original submission but have changed the wording to emphasise the point on line …

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Abstract

Line 30: What is an immunological route? Suggest

”...host-pathogen interaction, with diverse immunological processes leading to TB disease (10%) or stable lifelong association or elimination. We suggest these alternate relationships result from the prolonged co-evolution of the pathogen with humans and may even confer a survival advantage in the 90% of exposures that do not progress to disease.”

Thank you, we have reworded the abstract along the lines suggested above, but not identically to allow for other reviewer comments.

Introduction

Ln 43: It is misleading to suggest that the study of TB was the leading influence in establishing the Koch's postulates framework. Many other infections were involved, and Jacob Henle, one of Koch's teachers, is credited with the first clear formulation (see Evans AS. 1976 THE YALE JOURNAL OF BIOLOGY AND MEDICIN PMID: 782050).

We have downplayed the language, stating that TB “contributed” to the formulation if Koch’s postulated.

Ln 46: While the review rightly emphasises intracellular infection in macrophages, the importance and abundance of extracellular bacilli should not be ignored, particularly in transmission and in cavities.

We agree, and have added text on the importance of extracellular bacteria and transmission.

Ln: 56: This is misleading as primary disease prevention is implied, whereas the vaccine was given to individuals presumed to be already infected (TST or IGRA positive). Suggest ..."reduces by 50% progression to overt TB disease when given to those with immunological evidence of latent infection.

Thank you, edit made as suggested

Ln 62: Not sure why it is urgent. Suggest "high priority".

Wording changed as suggested.

Figure 1 needs clarification. The colour scale appears to signify the strength or vigour of the immune response so that disease is associated with high (orange/red) or low (green/blue) activity. The arrows seem to imply either a sequence or a route map when all we really have is an association with a plausible mechanistic link. They might also be taken to imply a hierarchy that is not appropriate. I'm not sure that the X-rays and arrows add anything, and the rectangle provides the key information on its own. Clarify please.

We have clarified the figure legend. We feel the X-rays give the clinical context, and so have kept them, and now state in the legend that this is highlighting that there are diverse pathways leading to active disease to try to emphasise the point the figure is illustrating.

Ln 149-157: I agree that the current dogma is that overt pulmonary disease is required to spread Mtb and fuel disease prevalence. It is vitally important to distinguish the spread of the organism from the occurrence of disease (which does not, of itself, spread). However, both epidemiological (e.g. Ryckman TS, et al. 2022Proc Natl Acad Sci U S A:10.1073/pnas.2211045119) and recent mechanistic (Dinkele R, et al. 2024iScience:10.1016/j.isci.2024.110731, Patterson B, et al. 2024Proc Natl Acad Sci U S A:10. E1073/pnas.2314813121, Warner DF, et al. 2025Nat Rev Microbiol:10.1038/s41579-025-01201-x) studies indicate the importance of asymptomatic infections, and those associated with sputum positivity have recently been recognised by WHO. I think it will be important to acknowledge the importance of this aspect and consider how immune responses may or may not contribute. I regard the view that Mtb is an obligate pathogen, dependent on overt pTB for transmission, as needing to be reviewed.

We agree that we did not give sufficient emphasis to the emerging evidence on asymptomatic infections, and that this may play an important part in transmission in high incidence settings. We now include a discussion on this, and citation of the papers above, on lines 168 – 170.

Ln 159: The terms colonise and colonisation are used, without a clear definition, several times. My view is that both refer to the establishment and replication of an organism on or within a host without associated damage. Where there is associated damage, this is often mediated by immune responses. In this header, I think "establishment in humanity" would be appropriate.

We agree with this point and have changed the header as suggested, and clarified our meaning when we use the term colonisation, which the reviewer correctly interprets.

Ln 181-: I strongly support the view that Mtb has contributed to human selection, even to the suggestion that humanity is adapted to maintain a long-term relationship with Mtb

Thank you, and we have expanded on this evidence as suggested by other reviewers.

Ln 189: improved.

Apologies, typo corrected.

Figure 2: I was also confused by this. The x-axis does not make sense, as a single property should increase. Moreover, does incidence refer to incidence in individuals with that specific balance of resistance and susceptibility, or contribution to overall global incidence - I suspect the latter (also, prevalence would make more sense). At the same time, the legend implies that those with high resistance to colonisation will be infrequent in the population, suggesting that the Y axis should be labelled "frequency in human population". Finally, I can't see what single label could apply to the X axis. While the implication that the majority of global infections reflect a balance between the resistance and susceptibilities is indicated, a frequency distribution does not seem an appropriate representation.

The reviewer is correct that the X axis is aiming to represent two variables, which is not logical, and so we have completely changed this figure to a simple one that we hope makes the point clearly and have amended the legend appropriately. We are aiming to highlight the selective pressures of Mtb on the human population over millennia.

Ln 244: Immunological failure - I agree with the statement but again find the figure (3) unhelpful. Do we start or end in the middle? Is the disease the outside - if so, why are different locations implied? The notion of a maze has some value, but the bacteria should start and finish in the same place by different routes.

We are attempting to illustrate the concept that escape from host immunological control can occur through different mechanisms. As this comment was just from one reviewer, we have left the figure unchanged but have expanded the legend to try to make the point that this is just a conceptual illustration of multiple routes to disease.

Ln 262 onward: I broadly agree with the points made about omic technologies, but would wish to see major emphasis on clear phenotyping of cases. There is something of a contradiction in the review between the emphasis on the multiplicity of immunological processes leading ultimately to disease and the recommendation to analyse via omics, which, in their most widely applied format, bundle these complexities into analyses of the humoral and cellular samples available in blood. Admittedly, the authors point out opportunities for 3-dimensional and single-cell analyses, but it is difficult to see where these end without extrapolation ad infinitum.

We totally agree that clear phenotyping of infection is critical, and expand on this further on lines 307 - 309.

Reviewer #2 (Recommendations for the authors):

I suggest expanding on the genetic determinants of Mtb/host co-evolution.

Thank you, we have now expanded on these sections as suggested.

Reviewer #3 (Recommendations for the authors):

We are in an era of exploding large-scale datasets from multi-omics profiling of Mtb and host interactions, offering an unprecedented lens to understand the complexity of the host immune response to Mtb-a pathogen that has infected human populations for thousands of years. The guiding philosophy for how to interpret this tremendous volume of data and what models can be built from it will be critical. In this context, the perspective article by Reichmann et al. raises an interesting concept: to "avoid unified immune mechanisms" when attempting to understand the immunology underpinning host-Mtb interactions. To support their arguments, the authors review studies and provide evidence from immune profiling, host and bacterial genetics, and showcase several inconsistencies. Overall, this perspective article is well articulated, and the concept is worthwhile for further exploration. A few comments for consideration:

Clarity on the notion of a "unified mechanism". Was there ever a single, clearly proposed unified immunological mechanism? For example, in lines 64-65, the authors criticize that almost all investigations into immune responses to Mtb are based on the premise that a unifying disease mechanism exists. However, after reading the article, it was not clear to me how previous studies attempted to unify the model or what that unifying mechanism was. While inconsistencies in findings certainly exist, they do not necessarily indicate that prior work was guided by a unified framework. I agree that interpreting and exploring data from a broader perspective is valuable, but I am not fully convinced that previous studies were uniformly "single-minded". In fact, the concept of heterogeneity in TB has been previously discussed (e.g., PMIDs: 19855401, 28736436).

We accept this point, and that we have overstated the argument and not acknowledged previous work sufficiently. We now downplay the language and cite the work as proposed.

However, we would propose that essentially all published studies imply that single mechanisms underly development of disease. The authors are not aware of any manuscript that concludes “Therefore, xxxx pathway is one of several that can lead to TB disease”, instead they state “Therefore, xxxx pathway leads to TB disease”. The implication of this language is that the mechanism described occurs in all patients, whilst in fact it likely only is involved in a subset. We have toned down the language and expand on this concept on line 268 – 270.

Evolutionary timeline and industrial-era framing. The evolutionary model needs updating. The manuscript cites a "70,000-year" origin for Mtb, but ancient-DNA studies place the most recent common ancestor at ~6,000 years BP (PMIDs: 25141181; 25848958). The Industrial Revolution is invoked multiple times as a driver of TB expansion, yet the magnitude of its contribution remains debated and, to my knowledge, lacks direct bacterial-genomics evidence for causal attribution; this should be framed as a hypothesis rather than a conclusion. In addition, the statement in lines 165-167 is inaccurate: at the genome level, Mtb has remained highly conserved since its most recent common ancestor-not specifically since the Industrial Revolution (PMID: 31448322).

We accept these points and have made the suggested amendments, as outlined in the public responses. Our understanding is that the evidence about the most common ancestor is controversial; if the divergence of human populations occurred concurrently with Mtb, then this must have been significantly earlier than 6,000 years ago, and so there are conflicting arguments in this domain.

Trained immunity and TB infection. The discussion of trained immunity could be expanded. Reference 59 suggests the induction of innate immune training, but reference 8 reports that revaccination does not confer protection against sustained TB infection, indicating that at least "re"-vaccination may not enhance protection. Furthermore, while BCG is often highlighted as a prototypical inducer of trained immunity, real-world infection occurs through Mtb itself. Importantly, a later study demonstrated that Mtb infection can also impart trained immunity (PMID: 33125891). Integrating these findings would provide a more nuanced view of how both vaccination and infection shape innate immune training in the TB context.

We thank the reviewer for these suggestions and have edited the relevant section to include these studies.

Pondría algo: En esta investigación, nos enfocamos en preferencias por justicia de mercado en pensiones, entendido cómo el grado en que las personas consideran que es justo que el bienestar de las pensiones dependa del ingreso y contribución individual.

creo que esto tiene que ver con la relevancia del concepto, y creo que lo dejaría antes de la operacionalización

El apartado es claro y autoexplicativo, bien. Solo tengo dos comentarios generales: 1.- Creo necesario explicitar vínculo entre meritocracia y pensiones (la relevancia de estudiar esta relación, así como investigaciones previas que respalden lo dicho), porque hasta ahora no se menciona nada sobre el objeto de estudio, por lo que se pierde un poco con el relato general. 2.- Gran parte del párrafo de medición se dedica a explicar la distinción entre percepciones y preferencias meritocráticas. Sin embargo, este marco no es utilizado en el paper, por lo que a mi parecer queda en el aire, e implícitamente pareciera que nos estamos pisando la cola. Pienso que sí hay que mencionarlo como una de las nuevas maneras para medir creencias meritocráticas, pero le daría más espacio a presentar estudios que usen la misma medición que estamos utilizando ahora. Por ej: estudio pionero, evolución de la medición (cambios en fraseos, categorías de respuestas, etc).

Sugeriría que este párrafo vaya después y que el caso chileno fuera de entrada en la sección. De lo contrario, se pierde un poco la presencia del contexto. Asimismo, se podría agregar un párrafo para hacer ese puente.

En esta seccion de bivariados solo se muestran asociaciones entre merito y mjp, y clase? Sugiero que:

i) se parta por clase, mostrando ese grafico que hicimos en el html de analysis ii) luego merito, eligiendo entre el scatter o la matriz de correlaciones iii) clase es fija, por lo que con un grafico de medias está bueno, pero merito no, por ende, podriamos incorporar el rol tiempo en lo bivariado

Pienso que si bien se observan varios flows, lo central en cuanto tendencia es que, por un lado, la gran mayoria está en contra de esta idea, pero por otro lado, hay un creciente grupo que si lo está (reflejado en el crecimiento del agree+strongly agree desde el 2018 al 2023 por ejemplo). Por eso creo que lo central de este dato es eso, mostrar que aunque la mayoría lo rechaza, hay un crecimiento en el acuerdo y en consecuencia una dismincion en el desacuerdo. Creo que sería bueno nombrar esas diferencias de numero en el parrfao, como está en el paper ya publicado

Esta idea está como no conectada con la que le sigue. Y la idea que le sigue (clase y actitudes) es más del parrafo anterior

Author response:

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

Public reviews:

Reviewer #1 (Public review):

In this important study, the authors characterized the transformation of neural representations of olfactory stimuli from the primary sensory cortex to multisensory regions in the medial temporal lobe and investigated how they were affected by non-associative learning. The authors used high-density silicon probe recordings from five different cortical regions while familiar vs. novel odors were presented to a head-restrained mouse. This is a timely study because unlike other sensory systems (e.g., vision), the progressive transformation of olfactory information is still poorly understood. The authors report that both odor identity and experience are encoded by all of these five cortical areas but nonetheless some themes emerge. Single neuron tuning of odor identity is broad in the sensory cortices but becomes narrowly tuned in hippocampal regions. Furthermore, while experience affects neuronal response magnitudes in early sensory cortices, it changes the proportion of active neurons in hippocampal regions. Thus, this study is an important step forward in the ongoing quest to understand how olfactory information is progressively transformed along the olfactory pathway.

The study is well-executed. The direct comparison of neuronal representations from five different brain regions is impressive. Conclusions are based on single neuronal level as well as population level decoding analyses. Among all the reported results, one stands out for being remarkably robust. The authors show that the anterior olfactory nucleus (AON), which receives direct input from the olfactory bulb output neurons, was far superior at decoding odor identity as well as novelty compared to all the other brain regions. This is perhaps surprising because the other primary sensory region - the piriform cortex - has been thought to be the canonical site for representing odor identity. A vast majority of studies have focused on aPCx, but direct comparisons between odor coding in the AON and aPCx are rare. The experimental design of this current study allowed the authors to do so and the AON was found to convincingly outperform aPCx. Although this result goes against the canonical model, it is consistent with a few recent studies including one that predicted this outcome based on anatomical and functional comparisons between the AON-projecting tufted cells vs. the aPCx-projecting mitral cells in the olfactory bulb (Chae, Banerjee et. al. 2022). Future experiments are needed to probe the circuit mechanisms that generate this important difference between the two primary olfactory cortices as well as their potential causal roles in odor identification.

The authors were also interested in how familiarity vs. novelty affects neuronal representation across all these brain regions. One weakness of this study is that neuronal responses were not measured during the process of habituation. Neuronal responses were measured after four days of daily exposure to a few odors (familiar) and then some other novel odors were introduced. This creates a confound because the novel vs. familiar stimuli are different odorants and that itself can lead to drastic differences in evoked neural responses. Although the authors try to rule out this confound by doing a clever decoding and Euclidian distance analysis, an alternate more straightforward strategy would have been to measure neuronal activity for each odorant during the process of habituation.

Reviewer #2 (Public review):

This manuscript investigates how olfactory representations are transformed along the cortico-hippocampal pathway in mice during a non-associative learning paradigm involving novel and familiar odors. By recording single-unit activity in several key brain regions (AON, aPCx, LEC, CA1, and SUB), the authors aim to elucidate how stimulus identity and experience are encoded and how these representations change across the pathway.

The study addresses an important question in sensory neuroscience regarding the interplay between sensory processing and signaling novelty/familiarity. It provides insights into how the brain processes and retains sensory experiences, suggesting that the earlier stations in the olfactory pathway, the AON aPCx, play a central role in detecting novelty and encoding odor, while areas deeper into the pathway (LEC, CA1 & Sub) are more sparse and encodes odor identity but not novelty/familiarity. However, there are several concerns related to methodology, data interpretation, and the strength of the conclusions drawn.

Strengths:

The authors combine the use of modern tools to obtain high-density recordings from large populations of neurons at different stages of the olfactory system (although mostly one region at a time) with elegant data analyses to study an important and interesting question.

Weaknesses:

(1) The first and biggest problem I have with this paper is that it is very confusing, and the results seem to be all over the place. In some parts, it seems like the AON and aPCx are more sensitive to novelty; in others, it seems the other way around. I find their metrics confusing and unconvincing. For example, the example cells in Figure 1C show an AON neuron with a very low spontaneous firing rate and a CA1 with a much higher firing rate, but the opposite is true in Figure 2A. So, what are we to make of Figure 2C that shows the difference in firing rates between novel vs. familiar odors measured as a difference in spikes/sec. This seems nearly meaningless. The authors could have used a difference in Z-scored responses to normalize different baseline activity levels. (This is just one example of a problem with the methodology.)

We appreciate the reviewer’s concerns regarding clarity and methodology. It is less clear why all neurons in a given brain area should have similar firing rates. Anatomically defined brain areas typically comprise of multiple cell types, which can have diverse baseline firing rates. Since we computed absolute firing rate differences per neuron (i.e., novel vs. familiar odor responses within the same neuron), baseline differences across neurons do not have a major impact.

The suggestion to use Z-scores instead of absolute firing rate differences is well taken. However, Z-scoring assumes that the underlying data are normally distributed, which is not the case in our dataset. Specifically, when analyzing odor-evoked firing rates on a per-neuron basis, only 4% of neurons exhibit a normal distribution. In cases of skewed distributions, Z-scoring can distort the data by exaggerating small variations, leading to misleading conclusions. We acknowledge that different analysis methods exist, we believe that our chosen approach best reflects the properties of the dataset and avoids potential misinterpretations introduced by inappropriate normalization techniques.

(2) There are a lot of high-level data analyses (e.g., decoding, analyzing decoding errors, calculating mutual information, calculating distances in state space, etc.) but very little neural data (except for Figure 2C, and see my comment above about how this is flawed). So, if responses to novel vs. familiar odors are different in the AON and aPCx, how are they different? Why is decoding accuracy better for novel odors in CA1 but better for familiar odors in SUB (Figure 3A)? The authors identify a small subset of neurons that have unusually high weights in the SVM analyses that contribute to decoding novelty, but they don't tell us which neurons these are and how they are responding differently to novel vs. familiar odors.

We performed additional analyses to address the reviewer’s feedback (Figures 2C-E and lines 118-132) and added more single-neuron data (Figures 1, S3 and S4).

(3) The authors call AON and aPCx "primary sensory cortices" and LEC, CA1, and Sub "multisensory areas". This is a straw man argument. For example, we now know that PCx encodes multimodal signals (Poo et al. 2021, Federman et al., 2024; Kehl et al., 2024), and LEC receives direct OB inputs, which has traditionally been the criterion for being considered a "primary olfactory cortical area". So, this terminology is outdated and wrong, and although it suits the authors' needs here in drawing distinctions, it is simplistic and not helpful moving forward.

We appreciate the reviewer’s concern regarding the classification of brain regions as “primary sensory” versus “multisensory.” Of note, the cited studies (Poo et al., 2021; Federman et al., 2024; Kehl et al., 2024) focus on posterior PCx (pPCx), while our recordings were conducted in very anterior section of anterior PCx. The aPCx and pPCx have distinct patterns of connectivity, both anatomically and functionally. To the best of our knowledge, there is no evidence for multimodal responses in aPCx, whereas there is for LEC, CA1 and SUB. Furthermore, our distinction is not based on a connectivity argument, as the reviewer suggests, but on differences in the α-Poisson ratio (Figure 1E and F).

To avoid confusion due to definitions of what constitutes a “primary sensory” region, we adopted a more neutral description throughout the manuscript.

(4) Why not simply report z-scored firing rates for all neurons as a function of trial number? (e.g., Jacobson & Friedrich, 2018). Figure 2C is not sufficient.

Regarding z-scores, please see response to 1). We further added a figure showing responses of all neurons to novel stimuli (using ROC instead of z-scoring, as described previously (e.g. Cohen et al. Nature 2012). We added the following figure to the supplementary for the completeness of the analysis (S2E).

For example, in the Discussion, they say, "novel stimuli caused larger increases in firing rates than familiar stimuli" (L. 270), but what does this mean?

This means that on average, the population of neurons exhibit higher firing rates in response to novel odors compared to familiar ones.

Odors typically increase the firing in some neurons and suppress firing in others. Where does the delta come from? Is this because novel odors more strongly activate neurons that increase their firing or because familiar odors more strongly suppress neurons?

We thank the reviewer for this valuable feedback and extended the characterization of firing rate properties, including a separate analysis of neurons i) significantly excited by odorants, ii) significantly inhibited by odorants and iii) not responsive to odorants. We added the analysis and corresponding discussion to the main manuscript (Figures 2C-E and lines 118-132)

(5) Lines 122-124 - If cells in AON and aPCx responded the same way to novel and familiar odors, then we would say that they only encode for odor and not at all for experience. So, I don't understand why the authors say these areas code for a "mixed representation of chemical identity and experience." "On the other hand," if LEC, CA1, and SUB are odor selective and only encode novel odors, then these areas, not AON and aPCx, are the jointly encoding chemical identity and experience. Also, I do not understand why, here, they say that AON and PCx respond to both while LEC, CA1, and SUB were selective for novel stimuli, but the authors then go on to argue that novelty is encoded in the AON and PCx, but not in the LEC, CA1, and SUB.

We appreciate the reviewer’s request for clarification. Throughout the brain areas we studied, odorant identity and experience can be decoded. However, the way information is represented is different between regions. We acknowledge that that “mixed” representation is a misleading term and removed it from the manuscript.

In AON and aPCx, neurons significantly respond to both novel and familiar odors. However, the magnitude of their responses to novel and familiar odors is sufficiently distinct to allow for decoding of odor experience (i.e., whether an odor is novel or familiar). Moreover, novelty engages more neurons in encoding the stimulus (Figure 2D). In neural space, the position of an odor’s representation in AON and aPCx shifts depending on whether it is novel or familiar, meaning that experience modifies the neural representation of odor identity. This suggests that in these regions the two representations are intertwined.

In contrast, some neurons in LEC, CA1, and SUB exhibit responses to novel odors, but few neurons respond to familiar odors at all. This suggests a more selective encoding of novelty.

(6) Lines 132-140 - As presented in the text and the figure, this section is poorly written and confusing. Their use of the word "shuffled" is a major source of this confusion, because this typically is the control that produces outcomes at the chance level. More importantly, they did the wrong analysis here. The better and, I think, the only way to do this analysis correctly is to train on some of the odors and test on an untrained odor (i.e., what Bernardi et al., 2021 called "cross-condition generalization performance"; CCGP).

We appreciate the feedback and thank the reviewer for the recommendation to implement cross-condition generalization performance (CCGP) as used in Bernardi et al., 2020. We acknowledge that the term "shuffled" may have caused confusion, as it typically refers to control analyses producing chance-level outcomes. In our case, by "shuffling" we shuffled the identity of novel and familiar odors to assess how much the decoder relies on odor identity when distinguishing novelty. This test provided insight into how novelty-based structure exists within neural activity beyond random grouping but does not directly assess generalization.

As suggested, we used CCGP to measure how well novelty-related representations generalize across different odors. Our findings show that in AON and aPCx, novelty-related information is indeed highly generalizable, supporting the idea that these regions encode novelty in a less odor-selective manner (Figure 2K).

Reviewer #3 (Public review):

In this manuscript, the authors investigate how odor-evoked neural activity is modulated by experience within the olfactory-hippocampal network. The authors perform extracellular recordings in the anterior olfactory nucleus (AON), the anterior piriform (aPCx) and lateral entorhinal cortex (LEC), the hippocampus (CA1), and the subiculum (SUB), in naïve mice and in mice repeatedly exposed to the same odorants. They determine the response properties of individual neurons and use population decoding analyses to assess the effect of experience on odor information coding across these regions.

The authors' findings show that odor identity is represented in all recorded areas, but that the response magnitude and selectivity of neurons are differentially modulated by experience across the olfactory-hippocampal pathway.

Overall, this work represents a valuable multi-region data set of odor-evoked neural activity. However, limitations in the interpretability of odor experience of the behavioral paradigm, and limitations in experimental design and analysis, restrict the conclusions that can be drawn from this study.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Some suggestions, in no particular order, to further improve the manuscript:

(1) The example neuronal responses for CA1 and SUB in Figure 1 are not very inspiring. To my eyes, the odor period response is not that different from the baseline period. In general, a thorough characterization of firing rate properties during the odor period between the different brain regions would be informative.

We thank the reviewer for this valuable feedback. We have replaced the example neurons from CA1 and SUB in Figure 1C. We further extended the characterization of firing rate properties, including a separate analysis of neurons i) significantly excited by odorants, ii) significantly inhibited by odorants and iii) not responsive to odorants. We added the analysis and corresponding discussion to the main manuscript (Figures 2C-E and lines 118-132)

(2) For the summary in Figure 1, why not show neuronal responses as z-scored firing rates as opposed to auROC?

We chose to use auROC instead of z-scored firing rates due to the non-normality of the dataset, which can distort results when using z-scores. Specifically, z-scoring can exaggerate small deviations in neurons with low responsiveness, potentially leading to misleading conclusions. auROC provides a more robust measure of response change that is less sensitive to these distortions because it does not assume any specific distribution. This approach has been used previously (e.g. Cohen et al. 2012, Nature).

(3) To study novelty, the authors presented odorants that were not used during four days of habituation. But this design makes it hard to dissociate odor identity from novelty. Why not track the response of the same odorants during the habituation process itself?

We respectfully disagree with the argument that using different stimuli as novel and familiar constitutes a confound in our analysis. In our study, we used multiple different, structurally dissimilar single molecule chemicals which were randomly assigned to novel and familiar categories in each animal. If individual stimuli did cause “drastic differences in evoked neural responses”, these would be evenly distributed between novel and familiar stimuli. It is therefore extremely unlikely that the clear differences we observed between novel and familiar conditions and between brain areas can be attributed to the contribution of individual stimuli, in particular given our analyses was performed at the population level. In fact, we observed that responses between novel and familiar conditions were qualitatively very similar in the short time window after odor onset (Figure 1G and H).

Importantly, the goal of this study was to investigate the impact of long-term habituation over more than 4 days, rather than short term habituation during one behavioral session. However, tracking the activity of large numbers of neurons across multiple days presents a significant technical challenge, due to the difficulty of identifying stable single-unit recordings over extended periods of time with sufficient certainty. Tools that facilitate tracking have recently been developed (e.g. Yuan AX et al., Elife. 2024) and it will be interesting to apply them to our dataset in the future.

(4) Since novel odors lead to greater sniffing and sniffing strongly influences firing rates in the olfactory system, the authors decided to focus on a 400 ms window with similar sniffing rates for both novel vs. familiar odors. Although I understand the rationale for this choice, I worry that this is too restrictive, and it may not capture the full extent of the phenomenology.

Could the authors model the effect of sniffing on firing rates of individual neurons from the data, and then check whether the odor response for novel context can be fully explained just by increased sniffing or not?

It is an interesting suggestion to extend the window of analysis and observe how responses evolve with sniffing (and other behavioral reactions). To address this, we added an additional figure to the supplementary material, showing the mean responses of all neurons to novel stimuli during the entire odor presentation window (Fig. S1B).

As suggested, we further created a Generalized Linear Model (GLM) for the entire 2s odor stimulation period, incorporating sniffing and novelty as independent variables. As expected, sniffing had a dominant impact on firing rate in all brain areas. A smaller proportion of neurons was modulated by novelty or by the interaction between novelty x breathing, suggesting the entrainment of neural activity by sniffing during the response to novel odors. These results support our decision to focus the analysis on the early 400ms window in order to dissociate the effects of novelty and behavioral responses. Taken together, our results suggest that odorant responses are modulated by novelty early during odorant processing, whereas at later stages sniffing becomes the predominant factor driving firing (Figure S2C-D).

(5) The authors conclude that aPCx has a subset of neurons dedicated to familiar odors based on the distribution of SVM weights in Figure 3D. To me, this is the weakest conclusion of the paper because although significant, the effect size is paltry; the central tendencies are hardly different for the two conditions in aPCx. Could the authors show the PSTHs of some of these neurons to make this point more convincing?

We appreciate the reviewer’s concern regarding the effect size. To strengthen our conclusion, we now include PSTHs of representative neurons in the least 10% and best 10% of neuronal population based on the SVM analysis (Figures S3 and S4). We hope this provides more clarity and support for the interpretation that there is a subset of neurons in aPCx that show greater sensitivity to familiar odors, despite the relatively modest central tendency differences.

In the revised manuscript, we discuss the effect size more explicitly in the text to provide context for its significance (lines 193 - 195).

Reviewer #2 (Recommendations for the authors):

(1) The authors only talk about "responsive" neurons. Does this include neurons whose activity increases significantly (activated) and neurons whose activity decreases (suppressed)?

Yes, the term "responsive" refers to neurons whose activity either increases significantly (excited) or decreases (inhibited) in response to the odor stimuli. We performed additional analyses to characterize responses separately for the different groups (Figure 2C-E and lines 118-132).

(2) Line 54 - The Schoonover paper doesn't show that cells lose their responses to odors, but rather that the population of cells that respond to odors changes with time. That is, population responses don't become more sparse

The fact that “the population of cells that respond to odors changes with time”, implies that some neurons lose their responsiveness (e.g. unit 2 in Figure 1 of Schoonover et al., 2021), while others become responsive (e.g. unit 1 in Figure 1 of Schoonover et al., 2021). Frequent responses reduce drift rate (Figure 4 of Schoonover et al., 2021), thus fewer neurons loose or gain responsiveness. We have revised the manuscript to clarify this.

(3) Line 104 - "Recurrent" is incorrectly used here. I think the authors mean "repeated" or something more like that.

Thank you for pointing this out. We replaced "recurrent" with "repeated".

(4) Figure 3D - What is the scale bar here?

We apologize for the accidental omission. The scale bar was be added to Figure 3D in the revised version of the manuscript.

(5) Line 377 - They say they lowered their electrodes to "200 um/s per second." This must be incorrect. Is this just a typo, or is it really 200 um/s, because that's really fast?

Thank you for pointing this out. It was 20 to 60 um/s, the change has been made in the manuscript.

(6) Line 431: The authors say they used auROC to calculate changes in firing rates (which I think is only shown in Figure 1D). Note that auROC measures the discriminability of two distributions, not the strength or change in the strength of response.

Indeed we used auROC to measure the discriminability of firing between baseline and during stimulus response. We have corrected the wording in the methods.

(7) Figure 1B: The anatomical locations of the five areas they recorded from are straightforward, and this figure is not hugely helpful. However, the reader would benefit tremendously by including an experimental schematic. As is, we needed to scour the text and methods sections to understand exactly what they did when.

We thank the reviewer for this suggestion. We included an experimental schematic in the supplementary material.

(8) Figure 1F(left): This plot is much less useful without showing a pre-odor window, even if only times after the odor onset were used for calculation alpha

We appreciate this concern, however the goal of Figure 1F is to illustrate the meaning of the alpha value itself. We chose not to include a pre-odor window comparison to avoid confusing the reader.

(9) Figure 2A: What are the bar plots above the raster plots? Are these firing rates? Are the bars overlaid or stacked? Where is the y-axis scale bar?

The bar plots above the raster plots represent a histogram of the spike count/trials over time, with a bin width of 50 ms. These bars are overlaid on the raster plot. We will include a y-axis scale bar in the revised figure to clarify the presentation.

(10) Figure 4G: This makes no sense. First, the Y axis is supposed to measure standard deviation, but the axis label is spikes/s. Second, if responses in the AON are much less reliable than responses in "deeper" areas, why is odor decoding in AON so much better than in the other areas?

We acknowledge the error in the axis label, and we will correct it to indicate the correct units. AON has a larger response variability but also larger responses magnitudes, which can explain the higher decoding accuracy.

(11) From the model and text, one predicts that the lifetime sparseness increases along the pathway. The authors should use this metric as well/instead of "odor selectivity" because of problems with arbitrary thresholding.

We acknowledge that lifetime sparseness, often computed using lifetime kurtosis, can be an informative measure of selectivity. However, we believe it has limitations that make it less suitable for our analysis. One key issue is that lifetime sparseness does not account for the stability of responses across multiple presentations of the same stimulus. In contrast, our odor selectivity measure incorporates trial-to-trial variability by considering responses over 10 trials and assessing significance using a Wilcoxon test compared to baseline. While the choice of a p-value threshold (e.g., 0.05) is somewhat arbitrary, it is a widely accepted statistical convention. Additionally, lifetime sparseness does not account for excitatory and inhibitory responses. For example, if a neuron X is strongly inhibited by odor A, strongly excited by odor B, and unresponsive to odors C and D, lifetime sparseness would classify it as highly selective for odor B, without capturing its inhibitory selectivity for odor A. The lifetime sparseness will be higher than if X was simply unresponsive for A.

Our odor selectivity measure addresses this by considering both excitation and inhibition as potential responses. Thus, while lifetime sparseness could provide a useful complementary perspective in another type of dataset, it does not fully capture the dynamics of odor selectivity here.

Author response 1.

Lifetime Kurtosis distribution per region.

Reviewer #3 (Recommendations for the authors):

Main points:

(1) The authors use a non-associative learning paradigm - repeated odor exposure - to test how experience modulates odor responses along the olfactory-hippocampal pathway. While repeated odor exposure clearly modulates odor-evoked neural activity, the relevance of this modulation and its differential effect across different brain areas are difficult to assess in the absence of any behavioral read-outs.

Our experimental paradigm involves a robust, reliable behavioral readout of non-associative learning. Novel olfactory stimuli evoke a well-characterized orienting reaction, which includes a multitude of physiological reactions, including exploratory sniffing, facial movements and pupil dilation (Modirshanechi et al., Trends Neuroscience 2023). In our study, we focused on exploration sniffing.

Compared to associative learning, non-associative learning might have received less attention. However, it is critically important because it forms the foundation for how organisms adapt to their environment through experience without forming associations. This is highlighted by the fact that non-instrumental stimuli can be remembered in large number (Standing, 1973) and with remarkable detail (Brady et al., 2008). While non-associative learning can thus create vast, implicit memory of stimuli in the environment, it is unclear how stimulus representations reflect this memory. Our study contributes to answering this question. We describe the impact of experience on olfactory sensory representations and reveal a transformation of representations from olfactory cortical to hippocampal structures. Our findings also indicate that sensory responses to familiar stimuli persist within sensory cortical and hippocampal regions, even after spontaneous orienting behaviors habituated. Further studies involving experimental manipulation techniques are needed to elucidate the causal mechanisms underlying the formation of stimulus memory during non-associative learning.

(2) The authors discuss the olfactory-hippocampal pathway as a transition from primary sensory (AON, aPCx) to associative areas (LEC, CA1, SUB). While this is reasonable, given the known circuit connectivity, other interpretations are possible. For example, AON, aPCx, and LEC receive direct inputs from the olfactory bulb ('primary cortex'), while CA1 and SUB do not; AON receives direct top-down inputs from CA1 ('associative cortex'), while aPCx does not. In fact, the data presented in this manuscript does not appear to support a consistent, smooth transformation from sensory to associative, as implied by the authors (e.g. Figure 4A, F, and G).

Thank you for this insightful comment. Indeed, there are complexities in the circuitry, and the relationships between different areas are not linear. We believe that AON and aPCx are distinctly different from LEC, CA1 and SUB, as the latter areas have been shown to integrate multimodal sensory information. To avoid confusion due to definitions of what constitutes a “primary sensory” region, we adopted a more neutral description throughout the manuscript. We also removed the term “gradual” to describe the transition of neural representations from olfactory cortical to hippocampal areas.

(3) The analysis of odor-evoked responses is focused on a 400 ms window to exclude differences in sniffing behavior. This window spans 200 ms before and after the first inhalation after odor onset. Inhalation onset initiates neural odor responses - why do the authors include neural data before inhalation onset?

The reason to include a brief time window prior to odor onset is to account for what is often called “partical” sniffs. In our experimental setup, odor delivery is not triggered by the animal’s inhalation. Therefore, it can happen that an animal has just begun to inhale when the stimulus is delivered. In this case, the animal is exposed to odorant molecules prior to the first complete inhalation after odor onset. We acknowledge that this limits the temporal resolution of our measurements, but it does not affect the comparison of sensory representations between different brain areas.

It would also be interesting to explore the effect of sniffing behavior (see point 2) on odor-evoked neural activity.

Thank you for your comment, we performed additional analysis including a GLM to address this question (Figure S2C-D).

Minor points:

(4) Figure 2A represents raster plots for 2 neurons per area - it is unclear how to distinguish between the 2 neurons in the plots.

Figure 2A shows one example neuron per brain area. Each neurons has two raster plot which indicate responses to either a novel (orange) or a familiar stimulus (blue). We have revised the figure caption for clarity.

(5) Overall, axes should be kept consistent and labeled in more detail. For example, Figure 2H and I are difficult to compare, given that the y-axis changes and that decoding accuracies are difficult to estimate without additional marks on the y-axis.

Axes are indeed different, because chance level decoding accuracy is different between those two figures. The decoding between novel and familiar odors has a chance level of 0.5, while chance level decoding odors is 0.1 (there are 10 odors to decode the identity from).

(6) Some parts of the discussion seem only loosely related to the data presented in this manuscript. For example, the statement that 'AON rather than aPCx should be considered as the primary sensory cortex in olfaction' seems out of context. Similarly, it would be helpful to provide data on the stability of subpopulations of neurons tuned to familiar odors, rather than simply speculate that they could be stable. The authors could summarize more speculative statements in an 'Ideas and Speculation' subsection.

Thank you for your comment. We appreciate your perspective on our hypotheses. We have revised the discussion accordingly. Specifically, we removed the discussion of stable subpopulations, since we have not performed longitudinal tracking in this study.

(7) The authors should try to reference relevant published work more comprehensively.

Thank you for your comment. We attempted to include relevant published work without exceeding the limit for references but might have overseen important contributions. We apologize to our colleagues, whose relevant work might not have been cited.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

The main contributions of this paper are: (1) a replication of the surprising prior finding that information about peripherally-presented stimuli can be decoded from foveal V1 (Williams et al 2008), (2) a new demonstration of cross-decoding between stimuli presented in the periphery and stimuli presented at the fovea, (3) a demonstration that the information present in the fovea is based on shape not semantic category, and (4) a demonstration that the strength of foveal information about peripheral targets is correlated with the univariate response in the same block in IPS.

Strengths:

The design and methods appear sound, and finding (2) above is new, and importantly constrains our understanding of this surprising phenomenon. The basic effect investigated here is so surprising that even though it has been replicated several times since it was first reported in 2008, it is useful to replicate it again.

We thank the reviewer for their summary. While we agree with many points, we would like to respectfully push back on the notion that this work is a replication of Williams et al. (2008). What our findings share with those of Williams is a report of surprising decoding at the fovea without foveal stimulation. Beyond this similarity, we treat these as related but clearly separate findings, for the following reasons:

(1) Foveal feedback, as shown by Williams et al. (2008) and others during fixation, was only observed during a shape discrimination task, specific to the presented stimulus. Control experiments without such a task (or a color-related task) did not show effects of foveal feedback. In contrast, in the present study, the participants’ task was merely to perform saccades towards stimuli, independently of target features. We thus show that foveal feedback can occur independently of a task related to stimulus features. This dissociation demonstrates that our study must be tapping into something different than reported by Williams.

(2) In a related study, Kroell and Rolfs (2022, 2025) demonstrated a connection between foveal feedback and saccade preparation, including the temporal details of the onset of this effect before saccade execution, highlighting the close link of this effect to saccade preparation. Here we used a very similar behavioral task to capture this saccade-related effect in neural recordings and investigate how early it occurs and what its nature is. Thus, there is a clear motivation for this study in the context of eye movement preparation that is separate from the previous work by Williams.

(3) Lastly, decoding in the experimental task was positively associated with activity in FEF and IPS, areas that have been reliably linked to saccade preparation. We have now also performed an additional analysis (see our response to Specific point 2 of Reviewer 2) showing that decoding in the control condition did not show the same association, further supporting the link of foveal feedback to saccade preparation. 

Despite our emphasis on these critical differences in studies, covert peripheral attention, as required by the task in Williams et al., and saccade preparation in natural vision, as in our study, are tightly coupled processes. Indeed, the task in Williams et al. would, during natural vision, likely involve an eye movement to the peripheral target. While speculative, a parsimonious and ecologically valid explanation is that both ours and earlier studies involve eye movement preparation, for which execution is suppressed, however, in studies enforcing fixation (e.g., Williams et al., 2008). We now discuss this idea of a shared underlying mechanism more extensively in the revised manuscript (pg 8 ln 228-240). 

Weaknesses:

(1) The paper, including in the title ("Feedback of peripheral saccade targets to early foveal cortex") seems to assume that the feedback to foveal cortex occurs in conjunction with saccade preparation. However, participants in the original Williams et al (2008) paper never made saccades to the peripheral stimuli. So, saccade preparation is not necessary for this effect to occur. Some acknowledgement and discussion of this prior evidence against the interpretation of the effect as due to saccade preparation would be useful. (e.g., one might argue that saccade preparation is automatic when attending to peripheral stimuli.)

We agree that the effects Williams et al. showed were not sufficiently discussed in the first version of this manuscript. To more clearly engage with these findings we now introduce saccade related foveal feedback (foveal prediction) and foveal feedback during fixation separately in the introduction (pg 2 ln 46-59).

We further added another section in the discussion called “Foveal feedback during saccade preparation” in which we discuss how our findings are related to Williams et al. and how they differ (pg 8 ln 211-240). 

As described in our previous response, we believe that our findings go beyond those described by Williams et al. (2008) and others in significant ways. However, during natural vision, the paradigm used by Williams et al. (2008) would likely be solved using an eye movement. Thus, while participants in Williams et al. (2008) did not execute saccades, it appears plausible that they have prepared saccades. Given the fact that covert peripheral attention and saccade preparation are tightly coupled processes (Kowler et al., 1995, Vis Res; Deubel & Schneider, 1996, Vis Res; Montagnini & Castet, 2007, J Vis; Rolfs & Carrasco, 2012, J Neurosci; Rolfs et al., 2011, Nat Neurosci), their results are parsimoniously explained by saccade preparation (but not execution) to a behaviorally relevant target.

(2) The most important new finding from this paper is the cross-decodability between stimuli presented in the fovea and stimuli presented in the periphery. This finding should be related to the prior behavioral finding (Yu & Shim, 2016) that when a foveal foil stimulus identical to a peripheral target is presented 150 ms after the onset of the peripheral target, visual discrimination of the peripheral target is improved, and this congruency effect occurred even though participants did not consciously perceive the foveal stimulus (Yu, Q., & Shim, W. M., 2016). Modulating foveal representation can influence visual discrimination in the periphery (Journal of Vision, 16(3), 15-15).

We thank the reviewer for highlighting this highly relevant reference. In the revised version of the manuscript, we now put more emphasis on the finding of cross-decodability (pg 2 ln 60-61). We now also discuss Yu et al.’s finding, which support our conclusion that foveal feedback and direct stimulus presentation share representational formats in early visual areas (pg 9 ln 277-279).

(3) The prior literature should be laid out more clearly. For example, most readers will not realize that the basic effect of decodability of peripherally-presented stimuli in the fovea was first reported in 2008, and that that original paper already showed that the effect cannot arise from spillover effects from peripheral retinotopic cortex because it was not present in a retinotopic location between the cortical locus corresponding to the peripheral target and the fovea. (For example, this claim on lines 56-57 is not correct: "it remains unknown 1) whether information is fed back all the way to early visual areas".) What is needed is a clear presentation of the prior findings in one place in the introduction to the paper, followed by an articulation and motivation of the new questions addressed in this paper. If I were writing the paper, I would focus on the cross-decodability between foveal and peripheral stimuli, as I think that is the most revealing finding.

We agree that the structure of the introduction did not sufficiently place our work in the context of prior literature. We have now expanded upon our Introduction section to discuss past studies of saccade- and fixation-related foveal feedback (pg 2 ln 49-59), laying out how this effect has been studied previously. We also removed the claim that "it remains unknown 1) whether information is fed back all the way to early visual areas", where our intention was to specifically focus on foveal prediction. We realize that this was not clear and hence removed this section. Instead, we now place a stronger focus on the cross-decodability finding (pg 2 ln 60-61).

Reviewer #2 (Public review):

Summary:

This study investigated whether the identity of a peripheral saccade target object is predictively fed back to the foveal retinotopic cortex during saccade preparation, a critical prediction of the foveal prediction hypothesis proposed by Kroell & Rolfs (2022). To achieve this, the authors leveraged a gaze-contingent fMRI paradigm, where the peripheral saccade target was removed before the eyes landed near it, and used multivariate decoding analysis to quantify identity information in the foveal cortex. The results showed that the identity of the saccade target object can be decoded based on foveal cortex activity, despite the fovea never directly viewing the object, and that the foveal feedback representation was similar to passive viewing and not explained by spillover effects. Additionally, exploratory analysis suggested IPS as a candidate region mediating such foveal decodability. Overall, these findings provide neural evidence for the foveal cortex processing the features of the saccade target object, potentially supporting the maintenance of perceptual stability across saccadic eye movements.

Strengths:

This study is well-motivated by previous theoretical findings (Kroell & Rolfs, 2022), aiming to provide neural evidence for a potential neural mechanism of trans-saccadic perceptual stability. The question is important, and the gaze-contingent fMRI paradigm is a solid methodological choice for the research goal. The use of stimuli allowing orthogonal decoding of stimulus category vs stimulus shape is a nice strength, and the resulting distinctions in decoded information by brain region are clean. The results will be of interest to readers in the field, and they fill in some untested questions regarding pre-saccadic remapping and foveal feedback.

We thank the reviewer for the positive assessment of our study.

Weaknesses:

The conclusions feel a bit over-reaching; some strong theoretical claims are not fully supported, and the framing of prior literature is currently too narrow. A critical weakness lies in the inability to test a distinction between these findings (claiming to demonstrate that "feedback during saccade preparation must underlie this effect") and foveal feedback previously found during passive fixation (Williams et al., 2008). Discussions (and perhaps control analysis/experiments) about how these findings are specific to the saccade target and the temporal constraints on these effects are lacking. The relationship between the concepts of foveal prediction, foveal feedback, and predictive remapping needs more thorough treatment. The choice to use only 4 stimuli is justified in the manuscript, but remains an important limitation. The IPS results are intriguing but could be strengthened by additional control analysis. Finally, the manuscript claims the study was pre-registered ("detailing the hypotheses, methodology, and planned analyses prior to data collection"), but on the OSF link provided, there is just a brief summary paragraph, and the website says "there have been no completed registrations of this project".

We thank the reviewer for these helpful considerations. We agree that some of the claims were not sufficiently supported by the evidence, and in the revised manuscript, we added nuance to those claims (pg 8 ln 211-240). Furthermore, we now address more directly the distinction between foveal feedback during fixation and foveal feedback (foveal prediction) during saccade preparation. In particular, we now describe the literature about these two effects separately in the introduction (pg 2 ln 46-59), and we have added a new section in the discussion (“Foveal feedback during saccade preparation”) that more thoroughly explains why a passive fixation condition would have been unlikely to produce the same results we find (pg 8 ln 211-227). We also adapted the section about “Saccadic remapping or foveal prediction”, clearly delineating foveal prediction from feature remapping and predictive updating of attention pointers. As recommended by the reviewer, we conducted the parametric modulation analyses on the control condition, strengthening the claim that our findings are saccade-related. These results were added as Supplementary Figure 2 and are discussed in (pg 7 ln 190-191) and (pg 8 ln 224-227). 

Lastly, we would like to apologize about a mistake we made with the pre-registration. We realized that the pre-registration had indeed not been submitted. We have now done so without changing the pre-registration itself, which can be seen from the recent activity of the preregistration (screenshot attached in the end). After consulting an open science expert at the University of Leipzig, we added a note of this mistake to the methods section of the revised manuscript (pg 10 ln 326-332). We could remove reference to this preregistration altogether, but would keep it at the discretion of the editor. 

Specifics:

(1) In the eccentricity-dependent decoding results (Figure 2B), are there any statistical tests to support the results being a U-shaped curve? The dip isn't especially pronounced. Is 4 degrees lower than the further ones? Are there alternative methods of quantifying this (e.g., fitting it to a linear and quadratic function)?

We statistically tested the U-shaped relationship using a weighted quadratic regression, which showed significant positive curvature for decoding between fovea and periphery in all early visual areas (V1: t(27) = 3.98, p = 0.008, V2: t(27) = 3.03, p = 0.02, V3: t(27)= 2.776, p = 0.025, one-sided). We now report these results in the revised manuscript (pg 5 ln 137-138).

(2) In the parametric modulation analysis, the evidence for IPS being the only region showing stronger fovea vs peripheral beta values was weak, especially given the exploratory nature of this analysis. The raw beta value can reflect other things, such as global brain fluctuations or signal-to-noise ratio. I would also want to see the results of the same analysis performed on the control condition decoding results.

We appreciate the reviewer’s suggestion and repeated the same parametric modulation analysis on the control condition to assess the influence of potential confounds on the overall beta values (Supplementary Figure 2). The results show a negative association between foveal decoding and FEF and IPS (likely because eye movements in the control condition lead to less foveal presentation of the stimulus) and a positive association with LO. Peripheral decoding was not associated with significant changes in any of the ROIs, indicating that global brain fluctuations alone are not responsible for the effects reported in the experimental condition. The results of this analysis thus show a specific positive association of IPS activity with the experimental condition, not the control condition, which is in line with the idea that the foveal feedback effect reported in this study may be related to saccade preparation.

(3) Many of the claims feel overstated. There is an emphasis throughout the manuscript (including claims in the abstract) that these findings demonstrate foveal prediction, specifically that "image-specific feedback during saccade preparation must underlie this effect." To my understanding, one of the key aspects of the foveal prediction phenomenon that ties it closely to trans-saccadic stability is its specificity to the saccade target but not to other objects in the environment. However, it is not clear to what degree the observed findings are specific to saccade preparation and the peripheral saccade target. Should the observers be asked to make a saccade to another fixation location, or simply maintain passive fixation, will foveal retinotopic cortex similarly contain the object's identity information? Without these control conditions, the results are consistent with foveal prediction, but do not definitively demonstrate that as the cause, so claims need to be toned down.

We fully agree with the reviewer and toned down claims about foveal prediction. We engage with the questions raised by the reviewer more thoroughly in the new discussion section “Foveal feedback during saccade preparation”.

In addition, we agree that another condition in which subjects make a saccade towards a different location would have been a great addition that we also considered, but due to concerns with statistical power did not add. While including such a condition exceeds the scope of the current study, we included this limitation in the Discussion section (pg 10 ln 316) and hope that future studies will address this question.

(4) Another critical aspect is the temporal locus of the feedback signal. In the paradigm, the authors ensured that the saccade target object was never foveated via the gaze-contingent procedure and a conservative data exclusion criterion, thus enabling the test of feedback signals to foveal retinotopic cortex. However, due to the temporal sluggishness of fMRI BOLD signals, it is unclear when the feedback signal arrives at the foveal retinotopic cortex. In other words, it is possible that the feedback signal arrives after the eyes land at the saccade target location. This possibility is also bolstered by Chambers et al. (2013)'s TMS study, where they found that TMS to the foveal cortex at 350-400 ms SOA interrupts the peripheral discrimination task. The authors should qualify their claims of the results occurring "during saccade preparation" (e.g., pg 1 ln 22) throughout the manuscript, and discuss the importance of temporal dynamics of the effect in supporting stability across saccades.

We fully agree that the sluggishness of the fMRI signal presents an important challenge in investigating foveal feedback. We have now included this limitation in the discussion (pg 10 ln 306-318). We also clarify that our argument connects to previous studies investigating the temporal dynamics of foveal feedback using similar tasks (pg 10 ln 313-316). Specifically, in their psychophysical work, Kroell and Rolfs (2022) and (2025) showed that foveal feedback occurs before saccade execution with a peak around 80 ms before the eye movement. 

(5) Relatedly, the claims that result in this paradigm reflect "activity exclusively related to predictive feedback" and "must originate from predictive rather than direct visual processes" (e.g., lines 60-65 and throughout) need to be toned down. The experimental design nicely rules out direct visual foveal stimulation, but predictive feedback is not the only alternative to that. The activation could also reflect mental imagery, visual working memory, attention, etc. Importantly, the experiment uses a block design, where the same exact image is presented multiple times over the block, and the activation is taken for the block as a whole. Thus, while at no point was the image presented at the fovea, there could still be more going on than temporally-specific and saccade-specific predictive feedback.

We agree that those claims could have misled the reader. Our intention was to state that the activation originates from feedback rather than direct foveal stimulation because of the nature of the design. We have now clarified these statements (pg 2 ln 65) and also included a discussion of other effects including imagery and working memory in the limitations section (pg 10 ln 306-313).

(6) The authors should avoid using the terms foveal feedback and foveal prediction interchangeably. To me, foveal feedback refers to the findings of Williams et al. (2008), where participants maintained passive fixation and discriminated objects in the periphery (see also Fan et al., 2016), whereas foveal prediction refers to the neural mechanism hypothesized by Kroell & Rolfs (2022), occurring before a saccade to the target object and contains task irrelevant feature information.

We agree, and we have now adopted a clearer distinction between these terms, referring to foveal prediction only when discussing the distinct predictive nature of the effect discovered by Kroell and Rolfs (2022). Otherwise we referred to this effect as foveal feedback.

(7) More broadly, the treatment of how foveal prediction relates to saccadic remapping is overly simplistic. The authors seem to be taking the perspective that remapping is an attentional phenomenon marked by remapping of only attentional/spatial pointers, but this is not the classic or widely accepted definition of remapping. Within the field of saccadic remapping, it is an ongoing debate whether (/how/where/when) information about stimulus content is remapped alongside spatial location (and also whether the attentional pointer concept is even neurophysiologically viable). This relationship between saccadic remapping and foveal prediction needs clarification and deeper treatment, in both the introduction and discussion.

We thank the reviewer for their remarks. We reformulated the discussion section on “Saccadic remapping or foveal prediction” to include the nuances about spatial and feature remapping laid out in the reviewer’s comment (pg 8-9 ln 241-269). We also put a stronger focus on the special role the fovea seems to be playing regarding the feedback of visual features (pg 8-9 ln 265-269).

(8) As part of this enhanced discussion, the findings should be better integrated with prior studies. E.g., there is some evidence for predictive remapping inducing integration of non-spatial features (some by the authors themselves; Harrison et al., 2013; Szinte et al., 2015). How do these findings relate to the observed results? Can the results simply be a special case of non-spatial feature integration between the currently attended and remapped location (fovea)? How are the results different from neurophysiological evidence for facilitation of the saccade target object's feature across the visual field (Burrow et al., 2014)? How might the results be reconciled with a prior fMRI study that failed to find decoding of stimulus content in remapped responses (Lescroart et al, 2016)? Might this reflect a difference between peripheral-to-peripheral vs peripheral-to-foveal remapping? A recent study by Chiu & Golomb (2025) provided supporting evidence for peripheral-to-fovea remapping (but not peripheral-to-peripheral remapping) of object-location binding (though in the post-saccadic time window), and suggested foveal prediction as the underlying mechanism.

We thank the reviewer for raising these intriguing questions. We now address them in the revised discussion. We argue that the findings by Harrison et al., 2013 and Szinte et al., 2015 of presaccadic integration of features across two peripheral locations can be explained by presaccadic updating of spatial attention pointers rather than remapping of feature information (pg 8 ln 248-253). The lack of evidence for periphery-to-periphery remapping (Lescroart et al, 2016) and the recent study by Chiu & Golomb (2025) showing object-location binding from periphery to fovea nicely align with our characterization of foveal processing as unique in predicting feature information of upcoming stimuli (pg 8-9 ln 265-269). Finally, we argue that the global (i.e., space-invariant) selection task-irrelevant saccadic target features (Burrows et al., 2014) is well-established at the neural level, but does not suffice to explain the spatially specific nature of foveal prediction (pg 8 ln 220-224). We now include these studies in the revised discussion section.

Reviewer #3 (Public review):

Summary:

In this paper, the authors used fMRI to determine whether peripherally viewed objects could be decoded from the foveal cortex, even when the objects themselves were never viewed foveally. Specifically, they investigated whether pre-saccadic target attributes (shape, semantic category) could be decoded from the foveal cortex. They found that object shape, but not semantic category, could be decoded, providing evidence that foveal feedback relies on low-mid-level information. The authors claim that this provides evidence for a mechanism underlying visual stability and object recognition across saccades.

Strengths:

I think this is another nice demonstration that peripheral information can be decoded from / is processed in the foveal cortex - the methods seem appropriate, and the experiments and analyses are carefully conducted, and the main results seem convincing. The paper itself was very clear and well-written.

We thank the reviewer for this positive evaluation of our work. As discussed in our response to Reviewer 1, we now elaborate on the differences between previous work showing decoding of peripheral information from foveal cortex from the effect shown here. While there are important similarities between these findings, foveal prediction in our study occurs in a saccade condition and in the absence of a task that is specific to stimulus features. 

Weaknesses:

There are a couple of reasons why I think the main theoretical conclusions drawn from the study might not be supported, and why a more thorough investigation might be needed to draw these conclusions.

(1) The authors used a blocked design, with each object being shown repeatedly in the same block. This meant that the stimulus was entirely predictable on each block, which weakens the authors' claims about this being a predictive mechanism that facilitates object recognition - if the stimulus is 100% predictable, there is no aspect of recognition or discrimination actually being tested. I think to strengthen these claims, an experiment would need to have unpredictable stimuli, and potentially combine behavioural reports with decoding to see whether this mechanism can be linked to facilitating object recognition across saccades.

We appreciate the reviewer’s point and would like to highlight that it was not our intention to claim a behavioral effect on object recognition. We believe that an ambiguous formulation in the original abstract may have been interpreted this way, and we thus removed this reference. We also speculated in our Discussion that a potential reason for foveal prediction could be a headstart in peripheral object recognition and in the revised manuscript more clearly highlight that this is a  potential future direction only.

(2)  Given that foveal feedback has been found in previous studies that don't incorporate saccades, how is this a mechanism that might specifically contribute to stability across saccades, rather than just being a general mechanism that aids the processing/discrimination of peripherally-viewed stimuli? I don't think this paper addresses this point, which would seem to be crucial to differentiate the results from those of previous studies.

We fully agree that this point had not been sufficiently addressed in the previous version of the manuscript. As described in our responses to similar comments from reviewers 1 and 2, we included an additional section in the Discussion (“Foveal feedback during saccade preparation”) to more clearly delineate the present study from previous findings of foveal feedback. Previous studies (Williams et al., 2008) only found foveal feedback during narrow discrimination tasks related to spatial features of the target stimulus, not during color-discrimination or fixation-only tasks, concluding that the observed effect must be related to the discrimination behavior. In contrast, we found foveal feedback (as evidenced by decoding of target features) during a saccade condition that was independent of the target features, suggesting a different role of foveal feedback than hypothesized by Williams et al. (2008).

Recommendations for the authors:  

Reviewer #2 (Recommendations for the authors):

(A) Minor comments:

(1)  The task should be clarified earlier in the manuscript.

We now characterise the task in the abstract and clarified its description in the third paragraph, right after introducing the main literature.

(2) Is there actually only 0.5 seconds between saccades? This feels very short/rushed.

The inter-trial-interval was 0.5 seconds, though effectively it varied because the target only appeared once participants fixated on the fixation dot. Note that this pacing is slower than the rate of saccades in natural vision (about 3 to 4 saccades per second).Participants did not report this paradigm as rushed.

(3) Typo on pg2 ln64 (whooe).

Fixed.

(4)  Can the authors also show individual data points for Figures 3 and 4?

We added individual data points for Figures 4 and S2

(5) The MNI coordinates on Figure 4A seem to be incorrect.

We took out those coordinates.

(6) Pg4 ln126 and pg6 ln194, why cite Williams et al. (2008)?

We included this reference here to acknowledge that Williams et al. raised the same issues. We added a “cf.” before this reference to clarify this.

(7) Pg7 ln207 Fabius et al. (2020) showed slow post-saccadic feature remapping, rather than predictive remapping of spatial attention.

We have corrected this mistake.

(8) The OSF link is valid, but I couldn't find a pre-registration.

The issue with the OSF link has been resolved. The pre-registration had been set up but not published. We now published it without changing the original pre-registration (see the screenshot attached).

(9) I couldn't access the OpenNeuro repository.

The issue with the OpenNeuro link has been resolved.

(B) Additional references you may wish to include:

(1) Burrows, B. E., Zirnsak, M., Akhlaghpour, H., Wang, M., & Moore, T.  (2014). Global selection of saccadic target features by neurons in area v4. Journal of Neuroscience.

(2) Chambers, C. D., Allen, C. P., Maizey, L., & Williams, M. A. (2013). Is delayed foveal feedback critical for extra-foveal perception?. Cortex.

(3) Chiu, T. Y., & Golomb, J. D. (2025). The influence of saccade target status on the reference frame of object-location binding. Journal of Experimental Psychology. General.

(4) Harrison, W. J., Retell, J. D., Remington, R. W., & Mattingley, J. B. (2013). Visual crowding at a distance during predictive remapping. Current Biology.

(5) Lescroart, M. D., Kanwisher, N., & Golomb, J. D. (2016). No evidence for automatic remapping of stimulus features or location found with fMRI. Frontiers in Systems Neuroscience.

(6) Moran, C., Johnson, P. A., Hogendoorn, H., & Landau, A. N. (2025). The representation of stimulus features during stable fixation and active vision. Journal of Neuroscience.

(7) Szinte, M., Jonikaitis, D., Rolfs, M., Cavanagh, P., & Deubel, H. (2016). Presaccadic motion integration between current and future retinotopic locations of attended objects. Journal of Neurophysiology.

We thank the reviewer for pointing out these references. We have included them in the revised version of the manuscript.

Reviewer #3 (Recommendations for the authors):

I just have a few minor points where I think some clarifications could be made.

(1) Line 64 - "whooe" should be "whoose" I think.

Fixed.

(2) Around line 53 - you might consider citing this review on foveal feedback - https://doi.org/10.1167/jov.20.12.2

We included the reference (pg 2 ln 55).

(3) Line 129 - you mention a u-shaped relationship for decoding - I wasn't quite sure of the significance/relevance of this relationship - it would be helpful to expand on this / clarify what this means.

We have expanded this section and added statistical tests of the u-shaped relationship in decoding using a weighted quadratic regression. We found significant positive curvature in all early visual areas between fovea and periphery (V1: t(27) = 3.98, p = 0.008, V2: t(27) = 3.03, p = 0.02, V3: t(27)= 2.776, p = 0.025). These findings support a u-shaped relationship. We now report these results in the revised manuscript (pg 5 ln 137-138).

(4) Figure 1 - it would be helpful to indicate how long the target was viewed in the "stim on" panels - I assume it was for the saccade latency, but it would be good to include those values in the main text.

We included that detail in the text (pg 3 ln 96-97).

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

This study examines whether changes in pupil size index prediction-error-related updating during associative learning, formalised as information gain via Kullback-Leibler (KL) divergence. Across two independent tasks, pupil responses scaled with KL divergence shortly after feedback, with the timing and direction of the response varying by task. Overall, the work supports the view that pupil size reflects information-theoretic processes in a context-dependent manner.

Strengths:

This study provides a novel and convincing contribution by linking pupil dilation to informationtheoretic measures, such as KL divergence, supporting Zénon's hypothesis that pupil responses reflect information gain during learning. The robust methodology, including two independent datasets with distinct task structures, enhances the reliability and generalisability of the findings. By carefully analysing early and late time windows, the authors capture the timing and direction of prediction-error-related responses, oPering new insights into the temporal dynamics of model updating. The use of an ideal-learner framework to quantify prediction errors, surprise, and uncertainty provides a principled account of the computational processes underlying pupil responses. The work also highlights the critical role of task context in shaping the direction and magnitude of these ePects, revealing the adaptability of predictive processing mechanisms. Importantly, the conclusions are supported by rigorous control analyses and preprocessing sanity checks, as well as convergent results from frequentist and Bayesian linear mixed-ePects modelling approaches.

Weaknesses:

Some aspects of directionality remain context-dependent, and on current evidence cannot be attributed specifically to whether average uncertainty increases or decreases across trials. DiPerences between the two tasks (e.g., sensory modality and learning regime) limit direct comparisons of ePect direction and make mechanistic attribution cautious. In addition, subjective factors such as confidence were not measured and could influence both predictionerror signals and pupil responses. Importantly, the authors explicitly acknowledge these limitations, and the manuscript clearly frames them as areas for future work rather than settled conclusions.

Reviewer #2 (Public review):

Summary:

The authors investigate whether pupil dilation reflects information gain during associative learning, formalised as Kullback-Leibler divergence within an ideal observer framework. They examine pupil responses in a late time window after feedback and compare these to informationtheoretic estimates (information gain, surprise, and entropy) derived from two diPerent tasks with contrasting uncertainty dynamics.

Strength:

The exploration of task evoked pupil dynamics beyond the immediate response/feedback period and then associating them with model estimates was interesting and inspiring. This oPered a new perspective on the relationship between pupil dilation and information processing.

Weakness:

However, the interpretability of the findings remains constrained by the fundamental diPerences between the two tasks (stimulus modality, feedback type, and learning structure), which confound the claimed context-dependent ePects. The later time-window pupil ePects, although intriguing, are small in magnitude and may reflect residual noise or task-specific arousal fluctuations rather than distinct information-processing signals. Thus, while the study oPers valuable methodological insight and contributes to ongoing debates about the role of the pupil in cognitive inference, its conclusions about the functional significance of late pupil responses should be treated with caution.

Reviewer #3 (Public review):

Summary:

Thank you for inviting me to review this manuscript entitled "Pupil dilation oPers a time-window on prediction error" by Colizoli and colleagues. The study examines prediction errors, information gain (Kullback-Leibler [KL] divergence), and uncertainty (entropy) from an information-theory perspective using two experimental tasks and pupillometry. The authors aim to test a theoretical proposal by Zénon (2019) that the pupil response reflects information gain (KL divergence). The conclusion of this work is that (post-feedback) pupil dilation in response to information gain is context dependent.

Strengths:

Use of an established Bayesian model to compute KL divergence and entropy.

Pupillometry data preprocessing and multiple robustness checks.

Weaknesses:

Operationalization of prediction errors based on frequency, accuracy, and their interaction:

The authors rely on a more model-agnostic definition of the prediction error in terms of stimulus frequency ("unsigned prediction error"), accuracy, and their interaction ("signed prediction error"). While I see the point, I would argue that this approach provides a simple approximation of the prediction error, but that a model-based approach would be more appropriate.

Model validation:

My impression is that the ideal learner model should work well in this case. However, the authors don't directly compare model behavior to participant behavior ("posterior predictive checks") to validate the model. Therefore, it is currently unclear if the model-derived terms like KL divergence and entropy provide reasonable estimates for the participant data.

Lack of a clear conclusion:

The authors conclude that this study shows for the first time that (post-feedback) pupil dilation in response to information gain is context dependent. However, the study does not oPer a unifying explanation for such context dependence. The discussion is quite detailed with respect to taskspecific ePects, but fails to provide an overarching perspective on the context-dependent nature of pupil signatures of information gain. This seems to be partly due to the strong diPerences between the experimental tasks.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

I highly appreciate the care and detail in the authors' response and thank them for the ePort invested in revising the manuscript. They addressed the core concerns to a high standard, and the manuscript has substantially improved in methodological rigour (through additional controls/sanity checks and complementary mixed-ePects analyses) and in clarity of interpretation (by explicitly acknowledging context-dependence and tempering stronger claims). The present version reads clearly and is much strengthened overall. I only have a few minor points below:

Minor suggestions:

Abstract:

In the abstract KL is introduced as abbreviation, but at first occurence it should be written out as "Kullback-Leibler (KL)" for readers not familiar with it.

We thank the reviewer for catching this error. It has been correct in the version of record.

Methods:

I appreciate the additional bayesian LME analysis. I only had a few things that I thought were missing from knowing the parameters: 1) what was the target acceptance rate (default of .95?), 2) which family was used to model the response distribution: (default) "gaussian" or robust "student-t"? Depending on the data a student-t would be preferred, but since the author's checked the fit & the results corroborate the correlation analysis, using the default would also be fine! Just add the information for completeness.

Thank you for bringing this to our attention. We have now noted that default parameters were used in all cases unless otherwise mentioned. 

Thank you once again for your time and consideration.

Reviewer #2 (Recommendations for the authors):

Thanks to the authors' ePort on revision. I am happy with this new version of manuscript.

Thank you once again for your time and consideration.

Reviewer #3 (Recommendations for the authors):

(1) Regarding comments #3 and #6 (first round) on model validation and posterior predictive checks, the authors replied that since their model is not a "generative" one, they can't perform posterior predictive checks. Crucially, in eq. 2, the authors present the p{tilde}^j_k variable denoting the learned probability of event k on trial j. I don't see why this can't be exploited for simulations. In my opinion, one could (and should) generate predictions based on this variable. The simplest implementation would translate the probability into a categorical choice (w/o fitting any free parameter). Based on this, they could assess whether the model and data are comparable.

We thank the reviewer for this clarification. The reviewer suggests using the probability distributions at each trial to predict which event should be chosen on each trial. More specifically, the event(s) with the highest probability on trial j could be used to generate a prediction for the choice of the participant on trial j. We agree that this would indeed be an interesting analysis. However, the response options of each task are limited to two-alternatives. In the cue-target task, four events are modeled (representing all possible cue-target conditions) while the participants’ response options are only “left” and “right”. Similarly, in the letter-color task, 36 events are modeled while the participants’ response options are “match” and “no-match”. In other words, we do not know which event (either four or 36, for the two tasks) the participant would have indicated on each trial. As an approximation to this fine-grained analysis, we investigated the relationship between the information-theoretic variables separately for error and correct trials. Our rationale was that we would have more insight into how the model fits depended on the participants’ actual behavior as compared with the ideal learner model.

(2) I recommend providing a plot of the linear mixed model analysis of the pupil data. Currently, results are only presented in the text and tables, but a figure would be much more useful.

We thank the reviewer for the suggestion to add a plot of the linear mixed model results. We appreciate the value of visualizing model estimates; however, we feel that the current presentation in the text and tables clearly conveys the relevant findings. For this reason, and to avoid further lengthening the manuscript, we prefer to retain the current format.

(3) I would consider only presenting the linear mixed ePects for the pupil data in the main results, and the correlation results in the supplement. It is currently quite long.

We thank the reviewer for this recommendation. We agree that the results section is detailed; however, we consider the correlation analyses to be integral to the interpretation of the pupil data and therefore prefer to keep them in the main text rather than move them to the supplement.

The following is the authors’ response to the original reviews

eLife Assessment

This important study seeks to examine the relationship between pupil size and information gain, showing opposite effects dependent upon whether the average uncertainty increases or decreases across trials. Given the broad implications for learning and perception, the findings will be of broad interest to researchers in cognitive neuroscience, decision-making, and computational modelling. Nevertheless, the evidence in support of the particular conclusion is at present incomplete - the conclusions would be strengthened if the authors could both clarify the differences between model-updating and prediction error in their account and clarify the patterns in the data.

Public Reviews:

Reviewer #1 (Public review):

Summary:

This study investigates whether pupil dilation reflects prediction error signals during associative learning, defined formally by Kullback-Leibler (KL) divergence, an information-theoretic measure of information gain. Two independent tasks with different entropy dynamics (decreasing and increasing uncertainty) were analyzed: the cue-target 2AFC task and the lettercolor 2AFC task. Results revealed that pupil responses scaled with KL divergence shortly after feedback onset, but the direction of this relationship depended on whether uncertainty (entropy) increased or decreased across trials. Furthermore, signed prediction errors (interaction between frequency and accuracy) emerged at different time windows across tasks, suggesting taskspecific temporal components of model updating. Overall, the findings highlight that pupil dilation reflects information-theoretic processes in a complex, context-dependent manner.

Strengths:

This study provides a novel and convincing contribution by linking pupil dilation to informationtheoretic measures, such as KL divergence, supporting Zénon's hypothesis that pupil responses reflect information gained during learning. The robust methodology, including two independent datasets with distinct entropy dynamics, enhances the reliability and generalisability of the findings. By carefully analysing early and late time windows, the authors capture the temporal dynamics of prediction error signals, offering new insights into the timing of model updates. The use of an ideal learner model to quantify prediction errors, surprise, and entropy provides a principled framework for understanding the computational processes underlying pupil responses. Furthermore, the study highlights the critical role of task context - specifically increasing versus decreasing entropy - in shaping the directionality and magnitude of these effects, revealing the adaptability of predictive processing mechanisms.

Weaknesses:

While this study offers important insights, several limitations remain. The two tasks differ significantly in design (e.g., sensory modality and learning type), complicating direct comparisons and limiting the interpretation of differences in pupil dynamics. Importantly, the apparent context-dependent reversal between pupil constriction and dilation in response to feedback raises concerns about how these opposing effects might confound the observed correlations with KL divergence. 

We agree with the reviewer’s concerns and acknowledge that the speculation concerning the directional effect of entropy across trials can not be fully substantiated by the current study. As the reviewer points out, the directional relationship between pupil dilation and information gain must be due to other factors, for instance, the sensory modality, learning type, or the reversal between pupil constriction and dilation across the two tasks. Also, we would like to note that ongoing experiments in our lab already contradict our original speculation. In line with the reviewer’s point, we noted these differences in the section on “Limitations and future research” in the Discussion. To better align the manuscript with the above mentioned points, we have made several changes in the Abstract, Introduction and Discussion summarized below: 

We have removed the following text from the Abstract and Introduction: “…, specifically related to increasing or decreasing average uncertainty (entropy) across trials.”

We have edited the following text in the Introduction (changes in italics) (p. 5):

“We analyzed two independent datasets featuring distinct associative learning paradigms, one characterized by increasing entropy and the other by decreasing entropy as the tasks progressed. By examining these different tasks, we aimed to identify commonalities (if any) in the results across varying contexts. Additionally, the contrasting directions of entropy in the two tasks enabled us to disentangle the correlation between stimulus-pair frequency and information gain in the postfeedback pupil response.

We have removed the following text from the Discussion:

“…and information gain in fact seems to be driven by increased uncertainty.”

“We speculate that this difference in the direction of scaling between information gain and the pupil response may depend on whether entropy was increasing or decreasing across trials.” 

“…which could explain the opposite direction of the relationship between pupil dilation and information gain”

“… and seems to relate to the direction of the entropy as learning progresses (i.e., either increasing or decreasing average uncertainty).” 

We have edited the following texts in the Discussion (changes in italics):

“For the first time, we show that the direction of the relationship between postfeedback pupil dilation and information gain (defined as KL divergence) was context dependent.” (p. 29):

Finally, we have added the following correction to the Discussion (p. 30):

“Although it is tempting to speculate that the direction of the relationship between pupil dilation and information gain may be due to either increasing or decreasing entropy as the task progressed, we must refrain from this conclusion. We note that the two tasks differ substantially in terms of design with other confounding variables and therefore cannot be directly compared to one another. We expand on these limitations in the section below (see Limitations and future research).”

Finally, subjective factors such as participants' confidence and internal belief states were not measured, despite their potential influence on prediction errors and pupil responses.

Thank you for the thoughtful comment. We agree with the reviewer that subjective factors, such as participants' confidence, can be important in understanding prediction errors and pupil responses. As per the reviewer’s point, we have included the following limitation in the Discussion (p. 33): 

“Finally, while we acknowledge the potential relevance of subjective factors, such as the participants’ overt confidence reports, in understanding prediction errors and pupil responses, the current study focused on the more objective, model-driven measure of information-theoretic variables. This approach aligns with our use of the ideal learner model, which estimates information-theoretic variables while being agnostic about the observer's subjective experience itself. Future research is needed to explore the relationship between information-gain signals in pupil dilation and the observer’s reported experience of or awareness about confidence in their decisions.” 

Reviewer #2 (Public review):

Summary:

The authors proposed that variability in post-feedback pupillary responses during the associative learning tasks can be explained by information gain, which is measured as KL divergence. They analysed pupil responses in a later time window (2.5s-3s after feedback onset) and correlated them with information-theory-based estimates from an ideal learner model (i.e., information gain-KL divergence, surprise-subjective probability, and entropy-average uncertainty) in two different associative decision-making tasks.

Strength:

The exploration of task-evoked pupil dynamics beyond the immediate response/feedback period and then associating them with model estimates was interesting and inspiring. This offered a new perspective on the relationship between pupil dilation and information processing.

Weakness:

However, disentangling these later effects from noise needs caution. Noise in pupillometry can arise from variations in stimuli and task engagement, as well as artefacts from earlier pupil dynamics. The increasing variance in the time series of pupillary responses (e.g., as shown in Figure 2D) highlights this concern.

It's also unclear what this complicated association between information gain and pupil dynamics actually means. The complexity of the two different tasks reported made the interpretation more difficult in the present manuscript.

We share the reviewer’s concerns. To make this point come across more clearly, we have added the following text to the Introduction (p. 5):

“The current study was motivated by Zenon’s hypothesis concerning the relationship between pupil dilation and information gain, particularly in light of the varying sources of signal and noise introduced by task context and pupil dynamics. By demonstrating how task context can influence which signals are reflected in pupil dilation, and highlighting the importance of considering their temporal dynamics, we aim to promote a more nuanced and model-driven approach to cognitive research using pupillometry.”

Reviewer #3 (Public review):

Summary:

This study examines prediction errors, information gain (Kullback-Leibler [KL] divergence), and uncertainty (entropy) from an information-theory perspective using two experimental tasks and pupillometry. The authors aim to test a theoretical proposal by Zénon (2019) that the pupil response reflects information gain (KL divergence). In particular, the study defines the prediction error in terms of KL divergence and speculates that changes in pupil size associated with KL divergence depend on entropy. Moreover, the authors examine the temporal characteristics of pupil correlates of prediction errors, which differed considerably across previous studies that employed different experimental paradigms. In my opinion, the study does not achieve these aims due to several methodological and theoretical issues.

Strengths:

(1)  Use of an established Bayesian model to compute KL divergence and entropy.

(2)  Pupillometry data preprocessing, including deconvolution.

Weaknesses:

(1) Definition of the prediction error in terms of KL divergence:

I'm concerned about the authors' theoretical assumption that the prediction error is defined in terms of KL divergence. The authors primarily refer to a review article by Zénon (2019): "Eye pupil signals information gain". It is my understanding that Zénon argues that KL divergence quantifies the update of a belief, not the prediction error: "In short, updates of the brain's internal model, quantified formally as the Kullback-Leibler (KL) divergence between prior and posterior beliefs, would be the common denominator to all these instances of pupillary dilation to cognition." (Zénon, 2019).

From my perspective, the update differs from the prediction error. Prediction error refers to the difference between outcome and expectation, while update refers to the difference between the prior and the posterior. The prediction error can drive the update, but the update is typically smaller, for example, because the prediction error is weighted by the learning rate to compute the update. My interpretation of Zénon (2019) is that they explicitly argue that KL divergence defines the update in terms of the described difference between prior and posterior, not the prediction error.

The authors also cite a few other papers, including Friston (2010), where I also could not find a definition of the prediction error in terms of KL divergence. For example [KL divergence:] "A non-commutative measure of the non-negative difference between two probability distributions." Similarly, Friston (2010) states: Bayesian Surprise - "A measure of salience based on the Kullback-Leibler divergence between the recognition density (which encodes posterior beliefs) and the prior density. It measures the information that can be recognized in the data." Finally, also in O'Reilly (2013), KL divergence is used to define the update of the internal model, not the prediction error.

The authors seem to mix up this common definition of the model update in terms of KL divergence and their definition of prediction error along the same lines. For example, on page 4: "KL divergence is a measure of the difference between two probability distributions. In the context of predictive processing, KL divergence can be used to quantify the mismatch between the probability distributions corresponding to the brain's expectations about incoming sensory input and the actual sensory input received, in other words, the prediction error (Friston, 2010; Spratling, 2017)."

Similarly (page 23): "In the current study, we investigated whether the pupil's response to decision outcome (i.e., feedback) in the context of associative learning reflects a prediction error as defined by KL divergence."

This is problematic because the results might actually have limited implications for the authors' main perspective (i.e., that the pupil encodes prediction errors) and could be better interpreted in terms of model updating. In my opinion, there are two potential ways to deal with this issue:

(a) Cite work that unambiguously supports the perspective that it is reasonable to define the prediction error in terms of KL divergence and that this has a link to pupillometry. In this case, it would be necessary to clearly explain the definition of the prediction error in terms of KL divergence and dissociate it from the definition in terms of model updating.

(b) If there is no prior work supporting the authors' current perspective on the prediction error, it might be necessary to revise the entire paper substantially and focus on the definition in terms of model updating.

We thank the reviewer for pointy out these inconsistencies in the manuscript and appreciate their suggestions for improvement. We take approach (a) recommended by the reviewer, and provide our reasoning as to why prediction error signals in pupil dilation are expected to correlate with information gain (defined as the KL divergence between posterior and prior belief distributions). This can be found in a new section in the introduction, copied here for convenience (p. 3-4):

“We reasoned that the link between prediction error signals and information gain in pupil dilation is through precision-weighting. Precision refers to the amount of uncertainty (inverse variance) of both the prior belief and sensory input in the prediction error signals [6,64–67]. More precise prediction errors receive more weighting, and therefore, have greater influence on model updating processes. The precisionweighting of prediction error signals may provide a mechanism for distinguishing between known and unknown sources of uncertainty, related to the inherent stochastic nature of a signal versus insufficient information of the part of the observer, respectively [65,67,68]. In Bayesian frameworks, information gain is fundamentally linked to prediction error, modulated by precision [65,66,69–75]. In non-hierarchical Bayesian models, information gain can be derived as a function of prediction errors and the precision of the prior and likelihood distributions, a relationship that can be approximately linear [70]. In hierarchical Bayesian inference, the update in beliefs (posterior mean changes) at each level is proportional to the precision-weighted prediction error; this update encodes the information gained from new observations [65,66,69,71,72]. Neuromodulatory arousal systems are well-situated to act as precision-weighting mechanisms in line with predictive processing frameworks [76,77]. Empirical evidence suggests that neuromodulatory systems broadcast precisionweighted prediction errors to cortical regions [11,59,66,78]. Therefore, the hypothesis that feedback-locked pupil dilation reflects a prediction error signal is similarly in line with Zenon’s main claim that pupil dilation generally reflects information gain, through precision-weighting of the prediction error. We expected a prediction error signal in pupil dilation to be proportional to the information gain.”

We have referenced previous work that has linked prediction error and information gain directly (p. 4): “The KL divergence between posterior and prior belief distributions has been previously considered to be a proxy of (precision-weighted) prediction errors [68,72].”

We have taken the following steps to remedy this error of equating “prediction error” directly with the information gain.

First, we have replaced “KL divergence” with “information gain” whenever possible throughout the manuscript for greater clarity. 

Second, we have edited the section in the introduction defining information gain substantially (p. 4): 

“Information gain can be operationalized within information theory as the KullbackLeibler (KL) divergence between the posterior and prior belief distributions of a Bayesian observer, representing a formalized quantity that is used to update internal models [29,79,80]. Itti and Baldi (2005)81 termed the KL divergence between posterior and prior belief distributions as “Bayesian surprise” and showed a link to the allocation of attention. The KL divergence between posterior and prior belief distributions has been previously considered to be a proxy of (precision-weighted) prediction errors[68,72]. According to Zénon’s hypothesis, if pupil dilation reflects information gain during the observation of an outcome event, such as feedback on decision accuracy, then pupil size will be expected to increase in proportion to how much novel sensory evidence is used to update current beliefs [29,63]. ” 

Finally, we have made several minor textual edits to the Abstract and main text wherever possible to further clarify the proposed relationship between prediction errors and information gain.

(2) Operationalization of prediction errors based on frequency, accuracy, and their interaction:

The authors also rely on a more model-agnostic definition of the prediction error in terms of stimulus frequency ("unsigned prediction error"), accuracy, and their interaction ("signed prediction error"). While I see the point here, I would argue that this approach offers a simple approximation to the prediction error, but it is possible that factors like difficulty and effort can influence the pupil signal at the same time, which the current approach does not take into account. I recommend computing prediction errors (defined in terms of the difference between outcome and expectation) based on a simple reinforcement-learning model and analyzing the data using a pupillometry regression model in which nuisance regressors are controlled, and results are corrected for multiple comparisons.

We agree with the reviewer’s suggestion that alternatively modeling the data in a reinforcement learning paradigm would be fruitful. We adopted the ideal learner model as we were primarily focused on Information Theory, stemming from our aim to test Zenon’s hypothesis that information gain drives pupil dilation. However, we agree with the reviewer that it is worthwhile to pursue different modeling approaches in future work. We have now included a complementary linear mixed model analysis in which we controlled for the effects of the information-theoretic variables on one another, while also including the nuisance regressors of pre-feedback baseline pupil dilation and reaction times (explained in more detail below in our response to your point #4). Results including correction for multiple comparisons was reported for all pupil time course data as detailed in Methods section 2.5. 

(3) The link between model-based (KL divergence) and model-agnostic (frequency- and accuracy-based) prediction errors:

I was expecting a validation analysis showing that KL divergence and model-agnostic prediction errors are correlated (in the behavioral data). This would be useful to validate the theoretical assumptions empirically.

The model limitations and the operalization of prediction error in terms of post-feedback processing do not seem to allow for a comparison of information gain and model-agnostic prediction errors in the behavioral data for the following reasons. First, the simple ideal learner model used here is not a generative model, and therefore, cannot replicate or simulate the participants responses (see also our response to your point #6 “model validation” below). Second, the behavioral dependent variables obtained are accuracy and reaction times, which both occur before feedback presentation. While accuracy and reaction times can serve as a marker of the participant’s (statistical) confidence/uncertainty following the decision interval, these behavioral measures cannot provide access to post-feedback information processing. The pupil dilation is of interest to us because the peripheral arousal system is able to provide a marker of post-feedback processing. Through the analysis presented in Figure 3, we indeed aimed to make the comparison of the model-based information gain to the model-agnostic prediction errors via the proxy variable of post-feedback pupil dilation instead of behavioral variables. To bridge the gap between the “behaviorally agnostic” model parameters and the actual performance of the participants, we examined the relationship between the model-based information gain and the post-feedback pupil dilation separately for error and correct trials as shown in Figure 3D-F & Figure 3J-L. We hope this addresses the reviewers concern and apologize in case we did not understand the reviewers suggestion here.

(4) Model-based analyses of pupil data:

I'm concerned about the authors' model-based analyses of the pupil data. The current approach is to simply compute a correlation for each model term separately (i.e., KL divergence, surprise, entropy). While the authors do show low correlations between these terms, single correlational analyses do not allow them to control for additional variables like outcome valence, prediction error (defined in terms of the difference between outcome and expectation), and additional nuisance variables like reaction time, as well as x and y coordinates of gaze.

Moreover, including entropy and KL divergence in the same regression model could, at least within each task, provide some insights into whether the pupil response to KL divergence depends on entropy. This could be achieved by including an interaction term between KL divergence and entropy in the model.

In line with the reviewer’s suggestions, we have included a complementary linear mixed model analysis in which we controlled for the effects of the information-theoretic variables on one another, while also including the nuisance regressors of pre-feedback baseline pupil dilation and reaction times. We compared the performance of two models on the post-feedback pupil dilation in each time window of interest: Modle 1 had no interaction between information gain and entropy and Model 2 included an interaction term as suggested. We did not include the x- and y- coordinates of gaze in the mixed linear model analysis, as there are multiple values of these coordinates per trial. Furthermore, regressing out the x and y- coordinates of gaze can potentially remove signal of interest in the pupil dilation data in addition to the gaze-related confounds and we did not measure absolute pupil size (Mathôt, Melmi & Castet, 2015; Hayes & Petrov, 2015). We present more sanity checks on the pre-processing pipeline as recommended by Reviewer 1.  

This new analysis resulted in several additions to the Methods (see Section 2.5) and Results. In sum, we found that including an interaction term for information gain and entropy did not lead to better model fits, but sometimes lead to significantly worse fits. Overall, the results of the linear mixed model corroborated the “simple” correlation analysis across the pupil time course while accounting for the relationship to the pre-feedback baseline pupil and preceeding reaction time differences. There was only one difference to note between the correlation and linear mixed modeling analyses: for the error trials in the cue-target 2AFC task, including entropy in the model accounted for the variance previously explained by surprise.

(5) Major differences between experimental tasks:

More generally, I'm not convinced that the authors' conclusion that the pupil response to KL divergence depends on entropy is sufficiently supported by the current design. The two tasks differ on different levels (stimuli, contingencies, when learning takes place), not just in terms of entropy. In my opinion, it would be necessary to rely on a common task with two conditions that differ primarily in terms of entropy while controlling for other potentially confounding factors. I'm afraid that seemingly minor task details can dramatically change pupil responses. The positive/negative difference in the correlation with KL divergence that the authors interpret to be driven by entropy may depend on another potentially confounding factor currently not controlled.

We agree with the reviewer’s concerns and acknowledge that the speculation concerning the directional effect of entropy across trials can not be fully substantiated by the currect study. We note that Review #1 had a similar concern. Our response to Reviewer #1 addresses this concern of Reviewer #3 as well. To better align the manuscript with the above mentioned points, we have made several changes that are detailed in our response to Reviewer #1’s public review (above). 

(6) Model validation:

My impression is that the ideal learner model should work well in this case. However, the authors don't directly compare model behavior to participant behavior ("posterior predictive checks") to validate the model. Therefore, it is currently unclear if the model-derived terms like KL divergence and entropy provide reasonable estimates for the participant data.

Based on our understanding, posterior predictive checks are used to assess the goodness of fit between generated (or simulated) data and observed data. Given that the “simple” ideal learner model employed in the current study is not a generative model, a posterior predictive check would not apply here (Gelman, Carlin, Stern, Dunson, Vehtari, & Rubin (2013). The ideal learner model is unable to simulate or replicate the participants’ responses and behaviors such as accuracy and reaction times; it simply computes the probability of seeing each stimulus type at each trial based on the prior distribution and the exact trial order of the stimuli presented to each participant. The model’s probabilities are computed directly from a Dirichlet distribution of values that represent the number of occurences of each stimulus-pair type for each task. The information-theoretic variables are then directly computed from these probabilities using standard formulas. The exact formulas used in the ideal learner model can be found in section 2.4.

We have now included a complementary linear mixed model analysis which also provides insight into the amount of explained variance of these information-theoretic predictors on the post-feedback pupil response, while also including the pre-feedback baseline pupil and reaction time differences (see section 3.3, Tables 3 & 4). The R² values ranged from 0.16 – 0.50 across all conditions tested.

(7) Discussion:

The authors interpret the directional effect of the pupil response w.r.t. KL divergence in terms of differences in entropy. However, I did not find a normative/computational explanation supporting this interpretation. Why should the pupil (or the central arousal system) respond differently to KL divergence depending on differences in entropy?

The current suggestion (page 24) that might go in this direction is that pupil responses are driven by uncertainty (entropy) rather than learning (quoting O'Reilly et al. (2013)). However, this might be inconsistent with the authors' overarching perspective based on Zénon (2019) stating that pupil responses reflect updating, which seems to imply learning, in my opinion. To go beyond the suggestion that the relationship between KL divergence and pupil size "needs more context" than previously assumed, I would recommend a deeper discussion of the computational underpinnings of the result.

Since we have removed the original speculative conclusion from the manuscript, we will refrain from discussing the computational underpinnings of a potential mechanism. To note as mentioned above, we have preliminary data from our own lab that contradicts our original hypothesis about the relationship between entropy and information gain on the post-feedback pupil response. 

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Apart from the points raised in the public review above, I'd like to use the opportunity here to provide a more detailed review of potential issues, questions, and queries I have:

(1) Constriction vs. Dilation Effects:

The study observes a context-dependent relationship between KL divergence and pupil responses, where pupil dilation and constriction appear to exhibit opposing effects. However, this phenomenon raises a critical concern: Could the initial pupil constriction to visual stimuli (e.g., in the cue-target task) confound correlations with KL divergence? This potential confound warrants further clarification or control analyses to ensure that the observed effects genuinely reflect prediction error signals and are not merely a result of low-level stimulus-driven responses.

We agree with the reviewers concern and have added the following information to the limitations section in the Discussion (changes in italics below; p. 32-33).

“First, the two associative learning paradigms differed in many ways and were not directly comparable. For instance, the shape of the mean pupil response function differed across the two tasks in accordance with a visual or auditory feedback stimulus (compare Supplementary Figure 3A with Supplementary Figure 3D), and it is unclear whether these overall response differences contributed to any differences obtained between task conditions within each task. We are unable to rule out whether so-called “low level” effects such as the initial constriction to visual stimuli in the cue-target 2AFC task as compared with the dilation in response auditory stimuli in letter-color 2AFC task could confound correlations with information gain. Future work should strive to disentangle how the specific aspects of the associative learning paradigms relate to prediction errors in pupil dilation by systematically manipulating design elements within each task.”

Here, I also was curious about Supplementary Figure 1, showing 'no difference' between the two tones (indicating 'error' or 'correct'). Was this the case for FDR-corrected or uncorrected cluster statistics? Especially since the main results also showed sig. differences only for uncorrected cluster statistics (Figure 2), but were n.s. for FDR corrected. I.e. can we be sure to rule out a confound of the tones here after all?

As per the reviewer’s suggestion, we verified that there were also no significant clusters after feedback onset before applying the correction for multiple comparisons. We have added this information to Supplemenatary section 1.2 as follows: 

“Results showed that the auditory tone dilated pupils on average (Supplementary Figure 1C). Crucially, however, the two tones did not differ from one another in either of the time windows of interest (Supplementary Figure 1D; no significant time points after feedback onset were obtained either before or after correcting for multiple comparisons using cluster-based permutation methods; see Section 2.5.” 

Supplementary Figure 1 is showing effects cluster-corrected for multiple comparisons using cluster-based permutation tests from the MNE software package in Python (see Methods section 2.5). We have clarified that the cluster-correction was based on permutation testing in the figure legend. 

(2) Participant-Specific Priors:

The ideal learner models do not account for individualised priors, assuming homogeneous learning behaviour across participants. Could incorporating participant-specific priors better reflect variability in how individuals update their beliefs during associative learning?

We have clarified in the Methods (see section 2.4) that the ideal learner models did account for participant-specific stimuli including participant-specific priors in the letter-color 2AFC task. We have added the following texts: 

“We also note that while the ideal learner model for the cue-target 2AFC task used a uniform (flat) prior distribution for all participants, the model parameters were based on the participant-specific cue-target counterbalancing conditions and randomized trial order.” (p. 13)

“The prior distributions used for the letter-color 2AFC task were estimated from the randomized letter-color pairs and randomized trial order presentation in the preceding odd-ball task; this resulted in participant-specific prior distributions for the ideal learner model of the letter-color 2AFC task. The model parameters were likewise estimated from the (participant-specific) randomized trial order presented in the letter-color 2AFC task.” (p. 13)

(3) Trial-by-Trial Variability:

The analysis does not account for random effects or inter-trial variability using mixed-effects models. Including such models could provide a more robust statistical framework and ensure the observed relationships are not influenced by unaccounted participant- or trial-specific factors.

We have included a complementary linear mixed model analysis in which “subject” was modeled as a random effect on the post-feedback pupil response in each time window of interest and for each task. Across all trials, the results of the linear mixed model corroborated the “simple” correlation analysis across the pupil time course while accounting for the relationship to the prefeedback baseline pupil and preceeding reaction time differences (see section 3.3, Tables 3 & 4).

(4) Preprocessing/Analysis choices:

Before anything else, I'd like to highlight the authors' effort in providing public code (and data) in a very readable and detailed format!

We appreciate the compliment - thank you for taking the time to look at the data and code provided.

I found the idea of regressing the effect of Blinks/Saccades on the pupil trace intriguing. However, I miss a complete picture here to understand how well this actually worked, especially since it seems to be performed on already interpolated data. My main points here are:

(4.1) Why is the deconvolution performed on already interpolated data and not on 'raw' data where there are actually peaks of information to fit?

To our understanding, at least one critical reason for interpolating the data before proceeding with the deconvolution analysis is that the raw data contain many missing values (i.e., NaNs) due to the presence of blinks. Interpolating over the missing data first ensures that there are valid numerical elements in the linear algebra equations. We refer the reviewer to the methods detailed in Knapen et al. (2016) for more details on this pre-processing method. 

(4.2) What is the model fit (e.g. R-squared)? If this was a poor fit for the regressors in the first place, can we trust the residuals (i.e. clean pupil trace)? Is it possible to plot the same Pupil trace of Figure 1D with a) the 'raw' pupil time-series, b) after interpolation only (both of course also mean-centered for comparison), on top of the residuals after deconvolution (already presented), so we can be sure that this is not driving the effects in a 'bad' way? I'd just like to make sure that this approach did not lead to artefacts in the residuals rather than removing them.

We thank the reviewer for this suggestion. In the Supplementary Materials, we have included a new figure (Supplementary Figure 2, copied below for convience), which illustrates the same conditions as in Figure 1D and Figure 2D, with 1) the raw data, and 2) the interpolated data before the nuisance regression. Both the raw data and interpolated data have been band-pass filtered as was done in the original pre-processing pipeline and converted to percent signal change. These figures can be compared directly to Figure 1D and Figure 2D, for the two tasks, respectively. 

Of note is that the raw data seem to be dominated by responses to blinks (and/or saccades). Crucially, the pattern of results remains overall unchaged between the interpolated-only and fully pre-processed version of the data for both tasks. 

In the Supplementary Materials (see Supplementary section 2), we have added the descriptives of the model fits from the deconvolution method. Model fits (R²) for the nuisance regression were generally low: cue-target 2AFC task, M = 0.03, SD = 0.02, range = [0.00, 0.07]; letter-color visual 2AFC, M = 0.08, SD = 0.04, range = [0.02, 0.16].

Furthermore, a Pearson correlation analysis between the interpolated and fully pre-processed data within the time windows of interest for both task indicated high correspondence: 

Cue-target 2AFC task

Early time window: M = 0.99, SD = 0.01, range = [0.955, 1.000]

Late time window: M = 0.99, SD = 0.01, range = [0.971, 1.000]

Letter-color visual 2AFC

Early time window: M = 0.95, SD = 0.04, range = [0.803, 0.998]

Late time window: M = 0.97, SD = 0.02, range = [0.908, 0.999]

In hindsight, including the deconvolution (nuisance regression) method may not have changed the pattern of results much. However, the decision to include this deconvolution method was not data-driven; instead, it was based on the literature establishing the importance of removing variance (up to 5 s) of these blinks and saccades from cognitive effects of interest in pupil dilation (Knapen et al., 2016). 

(4.3) Since this should also lead to predicted time series for the nuisance-regressors, can we see a similar effect (of what is reported for the pupil dilation) based on the blink/saccade traces of a) their predicted time series based on the deconvolution, which could indicate a problem with the interpretation of the pupil dilation effects, and b) the 'raw' blink/saccade events from the eye-tracker? I understand that this is a very exhaustive analysis so I would actually just be interested here in an averaged time-course / blink&saccade frequency of the same time-window in Figure 1D to complement the PD analysis as a sanity check.

Also included in the Supplementary Figure 2 is the data averaged as in Figure 1D and Figure 2D for the raw data and nuisance-predictor time courses (please refer to the bottom row of the sub-plots). No pattern was observed in either the raw data or the nuisance predictors as was shown in the residual time courses. 

(4.4) How many samples were removed from the time series due to blinks/saccades in the first place? 150ms for both events in both directions is quite a long bit of time so I wonder how much 'original' information of the pupil was actually left in the time windows of interest that were used for subsequent interpretations.

We thank the reviewer for bringing this issue to our attention. The size of the interpolation window was based on previous literature, indicating a range of 100-200 ms as acceptable (Urai et al., 2017; Knapen et al., 2016; Winn et al., 2018). The ratio of interpolated-to-original data (across the entire trial) varied greatly between participants and between trials: cue-target 2AFC task, M = 0.262, SD = 0.242, range = [0,1]; letter-color 2AFC task, M = 0.194, SD = 0.199, range = [0,1]. 

We have now included a conservative analysis in which only trials with more than half (threshold = 60%) of original data are included in the analyses. Crucially, we still observe the same pattern of effects as when all data are considered across both tasks (compare the second to last row in the Supplementary Figure 2 to Figure 1D and Figure 2D).

(4.5) Was the baseline correction performed on the percentage change unit?

Yes, the baseline correction was performed on the pupil timeseries after converting to percentsignal change. We have added that information to the Methods (section 2.3).

(4.6) What metric was used to define events in the derivative as 'peaks'? I assume some sort of threshold? How was this chosen?

The threshold was chosen in a data-driven manner and was kept consistent across both tasks. The following details have been added to the Methods:

“The size of the interpolation window preceding nuisance events was based on previous literature [13,39,99]. After interpolation based on data-markers and/or missing values, remaining blinks and saccades were estimated by testing the first derivative of the pupil dilation time series against a threshold rate of change. The threshold for identifying peaks in the temporal derivative is data-driven, partially based on past work[10,14,33]. The output of each participant’s pre-processing pipeline was checked visually. Once an appropriate threshold was established at the group level, it remained the same for all participants (minimum peak height of 10 units).” (p. 8 & 11).

(5) Multicollinearity Between Variables:

Lastly, the authors state on page 13: "Furthermore, it is expected that these explanatory variables will be correlated with one another. For this reason, we did not adopt a multiple regression approach to test the relationship between the information-theoretic variables and pupil response in a single model". However, the very purpose of multiple regression is to account for and disentangle the contributions of correlated predictors, no? I might have missed something here.

We apologize for the ambiguity of our explanation in the Methods section. We originally sought to assess the overall relationship between the post-feedback response and information gain (primarily), but also surprise and entropy. Our reasoning was that these variables are often investigated in isolation across different experiments (i.e., only investigating Shannon surprise), and we would like to know what the pattern of results would look like when comparing a single information-theoretic variable to the pupil response (one-by-one). We assumed that including additional explanatory variables (that we expected to show some degree of collinearity with each other) in a regression model would affect variance attributed to them as compared with the one-on-one relationships observed with the pupil response (Morrissey & Ruxton 2018). We also acknowledge the value of a multiple regression approach on our data. Based on the suggestions by the reviewers we have included a complementary linear mixed model analysis in which we controlled for the effects of the information-theoretic variables on one another, while also including the nuisance regressors of pre-feedback baseline pupil dilation and reaction times.  

This new analysis resulted in several additions to the Methods (see Section 2.5) and Results (see Tables 3 and 4). Overall, the results of the linear mixed model corroborated the “simple” correlation analysis across the pupil time course while accounting for the relationship to the prefeedback baseline pupil and preceeding reaction time differences. There was only one difference to note between the correlation and linear mixed modeling analyses: for the error trials in the cue-target 2AFC task, including entropy in the model accounted for the variance previously explained by surprise. 

Reviewer #2 (Recommendations for the authors):

(1) Given the inherent temporal dependencies in pupil dynamics, characterising later pupil responses as independent of earlier ones in a three-way repeated measures ANOVA may not be appropriate. A more suitable approach might involve incorporating the earlier pupil response as a covariate in the model.

We thank the reviewer for bringing this issue to our attention. From our understanding, a repeated-measures ANOVA with factor “time window” would be appropriate in the current context for the following reasons. First, autocorrelation (closely tied to sphericity) is generally not considered a problem when only two timepoints are compared from time series data (Field, 2013; Tabachnick & Fidell, 2019). Second, the repeated-measures component of the ANOVA takes the correlated variance between time points into account in the statistical inference. Finally, as a complementary analysis, we present the results testing the interaction between the frequency and accuracy conditions across the full time courses (see Figures 1D and 2D); in these pupil time courses, any difference between the early and late time windows can be judged by the reader visually and qualitatively. 

(2) Please clarify the correlations between KL divergence, surprise, entropy, and pupil response time series. Specifically, state whether these correlations account for the interrelationships between these information-theoretic measures. Given their strong correlations, partialing out these effects is crucial for accurate interpretation.

As mentioned above, based on the suggestions by the reviewers we have included a complementary linear mixed model analysis in which we controlled for the effects of the information-theoretic variables on one another, while also including the nuisance regressors of pre-feedback baseline pupil dilation and reaction times.  

This new analysis resulted in several additions to the Methods (see Section 2.5) and Results (see Tables 3 and 4). Overall, the results of the linear mixed model corroborated the “simple” correlation analysis across the pupil time course while accounting for the relationship to the prefeedback baseline pupil and preceeding reaction time differences. There was only one difference to note between the correlation and linear mixed modeling analyses: for the error trials in the cue-target 2AFC task, including entropy in the model accounted for the variance previously explained by surprise. 

(3) The effects observed in the late time windows appear weak (e.g., Figure 2E vs. 2F, and the generally low correlation coefficients in Figure 3). Please elaborate on the reliability and potential implications of these findings.

We have now included a complementary linear mixed model analysis which also provides insight into the amount of explained variance of these information-theoretic predictors on the post-feedback pupil response, while also including the pre-feedback baseline pupil and reaction time differences (see section 3.3, Tables 3 & 4). The R² values ranged from 0.16 – 0.50 across all conditions tested. Including the pre-feedback baseline pupil dilation as a predictor in the linear mixed model analysis consistently led to more explained variance in the post-feedback pupil response, as expected.  

(4) In Figure 3 (C-J), please clarify how the trial-by-trial correlations were computed (averaged across trials or subjects). Also, specify how the standard error of the mean (SEM) was calculated (using the number of participants or trials).

The trial-by-trial correlations between the pupil signal and model parameters were computed for each participant, then the coefficients were averaged across participants for statistical inference. We have added several clarifications in the text (see section 2.5 and legends of Figure 3 and Supplementary Figure 4).

We have added “the standard error of the mean across participants” to all figure labels.

(5) For all time axes (e.g., Figure 2D), please label the ticks at 0, 0.5, 1, 1.5, 2, 2.5, and 3 seconds. Clearly indicate the duration of the feedback on the time axes. This is particularly important for interpreting the pupil dilation responses evoked by auditory feedback.

We have labeled the x-ticks every 0.5 seconds in all figures and indicated the duration of the auditory feedback in the letter-color decision task and as well as the stimuli presented in the control tasks in the Supplementary Materials. 

Reviewer #3 (Recommendations for the authors):

(1) Introduction page 3: "In information theory, information gain quantifies the reduction of uncertainty about a random variable given the knowledge of another variable. In other words, information gain measures how much knowing about one variable improves the prediction or understanding of another variable."

(2) In my opinion, the description of information gain can be clarified. Currently, it is not very concrete and quite abstract. I would recommend explaining it in the context of belief updating.

We have removed these unclear statements in the Introduction. We now clearly state the following:

“Information gain can be operationalized within information theory as the KullbackLeibler (KL) divergence between the posterior and prior belief distributions of a Bayesian observer, representing a formalized quantity that is used to update internal models [29,79,80].” (p. 4)

(3) Page 4: The inconsistencies across studies are described in extreme detail. I recommend shortening this part and summarizing the inconsistencies instead of listing all of the findings separately.

As per the reviewer’s recommendation, we have shortened this part of the introduction to summarize the inconsistencies in a more concise manner as follows: 

“Previous studies have shown different temporal response dynamics of prediction error signals in pupil dilation following feedback on decision outcome: While some studies suggest that the prediction error signals arise around the peak (~1 s) of the canonical impulse response function of the pupil [11,30,41,61,62,90], other studies have shown evidence that prediction error signals (also) arise considerably later with respect to feedback on choice outcome [10,25,32,41,62]. A relatively slower prediction error signal following feedback presentation may suggest deeper cognitive processing, increased cognitive load from sustained attention or ongoing uncertainty, or that the brain is integrating multiple sources of information before updating its internal model. Taken together, the literature on prediction error signals in pupil dilation following feedback on decision outcome does not converge to produce a consistent temporal signature.” (p. 5)

We would like to note some additional minor corrections to the preprint:

We have clarified the direction of the effect in Supplementary Figure 3 with the following: 

“Participants who showed a larger mean difference between the 80% as compared with the 20% frequency conditions in accuracy also showed smaller differences (a larger mean difference in magnitude in the negative direction) in pupil responses between frequency conditions (see Supplementary Figure 4).”

The y-axis labels in Supplementary Figure 3 were incorrect and have been corrected as the following: “Pupil responses (80-20%)”.

We corrected typos, formatting and grammatical mistakes when discovered during the revision process. Some minor changes were made to improve clarity. Of course, we include a version of the manuscript with Tracked Changes as instructed for consideration.

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

Manuscript number: RC-2025-03206

Corresponding author(s____): Teresa M. Przytycka

General Statements

We thank all the reviewers for their time and their constructive criticism, based on which we have revised our manuscript. All review comments in are italics. Our responses are indicated in normal font except the excerpts from manuscript which are shown within double quote and in italics. The line numbers indicated here refer to those in the revised manuscript.

Point-by-point description of the revisions

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

This paper addresses the interesting question of how cell size may scale with organ size in different tissues. The approach is to mine data from the fly single cell atlas (FCA) which despite its name is a database of gene expression levels in single isolated nuclei. Using this data, they infer cell size based on ribosomal protein gene expression, and based on this approach infer that there are tissue and sex specific differences in scaling, some of which may be driven by differences in ribosomal protein gene expression.

Response: Indeed, using the FCA dataset, we infer sex-specific differences in both cell size and cell number, which we validated with targeted experiments. We show that Drosophila cell types scale through distinct strategies-via cell size, cell number, or a mix of both-in an allometric rather than uniform fashion. We further propose that these scaling differences are driven, at least in part, by variation in translational activity, reflected in the expression of ribosomal proteins, translation elongation factors, and Myc.

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I think the idea of mining this database is a clever one, however there a number of concerns about whether the existing data can really be used to draw the conclusions that are stated.

__Response: __We are pleased to see that the reviewer found the question and our approach interesting.

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*One concern has to do with the assumption that RP (ribosome protein) expression is a proxy for cell size. It is well established that ribosome abundance scales with cell size, but is there reason to believe that ribosome nuclear gene EXPRESSION correlates with ribosome abundance? *

I'm not saying that this can't be true, but it seems like a big assumption that needs to be justified with some data. Maybe this is well known in the Drosophila literature, but in that case the relevant literature really needs to be cited.

__Response: __To avoid any misunderstanding: we use sex-biased RP expression as an indicator of sex differences in cell size only within the same cell type or subtype, as defined by expression-based clustering in the FCA-not as a general estimator of cell size. This measure is applied strictly within the same clusters, never between different ones. To prevent overinterpretation, we replaced the term 'proxy' with 'indicator,' since the earlier wording might have implied that ribosomal gene expression was being used to estimate cell size more broadly.

We should have begun by providing more background on the well-established link between ribosomal protein gene dosage and cell growth. This context was missing from the introduction, so we have now added a full paragraph outlining what is known about this connection:

*Added at line 85: *

"Cell growth, which supports both cell enlargement and cell division, demands elevated protein synthesis, accomplished by boosting translation rates. Indeed, ribosome abundance is known to scale with cell size in many organisms (Schmoller and Skotheim 2015; Cadart and Heald 2022; Serbanescu et al. 2022). Long before it was known that DNA was the carrier of genetic information, Drosophila researchers had identified a large class of mutations known as "Minutes" (Schultz 1929). These were universally haplo-insufficient. A single wild type copy resulted in a tiny slowly growing fly, and the homozygous loss-of-function alleles were lethal. In clones, the Minute cells are clearly smaller and compete poorly with surrounding wild type cells. We now know that most of the Minute loci encode ribosomal proteins (Marygold et al. 2007). Similarly, the Drosophila diminutive locus, also characterized by small flies almost a century ago, is now known to encode the Myc oncogene (Gallant 2013). This is significant as Myc is a regulator of ribosomal protein encoding genes in metazoans, including Drosophila (Grewal et al. 2005). The ribosome is assembled in a specialized nuclear structure called the nucleolus (Ponti 2025). Across species, including Drosophila (Diegmiller et al. 2021) and C. elegans (Ma et al. 2018), nucleolar size scales with cell size and is broadly correlated with growth in cell size and/or cell number, processes that are directly relevant to sex-specific allometry. Collectively, these and many other studies offer compelling evidence that ribosomal biogenesis is positively associated with cell size and growth, underscoring the value of measuring ribosome biogenesis as a metric."

We understand that the reviewer is asking whether reduced RP mRNA expression directly leads to reduced functional ribosome assembly. We do not have a definitive answer to that specific question. However, we directly measured translation in fat body cells (section: Female bias in ribosomal gene expression in fat body cells leads to sex-biased protein synthesis), and the results show a clear correlation between RP gene expression and biosynthetic activity; even though we did not track every step from transcription to ribosome assembly to polysome loading across all cell types. This would indeed be an excellent direction for future work, including polysome profiling and related assays. Importantly, we did examine the nucleolus (Figure 4), where ribosome assembly occurs, and showed that nucleolar volume scales with RP gene expression. This strongly supports the presence of sex-specific differences in ribosome biogenesis.

Added at line 115:

"Building on the earlier studies noted above, as well as our direct measurements of translation bias in the fat body, nucleolar size, and cell size, we used sex-biased expression of ribosomal proteins as an indicator of sex differences in per-nucleus cell size."

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Second, the interpretation of RP expression as a proxy for cell size seems potentially at odds with the fact that some cells are multi-nucleate. Those cells are big because of multiple nuclei, and so they might not show any increase in ribosome expression per nucleus. presumably for multi-nucleate cells, RP expression if it reflects anything at all would be something to do with cell size PER nucleus.

Response: Yes, this is a very important point, and this is why we chose multinucleated indirect flight muscles for our direct experimental analysis. We show that in indirect flight muscle cells, adult cell size is greatly influenced by the sex-specific number of nuclei per cell. The female muscle cells are larger and have larger nuclei count per cell. Additionally, they also have higher expression of ribosomal protein coding genes. As the latter data are from the single nucleus sequencing atlas, this already demonstrates what this reviewer is asking for: per nucleus, female muscle cells express more ribosome protein coding mRNAs.

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*Third, it is well known that many tissues in Drosophila are polyploid or polytene. I don't know enough about the methodology used to produce the FCA to know whether this is somehow normalized. Otherwise, my hypothesis would be that nuclei showing higher RP expression might just be polyploid or polytene. You might say that this could be controlled by asking if all genes are similary upregulated, but that isn't the case since at least in polytene chromosomes it is well known that only a small number of genes are expressed at a given time, while many are silent. *

Response: Yes, this is an excellent point. As noted above, our study does not distinguish among the different potential causes of sex differences in ribosomal mRNA copy number, as these may vary across cell types. We now explicitly acknowledge it in the discussion (line 327). Importantly, even in the cases when ribosomal gene expression bias primarily reflects differences in DNA content, this still represents a plausible mechanistic route linking ribosomal gene expression to increased nucleolar ribosome biogenesis and, ultimately, larger cell size. This possibility does not alter our main conclusions.

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Overall, I think a lot more foundational work would need to be done in order to allow the inference of cell size from RP expression. In a way, it is a bit unfortunate that they chose to do this work in Drosophila where so many cells are polyploid, although I gather that even in humans some tissues have this issue, for example large neurons in the brain.

Response: We acknowledge that we did not clearly reference some of the foundational work in the literature. To address this, we have expanded the introduction to provide additional background and context. We also clarify that our fat body experiment offers independent support for the relationship between ribosomal gene expression bias, nuclear size bias, and corresponding biases in protein synthesis, thereby reinforcing the use of sex-specific ribosomal gene expression as an indicator of sex-specific cell size. Importantly, we assess this bias only within clusters, not between them. These clusters are derived from gene-expression-based clustering and are therefore relatively homogeneous. For example, as discussed in our response to Reviewer #3, the fat body contains several clusters that correspond to expression-defined subtypes of fat body cells. Our previous terminology may have inadvertently implied that we were using ribosomal gene expression to estimate cell size more broadly, which was not our intention.

As for the choice of the organism, most of the authors are Drosophila researchers and we benefit from the unique, highly replicated data from whole head and whole body of both sexes. Such data is necessary for a non-biased estimation of the differences in nuclear number.

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*Reviewer #1 (Significance (Required)):

The idea that gene regulatory networks could "program" differences in scaling by changing levels of ribosomal protein gene expression is a tremendously important one if it can be established, because it would show a simple way for size scaling to be placed under control of developmental regulatory pathways. My original concern when I first looked at the abstract was going to be that yeah the results are interesting but a mechanism is not provided, but as I read it, that concern went away. showing that RP gene expression, which could be programmed by various driving pathways, can affect allometric scaling, would be extremely impactful and really change how we think about scaling, but putting it into the framework of gene expression networks that control other aspects of developmewnht. it would not be necessary to show which pathways actually drive these expression differences, the fact that they are different would be interesting enough to make everyone want to read this paper. But as discussed above I am not, however, convinced by the evidence presented here. So while I think it would be very significant if true, I am not convinced that the conclusion is well supported. This doesn't mean I have a reason to think it is false, just that its not well supported for the reasons I have given.*

Response: We are grateful to the reviewer for this positive assessment of our findings despite lack of a specific mechanism. We also regret that our initial writing did not clearly situate our work within the foundational literature on the relationship between ribosomal biogenesis and scaling. The key contribution of our study is to demonstrate that sex-biased ribosomal biogenesis plays a role in allometric scaling, providing a basis for future mechanistic exploration. We hope that the revised manuscript now offers clear and compelling support for the conclusion that RP gene expression bias can influence allometric scaling.

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I hasten to point out that I could be entirely wrong, if the missing bits of logic (i.e. that RP expression matches ribosome abundance and that gene expression in the FCA dataset isn't influenced by ploidy of the nucleus). If suitable references can be provided to support these underlying assumptions, then in fact I think these concerns could be answered with very little effort. Otherwise, I think experiments would be needed to support these assumptions, and that might be non-trivial to do in a reasonable time frame. for that reason, in the next question I have put "cannot tell" for the time estimate.

Response: While gene expression in some FCA cell types may indeed be influenced by ploidy, our analysis does not depend on distinguishing among the possible sources of gene expression bias, which may vary across cell types. Rather, our key point is that-regardless of its origin-an increase in ribosomal gene expression is associated with enhanced ribosome biogenesis in the nucleolus and, ultimately, larger cell size. Thus, our main conclusions do not rely on any specific mechanism underlying RP gene expression upregulation. We now include additional references supporting the relationship between RP expression bias and cell size bias. We also strengthen the link between ribosomal gene expression and biosynthetic activity by clarifying its relationship with sex-biased Myc expression and the strong correlation with expression bias of EF1. We now include additional references supporting the relationship between RP expression bias and cell size bias. We also strengthen the link between ribosomal gene expression and biosynthetic activity by clarifying its relationship with sex-biased Myc expression and the strong correlation with expression bias of EF1.

We thank the reviewer for their thoughtful and constructive comments, which have prompted us to clarify both our reasoning and the relevant literature more fully.

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*Reviewer #2 (Evidence, reproducibility and clarity (Required)):

The authors analyzed the FlyAtlas single-nucleus dataset to identify sex differences in gene expression and cell numbers. This led them to focus on muscles, cardiomyocytes, and fat body cells. They then measured cell and nucleolus size across different tissues and showed that reducing Myc function decreases sex differences in fat body cells. Overall, the manuscript provides a characterization of dimorphic differences in cell and organ size across three tissues.*

Response: This is a nice synopsis of the work.

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Major Comments: The major claims of the manuscript are well supported by the reported experiments and analyses. While Reviewer #2 considered the major claims of the manuscript to be well supported, by the reported experiments and analysesStatistical analyses appear adequate.

Response: We agree, and we are glad that the reviewer found our work well supported.

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*Minor Comments: The following minor issues should be addressed through textual edits:In the Introduction:

"Disruptions in proportionality, whether due to undergrowth or overgrowth, can lead to reduced fitness or diseases such as cancer." Could the authors provide a reference for this statement, particularly for the claim that disruptions in proportion*

Response: We apologize for this omission. The following explanation is now included starting at line 39:

"For example, scaled cell growth is a driver of symmetry in Myc-dependent scaling of bone growth in the skeleton by chondrocyte proliferation (Ota et al. 2007; Zhou et al. 2011). Increased nucleolus size is a well known marker of cancer progression in a histopathological setting (Pianese 1896; Derenzini et al. 1998; Elhamamsy et al. 2022)."

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*The authors state:

"This study offers a comprehensive, cellular-resolution analysis of sexual size dimorphism in a model organism, uncovering how differences in cell number and size contribute to sex-specific body plans."*

The study cannot be considered comprehensive, as not all organs were examined.

Response: Indeed, "comprehensive" is a loaded word and in the revised manuscript we just omitted it.

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*The following sentence from the abstract is unclear:

"By uncovering how a conserved developmental system produces sex-specific proportions through distinct cellular strategies..."*

* What do the authors mean by a conserved developmental system? Do they refer to a commonly used developmental model, or to a developmental system that is evolutionarily conserved?*

Response: We acknowledge that the use of the word 'conserved' was inappropriate, and we have therefore removed it from the statement.

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*Reviewer #2 (Significance (Required)):

The manuscript presents a relevant exploration of sex-specific differences in cell size and cell number in Drosophila males and females. The limitations of the study are clearly acknowledged in the "Limitations" section. The work does not provide mechanistic insight into the causes or functional consequences of the observed differences. Nonetheless, the study extends our understanding of sexual dimorphism and establishes a foundation for future investigations into the autonomous and systemic mechanistic factors that regulate these differences.*

Response: Thank you.

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*Reviewer #3 (Evidence, reproducibility and clarity (Required)):

The manuscript by Pal and colleagues addresses an important question: the cellular mechanisms underlying sex differences in organ size. By leveraging single-nucleus transcriptomic data from the adult Drosophila Cell Atlas, the authors show that different cell types adopt distinct strategies to achieve sex differences in organ size-either by increasing cell size or by altering cell number. They then focus on three organs-the indirect flight muscles, the heart, and the fat body-and provide supporting evidence for their transcriptomic analyses.*

Response: This is a nice summary of the study. Thank you.

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This study tackles a highly relevant and often overlooked question, as our understanding of the molecular and cellular events driving sex differences remains incomplete. The work presents interesting observations; however, it is largely descriptive, establishing correlations without providing functional evidence or mechanistic insight.

Response: We agree that this is an often overlooked problem that has been difficult to address experimentally without single-cell genomics. Our work aims to help fill this gap. While the paper does contain descriptive elements, we believe such characterization is important at the early stages of developing a new area of inquiry. The study explores a unique dataset and includes experimental validation to support key observations. We also propose how allometry may be shaped by cell division and cell size, drawing on well-established molecular mechanisms. Thus, the reviewer's comment regarding a lack of mechanistic insight likely pertains to the absence of a direct connection to the sex-determination pathway, which is beyond the scope of the current study.

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Below are four main points that should be addressed before publication: 1. Introduction and contextualisation of prior work The introduction does not adequately present the current state of knowledge. Several key studies are missing or insufficiently discussed. In particular, the following works should be included and integrated into the introduction: - PMID: 26710087 - shows that the sex determination gene transformer regulates male-female differences in Drosophila body size. - PMID: 28064166 - describes how differences in Myc gene dosage contribute to sex differences in body size. - PMID: 26887495 - demonstrates that the intrinsic sexual identity of adult stem cells can control sex-biased organ size through sex-biased proliferation. - PMID: 28976974 - reveals that Sxl modulates body growth through both tissue-autonomous and non-autonomous mechanisms. - PMID: 39138201 - shows that transformer drives sex differences in organ size and body weight. Incorporating and discussing these references would provide a more comprehensive and up-to-date framework for the study.

Response: We agree that the literature suggested by the reviewer strengthens the introduction and improves the contextualization of prior work relevant to our study. Although much of it was previously included in the discussion section on cell-autonomous and hormonal regulation, it has now been moved to the introduction, along with the discussion of the papers suggested by the reviewer (beginning at line 58).

"In Drosophila melanogaster, adult females are substantially larger than males (Fig. 1A1), yet both sexes develop from genetically similar zygotes and share most organs and cell types. In wild type flies, sex is determined by the number of X chromosomes in embryos, with XX flies developing as females and X(Y) flies developing as males due to the activation and stable expression of Sex-lethal only in XX flies (Erickson and Quintero 2007). While it is not entirely clear how sexually dimorphic size is regulated, the sex determination pathway is implicated in size regulation. Sex-reversed flies often show a size based on the X chromosome number rather than sexual morphology. Female Sex-lethal contributes to larger female size independently of sexual identity (Cline 1984), and Sex-lethal expression in insulin producing neurons in the brain also impacts body size (Sawala and Gould 2017). Female-specific Transformer protein is produced as a consequence of female-specific Sex-lethal and also contributes to increased female size (Rideout et al. 2015). This size scaling also applies to individual organs. For example, the Drosophila female gut is longer than the male gut due Transformer activity (Hudry et al. 2016). It has also been suggested that Myc dose (it is X-linked) is a regulator of body size (Mathews et al. 2017), although the failed dosage compensation model proposed has not been demonstrated."

And again at line 74:

"These studies show that size is regulated, but they do not address whether scaling is uniform or non-uniform and the mechanism for sexual size differences (SSD). The origins of SSD can, in principle, arise from differences in (i) gene expression, (ii) the presence of sex-specific cell types, (iii) the number of cell-specific nuclei, or (iv) the size (per nucleus) of those cells. Previous research in Drosophila has largely focused on gene expression in sex-specific organs like the gonads (Arbeitman et al. 2002; Parisi et al. 2004; Graveley et al. 2011; Pal et al. 2023), which are governed by a well-characterized sex-determination pathway (Salz and Erickson 2010; Clough and Oliver 2012; Raz et al. 2023) However, whether and how scaling differences in shared, non-sex-specific tissues are achieved via changes in cell size and number remains largely unexamined (Fig. 1A2). These studies show that size is regulated, but they do not address whether scaling is uniform or non-uniform and the mechanism for size differences."

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2. Use of ribosomal gene expression as a proxy for cell size The authors use ribosomal gene expression levels as a proxy for cell size, but this assumption is not adequately justified. The cited references (refs. 20-22) focus on unicellular organisms (bacteria and yeast) or cleavage divisions in frog embryos, which are fundamentally different from adult Drosophila tissues. The authors should provide evidence that ribosome abundance scales with cell size across the distinct adult Drosophila cell types. Given that most adult fly tissues are post-mitotic, it is more likely that ribosomal gene expression reflects protein synthesis activity rather than cell size, particularly in secretory cell types.

Response: Reviewer 1 raised a similar point, and we agree. We recognize that the term "proxy" may have been misleading. We use this measure only in the context of sex bias within homogeneous cell clusters, and not between clusters, even when such clusters share the same cell-type annotation. To avoid overinterpretation, we changed "poxy" to "indicator".

In response to the reviewer's concern, we have expanded our discussion of the relevant supporting literature (additional text starting line 75). We have also directly measured translation in the fat body cells (section: Female bias in ribosomal gene expression in fat body cells leads to sex biased protein synthesis), which clearly demonstrates a correlation between ribosomal protein gene expression and biosynthetic activity. Although, we have not traced the chain of events from expression to ribosome assembly to polysome loading in all cell types, we did examine the nucleolus (Figure 4), where ribosomes are assembled, and we make a strong point that the volume of the nucleolus scales like ribosome protein gene expression. This provides strong evidence for sex-specific ribosome biogenesis contributing to cell size.

Furthermore, the observation that ribosomal gene expression likely reflects protein synthesis activity is not at odds with increased cell size: biosynthesis increases in larger cells (Schmoller and Skotheim 2015). We have added a panel to Figure 4 showing the relationship between ribosomal gene expression bias and the average expression bias of Eukaryotic Elongation Factor 1 (eEF1).

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3. Relationship between Myc and sex-biased Rp expression The proposed link between Myc and sex-biased Rp expression is unclear. Panels D and E of Figure 1 show no consistent relationship: some cell types with strong Rp sex bias exhibit either high or low female Myc bias, or even a male bias. The linear regression in Figure 4I (R = 0.07, p = 0.59) confirms the lack of correlation. The authors should clarify this point and adopt a more cautious interpretation regarding Myc as a potential regulator of sex-biased Rp expression and cell size differences. Experimentally, using Myc hypomorph or heterozygous conditions would be more appropriate than complete knockdown to test its role.

Response: Thank you for noting that the relationship between Myc expression bias and sex-biased RP expression required clarification. This response was prepared in consultation with Myc expert Dr. David Levens.

We demonstrate that both Myc and RP gene expression exhibit an overall female bias in the body. The absence of a strong correlation across cell clusters does not invalidate this conclusion. Myc is a well-established master regulator of ribosome biogenesis, but its quantitative effects are complex. According to recent models of Myc-mediated gene regulation (Nie et al. 2012; Lin et al. 2012), Myc upregulates all actively transcribed genes. Because this regulation is global, the relationship between changes in Myc expression and corresponding changes in ribosomal protein gene expression depends on cell type. Moreover, (Lorenzin et al. 2016) demonstrated that ribosomal protein genes saturate at relatively low levels of Myc, which helps explain why we observe a correlation in head cell clusters-where Myc expression is lower-but not in body clusters.

Importantly, on average, the female-specific Myc expression bias is stronger in body cell clusters than in head cell clusters, consistent with the stronger female bias in ribosomal protein gene expression observed in the head relative to the body.

To make this relationship more transparent, we combined the head and body clusters, which yielded a strong overall correlation (Fig. 4J, replacing the previous Fig. 4H).

To further strengthen the evidence linking ribosomal gene expression to cell size, we also examined the relationship between ribosomal gene expression bias and Elongation Factor 1 (eEF1) expression bias, a key component of protein biosynthesis during the elongation step of translation. The resulting correlation exceeds 0.9 (new Fig. 4H, added as an additional panel in Fig. 4).

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4. Conclusions about fat body cell number I have concerns about drawing conclusions on sex differences in fat body cell number from single-nucleus transcriptomic data for two reasons:

1- Drosophila fat body tissue is heterogeneous, comprising distinct subpopulations (e.g., visceral fat cells, subcuticular fat cells), some of which are sex-specific-such as fat cells associated with the spermathecae in females.

Response: Thank you for giving us the opportunity to clarify our analysis of the FCA data. Our approach does account for subpopulations within the fat body as well as within other cell types. Based on gene expression profiles, we identify three fat body clusters, all of which are reported in Table S3. One small female-specific cluster (

When all fat body clusters are combined into a single supercluster, this supercluster still shows a male bias. We have now clarified this point in the manuscript (line 113). Note that both subclusters of fat body are already shown in Fig. 1C and 1D.

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2- Adult fat body cells can be multinucleated (PMID: 13723227). Apparent sex differences in nucleus number may reflect differences in specific subpopulations or degrees of multinucleation rather than true differences in cell number. To strengthen the conclusions, the analysis should be performed at the level of fat body subpopulations, distinguishing clusters where possible. Additionally, quantifying nuclei relative to actual cell number-as done for muscle tissue-would clarify whether observed sex differences reflect true variation in cell number or differences in nuclear content per cell.

Response: Yes, some cells can be multinucleate. We specifically address this in the context of muscle cells, where multinucleation is prominent, and we also conducted experimental validation in this tissue. As noted above, our analysis is performed at the subpopulation level, since clusters are defined by expression similarity (Leiden resolution 4.0) rather than by annotation.

Because our work relies on single-nucleus data, each nucleus is treated as an individual unit of analysis. Nevertheless, we observe genuine nuclear differences within each cluster. Importantly, the presence of multinucleated cells does not alter our conclusions; it simply represents one form of variation in cell number that can be thought of as a subcomponent of cell/nuclei number.

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Minor corrections/points: 1-The term body size in the title does not accurately reflect the content of the paper. I recommend replacing it with organ size to better align with the study's focus.

Response: Thank you for the suggestion.

----------------------------------------------------------- 2-The term sexual size dimorphism is somewhat inaccurate in this context. Sex differences in size would be more appropriate. The term sexual dimorphism typically refers to traits that exhibit two distinct forms in males and females-such as primary or secondary sexual characteristics like sex organs or sex combs. In contrast, size is a quantitative trait that follows a normal distribution. Although the average female may be larger than the average male, the distributions overlap, making the term dimorphism imprecise.

Response: Thank you for the suggestion.

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3-In Figure 2E, there appears to be an inconsistency between the text, figure legend, and the data presented. The text and legend state that the total volume of dorsal longitudinal flight muscle cells was quantified, whereas the graph indicates measurements of nuclear size. This discrepancy should be clarified.

Response: Thank you for pointing this out. We figured out that Y-axis label in the graph was incorrect and it is now fixed.

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4-The authors proposed: "This increased biosynthetic activity in fat body cells may contribute to cell size differences, but also to the regulation of body size via production of factors that mediate body growth via interorgan communication". Please note that this hypothesis has already been tested functionally in PMID: 39138201 and was shown to be incorrect. Sex differences in body size are completely independent of fat body sexual identity or any intrinsic sex differences within fat cells.

__Response: __We thank the reviewer for the opportunity to discuss why the data shown in PMID 39138201 (Hérault et al. 2024) do not rule out a model in which the fat body contributes to the sex-specific regulation of body size via interorgan communication. The main reason data in Herault et al cannot rule out such a model is that they use wing size as a proxy for body size. This is in contrast to prior studies, such as (Rideout et al. 2015), in which pupal volume was used to directly measure body size and show a non-autonomous effect of sex determination gene transformer on body size. Measuring body size directly is a more precise readout of growth during the larval stages of development, as opposed to using adult wing area which reflects the growth of a single organ. It is also important to note that the diets used to rear flies in Herault and Rideout differ, which is an important consideration as females do not achieve their maximal size without high dietary protein levels (Millington et al. 2021). To ensure all these points are communicated to readers, we added text to this effect in the revised version of our manuscript.

Added at line 254:

"This increased biosynthetic activity in fat body cells may contribute to cell size differences, but also to the regulation of body size via production of factors that mediate body growth via interorgan communication (Colombani et al. 2003; Géminard et al. 2009; Rajan and Perrimon 2012; Sano et al. 2015; Koyama and Mirth 2016). Indeed, one study showed the sexual identity of the fat body influenced pupal volume, which is an accurate readout of larval growth (Rideout et al. 2015; Delanoue et al. 2010). While a recent study suggests that male-female differences in body size were regulated independently of fat body sexual identity (Hérault et al. 2024), this study measured the growth of a single organ, the wing, as a proxy for body size. Additional studies are therefore needed to resolve whether fat body protein synthesis plays an important role in regulating sex differences in body size."

-----------------------------------------------------------

*5-The authors state: "This demonstrate that Myc plays a key role in regulating the sex difference in nucleolar size." This is an overstatement given the functional data presented. The claim should be toned down to reflect the limited evidence.

**Referee cross-commenting**

I completely agree with the main comments of Reviewer 1, as they address the paper's core.*

Response: We have addressed the comments of Reviewer 1 in the response to reviewer's comments above.

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*Reviewer #3 (Significance (Required)):

The main novelty and strongest aspect of this study is its use of single-nucleus transcriptomic data from the adult Drosophila Cell Atlas to investigate how different cell types adopt distinct strategies to generate sex differences in organ size-either by increasing cell size or by altering cell number. Previous studies have largely focused on specific tissues, whereas this work provides a comprehensive, organism-wide view that encompasses all tissues, enabling direct cross-comparison between organs. This represents a clear advance in the field, primarily from a technical perspective, by leveraging organism-wide single-cell transcriptomics. The main limitations lie in the lack of functional experiments and mechanistic insights. Moreover, the proposed mechanism-differences in Myc gene dosage or expression levels-is not entirely novel, as Myc dosage has previously been implicated in contributing to sex differences in body size (PMID: 28064166).*

Response: We do have some functional testing in the 3 tissues, flight muscle, heart and fat body, however, providing mechanistic insights is beyond the scope of this paper. The paper suggested by the reviewer is an example of one attempt to provide such a mechanism, probably not the only one. We hope that our rich data that we have assembled in this paper provide resources for generating hypotheses and stimulate further research.

-----------------------------------------------------------

References

Cadart, Clotilde, and Rebecca Heald. 2022. "Scaling of Biosynthesis and Metabolism with Cell Size." Molecular Biology of the Cell 33 (9): pe5. https://doi.org/10.1091/mbc.E21-12-0627.

Diegmiller, Rocky, Caroline A. Doherty, Tomer Stern, Jasmin Imran Alsous, and Stanislav Y. Shvartsman. 2021. "Size Scaling in Collective Cell Growth." Development (Cambridge, England) 148 (18): dev199663. https://doi.org/10.1242/dev.199663.

Gallant, Peter. 2013. "Myc Function in Drosophila." Cold Spring Harbor Perspectives in Medicine 3 (10): a014324. https://doi.org/10.1101/cshperspect.a014324.

Grewal, Savraj S., Ling Li, Amir Orian, Robert N. Eisenman, and Bruce A. Edgar. 2005. "Myc-Dependent Regulation of Ribosomal RNA Synthesis during Drosophila Development." Nature Cell Biology 7 (3): 295-302. https://doi.org/10.1038/ncb1223.

Hérault, Chloé, Thomas Pihl, and Bruno Hudry. 2024. "Cellular Sex throughout the Organism Underlies Somatic Sexual Differentiation." Nature Communications 15 (1): 6925. https://doi.org/10.1038/s41467-024-51228-6.

Lin, Charles Y., Jakob Lovén, Peter B. Rahl, et al. 2012. "Transcriptional Amplification in Tumor Cells with Elevated C-Myc." Cell 151 (1): 56-67. https://doi.org/10.1016/j.cell.2012.08.026.

Lorenzin, Francesca, Uwe Benary, Apoorva Baluapuri, et al. 2016. "Different Promoter Affinities Account for Specificity in MYC-Dependent Gene Regulation." eLife 5 (July): e15161. https://doi.org/10.7554/eLife.15161.

Ma, Tian-Hsiang, Po-Hsiang Chen, Bertrand Chin-Ming Tan, and Szecheng J. Lo. 2018. "Size Scaling of Nucleolus in Caenorhabditis Elegans Embryos." Biomedical Journal 41 (5): 333-36. https://doi.org/10.1016/j.bj.2018.07.003.

Marygold, Steven J., John Roote, Gunter Reuter, et al. 2007. "The Ribosomal Protein Genes and Minute Loci of Drosophila Melanogaster." Genome Biology 8 (10): R216. https://doi.org/10.1186/gb-2007-8-10-r216.

Millington, Jason W., George P. Brownrigg, Charlotte Chao, et al. 2021. "Female-Biased Upregulation of Insulin Pathway Activity Mediates the Sex Difference in Drosophila Body Size Plasticity." eLife 10 (January): e58341. https://doi.org/10.7554/eLife.58341.

Nie, Zuqin, Gangqing Hu, Gang Wei, et al. 2012. "C-Myc Is a Universal Amplifier of Expressed Genes in Lymphocytes and Embryonic Stem Cells." Cell 151 (1): 68-79. https://doi.org/10.1016/j.cell.2012.08.033.

Ponti, Donatella. 2025. "The Nucleolus: A Central Hub for Ribosome Biogenesis and Cellular Regulatory Signals." International Journal of Molecular Sciences 26 (9): 4174. https://doi.org/10.3390/ijms26094174.

Rideout, Elizabeth J., Marcus S. Narsaiya, and Savraj S. Grewal. 2015. "The Sex Determination Gene Transformer Regulates Male-Female Differences in Drosophila Body Size." PLOS Genetics 11 (12): e1005683. https://doi.org/10.1371/journal.pgen.1005683.

Schmoller, Kurt M., and Jan M. Skotheim. 2015. "The Biosynthetic Basis of Cell Size Control." Trends in Cell Biology 25 (12): 793-802. https://doi.org/10.1016/j.tcb.2015.10.006.

Schultz, J. 1929. "The Minute Reaction in the Development of DROSOPHILA MELANOGASTER." Genetics 14 (4): 366-419. https://doi.org/10.1093/genetics/14.4.366.

Serbanescu, Diana, Nikola Ojkic, and Shiladitya Banerjee. 2022. "Cellular Resource Allocation Strategies for Cell Size and Shape Control in Bacteria." The FEBS Journal 289 (24): 7891-906. https://doi.org/10.1111/febs.16234.

DOI: 10.1038/s41467-025-67075-y

Resource: International Institute of Molecular and Cell Biology in Warsaw Biophysics and Structural Biology Core Facility (RRID:SCR_021630)

Curator: @scibot

SciCrunch record: RRID:SCR_021630

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Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This research group has consistently performed cutting-edge research aiming to understand the role of hormones in the control of social behaviors, specifically by utilizing the genetically-tractable teleost fish, medaka, and the current work is no exception. The overall claim they make, that estrogens modulate social behaviors in males and females is supported, with important caveats. For one, there is no evidence these estrogens are generated by "neurons" as would be assumed by their main claim that it is NEUROestrogens that drive this effect. While indeed the aromatase they have investigated is expressed solely in the brain, in most teleosts, brain aromatase is only present in glial cells (astrocytes, radial glia). The authors should change this description so as not to mislead the reader. Below I detail more specific strengths and weaknesses of this manuscript.

We thank the reviewer for this positive evaluation of our work and for the helpful comments and suggestions. Regarding the concern that the term “neuroestrogens” may be misleading, we addressed this in the previous revision by consistently replacing it throughout the manuscript with “brain-derived estrogens” or “brain estrogens.”

In addition, the following sentence was added to the Introduction (line 61): “In teleost brains, including those of medaka, aromatase is exclusively localized in radial glial cells, in contrast to its neuronal localization in rodent brains (Forlano et al., 2001; Diotel et al., 2010; Takeuchi and Okubo, 2013).”

Strenghth:

Excellent use of the medaka model to disentangle the control of social behavior by sex steroid hormones 

The findings are strong for the most part because deficits in the mutants are restored by the molecule (estrogens) that was no longer present due to the mutation 

Presentation of the approach and findings are clear, allowing the reader to make their own inferences and compare them with the authors' 

Includes multiple follow-up experiments, which leads to tests of internal replication and an impactful mechanistic proposal 

Findings are provocative not just for teleost researchers, but for other species since, as the authors point out, the data suggest mechanisms of estrogenic control of social behaviors may be evolutionary ancient 

We thank the reviewer again for their positive evaluation of our work.

Weakness:

As stated in the summary, the authors are attributing the estrogen source to neurons and there isn't evidence this is the case. The impact of the findings doesn't rest on this either

As mentioned above, we addressed this in the previous revision by replacing “neuroestrogens” with “brain-derived estrogens” or “brain estrogens” throughout the manuscript. In addition, the following sentence was added to the Introduction (line 61): “In teleost brains, including those of medaka, aromatase is exclusively localized in radial glial cells, in contrast to its neuronal localization in rodent brains (Forlano et al., 2001; Diotel et al., 2010; Takeuchi and Okubo, 2013).”

The d4 versus d8 esr2a mutants showed different results for aggression. The meaning and implications of this finding are not discussed, leaving the reader wondering

This comment is the same as one raised in the first review (Reviewer #1’s comment 2 on weaknesses), which we already addressed in our initial revision. For the reviewer’s convenience, we provide the response below:

Line 300: As the reviewer correctly noted, circles were significantly reduced in mutant males of the Δ8 line, whereas no significant reduction was observed in those of the Δ4 line. However, a tendency toward reduction was evident in the Δ4 line (P = 0.1512), and both lines showed significant differences in fin displays. Based on these findings, we believe our conclusion that esr2a^−/− males exhibit reduced aggression remains valid. To clarify this point and address potential reader concerns, we have revised the text as follows: “esr2a^−/− males exhibited significantly fewer fin displays (P = 0.0461 and 0.0293 for Δ8 and Δ4 lines, respectively) and circles (P = 0.0446 and 0.1512 for Δ8 and Δ4 lines, respectively) than their wild-type siblings (Fig. 5L; Fig. S8E), suggesting less aggression” was edited to read “esr2a^−/− males from both the Δ8 and Δ4 lines exhibited significantly fewer fin displays than their wild-type siblings (P = 0.0461 and 0.0293, respectively). Circles followed a similar pattern, with a significant reduction in the Δ8 line (P = 0.0446) and a comparable but non-significant decrease in the Δ4 line (P =0.1512) (Figure 5L, Figure 5—figure supplement 3E), showing less aggression.”

Lack of attribution of previous published work from other research groups that would provide the proper context of the present study

This comment is also the same as one raised in the first review (Reviewer #1’s comment 3 on weaknesses). In our previous revision, in response to this comment, we cited the relevant references (Hallgren et al., 2006; O’Connell and Hofmann, 2012; Huffman et al., 2013; Jalabert et al., 2015; Yong et al., 2017; Alward et al., 2020; Ogino et al., 2023) in the appropriate sections. We also added the following new references and revised the Introduction and Discussion accordingly:

(2) Alward BA, Laud VA, Skalnik CJ, York RA, Juntti SA, Fernald RD. 2020. Modular genetic control of social status in a cichlid fish. Proceedings of the National Academy of Sciences of the United States of America 117:28167–28174. DOI: https://doi.org/10.1073/pnas.2008925117

(39) O’Connell LA, Hofmann HA. 2012. Social status predicts how sex steroid receptors regulate complex behavior across levels of biological organization. Endocrinology 153:1341–1351. DOI:https://doi.org/10.1210/en.2011-1663

(54) Yong L, Thet Z, Zhu Y. 2017. Genetic editing of the androgen receptor contributes to impaired male courtship behavior in zebrafish. Journal of Experimental Biology 220:3017–3021.DOI:https://doi.org/10.1242/jeb.161596

There are a surprising number of citations not included; some of the ones not included argue against the authors' claims that their findings were "contrary to expectation"

In our previous revision, we cited the relevant references (Hallgren et al., 2006; O’Connell and Hofmann, 2012; Huffman et al., 2013; Jalabert et al., 2015) in the Introduction. We also revised the text to remove phrases such as “contrary to expectation” and “unexpected.”

The experimental design for studying aggression in males has flaws. A standard test like a residentintruder test should be used.

Following this comment, we have attempted additional aggression assays using the resident-intruder paradigm. However, these experiments did not produce consistent or interpretable results. As noted in our previous revision, medaka naturally form shoals and exhibit weak territoriality, and even slight differences in dominance between a resident and an intruder can markedly increase variability, reducing data reliability. Therefore, we believe that the approach used in the present study provides a more suitable assessment of aggression in medaka, regardless of territorial tendencies. We will continue to explore potential refinements in future studies and respectfully ask the reviewer to evaluate the present work based on the assay used here.

While they investigate males and females, there are fewer experiments and explanations for the female results, making it feel like a small addition or an aside

While we did not adopt this comment in our previous revision, we have carefully reconsidered the reviewers’ feedback and have now decided to remove the female data. This change allows us to present a more focused and cohesive story centered on males. The specific revisions are outlined below:

Abstract

Line 25: The text “, thereby revealing a previously unappreciated mode of action of brain-derived estrogens. We additionally show that female fish lacking Cyp19a1b are less receptive to male courtship and conversely court other females, highlighting the significance of brain-derived estrogens in establishing sex-typical behaviors in both sexes.” has been revised to “. Taken together, these findings reveal a previously unappreciated mode of action of brain-derived estrogens in shaping male-typical behaviors.”

Results

Line 88: The text “Loss of cyp19a1b function in these fish was verified by measuring brain and peripheral levels of sex steroids. As expected, brain estradiol-17β (E2) in both male and female homozygous mutants (cyp19a1b^−/−) was significantly reduced to 16% and 50%, respectively, of the levels in their wild-type (cyp19a1b^+/+) siblings (P = 0.0037, males; P = 0.0092, females) (Fig. 1, A and B). In males, brain E2 in heterozygotes (cyp19a1b^−/−) was also reduced to 45% of the level in wild-type siblings (P = 0.0284) (Fig. 1A), indicating a dosage effect of cyp19a1b mutation. In contrast, peripheral E2 levels were unaltered in both cyp19a1b^−/− males and females (Fig. S1, C and D), consistent with the expected functioning of Cyp19a1b primarily in the brain. Strikingly, brain levels of testosterone, as opposed to E2, increased 2.2-fold in cyp19a1b^−/− males relative to wild-type siblings (P = 0.0006) (Fig. 1A). Similarly, brain 11KT levels in cyp19a1b^−/− males and females increased 6.2- and 1.9-fold, respectively, versus wild-type siblings (P = 0.0007, males; P = 0.0316, females) (Fig. 1, A and B). These results show that cyp19a1b-deficient fish have reduced estrogen levels coupled with increased androgen levels in the brain, confirming the loss of cyp19a1b function. They also suggest that the majority of estrogens in the male brain and half of those in the female brain are synthesized locally in the brain. In addition, peripheral 11KT levels in cyp19a1b^−/− males and females increased 3.7- and 1.8-fold, respectively (P = 0.0789, males; P = 0.0118, females) (Fig. S1, C and D), indicating peripheral influence in addition to central effects.” has been revised to “Loss of cyp19a1b function in these fish was verified by measuring brain and peripheral levels of sex steroids in males. As expected, brain estradiol-17β (E2) in homozygous mutants (cyp19a1b^−/−) was significantly reduced to 16% of the levels in wild-type (cyp19a1b^+/+) siblings (P = 0.0037) (Figure 1A). Brain E2 in heterozygotes (cyp19a1b^+/−) was also reduced to 45% of wild-type levels (P = 0.0284) (Figure 1A), indicating a dosage effect of the cyp19a1b mutation. In contrast, peripheral E2 levels were unaltered in cyp19a1b^−/− males (Figure 1B), consistent with the expected functioning of Cyp19a1b primarily in the brain. Strikingly, brain testosterone levels, as opposed to E2, increased 2.2-fold in cyp19a1b^−/− males relative to wild-type siblings (P = 0.0006) (Figure 1A). Similarly, brain 11KT levels increased 6.2-fold (P = 0.0007) (Figure 1A). These results indicate that cyp19a1b-deficient males have reduced estrogen coupled with elevated androgen levels in the brain, confirming the loss of cyp19a1b function. They also suggest that the majority of estrogens in the male brain are synthesized locally in the brain. Peripheral 11KT levels also increased 3.7-fold in cyp19a1b^−/− males (P = 0.0789) (Figure 1B), indicating peripheral influence in addition to central effects.”

Line 211: “expression of vt in the pNVT of cyp19a1b^−/− males was significantly reduced to 18% as compared with cyp19a1b^+/+ males (P = 0.0040), a level comparable to that observed in females” has been revised to “expression of vt in the pNVT of cyp19a1b^−/− males was significantly reduced to 18% as compared with cyp19a1b^+/+ males (P = 0.0040).”

The subsection entitled “cyp19a1b-deficient females are less receptive to males and instead court other females,” which followed line 311, has been removed.

Discussion

The two paragraphs between lines 373 and 374, which addressed the female data, have been removed.

Materials and methods

Line 433: “males and females” has been changed to “males”.

Line 457: “focal fish” has been changed to “focal male”.

Line 458: “stimulus fish” has been changed to “stimulus female”.

Line 458: “Fig. 6, E and F, ” has been deleted.

Line 460: “; wild-type males in Fig. 6, A to C” has been deleted.

Line 466: The text “The period of interaction/recording was extended to 2 hours in tests of courtship displays received from the stimulus esr2b-deficient female and in tests of mating behavior between females, because they take longer to initiate courtship (12). In tests using an esr2b-deficient female as the stimulus fish, where the latency to spawn could not be calculated because these fish were unreceptive to males and did not spawn, the sexual motivation of the focal fish was instead assessed by counting the number of courtship displays and wrapping attempts in 30 min. The number of these mating acts was also counted in tests to evaluate the receptivity of females. In tests of mating behavior between two females, the stimulus female was marked with a small notch in the caudal fin to distinguish it from the focal female.” has been revised to “In tests using an esr2b-deficient female as the stimulus fish, the latency to spawn could not be calculated because the female was unreceptive to males and did not spawn. Therefore, the sexual motivation of the focal male was assessed by counting the number of courtship displays and wrapping attempts in 30 min. To evaluate courtship displays performed by stimulus esr2bdeficient females toward focal males, the recording period was extended to 2 hours, as these females take longer to initiate courtship (Nishiike et al., 2021). In all video analyses, the researcher was blind to the fish genotype and treatment.”

Line 499: “brains dissected from males and females of the cyp19a1b-deficient line (analysis of ara, arb, vt, gal, npba, and esr2b) and males of the esr1-, esr2a-, and esr2b-deficient lines” has been revised to “male brains from the cyp19a1b-deficient line (analysis of ara, arb, vt, and gal) and from the esr1-, esr2a-, and esr2b-deficient lines.”

Line 504: “After color development for 15 min (gal), 40 min (npba), 2 hours (vt), or overnight (ara, arb, and esr2b)” has been revised to “After color development for 15 min (gal), 2 hours (vt), or overnight (ara and arb).”

Line 516: “Thermo Fisher Scientific, Waltham, MA” has been changed to “Thermo Fisher Scientific” to avoid redundancy.

Line 565: The subsection entitled “Measurement of spatial distances between fish” has been removed.

Line 585: “6/10 cyp19a1b^+/+, 3/10 cyp19a1b^+/−, and 6/10 cyp19a1b^−/− females were excluded in Fig. 6B;” has been deleted.

References

The following references have been removed:

Capel B. 2017. Vertebrate sex determination: evolutionary plasticity of a fundamental switch. Nature Reviews Genetics 18:675–689. DOI: https://doi.org/10.1038/nrg.2017.60

Hiraki T, Nakasone K, Hosono K, Kawabata Y, Nagahama Y, Okubo K. 2014. Neuropeptide B is femalespecifically expressed in the telencephalic and preoptic nuclei of the medaka brain. Endocrinology 155:1021–1032. DOI: https://doi.org/10.1210/en.2013-1806

Juntti SA, Hilliard AT, Kent KR, Kumar A, Nguyen A, Jimenez MA, Loveland JL, Mourrain P, Fernald RD. 2016. A neural basis for control of cichlid female reproductive behavior by prostaglandin F2α. Current Biology 26:943–949. DOI: https://doi.org/10.1016/j.cub.2016.01.067

Kimchi T, Xu J, Dulac C. 2007. A functional circuit underlying male sexual behaviour in the female mouse brain. Nature 448:1009–1014. DOI: https://doi.org/10.1038/nature06089

Kobayashi M, Stacey N. 1993. Prostaglandin-induced female spawning behavior in goldfish (Carassius auratus) appears independent of ovarian influence. Hormones and Behavior 27:38–55.

DOI:https://doi.org/10.1006/hbeh.1993.1004

Liu H, Todd EV, Lokman PM, Lamm MS, Godwin JR, Gemmell NJ. 2017. Sexual plasticity: a fishy tale. Molecular Reproduction and Development 84:171–194. DOI: https://doi.org/10.1002/mrd.22691

Munakata A, Kobayashi M. 2010. Endocrine control of sexual behavior in teleost fish. General and Comparative Endocrinology 165:456–468. DOI: https://doi.org/10.1016/j.ygcen.2009.04.011

Nugent BM, Wright CL, Shetty AC, Hodes GE, Lenz KM, Mahurkar A, Russo SJ, Devine SE, McCarthy MM. 2015. Brain feminization requires active repression of masculinization via DNA methylation. Nature Neuroscience 18:690–697. DOI: https://doi.org/10.1038/nn.3988

Shaw K, Therrien M, Lu C, Liu X, Trudeau VL. 2023. Mutation of brain aromatase disrupts spawning behavior and reproductive health in female zebrafish. Frontiers in Endocrinology 14:1225199.

DOI:https://doi.org/10.3389/fendo.2023.1225199

Stacey NE. 1976. Effects of indomethacin and prostaglandins on the spawning behaviour of female goldfish. Prostaglandins 12:113–126. DOI: https://doi.org/10.1016/s0090-6980(76)80010-x

Figure 1

Panel B, which originally showed steroid levels in female brains, has been replaced with steroid levels in the periphery of males, originally presented in Figure S1, panel C. Accordingly, the legend “(A and B) Levels of E2, testosterone, and 11KT in the brain of adult cyp19a1b^+/+, cyp19a1b^+/−, and cyp19a1b^−/− males (A) and females (B) (n = 3 per genotype and sex).” has been revised to “(A, B) Levels of E2, testosterone, and 11KT in the brain (A) and periphery (B) of adult cyp19a1b^+/+, cyp19a1b^+/−, and cyp19a1b^−/− males (n = 3 per genotype).”

Figure 3

The female data have been deleted from Figure 3. The revised Figure 3 is presented.

The corresponding legend text has been revised as follows:

Line 862: “males and females (n = 4 and 5 per genotype for males and females, respectively)” has been changed to “males (n = 4 per genotype)”.

Line 864: “males and females (n = 4 except for cyp19a1b^+/+ males, where n = 3)” has been changed to “males (n = 3 and 4, respectively)”.

Figure 6

Figure 6 and its legend have been removed.

Figure 1—figure supplement 1

Panel C, showing male data, has been moved to Figure 1B, as described above, while panel D, showing female data, has been deleted. The corresponding legend “(C and D) Levels of E2, testosterone, and 11KT in the periphery of adult cyp19a1b^+/+, cyp19a1b^+/−, and cyp19a1b^−/− males (C) and females (D) (n = 3 per genotype and sex). Statistical differences were assessed by Bonferroni’s post hoc test (C and D). Error bars represent SEM. *P < 0.05.” has also been removed.

Line 804: Following this change, the figure title has been updated from “Generation of cyp19a1bdeficient medaka and evaluation of peripheral sex steroid levels” to “Generation of cyp19a1b-deficient medaka.”

The statistics comparing "experimental to experimental" and "control to experimental" isn't appropriate 

This comment is the same as one raised in the first review (Reviewer #1’s comment 7 on weaknesses), which we already addressed in our initial revision. For the reviewer’s convenience, we provide the response below:

The reviewer raised concerns about the statistical analysis used for Figures 4C and 4E, suggesting that Bonferroni’s test should be used instead of Dunnett’s test. However, Dunnett’s test is commonly used to compare treatment groups to a reference group that receives no treatment, as in our study. Since we do not compare the treated groups with each other, we believe Dunnett’s test is the most appropriate choice.

Line 576: The reviewer’s concern may have arisen from the phrase “comparisons between control and experimental groups” in the Materials and methods. We have revised it to “comparisons between untreated and E2-treated groups in Figure 4C and D” for clarity.

Reviewer #3 (Public Review):

Summary:

Taking advantage of the existence in fish of two genes coding for estrogen synthase, the enzyme aromatase, one mostly expressed in the brain (Cyp19a1b) and the other mostly found in the gonads (Cyp19a1a), this study investigates the role of brain-derived estrogens in the control of sexual and aggressive behavior in medaka. The constitutive deletion of Cyp19a1b markedly reduced brain estrogen content in males and to a lesser extent in females. These effects are accompanied by reduced sexual and aggressive behavior in males and reduced preference for males in females. These effects are reversed by adult treatment with supporting a role for estrogens. The deletion of Cyp19a1b is associated with a reduced expression of the genes coding for the two androgen receptors, ara and arb, in brain regions involved in the regulation of social behavior. The analysis of the gene expression and behavior of mutants of estrogen receptors indicates that these effects are likely mediated by the activation of the esr1 and esr2a isoforms. These results provide valuable insight into the role of estrogens in social behavior in the most abundant vertebrate taxon, however the conclusion of brain-derived estrogens awaits definitive confirmation.

We thank this reviewer for their positive evaluation of our work and comments that have improved the manuscript.

Strength:

Evaluation of the role of brain "specific" Cyp19a1 in male teleost fish, which as a taxon are more abundant and yet proportionally less studied that the most common birds and rodents. Therefore, evaluating the generalizability of results from higher vertebrates is important. This approach also offers great potential to study the role of brain estrogen production in females, an understudied question in all taxa.

Results obtained from multiple mutant lines converge to show that estrogen signaling, likely synthesized in the brain drives aspects of male sexual behavior.

The comparative discussion of the age-dependent abundance of brain aromatase in fish vs mammals and its role in organization vs activation is important beyond the study of the targeted species.  - The authors have made important corrections to tone down some of the conclusions which are more in line with the results. 

We thank the reviewer again for their positive evaluation of our work and the revisions we have made.

weaknesses:

No evaluation of the mRNA and protein products of Cyp19a1b and ESR2a are presented, such that there is no proper demonstration that the mutation indeed leads to aromatase reduction. The conclusion that these effects dependent on brain derived estrogens is therefore only supported by measures of E2 with an EIA kit that is not validated. No discussion of these shortcomings is provided in the discussion thus further weakening the conclusion manuscript.

In response to this and other comments, we have now provided direct validation that the cyp19a1b mutation in our medaka leads to loss of function. Real-time PCR analysis showed that cyp19a1b transcript levels in the brain were reduced by approximately half in cyp19a1b^+/− males and were nearly absent in cyp19a1b^−/− males, consistent with nonsense-mediated mRNA decay

In addition, AlphaFold 3-based structural modeling indicated that the mutant Cyp19a1b protein lacks essential motifs, including the aromatic region and heme-binding loop, and exhibits severe conformational distortion (see figure; key structural features are annotated as follows: membrane helix (blue), aromatic region (red), and heme-binding loop (orange)). 

Results:

Line 101: The following text has been added: “Loss of cyp19a1b function was further confirmed by measuring cyp19a1b transcript levels in the brain and by predicting the three-dimensional structure of the mutant protein. Real-time PCR revealed that transcript levels were reduced by half in cyp19a1b^+/− males and were nearly undetectable in cyp19a1b^−/− males, presumably as a result of nonsense-mediated mRNA decay (Lindeboom et al., 2019) (Figure 1C). The wild-type protein, modeled by AlphaFold 3, exhibited a typical cytochrome P450 fold, including the membrane helix, aromatic region, and hemebinding loop, all arranged in the expected configuration (Figure 1—figure supplement 1C). The mutant protein, in contrast, was severely truncated, retaining only the membrane helix (Figure 1—figure supplement 1C). The absence of essential domains strongly indicates that the allele encodes a nonfunctional Cyp19a1b protein. Together, transcript and structural analyses consistently demonstrate that the mutation generated in this study causes a complete loss of cyp19a1b function.”

Materials and methods

Line 438: A subsection entitled “Real-time PCR” has been added. The text of this subsection is as follows: “Total RNA was isolated from the brains of cyp19a1b^+/+, cyp19a1b^+/−, and cyp19a1b^−/− males using the RNeasy Plus Universal Mini Kit (Qiagen, Hilden, Germany). cDNA was synthesized with the SuperScript VILO cDNA Synthesis Kit (Thermo Fisher Scientific, Waltham, MA). Real-time PCR was performed on the LightCycler 480 System II using the LightCycler 480 SYBR Green I Master (Roche Diagnostics). Melting curve analysis was conducted to verify that a single amplicon was obtained in each sample. The β-actin gene (actb; GenBank accession number NM_001104808) was used to normalize the levels of target transcripts. The primers used for real-time PCR are shown in Supplementary file 2.”

Line 448: A subsection entitled “Protein structure prediction” has been added. The text of this subsection is as follows: “Structural predictions of Cyp19a1b proteins were conducted using AlphaFold 3 (Abramson et al., 2024). Amino acid sequences corresponding to the wild-type allele and the mutant allele generated in this study were submitted to the AlphaFold 3 prediction server. The resulting models were visualized with PyMOL (Schrödinger, New York, NY), and key structural features, including the membrane helix, aromatic region, and heme-binding loop, were annotated.”

References

The following two references have been added:

Abramson J, Adler J, Dunger J, Evans R, Green T, Pritzel A, Ronneberger O, Willmore L, Ballard AJ, Bambrick J, Bodenstein SW, Evans DA, Hung CC, O'Neill M, Reiman D, Tunyasuvunakool K, Wu Z, Žemgulytė A, Arvaniti E, Beattie C, Bertolli O, Bridgland A, Cherepanov A, Congreve M, CowenRivers AI, Cowie A, Figurnov M, Fuchs FB, Gladman H, Jain R, Khan YA, Low CMR, Perlin K, Potapenko A, Savy P, Singh S, Stecula A, Thillaisundaram A, Tong C, Yakneen S, Zhong ED, Zielinski M, Žídek A, Bapst V, Kohli P, Jaderberg M, Hassabis D, Jumper JM. 2024. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630:493–500. DOI: https://doi.org/10.1038/s41586-024-07487-w

Lindeboom RGH, Vermeulen M, Lehner B, Supek F. 2019. The impact of nonsense-mediated mRNA decay on genetic disease, gene editing and cancer immunotherapy. Nature Genetics 51:1645–1651.DOI:https://doi.org/10.1038/s41588-019-0517-5

Figure 1

The real-time PCR results described above have been incorporated in Figure 1, panel C, with the corresponding legend provided below (line 788).

(C) Brain cyp19a1b transcript levels in cyp19a1b^+/+, cyp19a1b^+/−, and cyp19a1b^−/− males (n = 6 per genotype). Mean value for cyp19a1b^+/+ males was arbitrarily set to 1.

The subsequent panels have been renumbered accordingly. The entirety of the revised Figure 1.

Figure 1—figure supplement 1

The AlphaFold 3-generated structural models described above have been incorporated in Figure 1— figure supplement 1, panel C, with the corresponding legend provided below (line 811).

(C) Predicted three-dimensional structures of wild-type (left) and mutant (right) Cyp19a1b proteins. Key structural features are annotated as follows: membrane helix (blue), aromatic region (red), and heme-binding loop (orange).

The entirety of the revised Figure 1—figure supplement 1 is presented

The information on the primers used for real-time PCR has been included in Supplementary file 2.

The functional deficiency of esr2a was already addressed in the previous revision. For clarity, we have reproduced the relevant information here.

A previous study reported that female medaka lacking esr2a fail to release eggs due to oviduct atresia (Kayo et al., 2019, Sci Rep 9:8868). Similarly, in this study, some esr2a-deficient females exhibited spawning behavior but were unable to release eggs, although the sample size was limited (Δ8 line: 2/3; Δ4 line: 1/1). In contrast, this was not observed in wild-type females (Δ8 line: 0/12; Δ4 line: 0/11). These results support the effective loss of esr2a function. To incorporate this information into the manuscript, the following text has been added to the Materials and methods (line 423): “A previous study reported that esr2a-deficient female medaka cannot release eggs due to oviduct atresia (Kayo et al., 2019). Likewise, some esr2a-deficient females generated in this study, despite the limited sample size, exhibited spawning behavior but were unable to release eggs (Δ8 line: 2/3; Δ4 line: 1/1), while such failure was not observed in wild-type females (Δ8 line: 0/12; Δ4 line: 0/11). These results support the effective loss of esr2a function.”

Most experiments are weakly powered (low sample size).

This comment is essentially the same as one raised in the first review (Reviewer #3’s comment 7 on weaknesses). We acknowledge the reviewer’s concern that the histological analyses were weakly powered due to the limited sample size. In our earlier revision, we responded as follows:

Histological analyses were conducted with a relatively small sample size, as our previous experience suggested that interindividual variability in the results would not be substantial. Since significant differences were detected in many analyses, further increasing the sample size was deemed unnecessary.

The variability of the mRNA content for a same target gene between experiments (genotype comparison vs E2 treatment comparison) raises questions about the reproducibility of the data (apparent disappearance of genotype effect).

This comment is the same as one raised in the first review (Reviewer #3’s comment 8 on weaknesses), which we already addressed in our initial revision. For the reviewer’s convenience, we provide the response below:

As the reviewer pointed out, the overall area of ara expression is larger in Figure 2J than in Figure 2F. However, the relative area ratios of ara expression among brain nuclei are consistent between the two figures, indicating the reproducibility of the results. Thus, this difference is unlikely to affect the conclusions of this study.

Additionally, the differences in ara expression in pPPp and arb expression in aPPp between wild-type and cyp19a1b-deficient males appear less pronounced in Figures 2J and 2K than in Figures 2F and 2H. This is likely attributable to the smaller sample size used in the experiments for Figures 2J and 2K, resulting in less distinct differences. However, as the same genotype-dependent trends are observed in both sets of figures, the conclusion that ara and arb expression is reduced in cyp19a1b-deficient male brains remains valid.

Conclusions:

Overall, the claims regarding role of estrogens originating in the brain on male sexual behavior is supported by converging evidence from multiple mutant lines. The role of brain-derived estrogens on gene expression in the brain is weaker as are the results in females. 

We appreciate the reviewer’s positive evaluation of our findings on male behavior. The concern regarding the role of brain-derived estrogens in gene expression has been addressed in our rebuttal, and the female data have been removed so that the analysis now focuses on males. The specific revisions for removing the female data are described in Response to reviewer #1’s comment 6 on weaknesses.

Recommendations For The Authors:

Reviewer #1 (Recommendations For The Authors):

The manuscript is improved slightly. I am thankful the authors addressed some concerns, but for several concerns the referees raised, the authors acknowledged them yet did not make corresponding changes to the manuscript or disagreed that they were issues at all without explanation. All reviewers had issues with the imbalanced focus on males versus females and the male aggression assay. Yet, they did not perform additional experiments or even make changes to the framing and scope of the manuscript. If the authors had removed the female data, they may have had a more cohesive story, but then they would still be left with inadequate behavior assays in the males. If the authors don't have the time or resources to perform the additional work, then they should have said so. However, the work would be incomplete relative to the claims. That is a key point here. If they change their scope and claims, the authors avoid overstating their findings. I want to see this work published because I believe it moves the field forward. But the authors need to be realistic in their interpretations of their data. 

In response to this and related comments, we have removed the female data and focused the manuscript on analyses in males. The specific revisions are described in Response to reviewer #1’s comment 6 on weaknesses. Additionally, we have validated that the cyp19a1b mutation in our medaka leads to loss of function (see Response to reviewer #3’s comment 1 on weaknesses), which further strengthens the reliability of our conclusions regarding male behavior.

I agree with the reviewer who said we need to see validation of the absence of functional cyp19a1 b in the brain. However, the results from staining for the protein and performing in situ could be quizzical. Indeed, there aren't antibodies that could distinguish between aromatase a and b, and it is not uncommon for expression of a mutated gene to be normal. One approach they could do is measure aromatase activity, but they are *sort of* doing that by measuring brain E2. It's not perfect, but we teleost folks are limited in these areas. At the very least, they should show the predicted protein structure of the mutated aromatase alleles. It could show clearly that the tertiary structure is utterly absent, giving more support to the fact that their aromatase gene is non-functional. 

As noted above, we have further validated the loss of cyp19a1b function by measuring cyp19a1b transcript levels in the brain and predicting the three-dimensional structure of the mutant protein. These analyses confirmed that cyp19a1b function is indeed lost, thereby increasing the reliability of our conclusions. For further details, please refer to Response to reviewer #3’s comment 1 on weaknesses.

With all of this said, the work is important, and it is possible that with a reframing of the impact of their work in the context of their findings, I could consider the work complete. I think with a proper reframing, the work is still impactful. 

In accordance with this feedback, and as described above, we have reframed the manuscript by removing the female data and focusing exclusively on males. This revision clarifies the scope of our study and reinforces the support for our conclusions. For further details, please refer to Response to reviewer #1’s comment 6 on weaknesses.

(1) Clearly state in the Figure 1 legend that each data point for male aggressive behaviors represents the total # of behaviors calculated over the 4 males in each experimental tank.

In response to this comment, we have revised the legend of Figure 1K (line 797). The original legend, “(K) Total number of each aggressive act observed among cyp19a1b^+/+, cyp19a1b^+/−, or cyp19a1^−/− males in the tank (n = 6, 7, and 5, respectively),” has been updated to “(K) Total number of each aggressive act performed by cyp19a1b^+/+, cyp19a1b^+/−, and cyp19a1b^−/− males. Each data point represents the sum of acts recorded for the 4 males of the same genotype in a single tank (n = 6, 7, and 5 tanks, respectively).” This clarifies that each data point reflects the total behaviors of the 4 males within each tank.

(2) The authors wrote under "Response to reviewer #1's major comment "...the development of male behaviors may require moderate neuroestrogen levels that are sufficient to induce the expression of ara and arb, but not esr2b, in the underlying neural circuitry": "This may account for the lack of aggression recovery in E2-treated cyp19a1b-deficient males in this study.".

What is meant by the latter statement? What accounts for the lack of aggression? The lack of increase in esr2b? Please clarify. 

Line 365: In response to this comment, “This may account for the lack of aggression recovery in E2treated cyp19a1b-deficient males in this study.” has been revised to “Considering this, the lack of aggression recovery in E2-treated cyp19a1b-deficient males in this study may be explained by the possibility that the E2 dose used was sufficient to induce not only ara and arb but also esr2b expression in aggression-relevant circuits, which potentially suppressed aggression.”

This revision clarifies that, while moderate brain estrogen levels are sufficient to promote male behaviors via induction of ara and arb, the E2 dose used in this study may have additionally induced esr2b in circuits relevant to aggression, potentially underlying the lack of aggression recovery.

(3) This is a continuation of my comment/concern directly above. If the induction of ara and arb aren't enough, then how can, as the authors state, androgen signaling be the primary driver of these behaviors? 

In response to this follow-up comment, we would like to clarify that, as described above, the lack of aggression recovery in E2-treated cyp19a1b-deficient males is not due to insufficient induction of ara and arb, but instead is likely because esr2b was also induced in aggression-relevant circuits, which may have suppressed aggression. Therefore, the concern that androgen signaling cannot be the primary driver of these behaviors is not applicable.

(4) The authors' point about sticking with the terminology for the ar genes as "ara" and "arb" is not convincing. The whole point of needing a change to match the field of neuroendocrinology as a whole (that is, across all vertebrates) is researchers, especially those with high standing like the Okubo group, adopt the new terminology. Indeed, the Okubo group is THE leader in medaka neuroendocrinology. It would go a long way if they began adopting the new terminology of "ar1" and "ar2". I understand this may be laborious to a degree, and each group can choose to use their terminology, but I'd be remiss if I didn't express my opinion that changing the terminology could help our field as a whole. 

We sincerely appreciate the reviewer’s thoughtful comments regarding nomenclature consistency in vertebrate neuroendocrinology. We understand the motivation behind the suggestion to adopt ar1 and ar2. However, we consider the established nomenclature of ara and arb to be more appropriate for the following reasons.

First, adopting the ar1/ar2 nomenclature would introduce a discrepancy between gene and protein symbols. According to the NCBI International Protein Nomenclature Guidelines (Section 2B.Abbreviations and symbols;

https://www.ncbi.nlm.nih.gov/genbank/internatprot_nomenguide/), the ZFIN Zebrafish Nomenclature Conventions (Section 2. PROTEINS:https://zfin.atlassian.net/wiki/spaces/general/pages/1818394635/ZFIN+Zebrafish+Nomenclature+Con ventions), and the author guidelines of many journal

(e.g.,https://academic.oup.com/molehr/pages/Gene_And_Protein_Nomenclature), gene and protein symbols should be identical (with proteins designated in non-italic font and with the first letter capitalized). Maintaining consistency between gene and protein symbols helps avoid unnecessary confusion. The ara/arb nomenclature allows this, whereas ar1/ar2 does not.

Second, the two androgen receptor genes in teleosts are paralogs derived from the third round of wholegenome duplication that occurred early in teleost evolution. For such duplicated genes, the ZFIN Zebrafish Nomenclature Conventions (Section 1.2. Duplicated genes) recommend appending the suffixes “a” and “b” to the approved symbol of the human or mouse ortholog. This convention clearly indicates that these genes are whole-genome duplication paralogs and provides an intuitive way to represent orthologous and paralogous relationships between teleost genes and those of other vertebrates. As a result, it has been widely adopted, and we consider it logical and beneficial to apply the same principle to androgen receptors.

In light of these considerations, we respectfully maintain that the ara/arb nomenclature is more suitable for the present manuscript than the alternative ar1/ar2 system.

(5) In the discussion please discuss these potentially unexpected findings.

(a) gal was unaffected in female cyp19a1 mutants, but they exhibit mating behaviors towards females. Given gal is higher in males and these females act like females, what does this mean about the function of gal/its utility in being a male-specific marker (is it one??)? 

(b) esr2b expression is higher in female cyp19a1 mutants. this is unexpected as well given esr2b is required for female-typical mating and is higher in females compared to males and E2 increases esr2b expression. please explain...well, what this means for our idea of what esr2b expression tell us. 

We thank the reviewer for the insightful comments. As the female data have been removed from the manuscript, discussion of these findings in female cyp19a1b mutants is no longer necessary.

Reviewer #3 (Recommendations For The Authors):

The authors have addressed a number of answers to the reviewer's comments, notably they provided missing methodological information and rephrased the text. However, the authors have not addressed the main issues raised by the reviewers. Notably, it is regrettable that the reduced amount of brain aromatase cannot be confirmed, this seems to be the primary step when validating a new mutant. Even if protein products of the two genes may not be discriminated (which I can understand), it should be possible to evaluate the expression of a common messenger and/or peptide and confirm that aromatase expression is reduced in the brain. Since Cyp19a1b is relatively more abundant in the brain Cyp19a1a, this would strengthen the conclusion and provide confidence that the mutant indeed does silence aromatase expression in the brain. Although these short comings are acknowledged in the rebuttal letter, this is not mentioned in the discussion. Doing so would make the manuscript more transparent and clearer. 

As noted in Response to reviewer #3’s comment 1 on weaknesses, we have validated the loss of Cyp19a1b function by measuring its transcript levels in the brain and predicting the three-dimensional structure of the mutant protein. These analyses confirmed that Cyp19a1b function is indeed lost, thereby increasing the reliability of our conclusions.

FigS1 - panels C&D please indicate in which tissue were hormones measured. Blood?

We thank the reviewer for pointing this out. In our study, “peripheral” refers to the caudal half of the body excluding the head and visceral organs, not blood. Accordingly, we have revised the figure legend and the description in the Materials and Methods section as follows:

Legend for Figure 1B (line 787) now reads: “Levels of E2, testosterone, and 11KT in the brain (A) and peripheral tissues (caudal half of the body) (B) of adult cyp19a1b^+/+, cyp19a1b^+/−, and cyp19a1b^−/− males (n = 3 per genotype).”

Materials and methods (line 431): The sentence “Total lipids were extracted from the brain and peripheral tissues (from the caudal half) of” has been revised to “Total lipids were extracted from the brain and from peripheral tissues, specifically the caudal half of the body excluding the head and visceral organs, of.”

Additional Alterations:

We have reformatted the text and supporting materials to comply with the journal’s Author Guidelines. The following changes have been made:

(1) Figures and supplementary files are now provided separately from the main text.

(2) The title page has been reformatted without any changes to its content.

(3) In-text citations have been changed from numerical references to the author–year format.

(4) Figure labels have been revised from “Fig. 1,” “Fig. S1,” etc., to “Figure 1,” “Figure 1—figure supplement 1,” etc.

(5) Table labels have been revised from “Table S1,” etc., to “Supplementary file 1,” etc.

(6) Line 324: The typo “is” has been corrected to “are”.

(7) Line 382: The section heading “Materials and Methods” has been changed to “Materials and methods” (lowercase “m”).

(8) Line 383: The Key Resources Table has been placed at the beginning of the Materials and methods section.

(9) Line 389: The sentence “Sexually mature adults (2–6 months) were used for experiments, and tissues were consistently sampled 1–5 hours after lights on.” has been revised to “Sexually mature adults (2–6 months) were used for experiments and assigned randomly to experimental groups. Tissues were consistently sampled 1–5 hours after lights on.”

(10)  Line 393: The sentence “All fish were handled in accordance with the guidelines of the Institutional Animal Care and Use Committee of the University of Tokyo.” has been removed.

(11)  Line 589: The following sentence has been added: “No power analysis was conducted due to the lack of relevant data; sample size was estimated based on previous studies reporting inter-individual variation in behavior and neural gene expression in medaka.”

(12)  Line 598: The reference list has been reordered from numerical sequence to alphabetical order by author.

(13)  In the figure legends, notations such as “A and B” have been revised to “A, B.”

'... en dat de grafieken enkel afhankelijk zijn van één variabel zoals een simpele y=x functie' Om verwarring te vermijden en extra duidelijkheid toe te brengen.

When the person doesn't acknowledge the truth rather hide it and the y use defense mechanisms such as regression denial dipsplacement etc.

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What is this?

Author response:

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

Public Reviews:

Reviewer #1 (Public Reviews):

Summary:

Argunşah et al. describe and investigate the mechanisms underlying the differential response dynamics of barrel vs septa domains of the whisker-related primary somatosensory cortex (S1). Upon repeated stimulation, the authors report that the response ratio between multi- and single-whisker stimulation increases in layer (L) 4 neurons of the septal domain, while remaining constant in barrel L4 neurons. This difference is attributed to the short-term plasticity properties of interneurons, particularly somatostatin-expressing (SST+) neurons. This claim is supported by the increased density of SST+ neurons found in L4 of the septa compared to barrels, along with a stronger response of (L2/3) SST+ neurons to repeated multi- vs single-whisker stimulation. The role of the synaptic protein Elfn1 is then examined. Elfn1 KO mice exhibited little to no functional domain separation between barrel and septa, with no significant difference in single- versus multi-whisker response ratios across barrel and septal domains. Consistently, a decoder trained on WT data fails to generalize to Elfn1 KO responses. Finally, the authors report a relative enrichment of S2- and M1-projecting cell densities in L4 of the septal domain compared to the barrel domain.

Strengths:

This paper describes and aims to study a circuit underlying differential response between barrel columns and septal domains of the primary somatosensory cortex. This work supports the view that barrel and septal domains contribute differently to processing single versus multi-whisker inputs, suggesting that the barrel cortex multiplexes sensory information coming from the whiskers in different domains.

We thank the reviewer for the very neat summary of our findings that barrel cortex multiplexes converging information in separate domains.

Weaknesses:

While the observed divergence in responses to repeated SWS vs MWS between the barrel and septal domains is intriguing, the presented evidence falls short of demonstrating that short-term plasticity in SST+ neurons critically underpins this difference. The absence of a mechanistic explanation for this observation limits the work’s significance. The measurement of SST neurons’ response is not specific to a particular domain, and the Elfn1 manipulation does not seem to be specific to either stimulus type or a particular domain.

We appreciate the reviewer’s perspective. Although further research is needed to understand the circuit mechanisms underlying the observed phenomenon, we believe our data suggest that altering the short-term dynamics of excitatory inputs onto SST neurons reduces the divergent spiking dynamics in barrels versus septa during repetitive single- and multi-whisker stimulation. Future work could examine how SST neurons, whose somata reside in barrels and septa, respond to different whisker stimuli and the circuits in which they are embedded. At this time, however, the authors believe there is no alternative way to test how the short-term dynamics of excitatory inputs onto SST neurons, as a whole, contribute to the temporal aspects of barrel versus septa spiking.

The study's reach is further constrained by the fact that results were obtained in anesthetized animals, which may not generalize to awake states.

We appreciate the reviewer’s concern regarding the generalizability of our findings from anesthetized animals to awake states. Anesthesia was employed to ensure precise individual whisker stimulation (and multi-whisker in the same animal), which is challenging in awake rodents due to active whisking. While anesthesia may alter higher-order processing, core mechanisms, such as short and long term plasticity in the barrel cortex, are preserved under anesthesia (Martin-Cortecero et al., 2014; Mégevand et al., 2009).

The statistical analysis appears inappropriate, with the use of repeated independent tests, dramatically boosting the false positive error rate.

Thank you for your feedback on our analysis using independent rank-based tests for each time point in wild-type (WT) animals. To address concerns regarding multiple comparisons and temporal dependencies (for Figure 1F and 4D for now but we will add more in our revision), we performed a repeated measures ANOVA for WT animals (13 Barrel, 8 Septa, 20 time points), which revealed a significant main effect of Condition (F(1,19) = 16.33, p < 0.001) and a significant Condition-Time interaction (F(19,361) = 2.37, p = 0.001). Post-hoc tests confirmed significant differences between Barrel and Septa at multiple time points (e.g., p < 0.0025 at times 3, 4, 6, 7, 8, 10, 11, 12, 16, 19 after Bonferroni posthoc correction), supporting a differential multi-whisker vs. single-whisker ratio response in WT animals. In contrast, a repeated measures ANOVA for knock-out (KO) animals (11 Barrel, 7 Septa, 20 time points) showed no significant main effect of Condition (F(1,14) = 0.17, p = 0.684) or Condition-Time interaction (F(19,266) = 0.73, p = 0.791), indicating that the BarrelSepta difference observed in WT animals is absent in KO animals.

Furthermore, the manuscript suffers from imprecision; its conclusions are occasionally vague or overstated. The authors suggest a role for SST+ neurons in the observed divergence in SWS/MWS responses between barrel and septal domains. However, this remains speculative, and some findings appear inconsistent. For instance, the increased response of SST+ neurons to MWS versus SWS is not confined to a specific domain. Why, then, would preferential recruitment of SST+ neurons lead to divergent dynamics between barrel and septal regions? The higher density of SST+ neurons in septal versus barrel L4 is not a sufficient explanation, particularly since the SWS/MWS response divergence is also observed in layers 2/3, where no difference in SST+ neuron density is found.

Moreover, SST+ neuron-mediated inhibition is not necessarily restricted to the layer in which the cell body resides. It remains unclear through which differential microcircuits (barrel vs septum) the enhanced recruitment of SST+ neurons could account for the divergent responses to repeated SWS versus MWS stimulation.

We fully appreciate the reviewer’s comment. We currently do not provide any evidence on the contribution of SST neurons in the barrels versus septa in layer 4 on the response divergence of spiking observed in SWS versus MWS. We only show that these neurons differentially distribute in the two domains in this layer. It is certainly known that there is molecular and circuit-based diversity of SST-positive neurons in different layers of the cortex, so it is plausible that this includes cells located in the two domains of vS1, something which has not been examined so far. Our data on their distribution are one piece of information that SST neurons may have a differential role in inhibiting barrel stellate cells versus septa ones. Morphological reconstructions of SST neurons in L4 of the somatosensory barrel cortex has shown that their dendrites and axons project locally and may confine to individual domains, even though not specifically examined (Fig. 3 of Scala F et al., 2019). The same study also showed that L4 SST cells receive excitatory input from local stellate cells) and is known that they are also directly excited by thalamocortical fibers (Beierlein et al., 2003; Tan et al., 2008), both of which facilitate.

As shown in our supplementary figure, the divergence is also observed in L2/3 where, as the reviewer also points out, where we do not have a differential distribution of SST cells, at least based on a columnar analysis extending from L4. There are multiple scenarios that could explain this “discrepancy” that one would need to examine further in future studies. One straightforward one is that the divergence in spiking in L2/3 domains may be inherited from L4 domains, where L4 SST act on. Another is that even though L2/3 SST neurons are not biased in their distribution their input-output function is, something which one would need to examine by detailed in vitro electrophysiological and perhaps optogenetic approaches in S1. Despite the distinctive differences that have been found between the L4 circuitry in S1 and V1 (Scala F et al., 2019), recent observations indicate that small but regular patches of V1 marked by the absence of muscarinic receptor 2 (M2) have high temporal acuity (Ji et al., 2015), and selectively receive input from SST interneurons (Meier et al., 2025). Regions lacking M2 have distinct input and output connectivity patterns from those that express M2 (Meier et al., 2021; Burkhalter et al., 2023). These findings, together with ours, suggest that SST cells preferentially innervate and regulate specific domains columns- in sensory cortices.

Regardless of the mechanism, the Elfn1 knock-out mouse line almost exclusively affects the incoming excitability onto SST neurons (see also reply to comment below), hence what can be supported by our data is that changing the incoming short-term synaptic plasticity onto these neurons brings the spiking dynamics between barrels and septa closer together.

The Elfn1 KO mouse model seems too unspecific to suggest the role of the short-term plasticity in SST+ neurons in the differential response to repeated SWS vs MWS stimulation across domains. Why would Elfn1-dependent short-term plasticity in SST+ neurons be specific to a pathway, or a stimulation type (SWS vs MWS)? Moreover, the authors report that Elfn1 knockout alters synapses onto VIP+ as well as SST+ neurons (Stachniak et al., 2021; previous version of this paper)-so why attribute the phenotype solely to SST+ circuitry? In fact, the functional distinctions between barrel and septal domains appear largely abolished in the Elfn1 KO.

Previous work by others and us has shown that globally removing Elfn1 selectively removes a synaptic process from the brain without altering brain anatomy or structure. This allows us to study how the temporal dynamics of inhibition shape activity, as opposed to inhibition from particular cell types. We will nevertheless update the text to discuss more global implications for SST interneuron dynamics and include a reference to VIP interneurons that contain Elfn1.

When comparing SWS to MWS, we find that MWS replaces the neighboring excitation which would normally be preferentially removed by short-term plasticity in SST interneurons, thus providing a stable control comparison across animals and genotypes. On average, VIP interneurons failed to show modulation by MWS. We were unable to measure a substantial contribution of VIP cells to this process and also note that the Elfn1 expressing multipolar neurons comprise only ~5% of VIP neurons (Connor and Peters, 1984; Stachniak et al., 2021), a fraction that may be lost when averaging from 138 VIP cells. Moreover, the effect of Elfn1 loss on VIP neurons is quite different and marginal compared to that of SST cells, suggesting that the primary impact of Elfn1 knockout is mediated through SST+ interneuron circuitry. Therefore, even if we cannot rule out that these 5% of VIP neurons contribute to barrel domain segregation, we are of the opinion that their influence would be very limited if any.

Reviewer #2 (Public Reviews):

Summary:

Argunsah and colleagues demonstrate that SST-expressing interneurons are concentrated in the mouse septa and differentially respond to repetitive multi-whisker inputs. Identifying how a specific neuronal phenotype impacts responses is an advance.

Strengths:

(1)  Careful physiological and imaging studies.

(2)  Novel result showing the role of SST+ neurons in shaping responses.

(3)  Good use of a knockout animal to further the main hypothesis.

(4)  Clear analytical techniques.

We thank the reviewer for their appreciation of the study.

Weaknesses:

No major weaknesses were identified by this reviewer. Overall, I appreciated the paper but feel it overlooked a few issues and had some recommendations on how additional clarifications could strengthen the paper. These include:

(1) Significant work from Jerry Chen on how S1 neurons that project to M1 versus S2 respond in a variety of behavioral tasks should be included (e.g. PMID: 26098757). Similarly, work from Barry Connor’s lab on intracortical versus thalamocortical inputs to SST neurons, as well as excitatory inputs onto these neurons (e.g. PMID: 12815025) should be included.

We thank the reviewer for these valuable resources that we overlooked. We will include Chen et al. (2015), Cruikshank et al. (2007) and Gibson et al. (1999) to contextualize S1 projections and SST+ inputs, strengthening the study’s foundation as well as Beierlein et al. (2003) which nicely show both local and thalamocortical facilitation of excitatory inputs onto L4 SST neurons, in contrast to PV cells. The paper also shows the gradual recruitment of SST neurons by thalamocortical inputs to provide feed-forward inhibition onto stellate cells (regular spiking) of the barrel cortex L4 in rat.

(2) Using Layer 2/3 as a proxy to what is happening in layer 4 (~line 234). Given that layer 2/3 cells integrate information from multiple barrels, as well as receiving direct VPm thalamocortical input, and given the time window that is being looked at can receive input from other cortical locations, it is not clear that layer 2/3 is a proxy for what is happening in layer 4.

We agree with the reviewer that what we observe in L2/3 is not necessarily what is taking place in L4 SST-positive cells. The data on L2/3 was included to show that these cells, as a population, can show divergent responses when it comes to SWS vs MWS, which is not seen in L2/3 VIP neurons. Regardless of the mechanisms underlying it, our overall data support that SST-positive neurons can change their activation based on the type of whisker stimulus and when the excitatory input dynamics onto these neurons change due to the removal of Elfn1 the recruitment of barrels vs septa spiking changes at the temporal domain. Having said that, the data shown in Supplementary Figure 3 on the response properties of L2/3 neurons above the septa vs above the barrels (one would say in the respective columns) do show the same divergence as in L4. This suggests that a circuit motif may exist that is common to both layers, involving SST neurons that sit in L4, L5 or even L2/3. This implies that despite the differences in the distribution of SST neurons in septa vs barrels of L4 there is an unidentified input-output spatial connectivity motif that engages in both L2/3 and L4. Please also see our response to a similar point raised by reviewer 1.

(3) Line 267, when discussing distinct temporal response, it is not well defined what this is referring to. Are the neurons no longer showing peaks to whisker stimulation, or are the responses lasting a longer time? It is unclear why PV+ interneurons which may not be impacted by the Elfn1 KO and receive strong thalamocortical inputs, are not constraining activity.

We thank the reviewer for their comment and will clarify the statement.

This convergence of response profiles was further clear in stimulus-aligned stacked images, where the emergent differences between barrels and septa under SWS were largely abolished in the KO (Figure 4B). A distinction between directly stimulated barrels and neighboring barrels persisted in the KO. In addition, the initial response continued to differ between barrel and septa and also septa and neighbor (Figure 4B). This initial stimulus selectivity potentially represents distinct feedforward thalamocortical activity, which includes PV+ interneuron recruitment that is not directly impacted by the Elfn1 KO (Sun et al., 2006; Tan et al., 2008). PV+ cells are strongly excited by thalamocortical inputs, but these exhibit short-term depression, as does their output, contrasting with the sustained facilitation observed in SST+ neurons. These findings suggest that in WT animals, activity spillover from principal barrels is normally constrained by the progressive engagement of SST+ interneurons in septal regions, driven by Elfn1-dependent facilitation at their excitatory synapses. In the absence of Elfn1, this local inhibitory mechanism is disrupted, leading to longer responses in barrels, delayed but stronger responses in septa, and persistently stronger responses in unstimulated neighbors, resulting in a loss of distinction between the responses of barrel and septa domains that normally diverge over time (see Author response image 1 below).

Author response image 1.

(A) Barrel responses are longer following whisker stimulation in KO. (B) Septal responses are slightly delayed but stronger in KO. (C) Unstimulated neighbors show longer persistent responses in KO.

(4) Line 585 “the earliest CSD sink was identified as layer 4…” were post-hoc measurements made to determine where the different shank leads were based on the post-hoc histology?

Post hoc histology was performed on plane-aligned brain sections which would allow us to detect barrels and septa, so as to confirm the insertion domains of each recorded shank. Layer specificity of each electrode therefore could therefore not be confirmed by histology as we did not have coronal sections in which to measure electrode depth.

(5) For the retrograde tracing studies, how were the M1 and S2 injections targeted (stereotaxically or physiologically)? How was it determined that the injections were in the whisker region (or not)?

During the retrograde virus injection, the location of M1 and S2 injections was determined by stereotaxic coordinates (Yamashita et al., 2018). After acquiring the light-sheet images, we were able to post hoc examine the injection site in 3D and confirm that the injections were successful in targeting the regions intended. Although it would have been informative to do so, we did not functionally determine the whisker-related M1 and whisker-related S2 region in this experiment.

(6) Were there any baseline differences in spontaneous activity in the septa versus barrel regions, and did this change in the KO animals?

Thank you for this interesting question. Our previous study found that there was a reduction in baseline activity in L4 barrel cortex of KO animals at postnatal day (P)12, but no differences were found at P21 (Stachniak et al., 2023).

Reviewer #3 (Public Reviews):

Summary:

This study investigates the functional differences between barrel and septal columns in the mouse somatosensory cortex, focusing on how local inhibitory dynamics, particularly involving Elfn1-expressing SST⁺ interneurons, may mediate temporal integration of multiwhisker (MW) stimuli in septa. Using a combination of in vivo multi-unit recordings, calcium imaging, and anatomical tracing, the authors propose that septa integrate MW input in an Elfn1-dependent manner, enabling functional segregation from barrel columns.

Strengths:

The core hypothesis is interesting and potentially impactful. While barrels have been extensively characterized, septa remain less understood, especially in mice, and this study's focus on septal integration of MW stimuli offers valuable insights into this underexplored area. If septa indeed act as selective integrators of distributed sensory input, this would add a novel computational role to cortical microcircuits beyond what is currently attributed to barrels alone. The narrative of this paper is intellectually stimulating.

We thank the reviewer for finding the study intellectually stimulating.

Weaknesses:

The methods used in the current study lack the spatial and cellular resolution needed to conclusively support the central claims. The main physiological findings are based on unsorted multi-unit activity (MUA) recorded via low-channel-count silicon probes. MUA inherently pools signals from multiple neurons across different distances and cell types, making it difficult to assign activity to specific columns (barrel vs. septa) or neuron classes (e.g., SST⁺ vs. excitatory).

The recording radius (~50-100 µm or more) and the narrow width of septa (~50-100 µm or less) make it likely that MUA from "septal" electrodes includes spikes from adjacent barrel neurons.

The authors do not provide spike sorting, unit isolation, or anatomical validation that would strengthen spatial attribution. Calcium imaging is restricted to SST⁺ and VIP⁺ interneurons in superficial layers (L2/3), while the main MUA recordings are from layer 4, creating a mismatch in laminar relevance.

We thank the reviewer for pointing out the possibility of contamination in septal electrodes. Importantly, it may not have been highlighted, although reported in the methods, but we used an extremely high threshold (7.5 std, in methods, line 583) for spike detection in order to overcome the issue raised here, which restricts such spatial contaminations. Since the spike amplitude decays rapidly with distance, at high thresholds, only nearby neurons contribute to our analysis, potentially one or two. We believe that this approach provides a very close approximation of single unit activity (SUA) in our reported data. We will include a sentence earlier in the manuscript to make this explicit and prevent further confusion.

Regarding the point on calcium imaging being performed on L2/3 SST and VIP cells instead of L4. Both reviewer 1 and 2 brought up the same issue and we responded as follows. As shown in our supplementary figure, the divergence is also observed in L2/3 where we do not have a differential distribution of SST cells, at least based on a columnar analysis extending from L4. There are multiple scenarios that could explain this “discrepancy” that one would need to examine further in future studies. One straightforward one is that the divergence in spiking in L2/3 domains may be inherited from L4 domains, where L4 SST act on. Another is that even though L2/3 SST neurons are not biased in their distribution their input-output function is, something which one would need to examine by detailed in vitro electrophysiological and perhaps optogenetic approaches in S1. Despite the distinctive differences that have been found between the L4 circuitry in S1 and V1 (Scala F et al., 2019), recent observations indicate that small but regular patches of V1 marked by the absence of muscarinic receptor 2 (M2) have high temporal acuity (Ji et al., 2015), and selectively receive input from SST interneurons (Meier et al., 2025). Regions lacking M2 have distinct input and output connectivity patterns from those that express M2 (Meier et al., 2021; Burkhalter et al., 2023). These findings, together with ours, suggest that SST cells preferentially innervate and regulate specific domains -columns- in sensory cortices.

Furthermore, while the role of Elfn1 in mediating short-term facilitation is supported by prior studies, no new evidence is presented in this paper to confirm that this synaptic mechanism is indeed disrupted in the knockout mice used here.

We thank Reviewer #3 for noting the absence of new evidence confirming Elfn1’s disruption of short-term facilitation in our knockout mice. We acknowledge that our study relies on previously strong published data demonstrating that Elfn1 mediates short-term synaptic facilitation of excitatory inputs onto SST+ interneurons (Sylwestrak and Ghosh, 2012; Tomioka et al., 2014; Stachniak et al., 2019, 2023). These studies consistently show that Elfn1 knockout abolishes facilitation in SST+ synapses, leading to altered temporal dynamics, which we hypothesize underlies the observed loss of barrel-septa response divergence in our Elfn1 KO mice (Figure 4). Nevertheless, to address the point raised, we will clarify in the revised manuscript (around lines 245-247 and 271-272) that our conclusions are based on these established findings, stating: “Building on prior evidence that Elfn1 knockout disrupts short-term facilitation in SST+ interneurons (Sylwestrak and Ghosh, 2012; Tomioka et al., 2014; Stachniak et al., 2019, 2023), we attribute the abolished barrel-septa divergence in Elfn1 KO mice to altered SST+ synaptic dynamics, though direct synaptic measurements were not performed here.”

Additionally, since Elfn1 is constitutively knocked out from development, the possibility of altered circuit formation-including changes in barrel structure and interneuron distribution, cannot be excluded and is not addressed.

We thank Reviewer #3 for raising the valid concern that constitutive Elfn1 knockout could potentially alter circuit formation, including barrel structure and interneuron distribution. To address this, we will clarify in the revised manuscript (around line ~271 and in the Discussion) that in our previous studies that included both whole-cell patch-clamp in acute brain slices ranging from postnatal day 11 to 22 (P11 - P21) and in vivo recordings from barrel cortex at P12 and P21, we saw no gross abnormalities in barrel structure, with Layer 4 barrels maintaining their characteristic size and organization, consistent with wildtype (WT) mice (Stachniak et al., 2019, 2023). While we cannot fully exclude subtle developmental changes, prior studies indicate that Elfn1 primarily modulates synaptic function rather than cortical cytoarchitecture (Tomioka et al., 2014). Elfn1 KO mice show no gross morphological or connectivity differences and the pattern and abundance of Elfn1 expressing cells (assessed by LacZ knock in) appears normal (Dolan and Mitchell, 2013).

We will add the following to the Discussion: “Although Elfn1 is constitutively knocked out, we find here and in previous studies that barrel structure is preserved (Stachniak et al., 2019, 2023). Further, the distribution of Elfn1 expressing interneurons is not different in KO mice, suggesting minimal developmental disruption (Dolan and Mitchell, 2013).

Nonetheless, we acknowledge that subtle circuit changes cannot be ruled out without the usage of time-depended conditional knockout of the gene.”

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors): 

(1) My biggest concern is regarding statistics. Did the authors repeatedly apply independent tests (Mann-Whitney) without any correction for multiple comparisons (Figures 1 and 4)? In that case, the chances of a spurious "significant" result rise dramatically. 

In response to the reviewer’s comment, we now present new statistical results by utilizing ANOVA and blended these results in the manuscript between lines 172 and 192 for WT data and 282 and 298 for Elfn1 KO data. This new statistical approach shows the same differences as we had previously reported, hence consolidating the statements made. 

(2) The findings only hint at a mechanism involving SST+ neurons for how SWS and MWS are processed differently in the barrel vs septal domains. As a direct test of SST+ neuron involvement in the divergence of barrel and septal responses, the authors might consider SST-specific manipulations - for example, inhibitory chemo- or optogenetics during SWS and MWS stimulation.

We thank the reviewer for this comment and agree that a direct manipulation of SST+ neurons via inhibitory chemo- or opto-genetics could provide further supporting evidence for the main claims in our study. We have opted out from performing these experiments for this manuscript as we feel they can be part of a future study.  At the same time, it is conceivable that such manipulations and depending on how they are performed may lead to larger and non-specific effects on cortical activity, since SST neurons will likely be completely shut down. So even though we certainly appreciate and value the strengths of such approaches, our experiments have addressed a more nuanced hypothesis, namely that the synaptic dynamics onto SST+ neurons matter for response divergence of septa versus barrels, which could not have been easily and concretely addressed by manipulating SST+ cell firing activity.  

(3) In general, it is hard to comprehend what microcircuit could lead to the observed divergence in the MWS/SWS ratio in the barrel vs septal domain. There preferential recruitment of SST+ neurons during MWS is not specific to a particular domain, and the higher density of SST+ neurons specifically in L4 septa cannot per se explain the diverging MWS/SWS ratio in L4 septal neurons since similar ratio divergence is observed across domains in L2/3 neurons without increase SST+ neuron density in L2/3. This view would also assume that SST+ inhibition remains contained to its own layer and domain. Is this the case? Is it that different microcircuits between barrels and septa differently shape the response to repeated MWS? This is partially discussed in the paper; can the authors develop on that? What would the proposed mechanism be? Can the short-term plasticity of the thalamic inputs (VPM vs POm) be part of the picture?

We thank the reviewer for raising this important point. We propose that the divergence in MWS/SWS ratios across barrel and septal domains arises from dynamic microcircuit interactions rather than static anatomical features such as SST+ density, which we describe and can provide a hint. In L2/3, where SST+ density is uniform, divergence persists, suggesting that trans-laminar and trans-domain interactions are key. Barrel domains, primarily receiving VPM inputs, exhibit short-term depression onto excitatory cells and engage PV+ and SST+ neurons to stabilize the MWS/SWS ratio, with Elfn1-dependent facilitation of SST+ neurons gradually increasing inhibition during repetitive SWS. Septal domains, in contrast, are targeted by facilitating POm inputs, combined with higher L4 SST+ density and Elfn1-mediated facilitation, producing progressive inhibitory buildup that amplifies the MWS/SWS ratio. SST+ projections in septa may extend trans-laminarly and laterally, influencing L2/3 and neighboring barrels, thereby explaining L2/3 divergence despite uniform SST+ density in L2/3. In this regards, direct laminar-dependent manipulations will be required to confirm whether L2/3 divergence is inherited from L4 dynamics. In Elfn1 KO mice, the loss of facilitation in SST+ neurons likely flattens these dynamics, disrupting functional segregation. Future experiments using VPM/POm-specific optogenetic activation and SST+ silencing will be critical to directly test this model.

We expanded the discussion accordingly.

(4) Can the decoder generalize between SWS and MWS? In this condition, if the decoder accuracy is higher for barrels than septa, it would support the idea that septa are processing the two stimuli differently. 

Our results show that septal decoding accuracy is generally higher than barrel accuracy when generalizing from multi-whisker stimulation (MWS) to single-whisker stimulation (SWS), indicating distinct information processing in septa compared to barrels.

In wild-type (WT) mice, septal accuracy exceeds barrel accuracy across all time windows (150ms, 51-95ms, 1-95ms), with the largest difference in the 51-95ms window (0.9944 vs. 0.9214 at pulse 20, 10Hz stimulation). This septal advantage grows with successive pulses, reflecting robust, separable neural responses, likely driven by the posterior medial nucleus (POm)’s strong MWS integration contrasting with minimal SWS activation. Barrel responses, driven by consistent ventral posteromedial nucleus (VPM) input for both stimuli, are less distinguishable, leading to lower accuracy.

In Elfn1 knockout (KO) mice, which disrupt excitatory drive to somatostatin-positive (SST+) interneurons, barrel accuracy is higher initially in the 1-50ms window (0.8045 vs. 0.7500 at pulse 1), suggesting reduced early septal distinctiveness. However, septal accuracy surpasses barrels in later pulses and time windows (e.g., 0.9714 vs. 0.9227 in 51-95ms at pulse 20), indicating restored septal processing. This supports the role of SST+ interneurons in shaping distinct MWS responses in septa, particularly in late-phase responses (51-95ms), where inhibitory modulation is prominent, as confirmed by calcium imaging showing stronger SST+ activation during MWS.

These findings demonstrate that septa process SWS and MWS differently, with higher decoding accuracy reflecting structured, POm- and SST+-driven response patterns. In Elfn1 KO mice, early deficits in septal processing highlight the importance of SST+ interneurons, with later recovery suggesting compensatory mechanisms. 

We have added Supplementary Figure 4 and included this interpretation between lines 338353. 

We thank the reviewer for suggesting this analysis.

(5) It is not clear to me how the authors achieve SWS. How is it that the pipette tip "placed in contact with the principal whisker" does not detach from the principal whisker or stimulate other whiskers? Please clarify the methods. 

Targeting the specific principal whisker is performed under the stereoscope.  

Specifically, we have added this statement in line 628:

“We trimmed the whiskers where necessary, to avoid them touching each other and to avoid stimulating other whiskers. By putting the pipette tip very close (almost touching) to the principal whisker, the movement of the tip (limited to 1mm) would reliably move the targeted whisker. The specificity of the stimulation of the selected principal whisker was observed under the stereoscope.”

(6) The method for calculating decoder accuracy is not clearly described-how can accuracy exceed 1? The authors should clarify this metric and provide measures of variability (e.g., confidence intervals or standard deviations across runs) to assess the significance of their comparisons. Additionally, using a consistent scale across all plots would improve interoperability. 

We thank the reviewer for raising this point. We have now changed the way accuracies are calculated and adopted a common scale among different plots (see updated Figure 5). We have also changed the methods section accordingly.

(7) Figure 1: The sample size is not specified. It looks like the numbers match the description in the methods, but the sample size should be clearly stated here. 

These are the numbers the reviewer is inquiring about. 

WT: (WT) animals: a 280 × 95 × 20 matrix for the stimulated barrel (14 Barrels, 95ms, 20 pulses), a 180 × 95 × 20 matrix for the septa (9 Septa, 95ms, 20 pulses), and a 360 × 95 × 20 matrix for the neighboring barrel (18 Neighboring barrels, 95ms, 20 pulses). N=4 mice.

KO: 11-barrel columns, 7 septal columns, 11 unstimulated neighbors from N=4 mice.

Panels D-F are missing axes and axis labels (firing rate, p-value). Panel D is mislabeled (left, middle, and right). I can't seem to find the yellow line. 

Thank you for this observation. We made changes in the figures to make them easier to navigate based on the collective feedback from the reviewers.

Why is changing the way to compare the differences in the responses to repeated stimulation between SWS and MWS? 

To assess temporal accumulation of information, we compared responses to repeated single-whisker stimulation (SWS) and multi-whisker stimulation (MWS) using an accumulative decoding approach rather than simple per-pulse firing rates. This method captures domain-specific integration dynamics over successive pulses.

The use of the term "principal whisker" is confusing, as it could refer to the whisker that corresponds to the recorded barrel. 

When we use the term principal whisker, the intention is indeed to refer to the whisker corresponding to the recorded barrel during single whisker stimulation. The term principal whisker is removed from Figure legend 1 and legend S1C where it may have led to  ambiguity.    

Why the statement "after the start of active whisking"? Mice are under anesthesia here; it does not appear to be relevant for the figure. 

“After the start of active whisking” refers to the state of the barrel cortex circuitry at the time of recordings. The particular reference we use comes from the habit of assessing sensory processing also from a developmental point of view. The reviewer is correct that it has nothing to do the with the status of the experiment. Nevertheless, since the reviewer found that it may create confusion, we have now taken it out. 

(8) Figure 3: The y-axis label is missing for panel C. 

This is now fixed. (dF/F).

(9) Figure 4: Axis labels are missing.

Added.

Minor: 

(10) Line 36: "progressive increase in septal spiking activity upon multi-whisker stimulation". There is no increase in septal spiking activity upon MWS; the ratio MWS/SWS increases.

We have changed the sentence as follows: Genetic removal of Elfn1, which regulates the incoming excitatory synaptic dynamics onto SST+ interneurons, leads to the loss of the progressive increase in septal spiking ratio (MWS/SWS) upon stimulation.

(11) Line 105: domain-specific, rather than column-specific, for consistency.

We have changed it.

(12) Lines 173-174: "a divergence between barrel and septa domain activity also occurred in Layer 4 from the 2nd pulse onward (Figure 1E)". The authors only show a restricted number of comparisons. Why not show the p-values as for SWS?

The statistics is now presented in current Figure 1E.

(13) Lines 151-153: "Correspondingly, when a single whisker is stimulated repeatedly, the response to the first pulse is principally bottom-up thalamic-driven responses, while the later pulses in the train are expected to also gradually engage cortico-thalamo-cortical and cortico-cortical loops." Can the authors please provide a reference?

We have now added the following references : (Kyriazi and Simons, 1993; Middleton et al., 2010; Russo et al., 2025).

(14) Lines 184-186: "Our electrophysiological experiments show a significant divergence of responses over time upon both SWS and MWS in L4 between barrels (principal and neighboring) and adjacent septa, with minimal initial difference". The only difference between the neighboring barrel and septa is the responses to the initial pulse. Can the author clarify? 

We have now changed the sentence as follows: Our electrophysiological experiments show a significant divergence of responses between domains upon both SWS and MWS in L4. (Line 198 now)

(15) Line 214: "suggest these interneurons may play a role in diverging responses between barrels and septa upon SWS". Why SWS specifically?

We have changed the sentence as follows: These results confirmed that SST+ and VIP+ interneurons have higher densities in septa compared to barrels in L4 and suggest these interneurons may play a role in diverging responses between barrels and septa. (Line 231 now).

(16) Line 235: "This result suggests that differential activation of SST+ interneurons is more likely to be involved in the domain-specific temporal ratio differences between barrels and septa". Why? The results here are not domain-specific.

We have now revised this statement to: This result suggested that temporal ratio differences specific to barrels and septa might involve differential activation of SST+ interneurons rather than VIP+ interneurons.

(17) Lines 241-243: "SST+ interneurons in the cortex are known to show distinct short-term synaptic plasticity, particularly strong facilitation of excitatory inputs, which enables them to regulate the temporal dynamics of cortical circuits." Please provide a reference.

We have now added the following references: (Grier et al., 2023; Liguz-Lecznar et al., 2016).

(18) Lines 245-247: "A key regulator of this plasticity is the synaptic protein Elfn1, which mediates short-term synaptic facilitation of excitation on SST+ interneurons (Stachniak et al., 2021, 2019; Tomioka et al., 2014)". Is Stachniak et al., 2021 not about the role of Elf1n in excitatory-to-VIP+ neuron synapses?

The reviewer correctly spotted this discrepancy . This reference has now been removed from this statement.

(19) Lines 271-272: "Building on our findings that Elfn1-dependent facilitation in SST+ interneurons is critical for maintaining barrel-septa response divergence". The authors did not show that.

We have now changed the statement to: Building on our findings that Elfn1 is critical for maintaining barrel-septa response divergence  

(20) Line 280: second firing peak, not "peal".

Thank you, it is now fixed.

(21) Lines 304-305: "These results highlight the critical role of Elfn1 in facilitating the temporal integration of 305 sensory inputs through its effects on SST+ interneurons". This claim is also overstated. 

We have now changed the statement to: These results highlight the contribution of Elfn1 to the temporal integration of sensory inputs. (Line 362)

(22) Line 329: Any reason why not cite Chen et al., Nature 2013?

We have now added this reference, as also pointed out by reviewer 1.

(23) Line 341-342: "wS1" and "wS2" instead of S1 and S2 for consistency.

Thanks, we have now updated the terms.

Reviewer #2 (Recommendations for the authors): 

(1) Figure 3D - the SW conditions are labeled but not the MW conditions (two right graphs) - they should be labeled similarly (SSTMW, VIPMW). 

The two right graphs in Figure 3D represent paired SW vs MW comparisons of the evoked responses for SST and VIP populations, respectively.

(2) Figure 6 D and E I think it would be better if the Depth measurements were to be on the yaxis, which is more typical of these types of plots. 

We thank the reviewer for this comment. Although we appreciate this may be the case, we feel that the current presentation may be easier for the reader to navigate, and we have hence kept it. 

(3) Having an operational definition of septa versus barrel would be useful. As the authors point out, this is a tough distinction in a mouse, and often you read papers that use Barrel Wall versus Barrel Hollow/Center - operationally defining how these areas were distinguished would be helpful. 

We thank the reviewer for this comment and understand the point made.

We have now updated the methods section in line 611: 

DiI marks contained within the vGlut2 staining were defined as barrel recordings, while DiI marks outside vGlut2 staining were septal recordings.

Reviewer #3 (Recommendations for the authors): 

To support the manuscript's major claims, the authors should consider the following:

(1) Validate the septal identity of the neurons studied, either anatomically or functionally at the single-cell level (e.g., via Ca²⁺ imaging with confirmed barrel/septa mapping). 

We thank the reviewer for this suggestion, but we feel that these extensive experiments are beyond the scope of this study. 

(2) Provide both anatomical and physiological evidence to assess the possibility of altered cortical development in Elfn1 KO mice, including potential changes in barrel structure or SST⁺ cell distribution. 

To address the reviewer’s point, we have now added the following to the Discussion: “Although Elfn1 is constitutively knocked out, we find here and in previous studies that barrel structure is preserved (Stachniak et al., 2019, 2023). Further, the distribution of Elfn1 expressing interneurons is not different in KO mice, suggesting minimal developmental disruption (Dolan and Mitchell, 2013). Nonetheless, we acknowledge that subtle circuit changes cannot be ruled out without conditional knockouts.”,

(3) Examine the sensory responses of SST⁺ and VIP⁺ interneurons in deeper cortical layers, particularly layer 4, which is central to the study's main conclusions.

We thank the reviewer for this suggestion and appreciate the value it would bring to the study. We nevertheless feel that these extensive experiments are beyond the scope of this study and hence opted out from performing them. 

Minor Comments:

(1)  The authors used a CLARITY-based passive clearing protocol, which is known to sometimes induce tissue swelling or distortion. This may affect anatomical precision, especially when assigning neurons to narrow domains such as septa versus barrels. Please clarify whether tissue expansion was measured, corrected, or otherwise accounted for during analysis.

Yes, the tissue expansion was accounted during analysis for the laminar specification. We excluded the brains with severe distortion. 

(2) While the anatomical data are plotted as a function of "depth from the top of layer 4," the manuscript does not specify the precise depth ranges used to define individual cortical layers in the cleared tissue. Given the importance of laminar specificity in projection and cell type analyses, the criteria and boundaries used to delineate each layer should be explicitly stated.

Thank you for pointing this out. We now include the criteria for delineating each layer in the manuscript. “Given that the depth of Layer 4 (L4) can be reliably measured due to its welldefined barrel boundaries, and that the relative widths of other layers have been previously characterized (El-Boustani et al., 2018), we estimated laminar boundaries proportionally. Specifically, Layer 2/3 was set to approximately 1.3–1.5 times the width of L4, Layer 5a to ~0.5 times, and Layer 5b to a similar width as L4. Assuming uniform tissue expansion across the cortical column, we extrapolated the remaining laminar thicknesses proportionally.”

(3)  In several key comparisons (e.g., SST⁺ vs. VIP⁺ interneurons, or S2-projecting vs. M1projecting neurons), it is unclear whether the same barrel columns were analyzed across conditions. Given the anatomical and functional heterogeneity across wS1 columns, failing to control for this may introduce significant confounds. We recommend analyzing matched columns across groups or, if not feasible, clearly acknowledging this limitation in the manuscript.

We thank the reviewer for raising this important point. For the comparison of SST⁺ versus VIP⁺ interneurons, it would in principle have been possible to analyze the same barrel columns across groups. However, because some of the cleared brains did not reach the optimal level of clarity, our choice of columns was limited, and we were not always able to obtain sufficiently clear data from the same columns in both groups. Similarly, for the analysis of S2- versus M1-projecting neurons, variability in the position and spread of retrograde virus injections made it difficult to ensure measurements from identical barrel columns. We have now added a statement in the Discussion to acknowledge this limitation.

(4) Figure 1C: Clarify what each point in the t-SNE plot represents-e.g., a single trial, a recording channel, or an averaged response. Also, describe the input features used for dimensionality reduction, including time windows and preprocessing steps.

In response to the reviewer’s comment, we have now added the following in the methods: In summary, each point in the t-SNE plots represents an averaged response across 20 trials for a specific domain (barrel, septa, or neighbor) and genotype (WT or KO), with approximately 14 points per domain derived from the 280 trials in each dataset. The input features are preprocessed by averaging blocks of 20 trials into 1900-dimensional vectors (95ms × 20), which are then reduced to 2D using t-SNE with the specified parameters. This approach effectively highlights the segregation and clustering patterns of neural responses across cortical domains in both WT and KO conditions.

(5) Figures 1D, E (left panels): The y-axes lack unit labeling and scale bars. Please indicate whether values are in spikes/sec, spikes/bin, or normalized units.

We have now clarified this. 

(6) Figures 1D, E (right panels): The color bars lack units. Specify whether the values represent raw firing rates, z-scores, or other normalized measures. Replace the vague term "Matrix representation" with a clearer label such as "Pulse-aligned firing heatmap."

Thank you, we have now done it.

(7) Figure 1E (bottom panel): There appears to be no legend referring to these panels. Please define labels such as "B" and "S." 

Thank you, we have now done it.

(8) Figure 1E legend: If it duplicates the legend from Figure 1D, this should be made explicit or integrated accordingly. 

We have changed the structure of this figure.

(9) Figure 1F: Define "AUC" and explain how it was computed (e.g., area under the firing rate curve over 0-50 ms). Indicate whether the plotted values represent percentages and, if so, label the y-axis accordingly. If normalization was applied, describe the procedure. Include sample sizes (n) and specify what each data point represents (e.g., animal, recording site). 

The following paragraph has been added in the methods section:

The Area Under the Curve (AUC) was computed as the integral of the smoothed firing rate (spikes per millisecond) over a 50ms window following each whisker stimulation pulse, using trapezoidal integration. Firing rate data for layer 4 barrel and septal regions in wild-type (WT) and knockout (KO) mice were smoothed with a 3-point moving average and averaged across blocks of 20 trials. Plotted values represent the percentage ratio of multi-whisker (MW) to single whisker (SW) AUC with error bars showing the standard error of the mean. Each data point reflects the mean AUC ratio for a stimulation pulse across approximately 11 blocks (220 trials total). The y-axis indicates percentages.

(10) Figure 3C: Add units to the vertical axis.

We have added them.

(11) Figure 3D: Specify what each line represents (e.g., average of n cells, individual responses?). 

Each line represents an average response of a neuron.  

(12) Figure 4C legend: Same with what?". No legend refers to the bottom panels - please revise to clarify. 

Thank you. We have now changed the figure structure and legends and fixed the missing information issue.

(13) Supplementary Figure 1B: Indicate the physical length of the scale bar in micrometers. 

This has been fixed. The scale bar is 250um.

(14) Indicate the catalog number or product name of the 8×8 silicon probe used for recordings.

We have added this information. It is the A8x8-Edge-5mm-100-200-177-A64

References

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(2) Burkhalter, A., D’Souza, R. D. & Ji, W. (2023). Integration of feedforward and feedback information streams in the modular architecture of mouse visual cortex. Annu. Rev. Neurosci. 46, 259–280.

(3) Chen, J. L., Margolis, D. J., Stankov, A., Sumanovski, L. T., Schneider, B. L. & Helmchen, F. (2015). Pathway-specific reorganization of projection neurons in somatosensory cortex during learning. Nat. Neurosci. 18, 1101–1108.

(4) Connor, J. R. & Peters, A. (1984). Vasoactive intestinal polypeptide-immunoreactive neurons in rat visual cortex. Neuroscience 12, 1027–1044.

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(6) Dolan, J. & Mitchell, K. J. (2013). Mutation of Elfn1 in mice causes seizures and hyperactivity. PLoS One 8, e80491.

(7) Gibson, J. R., Beierlein, M. & Connors, B. W. (1999). Two networks of electrically coupled inhibitory neurons in neocortex. Nature 402, 75–79.

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(9) Martin-Cortecero, J. & Nuñez, A. (2014). Tactile response adaptation to whisker stimulation in the lemniscal somatosensory pathway of rats. Brain Res. 1591, 27–37.

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(11) Meier, A. M., Wang, Q., Ji, W., Ganachaud, J. & Burkhalter, A. (2021). Modular network between postrhinal visual cortex, amygdala, and entorhinal cortex. J. Neurosci. 41, 4809– 4825.

(12) Meier, A. M., D’Souza, R. D., Ji, W., Han, E. B. & Burkhalter, A. (2025). Interdigitating modules for visual processing during locomotion and rest in mouse V1. bioRxiv 2025.02.21.639505.

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(14) Stachniak, T. J., Sylwestrak, E. L., Scheiffele, P., Hall, B. J. & Ghosh, A. (2019). Elfn1induced constitutive activation of mGluR7 determines frequency-dependent recruitment of somatostatin interneurons. J. Neurosci. 39, 4461–4475.

(15) Stachniak, T. J., Kastli, R., Hanley, O., Argunsah, A. Ö., van der Valk, E. G. T., Kanatouris, G. & Karayannis, T. (2021). Postmitotic Prox1 expression controls the final specification of cortical VIP interneuron subtypes. J. Neurosci. 41, 8150–8166.

(16) Stachniak, T. J., Argunsah, A. Ö., Yang, J. W., Cai, L. & Karayannis, T. (2023). Presynaptic kainate receptors onto somatostatin interneurons are recruited by activity throughout development and contribute to cortical sensory adaptation. J. Neurosci. 43, 7101–7118.

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Author response:

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

Reviewer #1 (Public review):

Summary:

This manuscript by Pournejati et al investigates how BK (big potassium) channels and CaV1.3 (a subtype of voltage-gated calcium channels) become functionally coupled by exploring whether their ensembles form early-during synthesis and intracellular trafficking-rather than only after insertion into the plasma membrane. To this end, the authors use the PLA technique to assess the formation of ion channel associations in the different compartments (ER, Golgi or PM), single-molecule RNA in situ hybridization (RNAscope), and super-resolution microscopy.

Strengths:

The manuscript is well written and addresses an interesting question, combining a range of imaging techniques. The findings are generally well-presented and offer important insights into the spatial organization of ion channel complexes, both in heterologous and endogenous systems.

Weaknesses:

The authors have improved their manuscript after revisions, and some previous concerns have been addressed.

Still, the main concern about this work is that the current experiments do not quantitatively or mechanistically link the ensembles observed intracellularly (in the endoplasmic reticulum (ER) or Golgi) to those found at the plasma membrane (PM). As a result, it is difficult to fully integrate the findings into a coherent model of trafficking. Specifically, the manuscript does not address what proportion of ensembles detected at the PM originated in the ER. Without data on the turnover or halflife of these ensembles at the PM, it remains unclear how many persist through trafficking versus forming de novo at the membrane. The authors report the percentage of PLApositive ensembles localized to various compartments, but this only reflects the distribution of pre-formed ensembles. What remains unknown is the proportion of total BK and Ca_V1.3 channels (not just those in ensembles) that are engaged in these complexes within each compartment. Without this, it is difficult to determine whether ensembles form in the ER and are then trafficked to the PM, or if independent ensemble formation also occurs at the membrane. To support the model of intracellular assembly followed by coordinated trafficking, it would be important to quantify the fraction of the total channel population that exists as ensembles in each compartment. A comparable ensemble-to-total ratio across ER and PM would strengthen the argument for directed trafficking of pre-assembled channel complexes.

We appreciate the reviewer’s thoughtful comment and agree that quantitatively linking intracellular hetero-clusters to those at the plasma membrane is an important and unresolved question. Our current study does not determine what proportion of ensembles at the plasma membrane originated during trafficking. It also does not quantify the fraction of total BK and Ca_V1.3 channels engaged in these complexes within each compartment. Addressing this requires simultaneous measurement of multiple parameters—total BK channels, total Ca_V1.3 channels, hetero-cluster formation (via PLA), and compartment identity—in the same cell. This is technically challenging. The antibodies used for channel detection are also required for the proximity ligation assay, which makes these measurements incompatible within a single experiment.

To overcome these limitations, we are developing new genetically encoded tools to enable real-time tracking of BK and Ca_V1.3 dynamics in live cells. These approaches will enable us to monitor channel trafficking and the formation of hetero-clusters, as detected by colocalization. This kind of experiments will provide insight into their origin and turnover. While these experiments are beyond the scope of the current study, the findings in our current manuscript provide the first direct evidence that BK and CaV channels can form hetero-clusters intracellularly prior to reaching the plasma membrane. This mechanistic insight reveals a previously unrecognized step in channel organization and lays the foundation for future work aimed at quantifying ensemble-to-total ratios and determining whether coordinated trafficking of pre-assembled complexes occurs.

This limitation is acknowledged in the discussion section, page 23. It reads: “Our findings highlight the intracellular assembly of BK-Ca_V1.3 hetero-clusters, though limitations in resolution and organelle-specific analysis prevent precise quantification of the proportion of intracellular complexes that ultimately persist on the cell surface.”

Reviewer #2 (Public review):

Summary:

The co-localization of large conductance calcium- and voltage activated potassium (BK) channels with voltage-gated calcium channels (CaV) at the plasma membrane is important for the functional role of these channels in controlling cell excitability and physiology in a variety of systems.

An important question in the field is where and how do BK and CaV channels assemble as 'ensembles' to allow this coordinated regulation - is this through preassembly early in the biosynthetic pathway, during trafficking to the cell surface or once channels are integrated into the plasma membrane. These questions also have broader implications for assembly of other ion channel complexes

Using an imaging based approach, this paper addresses the spatial distribution of BKCaV ensembles using both overexpression strategies in tsa201 and INS-1 cells and analysis of endogenous channels in INS-1 cells using proximity ligation and superesolution approaches. In addition, the authors analyse the spatial distribution of mRNAs encoding BK and Cav1.3.

The key conclusion of the paper that BK and Ca_V1.3 are co-localised as ensembles intracellularly in the ER and Golgi is well supported by the evidence.However, whether they are preferentially co-translated at the ER, requires further work. Moreover, whether intracellular pre-assembly of BK-Ca_V1.3 complexes is the major mechanism for functional complexes at the plasma membrane in these models requires more definitive evidence including both refinement of analysis of current data as well as potentially additional experiments.

The reviewer raises the question of whether BK and Ca_V1.3 channels are preferentially co-translated. In fact, I would like to propose that co-translation has not yet been clearly defined for this type of interaction between ion channels. In our current work, we 1) observed the colocalization between BK and Ca_V1.3 mRNAs and 2) determined that 70% of BK mRNA in active translation also colocalizes with Ca_V1.3 mRNA. We think these results favor the idea of translational complexes that can underlie the process of co-translation. However, and in total agreement with the Reviewer, the conclusion that the mRNA for the two ion channels is cotranslated would require further experimentation. For instance, mRNA coregulation is one aspect that could help to define co-translation. 

To avoid overinterpretation, we have revised the manuscript to remove references to “co-translation” in the Results section and included the word “potential” when referring to co-translation in the Discussion section. We also clarified the limitations of our evidence in the Discussion that can be found on page 25: “It is important to note that while our data suggest mRNA coordination, additional experiments are required to directly assess co-translation.”

Strengths & Weaknesses

(1) Using proximity ligation assays of overexpressed BK and CaV1.3 in tsa201 and INS1 cells the authors provide strong evidence that BK and CaV can exist as ensembles (ie channels within 40 nm) at both the plasma membrane and intracellular membranes, including ER and Golgi. They also provide evidence for endogenous ensemble assembly at the Golgi in INS-1 cells and it would have been useful to determine if endogenous complexes are also observe in the ER of INS-1 cells. There are some useful controls but the specificity of ensemble formation would be better determined using other transmembrane proteins rather than peripheral proteins (eg Golgi 58K).

We thank the reviewer for their thoughtful feedback and for recognizing the strength of our proximity ligation assay data supporting BK–Ca_V1.3 hetero-clusters formation at both the plasma membrane and intracellular compartments. As for specificity controls, we appreciate the suggestion to use transmembrane markers. To strengthen our conclusion, we have performed an additional experiment comparing the number of PLA puncta formed by the interaction of Ca_V1.3 and BK channels with the number of PLA puncta formed by the interaction of Ca_V1.3 channels and ryanodine receptors in INS-1 cells. As shown in the figure below, the number of interactions between Ca_V1.3 and BK channels is significantly higher than that between Ca_V1.3 and RyR₂. Of note, RyR₂ is a protein resident of the ER. These results provide additional evidence of the existence of endogenous complex formation in INS-1 cells. We have added this figure as a supplement.

(2) Ensemble assembly was also analysed using super-resolution (dSTORM) imaging in INS-1 cells. In these cells only 7.5% of BK and CaV particles (endogenous?) co-localise that was only marginally above chance based on scrambled images. More detailed quantification and validation of potential 'ensembles' needs to be made for example by exploring nearest neighbour characteristics (but see point 4 below) to define proportion of ensembles versus clusters of BK or Cav1.3 channels alone etc. For example, it is mentioned that a distribution of distances between BK and Cav is seen but data are not shown.

We thank the reviewer for this comment. To address the request for more detailed quantification and validation of ensembles, we performed additional analyses:

Proportion of ensembles vs isolated clusters: We quantified clusters within 200 nm and found that 37 ± 3% of BK clusters are near one or more CaV1.3 clusters, whereas 15 ± 2% of CaV1.3 clusters are near BK clusters. Figure 8– Supplementary 1A

Distance distribution: As shown in Figure 8–Supplementary 1B, the nearestneighbor distance distribution for BK-to-CaV1.3 in INS-1 cells (magenta) is shifted toward shorter distances compared to randomized controls (gray), supporting preferential localization of BK–CaV1.3 hetero-clusters.

Together, these analyses confirm that BK–CaV1.3 ensembles occur more frequently than expected by chance and exhibit an asymmetric organization favoring BK proximity to CaV1.3 in INS-1 cells. We have included these data and figures in the revised manuscript, as well as description in the Results section. 

(3) The evidence that the intracellular ensemble formation is in large part driven by cotranslation, based on co-localisation of mRNAs using RNAscope, requires additional critical controls and analysis. The authors now include data of co-localised BK protein that is suggestive but does not show co-translation. Secondly, while they have improved the description of some controls mRNA co-localisation needs to be measured in both directions (eg BK - SCN9A as well as SCN9A to BK) especially if the mRNAs are expressed at very different levels. The relative expression levels need to be clearly defined in the paper. Authors also use a randomized image of BK mRNA to show specificity of co-localisation with Cav1.3 mRNA, however the mRNA distribution would not be expected to be random across the cell but constrained by ER morphology if cotranslated so using ER labelling as a mask would be useful?

We thank the reviewer for these constructive suggestions. We measured mRNA colocalization in both directions as recommended. As shown in the figure below, colocalization between KCNMA1 and SCN9A transcripts was comparable in both directions, with no statistically significant difference, supporting the specificity of the observed associations. We decided not to add this to the original figure to keep the figure simple. 

We agree that co-localization of BK protein with BK mRNA is not conclusive evidence of co-translation, and we do not intend to mislead readers in our conclusion. Consequently, we were careful in avoiding the use of co-translation in the result section and added the word “potential” when referring to co-translation in the Discussion section. We added a sentence in the discussion to caution our interpretation: “It is important to note that while our data suggest mRNA coordination, additional experiments are required to directly assess cotranslation.”

Author response image 1.

(4) The authors attempt to define if plasma membrane assemblies of BK and CaV occur soon after synthesis. However, because the expression of BK and CaV occur at different times after transient transfection of plasmids more definitive experiments are required. For example, using inducible constructs to allow precise and synchronised timing of transcription. This would also provide critical evidence that co-assembly occurs very early in synthesis pathways - ie detecting complexes at ER before any complexes 

We appreciate the reviewer’s insightful suggestion regarding the use of inducible constructs to synchronize transcription timing. This is an excellent approach and would allow direct testing of whether co-assembly occurs early in the synthesis pathway, including detection of complexes at the ER prior to plasma membrane localization. These experiments are beyond the scope of the present work but represent an important direction for future studies.

We have added the following sentence to the Discussion section (page 24) to highlight this idea. “Future experiments using inducible constructs to precisely control transcription timing will enable more precise quantification of heterocluster formation in the ER compartment prior to plasma membrane insertion and reduce the variability introduced by differences in expression timing after plasmid transfection.” 

(5) While the authors have improved the definition of hetero-clusters etc it is still not clear in superesolution analysis, how they separate a BK tetramer from a cluster of BK tetramers with the monoclonal antibody employed ie each BK channel will have 4 binding sites (4 subunits in tetramer) whereas Cav1.3 has one binding site per channel. Thus, how do authors discriminate between a single BK tetramer (molecular cluster) with potential 4 antibodies bound compared to a cluster of 4 independent BK channels.

We appreciate the reviewer’s thoughtful comment regarding the interpretation of super-resolution data. We agree that distinguishing a single BK tetramer from a cluster of multiple BK channels is challenging when using an antibody that can bind up to four sites per channel. To clarify, our analysis does not attempt to resolve individual subunits within a tetramer; rather, it focuses on the nanoscale spatial proximity of BK and Ca_V1.3 signals.

We want to note that this limitation applies only to the super-resolution maps in Figures 8C and 9D and does not affect Airyscan-based analyses or measurements of BK–Ca_V1.3 proximity.

To address how we might distinguish between a single BK tetramer and a cluster of multiple BK channels, we considered two contrasting scenarios. In the first case, we assume that all four α-subunits within a tetramer are labeled. Based on cryoEM structures, a BK tetramer measures approximately 13 nm × 13 nm (≈169 nm²). Adding two antibody layers (primary and secondary) would increase the footprint by ~14 nm in each direction, resulting in an estimated area of ~41 nm × 41 nm (≈1681 nm²). Under this assumption, particles smaller than ~1681 nm² would likely represent individual tetramers, whereas larger particles would correspond to clusters of multiple tetramers. 

In the second scenario, we propose that steric constraints at the S9–S10 segment, where the antibody binds, limit labeling to a single antibody per tetramer. If true, the localization precision would approximate 14 nm × 14 nm—the combined size of the antibody complex and the channel—close to the resolution limit of the microscope. To test this, we performed a control experiment using two antibodies targeting the BK C-terminal domain, raised in different species and labeled with distinct fluorophores. Super-resolution imaging revealed that only ~12% of particles were colocalized, suggesting that most channels bind a single antibody.

If multiple antibodies could bind each tetramer, we would expect much greater colocalization.

Although these data are not included in the manuscript, we have added the following clarification to the Results section (page 19): “It is important to note that this technique does not allow us to distinguish between labeling of four BK αsubunits within a tetramer and labeling of multiple BK channel clusters. Hence, particles smaller than ~1680 nm² may represent either a single tetramer or a cluster. This limitation applies to Figures 8C and 9D and does not affect measurements of BK–Ca_V1.3 proximity.”

Author response image 2.

(6) The post-hoc tests used for one way ANOVA and ANOVA statistics need to be defined throughout

We thank the reviewer for highlighting the need for clarity regarding our statistical analyses. We have now specified the post-hoc tests used for all one-way ANOVA and ANOVA comparisons throughout the manuscript, and updated figure legends.

Reviewer #3 (Public review):

Summary:

The authors present a clearly written and beautifully presented piece of work demonstrating clear evidence to support the idea that BK channels and Cav1.3 channels can co-assemble prior to their assertion in the plasma membrane.

Strengths:

The experimental records shown back up their hypotheses and the authors are to be congratulated for the large number of control experiments shown in the ms.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

The authors have sufficiently addressed the specific points previously raised and the manuscript has improved clarity in those aspects. My main concern, which still remains, is stated in the public review.

Reviewer #3 (Recommendations for the authors):

I am content that the authors have attempted to fully address my previous criticisms.

I have only three suggestions

(1) I think the word Homo-clusters at the bottom right of Figure 1 is erroneously included.

We thank the reviewer for bringing this to our attention. The figure has been corrected accordingly.

(2) The authors should, for completeness, to refer to the beta, gamma and LINGO subunit families in the Introduction and include appropriate references:

Knaus, H. G., Folander, K., Garcia-Calvo, M., Garcia, M. L., Kaczorowski, G. J., Smith, M., & Swanson, R. (1994). Primary sequence and immunological characterization of betasubunit of high conductance Ca2+-activated K+ channel from smooth muscle. The Journal of Biological Chemistry, 269(25), 17274-17278.

Brenner, R., Jegla, T. J., Wickenden, A., Liu, Y., & Aldrich, R. W. (2000a). Cloning and functional characterization of novel large conductance calcium-activated potassium channel beta subunits, hKCNMB3 and hKCNMB4. The Journal of Biological Chemistry, 275(9), 6453-6461.

Yan, J & R.W. Aldrich. (2010) LRRC26 auxiliary protein allows BK channel activation at resting voltage without calcium. Nature. 466(7305):513-516

Yan, J & R.W. Aldrich. (2012) BK potassium channel modulation by leucine-rich repeatcontaining proteins. Proceedings of the National Academy of Sciences 109(20):7917-22

Dudem, S, Large RJ, Kulkarni S, McClafferty H, Tikhonova IG, Sergeant, GP, Thornbury, KD, Shipston, MJ, Perrino BA & Hollywood MA (2020). LINGO1 is a novel regulatory subunit of large conductance, Ca2+-activated potassium channels. Proceedings of the National Academy of Sciences 117 (4) 2194-2200

Dudem, S., Boon, P. X., Mullins, N., McClafferty, H., Shipston, M. J., Wilkinson, R. D. A., Lobb, I., Sergeant, G. P., Thornbury, K. D., Tikhonova, I. G., & Hollywood, M. A. (2023). Oxidation modulates LINGO2-induced inactivation of large conductance, Ca2+-activated potassium channels. The Journal of Biological Chemistry, 299 (3) 102975.

We agree with the reviewer’s suggestion and have revised the Introduction to include references to the beta, gamma, and LINGO subunit families. Appropriate citations have been added to ensure completeness and contextual relevance.

Additionally, BK channels are modulated by auxiliary subunits, which fine-tune BK channel gating properties to adapt to different physiological conditions. The β, γ, and LINGO1 subunits each contribute distinct structural and regulatory features: β-subunits modulate Ca²⁺ sensitivity and can induce inactivation; γ-subunits shift voltage-dependent activation to more negative potentials; and LINGO1 reduces surface expression and promotes rapid inactivation (18-24). These interactions ensure precise control over channel activity, allowing BK channels to integrate voltage and calcium signals dynamically in various cell types.

(3) I think it may be more appropriate to include the sentence "The probes against the mRNAs of interest and tested in this work were designed by Advanced Cell Diagnostics." (P16, right hand column, L12-14) in the appropriate section of the Methods, rather than in Results.

We thank the reviewer for this helpful suggestion. In response, we have relocated the sentence to the appropriate section of the Methods, where it now appears with relevant context.

Se podría escribir un último párrafo breve respecto a esta hipótesis de interacción, con tal de que se despeje su carácter explotario, y así también dar espacio a justificar porqué se decidió moderar por nivel de gobernanza y no por desigualdad, por ejemplo

Muy completo y claro el apartado. No hay mayores sugerencias que las ya anotadas

En este apartado aclararía la distinción entre confianza y cohesión social, ya que se suelen entender como sinónimos. En la misma línea, señalaría que elementos como la desigualdad y el bienestar son consecuencias de la cohesión social y no condiciones constitutivas como tal (citar Schiefer & van der Noll, 2017).

Reviewer #2 (Public review):

Summary:

I appreciate the considerable work the authors have done on the revision. The manuscript is markedly improved.

Strengths still include the strong theoretical basis, well-done experiments, and clear links to LFP / spectral analyses that have links to human data. The task is now more clearly explained, and the neural correlates better articulated.

Weaknesses:

I had remaining questions, many related to my previous questions.<br /> (1) The results have some complexity, but I still had questions about which is resource and which is resistance based. The authors say in the last sentence of the discussion: "Prominent pre-choice theta power was associated with a behavioral strategy characterized by a strong bias towards a resistance-based strategy, whereas the neural signature of ival-tracking was associated with a strong bias towards a resource-based strategy.".<br /> I might suggest making this simpler and clear in the abstract and the first paragraph of the discussion. A simple statement like 'pre-choice theta was biased towards resistance whereas single neurons were biased towards resources" might make this idea come across?

(2) I think most readers would like to see raw single trial LFP traces in Figure 3, single unit rasters in Figure 4, and spike-field records in Figure 5.

(3) What limitations are there to this work? I wonder if readers might benefit from some contextualization - the sample size, heterogenous behavior - lack of cell-type specificity - using PC3 to define spectral relationships - I might suggest pointing these out.

(4) I still wasn't sure what 4 Hz vs. theta 6-12 Hz meant - is it all based on PC3's pos/neg correlation? I wonder if showing a scatter plot with the y-axis being PC3 and the x-axis being theta 4 Hz power would help distinguish these? Is this the first time this sort of analysis has been done? If so, it requires clearer definitions.

Reviewer #1 (Public review):

Summary:

In this manuscript, Green et al. attempt to use large-scale protein structure analysis to find signals of selection and clustering related to antibiotic resistance. This was applied to the whole proteome of Mycobacterium tuberculosis, with a specific focus on the smaller set of known antibiotic-resistance-related proteins.

Strengths:

The use of geospatial analysis to detect signals of selection and clustering on the structural level is really intriguing. This could have a wider use beyond the AMR-focussed work here and could be applied to a more general evolutionary analysis context. Much of the strength of this work lies in breaking ground into this structural evolution space, something rarely seen in such pathogen data. Additional further research can be done to build on this foundation, and the work presented here will be important for the field.

The size of the dataset and use of protein structure prediction via AlphaFold, giving such a consistent signal within the dataset, is also of great interest and shows the power of these approaches to allow us to integrate protein structure more confidently into evolution and selection analyses.

Weaknesses:

There are several issues with the evolutionary analysis and assumptions made in the paper, which perhaps overstate the findings, or require refining to take into account other factors that may be at play.

(1) The focus on antimicrobial resistance (AMR) throughout the paper contains the findings within that lens. This results in a few different weaknesses:

(a) While the large size of the analysis is highlighted in the abstract and elsewhere, in reality, only a few proteins are studied in depth. These are proteins already associated with AMR by many other studies, somewhat retreading old ground and reducing the novelty.

(b) Beyond the AMR-associated proteins, the proteome work is of great interest, but only casually interrogated and only in the context of AMR. There appears to be an assumption that all signals of positive selection detected are related to AMR, whereas something like cas10 is part of the CRISPR machinery, a set of proteins often under positive selection, and thus unlikely to be AMR-related.

(2) The strength of the signal from the structural information and the novelty of the structural incorporation into prediction are perhaps overstated.

(a) A drop of 13% in F1 for a gain of 2% in PPV is quite the trade-off. This is not as indicative of a strong predictor that could be used as the abstract claims. While the approach is novel and this is a good finding for a first attempt at such complex analysis, this is perhaps not as significant as the authors claim

(b) In relation to this, there is a lack of situating these findings within the wider research landscape. For instance, the use of structure for predicting resistance has been done, for example, in PncA (https://academic.oup.com/jacamr/article/6/2/dlae037/7630603, https://www.sciencedirect.com/science/article/pii/S1476927125003664, https://www.nature.com/articles/s41598-020-58635-x) and in RpoB (https://www.nature.com/articles/s41598-020-74648-y). These, and other such works, should be acknowledged as the novelty of this work is perhaps not as stark as the authors present it to be.

(3) The authors postulate that neutral AA substitutions would be randomly distributed in the protein structure and thus use random mutations as a negative control to simulate this neutral evolution. However, I am unsure if this is a true negative control for neutral evolution. The vast majority of residues would be under purifying selection, not neutral selection, especially in core proteins like rpoB and gyrA. Therefore, most of these residues would never be mutated in a real-world dataset. Therefore, you are not testing positive selection against neutral selection; you are testing positive against purifying, which will have a much stronger signal. This is likely to, in turn, overestimate the signal of positive selection. This would be better accounted for using a model of neutral evolution, although this is complex and perhaps outside the scope. Still, it needs to be made clear that these negative controls are not representative of neutral evolution.

(4) In a similar vein, the use of 15 Å as a cut-off for stating co-localisation feels quite arbitrary. The average radius of a globular protein is about 20 Å, so this could be quite a large patch of a protein. I think it may be good to situate the cut-off for a 'single location' within a size estimator of the entire protein, as 15 Å could be a neighbourhood in a large protein, but be the whole protein for smaller ones.

<< search

Cela nous fait réaliser que tout repose sur des outils capables de lire ces formats finalement et que dès qu’il y a le moindre soucis, le moindre décalage, les problèmes commencent. C’est super interessant

Parresía (Sócrates y Foucault)

長いし、トルでも意味は通じるかなと

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Le Chagrin et la Pitié : Analyse d'un Film Révolutionnaire

Résumé Exécutif

Ce document de synthèse analyse le film documentaire Le Chagrin et la Pitié de Marcel Ophuls, en s'appuyant sur les perspectives et témoignages présentés dans le documentaire d'ARTE. Sorti en 1971, Le Chagrin et la Pitié a provoqué une rupture fondamentale dans la mémoire collective française concernant la période de l'Occupation.

Les points essentiels sont les suivants :

• Destruction du Mythe Résistancialiste : Le film a été le premier à confronter frontalement et à déconstruire le mythe gaulliste d'une France majoritairement unie dans la Résistance.

Il a révélé une réalité bien plus complexe, faite de collaboration, d'attentisme, d'ignorance volontaire et d'actes héroïques isolés.

• Une Méthodologie d'Interview Novatrice : Marcel Ophuls a développé un art de l'interview unique, mêlant douceur apparente, humour et questions incisives.

En transformant les témoins en "personnages" au sens fort, il a créé une "dramaturgie du témoignage" qui expose les ambiguïtés et les contradictions de la période.

• Censure et Succès Paradoxal : Initialement conçu pour la télévision, le film a été refusé par l'ORTF, la télévision d'État, au motif qu'il "détruit des mythes dont la France a encore besoin".

Cette censure a paradoxalement amplifié son impact, le transformant en un événement culturel majeur lors de sa sortie en salles, où il a connu un immense succès public.

• Un Catalyseur de Mémoire : Le film a déclenché un débat public sans précédent sur la responsabilité de l'État français et de citoyens français dans la collaboration et la déportation des Juifs.

Il a ouvert la voie à de nouvelles œuvres cinématographiques et aux travaux d'historiens comme Robert Paxton.

• Héritage Politique et Sociétal Durable : L'onde de choc du film a eu des répercussions à long terme, influençant la société française dans son rapport à son passé.

Son héritage est perceptible jusque dans le discours de Jacques Chirac en 1995, reconnaissant officiellement la responsabilité de l'État français dans la Shoah, un discours considéré comme un prolongement direct du travail de mémoire initié par le film.

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Un Séisme Cinématographique et Culturel

Le 14 avril 1971, une petite salle de cinéma du Quartier Latin à Paris projette pour la première fois Le Chagrin et la Pitié.

Ce documentaire, réalisé par Marcel Ophuls, alors âgé de 42 ans, offre une "vision décapante" des années d'Occupation, loin de la mythologie héroïque officielle.

Produit par les télévisions allemande et suisse, il est rapidement acclamé à l'international, acheté par 27 pays et sélectionné aux Oscars.

En France, cependant, l'accueil est radicalement différent. L'ORTF (Office de Radiodiffusion-Télévision Française) refuse d'acheter et de diffuser ce qu'elle considère comme un "film hérétique".

Cette décision déclenche de violentes controverses et érige le film en symbole d'un "duel entre la génération post-68 et le pouvoir".

Le film devient célèbre, paradoxalement, parce qu'on ne l'a pas montré à la télévision.

Le titre lui-même, inspiré par le témoignage d'un résistant qui confie que ses sentiments les plus fréquents furent "le chagrin et la pitié", est décrit comme "extraordinairement romanesque" et "impitoyable", reflétant la complexité d'une période où les lignes morales étaient brouillées.

La Genèse du Projet : De l'ORTF à l'Exil

Le Parcours de Marcel Ophuls

Rien ne prédestinait Marcel Ophuls à briser le mythe gaulliste, si ce n'est son parcours personnel.

Né en Allemagne en 1927, fils du cinéaste Max Ophuls, il fuit le nazisme avec sa famille pour la France, puis pour Hollywood.

Devenu citoyen américain, il rentre en France après-guerre.

Après des débuts comme assistant réalisateur et une amitié avec François Truffaut, il connaît un échec commercial qui le pousse, "très à contre-cœur" et pour des "raisons alimentaires", à rejoindre la télévision française en 1966.

L'Invention d'un Style

À l'ORTF, au sein de l'équipe de l'émission "Zoom", Ophuls développe son style.

Utilisant les nouvelles technologies légères (caméra 16mm, enregistreur Nagra), il pratique un "journalisme subjectif", allant à la rencontre des Français (femmes, ouvriers, jeunes) et perfectionnant ce qui est décrit comme un "art de l'interview".

Du Projet Interrompu à la Production Indépendante

Le projet initial est une suite à deux émissions sur Munich 1938, visant à explorer les conséquences de l'Occupation. Le mouvement de Mai 68 et la grève qui s'ensuit à l'ORTF interrompent le projet.

Ophuls, ainsi que les producteurs André Harris et Alain de Sédouy, sont licenciés.

Le groupe trouve refuge auprès d'une nouvelle société de production suisse et Ophuls convainc la télévision allemande (NDR) de financer 70% du film.

Le tournage est lancé au printemps 1969, né de la censure et de la nécessité de trouver du travail ailleurs.

Une Méthodologie Révolutionnaire : La Dramaturgie du Témoignage

L'Art de l'Interview

Marcel Ophuls rejette l'étiquette de "cinéma vérité", qu'il juge "horriblement prétentieux".

Sa méthode consiste à créer une "dramaturgie du témoignage" où les personnes interrogées deviennent de véritables personnages.

Il aborde ses sujets "en douceur, en rigolant", utilisant parfois l'"humour juif" pour désarmer, mais son approche est fondamentalement sans concession.

Il laisse ses témoins "dérouler leurs pensées", manifestant une forme de respect pour leur parole tout en maintenant une distance critique, voire un "manque d'empathie".

Cette approche permet de révéler les fissures, les non-dits et les justifications a posteriori.

Témoignages Emblématiques

Le film est construit autour d'une mosaïque de témoignages qui, mis en regard, créent une vision polyphonique et troublante de la France occupée.

• Le choix de Clermont-Ferrand comme microcosme s'est avéré judicieux en raison de sa proximité avec Vichy et de la présence de toutes les facettes de l'époque :

*pétainisme, * Milice, et * Résistance.

Témoin(s)

Rôle / Statut

Thème Principal du Témoignage

Les frères Klein

Commerçants

La banalité de l'antisémitisme et le manque de solidarité. Leur annonce dans Le Moniteur pour se déclarer "catholique" et non "juif" est une séquence phare.

René de Chambrun

Gendre de Pierre Laval

La défense sophistique de Vichy, argumentant que le régime aurait sauvé une partie des Juifs français.

Ophuls le confronte directement à la caméra sur le droit moral d'un État à "choisir entre deux groupes humains".

Christian de la Mazière

Ancien de la Waffen-SS française

L'engagement fasciste assumé ("jeune fasciste").

Son témoignage, qualifié de "glaçant" et "authentique", crève l'écran et met mal à l'aise toutes les consciences.

Il conclut le film par un appel à la prudence adressé à la jeunesse de 68.

Pierre Mendès France

Homme politique, résistant

La dignité face à la persécution.

Son récit de l'arrestation de son père et de la naissance de sa fille, qu'il n'avait jamais vue, est un moment d'émotion intense.

Les frères Grave

Paysans résistants

L'héroïsme ordinaire et modeste. Leur témoignage sur les débuts de la résistance en Auvergne, où ils chantaient L'Internationale car Pétain avait annexé La Marseillaise, illustre l'engagement populaire.

Claude Lévi-Strauss

Anthropologue

Le regard extérieur et moral. Il juge sévèrement l'État français pour avoir "renié le droit d'asile traditionnel de la France" en livrant des ressortissants qu'il devait protéger.

Témoins des "tondues"

Spectateurs de la Libération

La violence misogyne de l'épuration.

La séquence, associée à une chanson de Brassens, est qualifiée de "transgressive" et a profondément marqué les féministes émergentes de l'époque.

La Censure et la Controverse : Un Mythe Intouchable

Le Refus de l'ORTF

La direction de la télévision d'État justifie sa décision de ne pas diffuser le film par une phrase devenue célèbre :

"Ce film détruit des mythes dont la France a encore besoin."

Cette déclaration révèle une volonté explicite du pouvoir politique de maintenir une version officielle de l'Histoire, occultant les aspects les plus sombres de la période.

L'Opposition de Simone Veil et des Résistants

Une opposition significative est venue de figures respectées, notamment Simone Veil.

Ayant elle-même survécu à la déportation, elle estimait que le film "entachait de collaboration l'ensemble de la société française" et ne rendait pas justice aux nombreux Français courageux qui, sans être des résistants armés, avaient aidé des Juifs.

Les commentateurs du documentaire suggèrent que sa position, bien que sincère, a servi de paravent aux "pétinistes à Légion d'honneur" de l'ORTF.

De nombreux anciens résistants ont également fait pression, craignant que le film ne donne une "mauvaise image de la France".

L'Affaire Bousquet

Le film expose la présence au sommet de la société de figures de la collaboration.

Une séquence montre René Bousquet, secrétaire général de la police de Vichy et organisateur de la rafle du Veld'Hiv, devenu après-guerre un puissant directeur de la Banque d'Indochine.

La banque a contacté les producteurs suisses pour leur demander de supprimer le passage en échange de contreparties financières, ce que ces derniers ont refusé.

Cette affaire illustre à quel point les responsables de l'époque étaient encore en poste et influents.

L'Héritage d'un Film-Événement

Une Rupture Mémorielle

Le Chagrin et la Pitié a provoqué un "basculement mémoriel".

Il a forcé la société française à regarder en face la collaboration de l'État et le comportement d'une partie de sa population.

Pour la première fois, la parole se libère, comme en témoigne le nombre sans précédent de lettres envoyées au journal Le Monde en 1971, où les citoyens débattent avec passion de la période.

Le film a rendu impossible de "remettre la poussière sous le tapis".

Impact International

Aux États-Unis, le film sort en 1972 dans un contexte marqué par la guerre du Vietnam et le scandale du Watergate.

La critique américaine y voit un miroir, posant la question : "Dans des circonstances comparables, avons-nous bien agi ?".

Le film change également la perception américaine de la Libération, révélant que les GIs ont débarqué non seulement dans un pays occupé, mais aussi dans un pays qui avait "sereinement organisé sa collaboration avec l'occupant".

Un Catalyseur pour l'Histoire et le Cinéma

Le film est considéré comme un "facilitateur" qui a permis l'émergence d'autres œuvres traitant de l'Occupation sous un angle critique, comme Lacombe Lucien de Louis Malle ou Monsieur Klein de Joseph Losey.

Il a également préparé le terrain pour l'accueil du livre de l'historien américain Robert Paxton, La France de Vichy, qui, par une approche archivistique, confirmait les conclusions du film.

Ophuls et Paxton sont vus comme partageant le même "esprit" en osant juger Vichy.

Vers la Reconnaissance Politique

L'impact du film s'étend sur plusieurs décennies. Le débat qu'il a ouvert est considéré comme une étape essentielle menant à la reconnaissance officielle de la responsabilité de la France.

Un intervenant établit une continuité directe : "Il n'y a pas de discours de Chirac en 1995 s'il n'y a pas le chagrin à la pitié."

Ce discours, où Jacques Chirac déclare que "la folie criminelle de l'occupant a été secondée par des Français, secondée par l'État français", marque l'aboutissement du processus de mémoire que le film avait brutalement initié 24 ans plus tôt.

C'est la preuve qu'un film, "somme toute assez rare", peut "changer les choses" et "changer des vies".

Une nouvelle fois, nous pouvons nous rendre compte que les premiers réseaux sociaux sont nés d’un effort progressif et collectif. Je trouve très interessant de voir à quel point il y avait déjà cet esprit de partage et de communauté avant que les réseaux sociaux ne deviennent un phénomène mondial

Puisque l'idée du web était de pouvoir accéder à des fichiers présents sur d'autres ordinateurs à travers des adresse uniques et en utilisant l'infrastructure d'internet pour y accéder.

Autrement dit le backend et le frontend

Synthèse de la Séance Plénière du Conseil Économique, Social et Environnemental

Résumé

La séance plénière du Conseil économique, social et environnemental (CESE) s'est articulée autour de deux axes majeurs :

l'examen et l'adoption unanime d'un avis crucial sur les droits et les besoins fondamentaux de l'enfant,

et une série d'interventions sur des sujets d'actualité reflétant les préoccupations de la société civile.

L'avis intitulé "Satisfaire les besoins fondamentaux des enfants et garantir leurs droits dans tous les temps et espaces de leur vie quotidienne" a été adopté à l'unanimité (130 voix pour).

Conçu en complément des travaux de la Convention Citoyenne sur le même sujet, cet avis dresse un constat sévère de la situation des enfants en France, marquée par des inégalités croissantes (sociales, territoriales, économiques) et un décalage persistant entre les droits proclamés et leur application réelle. Le document met en lumière une société pensée "par et pour les adultes", qui peine à placer l'enfant au cœur de ses préoccupations.

Les préconisations phares incluent l'instauration d'une "clause impact enfance" dans chaque texte de loi, une réforme ambitieuse des rythmes scolaires, la garantie d'un accès équitable aux loisirs et aux vacances, et la création d'un "service public de la continuité éducative" pour coordonner l'ensemble des acteurs.

L'intervention de Claire Hédon, Défenseure des droits, a renforcé ce diagnostic par des données chiffrées alarmantes sur les atteintes aux droits de l'enfant, notamment pour les plus vulnérables.

En amont de ce débat, la séance d'expression libre a permis d'aborder des enjeux variés :

• la remise en cause de la légitimité de la participation citoyenne,

• les coupes drastiques dans l'aide publique au développement,

• les menaces sur le système de santé,

• la dérégulation environnementale au niveau européen, les dangers des nouveaux OGM,

• la hausse des accidents du travail,

• la pression exercée sur les demandeurs d'emploi,

• et les appels à une souveraineté alimentaire concrète.

Enfin, la présentation du budget du CESE a révélé une situation financière tendue, marquée par une baisse des dotations de l'État et menacée par de nouvelles coupes potentielles votées par le Sénat, mettant en péril la capacité de l'institution à mener ses missions, notamment l'organisation de futures conventions citoyennes.

I. Session d'Expression Libre : Un Panorama des Préoccupations Sociétales

Avant l'examen de l'avis sur l'enfance, plusieurs intervenants ont exprimé les préoccupations de leurs groupes respectifs sur des sujets d'actualité.

• Défense de la Participation Citoyenne (Agatha Mel) :

Au nom des organisations étudiantes, une défense de la Convention Citoyenne sur les temps de l'enfant a été formulée, dénonçant les "procès d'illégitimité, d'incompétence et de manipulation" et appelant à un débat sérieux sur le fond du rapport, sans caricaturer le travail des citoyens.

• Aide Publique au Développement (Jean-Marc Boivin) :

Le groupe des associations a alerté sur les coupes "drastiques et disproportionnées" (-60 % en 2 ans) dans le budget de l'aide publique au développement, entraînant la fermeture de 1300 projets, la suppression de 10 000 emplois et impactant plus de 15 millions de personnes.

• Impact sur la Santé (Dominique Joseph) :

La Mutualité Française a qualifié d'irresponsable l'augmentation de la taxe sur les complémentaires santé, la qualifiant de "TVA sur la santé", et a souligné la nécessité d'une réforme de fond du système de protection sociale.

• Dérégulation Environnementale (Florent Compnibus) :

Le groupe environnement a dénoncé le projet législatif européen "Omnibus" comme une "dérégulation massive" et un "abandon pur et simple du principe de précaution", instaurant des autorisations illimitées pour les pesticides et biocides et affaiblissant le devoir de vigilance des entreprises.

• Opposition aux Nouveaux OGM (Éric Meer) :

Le groupe alternative sociale et écologique a critiqué l'accord européen sur les nouvelles techniques génomiques (NGT), y voyant une "fuite en avant technologique" qui favorise le brevetage, la dépendance des paysans et prive les consommateurs de traçabilité.

• Accidents du Travail (Ingrid Clément) :

La CFDT a qualifié 2024 d'"année noire" avec 774 décès au travail (deux par jour), une augmentation de 26 % des accidents pour les femmes, et une hausse des troubles musculosquelettiques et des affections psychiques, appelant à renforcer la prévention primaire.

• Pression sur les Demandeurs d'Emploi (Isabelle Dor) :

Le groupe des associations a relayé des témoignages de personnes suivies par France Travail décrivant "infantilisation", "pression folle" et menaces de radiation, illustrant des situations qualifiées d'ubuesques pour les bénéficiaires du RSA et les travailleurs pauvres.

• Soutien à la Solidarité Syndicale (Alain le corps) :

La CGT a dénoncé la mise en examen de sa secrétaire générale, Sophie Binet, pour avoir utilisé l'expression "les rats quittent le navire", affirmant qu'il s'agit "non pas une injure, mais le constat amer d'un comportement irresponsable".

• Souveraineté Alimentaire (Henriespéré) :

Le groupe de l'agriculture a relayé les propos de la ministre sur la "guerre agricole" qui se prépare, appelant à passer "des discours aux actes" pour relancer les filières agricoles françaises via l'innovation et la réciprocité des normes.

II. L'Avis du CESE sur les Besoins et les Droits Fondamentaux de l'Enfant

Le cœur de la séance a été consacré à l'avis "Satisfaire les besoins fondamentaux des enfants et garantir leurs droits", élaboré par la commission éducation, culture et communication.

Cet avis constitue la contribution de la société civile organisée en parallèle de la Convention Citoyenne sur les temps de l'enfant, saisie par le Premier ministre.

A. Le Discours de la Défenseure des Droits (Claire Hédon)

En introduction, Claire Hédon, Défenseure des droits et des enfants, a livré une intervention dense, soulignant l'écart entre le "droit annoncé et son effectivité".

• Volume des Saisines : L'institution a reçu 3 073 réclamations relatives à des atteintes aux droits de l'enfant en 2024. 30 % de ces réclamations concernent la scolarisation d'élèves en situation de handicap.

• Consultation des Enfants : Pour préparer son rapport 2025, plus de 1 600 enfants et jeunes ont été écoutés, soulignant l'importance de leur parole "trop souvent absente du débat public".

• Accès aux Loisirs : Un chiffre marquant illustre les inégalités massives : 71 % des enfants issus de familles modestes ne pratiquent aucune activité sportive ou culturelle, contre seulement 38 % des familles aisées.

La situation est encore plus critique en Outre-mer, où les équipements sont quatre fois moins nombreux qu'en métropole à Mayotte.

• Temps d'Écran : Le temps passé devant les écrans augmente fortement, atteignant en moyenne 4h48 par jour chez les 11-14 ans (hors école) et jusqu'à 5h10 chez les 16 ans, avec des conséquences graves sur le sommeil et la santé mentale.

• Droit à l'Éducation : La Défenseure a alerté sur les heures d'enseignement perdues, citant le cas d'élèves de CP à Marseille sans cours pendant un mois, et le chiffre de 27 000 jeunes sans affectation au lycée début 2024 sur tout le territoire.

• Impact Climatique : Le réchauffement climatique menace la continuité du service public de l'éducation.

D'ici 2030, près de 7 000 écoles maternelles seront exposées à des vagues de chaleur supérieures à 35°C.

B. Présentation du Projet d'Avis par la Commission

Les rapporteurs ont présenté un projet d'avis structuré autour d'un principe fondamental : l'enfant est une personne à part entière.

Le fil rouge de l'analyse est un triptyque : droits de l'enfant, satisfaction de ses besoins et lutte contre les inégalités.

Constats et Enjeux Majeurs

• Des Droits Peu Effectifs : Malgré la ratification de la Convention internationale des droits de l'enfant, la réalité quotidienne est marquée par des droits non respectés, comme le soulignent les rapports de l'ONU et de la Défenseure des droits.

• Des Inégalités Croissantes : Les inégalités sociales, économiques, territoriales et environnementales percutent de plein fouet la vie des enfants.

34,3 % des familles monoparentales vivent en situation de pauvreté.

À la veille de la rentrée 2025, au moins 2 159 enfants sont restés sans solution d'hébergement.

• Une Société "Adulto-centrée" : L'organisation sociale, notamment les rythmes de travail et les temps scolaires, est pensée pour les adultes, laissant peu de place aux besoins biologiques et psychologiques des enfants.

• L'Enfant "de l'intérieur" : En 20 ans, le périmètre de déplacement autonome des enfants a chuté de plusieurs kilomètres à moins de 300 mètres.

Quatre enfants sur 10 (3-10 ans) ne jouent jamais dehors pendant la semaine.

Préconisations Clés

L'avis formule 19 préconisations pour répondre à ces enjeux. Les plus structurantes sont :

Thématique

Préconisation Phare

Description

Gouvernance et Législation

Créer une clause "impact enfance"

Intégrer dans l'évaluation de chaque projet de loi ou de règlement une analyse de ses conséquences sur les droits et le bien-être des enfants.

Temps Scolaire

Affirmer que le statu quo n'est plus tenable

Appeler à revoir l'organisation des journées et des semaines scolaires, en préconisant une alternance de 7 semaines de cours et 2 semaines de vacances, tout en maintenant 8 semaines l'été.

Droit aux Vacances et Loisirs

Garantir un accès équitable pour tous

Développer une information ciblée, mettre en place une tarification sociale et soutenir financièrement les structures d'accueil collectif pour lutter contre les inégalités d'accès.

Lien à la Nature

Valoriser et accompagner l'éducation "au dehors"

Déployer des aménagements tels que la végétalisation des cours d'école, les aires éducatives et les plans locaux d'éducation à la nature pour reconnecter les enfants à leur environnement.

Coordination des Acteurs

Créer un service public de la continuité éducative

Articuler les outils existants (PEDT, CTG) pour garantir à chaque enfant un accès à des temps éducatifs variés, cohérents et de qualité, en mobilisant l'ensemble des acteurs (école, familles, associations, collectivités).

Parentalité et Travail

Créer un droit attaché aux obligations parentales

Transposer la directive européenne sur l'équilibre vie pro/vie perso pour permettre aux parents de recourir à des formules souples de travail.

Financement

Assurer un effort budgétaire conséquent et pérenne

Reconnaître l'éducation comme un investissement d'avenir et non comme une simple dépense, en garantissant les moyens nécessaires à l'État, la Sécurité sociale et aux collectivités pour mener des politiques publiques ambitieuses.

C. Réception et Adoption de l'Avis

L'ensemble des groupes politiques et de la société civile présents au CESE ont salué la qualité et l'ambition de l'avis.

Les déclarations ont convergé sur le diagnostic des inégalités croissantes et la nécessité d'une action politique forte.

Le projet d'avis a été adopté à l'unanimité des 130 votants.

En complément, la députée Florence Erroin-Léoté a annoncé son intention de porter une proposition de loi sur le droit au loisir des enfants, s'appuyant sur les travaux de la Convention Citoyenne et du CESE pour faire du temps libre un "lieu éducatif, de mixité, d'émancipation et de démocratie vivante".

III. Le Budget du CESE : Enjeux et Vulnérabilités

La séance s'est conclue par la présentation du budget du CESE, qui a mis en lumière une situation financière préoccupante.

• Contexte de Pression Budgétaire : Le président a rappelé qu'au même moment, le Sénat votait une baisse de 5 millions d'euros du budget du CESE, contre l'avis de sa propre commission des finances et du gouvernement.

• Baisse des Recettes : Le budget présenté montre une érosion continue des recettes, notamment la fin de la dotation spécifique de 4 millions d'euros pour l'organisation des conventions citoyennes.

De plus, les travaux de rénovation du Palais d'Iéna vont priver le CESE d'environ 1,6 million d'euros de recettes de valorisation (location d'espaces) en 2026.

• Un Budget 2026 à l'Équilibre Fragile : Le budget pour 2026 est présenté comme étant à l'équilibre, mais cet équilibre est atteint en n'incluant pas le financement d'une nouvelle convention citoyenne et en réduisant certains postes comme la communication.

• Incapacité à Financer de Nouvelles Missions : Le questeur a été clair : "en l'état, [...] on est demain incapable de refaire une convention citoyenne à 4 millions d'euros".

L'organisation de telles missions dépendra désormais de la capacité du CESE à obtenir des financements ad hoc auprès du gouvernement pour chaque commande.

• Investissement Immobilier Massif : La présentation a souligné que les réserves de trésorerie accumulées sont désormais engagées dans un plan pluriannuel d'investissement indispensable pour la rénovation du bâtiment, rattrapant des décennies de sous-investissement.

Dossier d'Information : L'Impact du Smartphone et de l'IA sur l'Adolescence

Résumé

Cette synthèse examine l'analyse de l'anthropologue David Le Breton sur les transformations profondes induites par l'omniprésence du smartphone et de l'intelligence artificielle (IA) dans la vie des adolescents.

Le constat central est celui d'une rupture anthropologique majeure, marquée par le remplacement de la "conversation" – un échange incarné, empathique et réciproque – par la "communication" numérique, une interaction désincarnée, utilitariste et source d'isolement.

Les points critiques à retenir sont :

• La Fin de la Conversation : L'interaction en face à face est constamment rompue par les notifications, dévalorisant la présence physique au profit d'un univers virtuel.

Cette fragmentation du lien social direct entraîne une érosion documentée de l'empathie chez les jeunes générations.

• L'Ascension du Compagnon IA : Pour combler le vide affectif et social, les adolescents se tournent vers des chatbots, des "compagnons secrets" virtuels qui offrent une attention constante et sans jugement.

Cette relation, bien que narcissiquement rassurante, amplifie l'isolement et transforme l'utilisateur en produit, ses données étant captées et valorisées.

• Des Conséquences Cognitives et Physiques Sévères : L'exposition massive aux écrans est corrélée à un affaiblissement des capacités de concentration, de lecture approfondie et de pensée critique.

Elle favorise une sédentarité accrue, entraînant des problèmes de santé (douleurs cervicales, myopie) et une baisse drastique de l'activité physique par rapport aux générations précédentes.

• Une Crise de Santé Mentale Planétaire : David Le Breton, s'appuyant sur de multiples travaux, établit un lien direct entre l'explosion de l'anxiété, de la dépression, des tentatives de suicide et des scarifications chez les adolescents depuis 2010 et l'adoption généralisée du smartphone connecté à Internet.

• Enjeux Sociétaux et Éthiques : Au-delà de l'individu, l'analyse pointe vers une homogénéisation culturelle mondiale ("MacWorld"), la vulnérabilité accrue aux fausses nouvelles, et les graves implications éthiques et environnementales de la technologie (travail des enfants, exploitation de métaux rares, pollution des data centers).

En conclusion, loin d'être un simple outil, le smartphone dopé à l'IA façonne une nouvelle anthropologie où la simulation du lien supplante l'expérience réelle, avec des conséquences délétères sur le développement individuel et la cohésion sociale.

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1. Contexte de l'Analyse

La présente analyse se fonde sur les propos de David Le Breton, professeur émérite d'anthropologie à l'Université de Strasbourg, reconnu pour ses travaux sur les conduites à risque, le corps, et plus récemment sur le ralentissement et la marche.

Son intervention s'inscrit dans une réflexion plus large sur la santé mentale des jeunes et l'impact de l'intelligence artificielle (IA) sur la société.

2. La Rupture Anthropologique : L'Avant et l'Après Smartphone

David Le Breton postule qu'une rupture anthropologique fondamentale a eu lieu autour des années 2008-2009 avec l'avènement de l'Internet à haut débit sur les smartphones.

Ce changement a transformé radicalement l'espace public et les interactions humaines.

• Une "Société Spectrale" : Les villes sont désormais "hantées par des espèces de fantômes qui sont hypnotisés par leur téléphone portable et qui ne voient plus rien du tout à leur entour".

• Perte d'Attention à l'Environnement : Cet état d'hypnose crée des dangers physiques (piétons et cyclistes inattentifs) et sociaux, car l'attention n'est plus portée à l'environnement immédiat ou aux autres personnes présentes.

• Le Monde d'Avant : Il y a une vingtaine d'années, le monde était radicalement différent.

Même avec les premiers téléphones portables, l'attention au monde environnant n'était pas abolie comme elle l'est aujourd'hui par l'hypnose de l'écran du smartphone.

3. Distinction Fondamentale : Conversation contre Communication

Le cœur de l'analyse de Le Breton repose sur une distinction anthropologique essentielle entre deux modes d'interaction.

Caractéristique

La Conversation

La Communication (numérique)

Cadre

Visage à visage, présence physique.

À distance, anonymat fréquent.

Corps

Central (mimiques, expressions, gestes).

Absent, désincarné.

Temporalité

Imprévisible, inclut le temps du silence et de la réflexion.

Urgence, efficacité, utilitarisme. Le silence est perçu comme une "panne".

Qualité du lien

Écoute, attention, empathie, réciprocité.

Centrée sur soi, instrumentale.

David Le Breton cite son propre ouvrage pour souligner ce point :

La conversation à l'implique de l'empathie c'est-à-dire une capacité à se mettre à la place de l'autre et à ne pas être étranger à ses ressentis.

Cette qualité disparaît dans la communication à distance [...] l'autre se transforme alors en fiction sans épaisseur.

4. Données Clés sur le Temps d'Écran

L'intervention initiale d'Axel fournit des chiffres qui contextualisent l'ampleur du phénomène, basés notamment sur un rapport de l'ARCOM d'avril 2025.

Catégorie d'Âge

Temps d'Écran en 2011

Temps d'Écran en 2022/récent

1-6 ans

1h 47min

2h 03min

7-12 ans

2h 51min

4h 12min

13-19 ans

4h 20min

5h 10min

15-24 ans

(non spécifié)

5h 48min (dépasse les 50-64 ans)

50-64 ans

(non spécifié)

5h 27min (principalement TV en direct)

Ces données montrent une augmentation astronomique du temps passé devant les écrans en une décennie, les jeunes de 15-24 ans étant désormais les plus grands consommateurs, principalement via le smartphone. Pour certains adolescents, ce temps peut dépasser les dix heures par jour.

5. L'Adolescent et le Compagnon Virtuel (IA)

Face à un lien social qui s'effrite et à une désertion affective des proches, l'IA, via les chatbots, offre une solution de substitution qui devient un phénomène central de l'adolescence contemporaine.

• Le "Doudou de Substitution" : L'IA permet de fabriquer un "compagnon secret fictionnel" pour combler un manque affectif.

Le jeune programme ce personnage virtuel (nom, voix, personnalité) pour en faire un interlocuteur idéal.

• Un Bouclier de Sens : Le chatbot est toujours disponible, bienveillant, sans jugement, et procure un sentiment de maîtrise et de reconnaissance.

Il devient un "bouclier de sens pour conjurer les désarrois, les souffrances".

• L'Illusion de la Réciprocité : L'adolescent interagit avec le chatbot comme avec une personne réelle, oubliant qu'il s'agit d'un programme conçu pour capter ses données et le maintenir connecté le plus longtemps possible.

• La Violence de l'Indifférence : Cette quête d'attention virtuelle naît souvent d'un manque d'attention réelle, illustré par l'anecdote poignante d'une petite fille disant à son père hypnotisé par son portable :

Papa je veux que tu m'écoutes avec les yeux.

6. Conséquences sur le Lien Social et l'Érosion de l'Empathie

L'hyper-connexion paradoxalement génère un isolement profond et une dégradation des compétences sociales.

• La Liquidation de l'Interlocuteur : La présence physique d'un ami ou d'un parent est immédiatement "liquidée" dès qu'une notification apparaît.

L'interlocuteur réel a "moins d'épaisseur ontologiquement que les autres virtuels".

• La Simulation du Lien : Les "centaines d'amis" des réseaux sociaux ne valent pas un ou deux amis réels capables d'un geste de réconfort physique.

La communication numérique simule le lien social mais ne crée ni intimité ni raisons de vivre.

• Le Déclin de l'Empathie : Une étude menée par la sociologue Sherry Turkle sur 14 000 étudiants sur 30 ans montre que depuis les années 2000, "les jeunes témoignent d'un moindre intérêt pour les autres".

Les auteurs de l'étude établissent un lien direct entre ce retrait de l'empathie et la croissance de l'accès aux jeux en ligne et aux réseaux sociaux.

7. Impacts Cognitifs, Physiques et Comportementaux

La surexposition aux écrans et la délégation de la pensée à l'IA ont des effets directs et mesurables sur le développement des jeunes.

7.1. Impacts Cognitifs

• Difficulté de Lecture : La communication "synchopée, simple, permanente, ultra rapide" rend difficile la lecture de textes longs et élaborés, y compris des SMS de plus de quelques phrases.

• Faible Culture Générale : La croyance que toute information est accessible en un clic décourage l'apprentissage en profondeur.

Les étudiants "peinent à lire simplement quelques pages d'un article ou d'un livre".

• Apprentissage de la Passivité : Le recours systématique à l'IA pour obtenir des réponses immédiates (ex: ChatGPT pour un devoir) empêche le développement de la recherche personnelle, de la nuance et de la pensée critique.

• Externalisation de la Mémoire : L'usage du clavier et la possibilité de tout retrouver en ligne affaiblissent la mémorisation, qui est un processus affectif et contextuel, et non un simple stockage d'informations.

7.2. Impacts Physiques et Comportementaux

• Sédentarité Extrême : Une recherche du médecin William Bird montre qu'en quelques décennies, la distance parcourue par un enfant de 8 ans autour de son domicile est passée de 9 km à 300 mètres.

• Baisse des Performances Physiques : Les adolescents des années 70 étaient "deux fois plus actifs". Un 800 mètres qui se courait en 3 minutes en prend aujourd'hui 4.

• Problèmes de Santé : Le développement planétaire des douleurs cervicales et dorsales, ainsi que de la myopie, est directement lié à la posture penchée sur l'écran.

8. La Crise de la Santé Mentale Adolescente

David Le Breton conclut son analyse sur un bilan humain alarmant, établissant une corrélation temporelle forte entre la généralisation du smartphone et l'explosion des troubles psychiques chez les jeunes à partir de 2010.

En se référant aux travaux du psychologue Jonathan Haidt ("Génération anxieuse"), il affirme que jamais dans l'histoire on n'a connu une telle ampleur de souffrances adolescentes :

• Anxiété et Dépression

• Sentiment d'Isolement

• Tentatives de Suicide et Suicides

• Scarifications (particulièrement chez les filles)

Cette crise est également visible chez les tout-petits, avec des retards de langage chez des enfants surexposés aux écrans, privés des interactions parentales cruciales à leur développement.

9. Enjeux Éthiques, Culturels et Environnementaux

L'impact du smartphone et de l'IA dépasse la sphère individuelle pour toucher l'ensemble de la société.

• Manipulation et Harcèlement : L'IA permet de créer facilement des "deepfakes" ou "deepnudes" pour humilier, discréditer ou faire chanter des individus, les adolescentes étant des victimes fréquentes.

• Homogénéisation Culturelle ("MacWorld") : Les technologies créent une culture mondiale unifiée par les mêmes films, musiques, séries et modes de consommation, liquidant les cultures locales et les savoir-faire traditionnels.

• Hypocrisie de la Silicon Valley : Les dirigeants des géants du numérique protègent leurs propres enfants des technologies qu'ils promeuvent, en les inscrivant dans des écoles (ex: Waldorf) où le numérique est banni, conscients de ses dangers.

• Impacts Environnementaux et Géopolitiques : Le numérique a une empreinte écologique massive (data centers, consommation d'énergie) et repose sur l'exploitation de métaux rares, alimentant des conflits géopolitiques et le travail d'enfants dans certains pays.

Ces aspects sont souvent occultés dans les débats sur le climat.

10. Conclusion et Posture de l'Analyste

David Le Breton insiste sur le fait que son analyse n'est pas celle d'un "moraliste" mais celle d'un sociologue et anthropologue qui observe et documente une réalité.

Son travail vise à pointer des faits observables et documentés par de nombreuses études, soulignant que jamais dans l'histoire le lien social n'a été aussi "abîmé".

Le monde hyper-connecté a coïncidé avec le début de "l'hyperindividualisation de nos sociétés", menant au paysage social et psychologique actuel.

Revisión para la clase de mañana. Información para la resolución del caso

L2 norm, Euclidiean distance

Author response:

(1) General Statements

We thank the Reviewers for a fair review of our work and helpful suggestions. We have significantly revised the manuscript in response to these suggestions. We provide a point-by-point response to the Reviewers below but wanted to highlight in our response a recurring concern related to the strong cell cycle arrest observed upon the acute FAM53C knock-down being different than the limited phenotypes in other contexts, including the knockout mice and DepMap data.

First, we now show that we can recapitulate the strong G1 arrest resulting from the FAM53C knock-down using two independent siRNAs in RPE-1 cells, supporting the specificity of the effects.

Second, the G1 arrest that results from the FAM53C knock-down is also observed in cells with inactive p53, suggesting it is not due to a non-specific stress response due to “toxic” siRNAs. In addition, the arrest is dependent on RB, which fits with the genetic and biochemical data placing FAM53C upstream of RB, further supporting a specific phenotype.

Third, we have performed experiments in other human cells, including cancer cell lines. As would be expected for cancer cells, the G1 arrest is less pronounced but is still significant, indicating that the G1 arrest is not unique to RPE-1 cells.

Fourth, it is not unexpected that compensatory mechanisms would be activated upon loss of FAM53C during development or in cancer – which may explain the lack of phenotypes in vivo or upon long-term knockout. This has been true for many cell cycle regulators, either because of compensation by other family members that have overlapping functions, or by a larger scale rewiring of signaling pathways. 

(2) Point-by-point description of the revisions

Reviewer #1 (Evidence, reproducibility and clarity): 

Summary: 

Taylar Hammond and colleagues identified new regulators of the G1/S transition of the cell cycle.

They did so by screening public available data from the Cancer Dependency Map, and identified FAM53C as a positive regulator of the G1/S transition. Using biochemical assays they then show that FAM53 interacts with the DYRK1A kinase to inhibit its function. DYRK1A in its is known to induce degradation of cyclin D, leading the authors to propose a model in which DYRK1Adependent cyclin D degradation is inhibited by FAM53C to permit S-phase entry. Finally the authors assess the effect of FAM53C deletion in a cortical organoid model, and in Fam53c knockout mice. Whereas proliferation of the organoids is indeed inhibited, mice show virtually no phenotype.  

Major comments: 

The authors show convincing evidence that FAM53C loss can reduce S-phase entry in cell cultures, and that it can bind to DYRK1A. However, FAM53 has multiple other binding partners and I am not entirely convinced that negative regulation of DYRK1A is the predominant mechanism to explain its effects on S-phase entry. Some of the claims that are made based on the biochemical assays, and on the physiological effects of FAM53C are overstated. In addition, some choices made methodology and data representation need further attention. 

(1) The authors do note that P21 levels increase upon FAM53C. They show convincing evidence that this is not a P53-dependent response. But the claim that " p21 upregulation alone cannot explain the G1 arrest in FAM53C-deficient cells (line 138-139) is misleading. A p53-independent p21 response could still be highly relevant. The authors could test if FAM53C knockdown inhibits proliferation after p21 knockdown or p21 deletion in RPE1 cells. 

The Reviewer raises a great point. Our initial statement needed to be clarified and also need more experimental support. We have performed experiments where we knocked down FAM53C and p21 individually, as well as in combination, in RPE-1 cells. These experiment show that p21 knock-down is not sufficient to negate the cell cycle arrest resulting from the FAM53C knockdown in RPE-1 cells (Figure 4B,C and Figure S4C,D).

We now extended these experiments to conditions where we inhibited DYRK1A, and we also compared these data to experiments in p53-null RPE-1 cells. Altogether, these experiments point to activation of p53 downstream of DYRK1A activation upon FAM53C knock-down, and indicate that p21 is not the only critical p53 target in the cell cycle arrest observed in FAM53C knock-down cells (Figure 4 and Figure S4).

(2) The authors do not convincingly show that FAM53C acts as a DYRK1A inhibitor in cells. Figures 4B+C and S4B+C show extremely faint P-CycD1 bands, and tiny differences in ratios. The P values are hovering around the 0.05, so n=3 is clearly underpowered here. Total CycD1 levels also correlate with FAM53C levels, which seems to affect the ratios more than the tiny pCycD1 bands. Why is there still a pCycD1 band visible in 4B in the GFP + BTZ + DYRK1Ai condition? And if I look at the data points I honestly don't understand how the authors can conclude from S4C that knockdown of siFAM53C increases (DYRK1A dependent) increases in pCycD1 (relative to total CycD1). In figure 5C, no blot scans are even shown, and again the differences look tiny. So the authors should either find a way to make these assays more robust, or alter their claims appropriately. 

We appreciate these comments from the Reviewer and have significantly revised the manuscript to address them.

The analysis of Cyclin D phosphorylation and stability are complicated by the upregulation of p21 upon FAM53C knock-down, in particular because p21 can be part of Cyclin D complexes, which may affect its protein levels in cells (as was nicely showed in a previous study from the lab of Tobias Meyer – Chen et al., Mol Cell, 2013). Instead of focusing on Cyclin D levels and stability, we refocused the manuscript on RB and p53 downstream of FAM53C loss.

We removed previous panel 4B from the revised manuscript. For panels 4E and S4B (now panels S3J and S3K)), we used a true “immunoassay” (as indicated in the legend – not an immunoblot), which is much more quantitative and avoids error-prone steps in standard immunoblots (“Western blots”). Briefly, this system was developed by ProteinSimple. It uses capillary transfer of proteins and ELISA-like quantification with up to 6 logs of dynamic range (see their web site https://www.proteinsimple.com/wes.html). The “bands” we show are just a representation of the luminescence signals in capillaries. We made sure to further clarify the figure legends in the revised manuscript.

The representative Western blot images for 5C-D (now 5F-G) in the original submission are shown in Figure 5E, we apologize if this was not clear. The differences are small, which we acknowledge in the revised manuscript. Note that several factors can affect Cyclin D levels in cells, including the growth rate and the stage of the cell cycle. Our FACS analysis shows that normal organoids have ~63% of cells in G1 and ~13% in S phase; the overall lower proportion of S-phase cells in organoids may make the immunoblot difference appear smaller, with fewer cycling cells resulting in decreased Cyclin D phosphorylation.

Nevertheless, the Reviewer brings up a good point and comments from this Reviewer and the others made us re-think how to best interpret our results. As discussed above, we re-read carefully the Meyer paper and think that FAM53C’s role and DYRK1A activity in cells may be understood when considering levels of both CycD and p21 at the same time in a continuum. While our genetic and biochemical data support a role for FAM53C in DYRK1A inhibition, it is likely that the regulation of cell cycle progression by FAM53C is not exclusively due to this inhibition. As discussed above and below, we noted an upregulation of p21 upon FAM53C knock-down, and activation of p53 and its targets likely contributes significantly to the phenotypes observed. We added new experiments to support this more complex model (Figure 4 and Figure S4, with new model in S4L).

(3) The experiments to test if DYRK1A inhibition could rescue the G1 arrest observed upon FAM53C knockdown are not entirely convincing either. It would be much more convincing if they also perform cell counting experiments as they have done in Figures 1F and 1G, to complement the flow cytometry assays. I suggest that the authors do these cell counting experiments in RPE1 +/- P53 cells as well as HCT116 cells. In addition, did the authors test if P21 is induced by DYRK1Ai in HCT116 cells? 

We repeated the experiments with the DYRK1A inhibitor and counted the cells. In p53-null RPE1 cells, we found that cell numbers do not increase in these conditions where we had observed a cell cycle re-entry (Fig. 4E), which was accompanied by apoptotic cell death (Fig. S4I). Thus, cells re-enter the cell cycle but die as they progress through S-phase and G2/M. We note that inhibition of DYRK1A has been shown to decrease expression of G2/M regulators (PMID: 38839871), which may contribute to the inability of cells treated to DYRK1Ai to divide. Because our data in RPE-1 cells showed that p21 knock-down was not sufficient to allow the FAM53C knock-down cells to re-enter the cell cycle, we did not further analyze p21 in HCT-116 cells.

(4) The data in Figure 5C and 5D are identical, although they are supposed to represent either pCycD1 ratios or p21 levels. This is a problem because at least one of the two cannot be true. Please provide the proper data and show (representative) images of both data types.

We apologize for these duplicated panels in the original submission. We now replaced the wrong panel with the correct data (Fig. 5F,G). 

(5) Line 246: "Fam53c knockout mice display developmental and behavioral defects." I don't agree with this claim. The mutant mice are born at almost the expected Mendelian ratios, the body weight development is not consistently altered. But more importantly, no differences in adult survival or microscopic pathology were seen. The authors put strong emphasis on the IMPC behavioral analysis, but they should be more cautious. The IMPC mouse cohorts are tested for many other phenotypes related to behavior and neurological symptoms and apparently none of these other traits were changed in the IMPC Famc53c-/- cohort. Thus, the decreased exploration in a new environment could very well be a chance finding. The authors need to take away claims about developmental and behavioral defects from the abstract, results and discussion sections; the data are just too weak to justify this. 

We agree with the Reviewer that, although we observed significant p-values, this original statement may not be appropriate in the biological sense. We made sure in the revised manuscript to carefully present these data.

Minor comments: 

(6) Can the authors provide a rationale for each of the proteins they chose to generate the list of the 38 proteins in the DepMap analysis? I looked at the list and it seems to me that they do not all have described functions in the G1/S transition. The analysis may thus be biased. 

To address this point, we updated Table S1 (2nd tab) to provide a better rationale for the 38 factors chosen. Our focus was on the canonical RB pathway and we included RB binding proteins whose function had suggested they may also be playing a role in the G1/S transition. We do agree that there is some bias in this selection (e.g., there are more RB binding factors described) but we hope the Reviewer will agree with us that this list and the subsequent analysis identified expected factors, including FAM53C. Future studies using this approach and others will certainly identify new regulators of cell cycle progression.

(7) Figure 1B is confusing to me. Are these just some (arbitrarily) chosen examples? Consider leaving this heatmap out altogether, of explain in more detail. 

We agree with the Reviewer that this panel was not necessarily useful and possibly in the wrong place, and we removed it from the manuscript. We replaced it with a cartoon of top hits in the screen.

(8) The y-axes in Figures 2C, 2D, 2E, and 4D are misleading because they do not start at 0. Please let the axis start at 0, or make axis breaks. 

We re-graphed these panels.

(9) Line 229: " Consequences ... brain development." This subheader is misleading, because the in vitro cortical organoid system is a rather simplistic model for brain development, and far away from physiological brain development. Please alter the header. 

We changed the header to “Consequences of FAM53C inactivation in human cortical organoids in culture”.

(10) Figure S5F: the gating strategy is not clear to me. In particular, how do the authors know the difference between subG1 and G1 DAPI signals? Do they interpret the subG1 as apoptotic cells? If yes, why are there so many? Are the culturing or harvesting conditions of these organoids suboptimal? Perhaps the authors could consider doing IF stainings on EdU or BrdU on paraffin sections of organoids to obtain cleaner data?

Thank you for your feedback. The subG1 population in the original Figure S5F represents cells that died during the dissociation step of the organoids for FACS analysis. To address this point, we performed live & dead staining to exclude dead cells and provide clearer data. We refined gating strategy for better clarity in the new S5F panel.

(11) Figure S6A; the labeling seems incorrect. I would think that red is heterozygous here, and grey mutant. 

We fixed this mistake, thank you. 

Reviewer #1 (Significance): 

The finding that the poorly studied gene FAM53C controls the G1/S transition in cell lines is novel and interesting for the cell cycle field. However, the lack of phenotypes in Famc53-/- mice makes this finding less interesting for a broader audience. Furthermore, the mechanisms are incompletely dissected. The importance of a p53-indepent induction of p21 is not ruled out. And while the direct inhibitory interaction between FAM53C and DYRK1A is convincing (and also reported by others; PMID: 37802655), the authors do not (yet) convincingly show that DYRK1A inhibition can rescue a cell proliferation defect in FAM53C-deficient cells. 

Altogether, this study can be of interest to basic researchers in the cell cycle field. 

I am a cell biologist studying cell cycle fate decisions, and adaptation of cancer cells & stem cells to (drug-induced) stress. My technical expertise aligns well with the work presented throughout this paper, although I am not familiar with biolayer interferometry. 

Reviewer #2 (Evidence, reproducibility and clarity): 

Summary 

In this study Hammond et al. investigated the role of Dual-specificity Tyrosine Phosphorylation regulated Kinase 1A (DYRK1) in G1/S transition. By exploiting Dependency Map portal, they identified a previously unexplored protein FAM53C as potential regulator of G1/S transition. Using RNAi, they confirmed that depletion of FAM53C suppressed proliferation of human RPE1 cells and that this phenotype was dependent on the presence protein RB. In addition, they noted increased level of CDKN1A transcript and p21 protein that could explain G1 arrest of FAM53Cdepleted cells but surprisingly, they did not observe activation of other p53 target genes. Proteomic analysis identified DYRK1 as one of the main interactors of FAM53C and the interaction was confirmed in vitro. Further, they showed that purified FAM53C blocked the ability of DYRK1 to phosphorylate cyclin D in vitro although the activity of DYRK1 was likely not inhibited (judging from the modification of FAM53C itself). Instead, it seems more likely that FAM53C competes with cyclin D in this assay. Authors claim that the G1 arrest caused by depletion of FAM53C was rescued by inhibition of DYRK1 but this was true only in cells lacking functional p53. This is quite confusing as DYRK1 inhibition reduced the fraction of G1 cells in p53 wild type cells as well as in p53 knock-outs, suggesting that FAM53C may not be required for regulation of DYRK1 function. Instead of focusing on the impact of FAM53C on cell cycle progression, authors moved towards investigating its potential (and perhaps more complex) roles in differentiation of IPSCs into cortical organoids and in mice. They observed a lower level of proliferating cells in the organoids but if that reflects an increased activity of DYRK1 or if it is just an off target effect of the genetic manipulation remains unclear. Even less clear is the phenotype in FAM53C knock-out mice. Authors did not observe any significant changes in survival nor in organ development but they noted some behavioral differences. Weather and how these are connected to the rate of cellular proliferation was not explored. In the summary, the study identified previously unknown role of FAM53C in proliferation but failed to explain the mechanism and its physiological relevance at the level of tissues and organism. Although some of the data might be of interest, in current form the data is too preliminary to justify publication.

Major points 

(1) Whole study is based on one siRNA to Fam53C and its specificity was not validated. Level of the knock down was shown only in the first figure and not in the other experiments. The observed phenotypes in the cell cycle progression may be affected by variable knock-down efficiency and/or potential off target effects. 

We thank the Reviewer for raising this important point. First, we need to clarify that our experiments were performed with a pool of siRNAs (not one siRNA). Second, commercial antibodies against FAM53C are not of the best quality and it has been challenging to detect FAM53C using these antibodies in our hands – the results are often variable. In addition, to better address the Reviewer’s point and control for the phenotypes we have observed, we performed two additional series of experiments: first, we have confirmed G1 arrest in RPE-1 cells with individual siRNAs, providing more confidence for the specificity of this arrest (Fig. S1B); second, we have new data indicating that other cell lines arrest in G1 upon FAM53C knock-down (Fig. S1E,F and Fig. 4F).

(2) Experiments focusing on the cell cycle progression were done in a single cell line RPE1 that showed a strong sensitivity to FAM53C depletion. In contrast, phenotypes in IPSCs and in mice were only mild suggesting that there might be large differences across various cell types in the expression and function of FAM53C. Therefore, it is important to reproduce the observations in other cell types. 

As mentioned above, we have new data indicating that other cell lines arrest in G1 upon FAM53C knock-down (three cancer cell lines) (Fig. S1E,F and Fig. 4F).

(3) Authors state that FAM53C is a direct inhibitor of DYRK1A kinase activity (Line 203), however this model is not supported by the data in Fig 4A. FAM53C seems to be a good substrate of DYRK1 even at high concentrations when phosphorylations of cyclin D is reduced. It rather suggests that DYRK1 is not inhibited by FAM53C but perhaps FAM53C competes with cyclin D. Further, authors should address if the phosphorylation of cyclin D is responsible for the observed cell cycle phenotype. Is this Cyclin D-Thr286 phosphorylation, or are there other sites involved? 

We revised the text of the manuscript to include the possibility that FAM53C could act as a competitive substrate and/or an inhibitor.

We removed most of the Cyclin D phosphorylation/stability data from the revised manuscript. As the Reviewers pointed out, some of these data were statistically significant but the biological effects were small. As discussed above in our response to Reviewer #1, the analysis of Cyclin D phosphorylation and stability are complicated by the upregulation of p21 upon FAM53C knockdown, in particular because p21 can be part of Cyclin D complexes, which may affect its protein levels in cells (as was nicely showed in a previous study from the lab of Tobias Meyer – Chen et al., Mol Cell, 2013). Instead of focusing on Cyclin D levels and stability, we refocused the manuscript on RB and p53 downstream of FAM53C loss.

We note, however, that we used specific Thr286 phospho-antibodies, which have been used extensively in the field. Our data in Figure 1 with palbociclib place FAM53C upstream of Cyclin D/CDK4,6. We performed Cyclin D overexpression experiments but RPE-1 cells did not tolerate high expression of Cyclin D1 (T286A mutant) and we have not been able to conduct more ‘genetic’ studies. 

(4) At many places, information on statistical tests is missing and SDs are not shown in the plots. For instance, what statistics was used in Fig 4C? Impact of FAM53C on cyclin D phosphorylation does not seem to be significant. In the same experiment, does DYRK1 inhibitor prevent modification of cyclin D? 

As discussed above, we removed some of these data and re-focused the manuscript on p53-p21 as a second pathway activated by loss of FAM53C.

(5) Validation of SM13797 compound in terms of specificity to DYRK1 was not performed. 

This is an important point. We had cited an abstract from the company (Biosplice) but we agree that providing data is critical. We have now revised the manuscript with a new analysis of the compound’s specificity using kinase assays. These data are shown in Fig. S3F-H.

(6) A fraction of cells in G1 is a very easy readout but it does not measure progression through the G1 phase. Extension of the S phase or G2 delay would indirectly also result in reduction of the G1 fraction. Instead, authors could measure the dynamics of entry to S phase in cells released from a G1 block or from mitotic shake off. 

The Reviewer made a good point. As discussed in our response to Reviewer #1, with p53-null RPE-1 cells, we found that cell numbers do not increase in these conditions where we had observed a cell cycle re-entry (Fig. 4E), which was accompanied by apoptotic cell death (Fig. S4I). Thus, cells re-enter the cell cycle but die as they progress through S-phase and G2/M. We note that inhibition of DYRK1A has been shown to decrease expression of G2/M regulators (PMID: 38839871), which may contribute to the inability of cells treated to DYRK1Ai to divide.

Because our data in RPE-1 cells showed that p21 knock-down was not sufficient to allow the FAM53C knock-down cells to re-enter the cell cycle, we did not further analyze p21 in HCT-116 cells. These data indicate that G1 entry by flow cytometry will not always translate into proliferation.

Other points:

(7) Fig. 2C, 2D, 2E graphs should begin with 0 

We remade these graphs.

(8) Fig. 5D shows that the difference in p21 levels is not significant in FAM53C-KO cells but difference is mentioned in the text. 

We replaced the panel by the correct panel; we apologize for this error.

(9) Fig. 6D comparison of datasets of extremely different sizes does not seem to be appropriate

We agree and revised the text. We hope that the Reviewer will agree with us that it is worth showing these data, which are clearly preliminary but provide evidence of a possible role for FAM53C in the brain.

(10) Could there be alternative splicing in mice generating a partially functional protein without exon 4? Did authors confirm that the animal model does not express FAM53C? 

We performed RNA sequencing of mouse embryonic fibroblasts derived from control and mutant mice. We clearly identified fewer reads in exon 4 in the knockout cells, and no other obvious change in the transcript (data not shown). However, immunoblot with mouse cells for FAM53C never worked well in our hands. We made sure to add this caveat to the revised manuscript.

Reviewer #2 (Significance): 

Main problem of this study is that the advanced experimental models in IPSCs and mice did not confirm the observations in the cell lines and thus the whole manuscript does not hold together. Although I acknowledge the effort the authors invested in these experiments, the data do not contribute to the main conclusion of the paper that FAM53C/DYRK1 regulates G1/S transition. 

Reviewer #3 (Evidence, reproducibility and clarity: 

This paper identifies FAM53C as a novel regulator of cell cycle progression, particularly at the G1/S transition, by inhibiting DYRK1A. Using data from the Cancer Dependency Map, the authors suggest that FAM53C acts upstream of the Cyclin D-CDK4/6-RB axis by inhibiting DYRK1A.  Specifically, their experiments suggest that FAM53C Knockdown induces G1 arrest in cells, reducing proliferation without triggering apoptosis. DYRK1A Inhibition rescues G1 arrest in P53KO cells, suggesting FAM53C normally suppresses DYRK1A activity. Mass Spectrometry and biochemical assays confirm that FAM53C directly interacts with and inhibits DYRK1A. FAM53C Knockout in Human Cortical Organoids and Mice leads to cell cycle defects, growth impairments, and behavioral changes, reinforcing its biological importance. 

Strength of the paper: 

The study introduces a novel cell cycle control signalling module upstream of CDK4/6 in G1/S regulation which could have significant impact. The identification of FAM53C using a depmap correlation analysis is a nice example of the power of this dataset. The experiments are carried out mostly in a convincing manner and support the conclusions of the manuscript. 

Critique: 

(1) The experiments rely heavily on siRNA transfections without the appropriate controls. There are so many cases of off-target effects of siRNA in the literature, and specifically for a strong phenotype on S-phase as described here, I would expect to see solid results by additional experiments. This is especially important since the ko mice do not show any significant developmental cell cycle phenotypes. Moreover, FAM53C does not show a strong fitness effect in the depmap dataset, suggesting that it is largely non-essential in most cancer cell lines. For this paper to reach publication in a high-standard journal, I would expect that the authors show a rescue of the S-phase phenotype using an siRNA-resistant cDNA, and show similar S-phase defects using an acute knock out approach with lentiviral gRNA/Cas9 delivery. 

We thank the Reviewer for this comment. Please refer to the initial response to the three Reviewers, where we discuss our use of single siRNAs and our results in multiple cell lines. Briefly, we can recapitulate the G1 arrest upon FAM53C knock-down using two independent siRNAs in RPE-1 cells. We also observe the same G1 arrest in p53 knockout cells, suggesting it is not due to a non-specific stress response. In addition, the arrest is dependent on RB, which fits with the genetic and biochemical data placing FAM53C upstream of RB, further supporting a specific phenotype. Human cancer cell lines also arrest in G1 upon FAM53C knock-down, not just RPE-1 cells. Finally, we hope the Reviewer will agree with us that compensatory mechanisms are very common in the cell cycle – which may explain the lack of phenotypes in vivo or upon long-term knockout of FAM53C.

(2) The S-phase phenotype following FAM53C should be demonstrated in a larger variety of TP53WT and mutant cell lines. Given that this paper introduces a new G1/S control element, I think this is important for credibility. Ideally, this should be done with acute gRNA/Cas9 gene deletion using a lentiviral delivery system; but if the siRNA rescue experiments work and validate an on-target effect, siRNA would be an appropriate alternative. 

We now show data with three cancer cell lines (U2OS, A549, and HCT-116 – Fig. S1E,F and Fig. 4F), in addition to our results in RPE-1 cells and in human cortical organoids. We note that the knock-down experiments are complemented by overexpression data (Fig. 1G-I), by genetic data (our original DepMap screen), and our biochemical data (showing direct binding of FAM53C to DYRK1A).

(3) The western blot images shown in the MS appear heavily over-processed and saturated (See for example S4B, 4A, B, and E). Perhaps the authors should provide the original un-processed data of the entire gels? 

For several of our panels (e.g., 4E and S4B, now panels S3J and S3K)), we used a true “immunoassay” (as indicated in the legend – not an immunoblot), which is much more quantitative and avoids error-prone steps in standard immunoblots (“Western blots”). Briefly, this system was developed by ProteinSimple. It uses capillary transfer of proteins and ELISA-like quantification with up to 6 logs of dynamic range (see their web site https://www.proteinsimple.com/wes.html). The “bands” we show are just a representation of the luminescence signals in capillaries. We made sure to further clarify the figure legends in the revised manuscript.

Data in 4A are also not a western blot but a radiograph.

For immunoblots, we will provide all the source data with uncropped blots with the final submission.

(4) A critical experiment for the proposed mechanism is the rescue of the FAM53C S-phase reduction using DYRK1A inhibition shown in Figure 4. The legend here states that the data were extracted from BrdU incorporation assays, but in Figure S4D only the PI histograms are shown, and the S-phase population is not quantified. The authors should show the BrdU scatterplot and quantify the phenotype using the S-phase population in these plots. G1 measurements from PI histograms are not precise enough to allow for conclusions. Also, why are the intensities of the PI peaks so variable in these plots? Compare, for example, the HCT116 upper and lower panels where the siRNA appears to have caused an increase in ploidy. 

We apologize for the confusion and we fixed these errors, for most of the analyses, we used PI to measure G1 and S-phase entry. We added relevant flow cytometry plots to supplemental figures (Fig. S1G, H, I, as well as Fig. S4E and S4K, and Fig. S5F).

(5) There's an apparent contradiction in how RB deletion rescues the G1 arrest (Figure 2) while p21 seems to maintain the arrest even when DYRK1A is inhibited. Is p21 not induced when FAM53C is depleted in RB ko cells? This should be measured and discussed. 

This comment and comments from the two other Reviewers made us reconsider our model. We re-read carefully the Meyer paper and think that DYRK1A activity may be understood when considering levels of both CycD and p21 at the same time in a continuum (as was nicely showed in a previous study from the lab of Tobias Meyer – Chen et al., Mol Cell, 2013). While our genetic and biochemical data support a role for FAM53C in DYRK1A inhibition, it is obvious that the regulation of cell cycle progression by FAM53C is not exclusively due to this inhibition. As discussed above and below, we noted an upregulation of p21 upon FAM53C knock-down, and activation of p53 and its targets likely contributes significantly to the phenotypes observed. We added new experiments to support this more complex model (Figure 4 and Figure S4, with new model in S4L).

Reviewer #3 (Significance): 

In conclusion, I believe that this MS could potentially be important for the cell cycle field and also provide a new target pathway that could be relevant for cancer therapy. However, the paper has quite a few gaps and inconsistencies that need to be addressed with further experiments. My main worry is that the acute depletion phenotypes appear so strong, while the gene is nonessential in mice and shows only a minor fitness effect in the depmap screens. More convincing controls are necessary to rule out experimental artefacts that misguide the interpretation of the results.

We appreciate this comment and hope that the Reviewer will agree it is still important to share our data with the field, even if the phenotypes in mice are modest.

Reviewer #2 (Public review):

The Revision title and abstract are not updated enough to distinguish the special niche piRNA clusters from the more prominent major dual strand piRNA clusters that are widely known in the field for Drosophila, like 42AB and 38C. This revision mainly adds the term "piRNA source loci (piSL)" that is too vague and not a well-accepted name that would distinguish just these particularly niche piRNA clusters from major dual strand piRNA clusters like 42AB and 38C. This piSL term is problematic because it seems to imply these piSL's are connected to or would eventually become major dual strand piRNA clusters, but there is zero evidence in this study for any genetic or evolutionary connection between these two distinct types of piRNA sources. This revision still lacks the necessary changes needed to point out like in the abstract that major dual strand piRNA clusters like 42AB, 38C, 80F, and 102F in Drosophila that make up the bulk of piRNAs cannot be shown to be impacted by changes aimed at depleting ADMA-histones from these loci, and the authors' current evidence is still only limited to showing in these few 'niche' piRNA clusters that ADMA-histones may exhibit a direct interaction with Rhino as supported only by the knockdown of Drosophila Art4.

The author's rebuttal letter argues that 42AB and 38C are just conserved piRNA clusters that may no longer be regulated by ADMA. This is still a weak claim for dismissing the potential genetic redundancy problem when this study can only report strong knockdown of Art4. First, the dual strand 42AB piRNA cluster's conservation as a Drosophilid piRNA cluster is actually still a relatively recent evolutionary innovation in just D.simulans and D.melanogaster that are less than 3MYA diverged. This 42AB cluster is no longer conserved in D.sechelia and is also younger than the uni-strand Flamenco piRNA cluster that is conserve to 7MYA. The evolutionary arguments by the authors are not well-grounded. Second, the 42AB and 38C are the largest major dual strand piRNA clusters with very significant localization of Rhino and impact from Rhino loss of function, and if this paper's central thesis is that ADMA-histones directed by Art1 or Art4 is critical for the expression of dual-strand piRNA cluster loci by impacting Rhino, the current data still remain weak with no new experiments to help bolster their claims.

The author's rebuttal letter argues that the challenges they faced in trying to knock down Art1 in the fly was thwarted by reagent issues, and the explanations are unsatisfactory. They claim they only tested two RNAi cross lines to try to knock down Art1: the strain BDSC #36891, y[1] sc[*] v[1] sev[21]; P{y[+t7.7], v[+t1.8]=TRiP.GL01072}attP2/TM3, Sb[1] that they said they could not obtain this strain to be alive from the stock center? And then testing an alternative line VDRC #v110391P{KK101196}VIE-260B that displayed mediocre knockdown, the authors seemed to suggest they have given up trying to make this very important experiment work? They should have tried to figure out with the BDSC, a venerable stock center for Drosophila genetic tools, why they could not receive that fly strain alive (shipping flies at the economy rate internationally may be cheaper but often is too strenuous for flies to survive), and the authors have not acknowledged testing two other available knockdown lines for Art1: BDSC #31348, y[1] v[1]; P{y[+t7.7] v[+t1.8]=TRiP.JF01306}attP2 dsRNA and VDRC #w1118 P{GD11959}v40388. Trying to get good knockdown of Art1 would be a critical must-have experiment to address whether this arginine methyltransferase has an in vivo impact on ADMA-histones in the Drosophila ovary and showing an impact on 42AB and 38C. The revision does not address this major deficiency in impact on these two major dual strand piRNA clusters, only the very few niche piRNA clusters that are responsive to Art4 knockdown.

The rebuttal letter argues that "Therefore, conserved clusters such as 42AB and 38C may no longer be regulated by ADMA." but then the revision discussion is still speculating much too wildly that the piRNA source loci are then precursors for the eventual large piRNA clusters of 42AB and 38C. This renaming of the term piRNA source loci and the model in Fig. 7C is still misleading because 42AB and 38C are the main largest dual-strand piRNA clusters, and the pictures depict the ADMA-histones as recruiting Rhino and then Kipferl at a piRNA cluster. The term "piRNA source loci" does not sound distinct enough to separate it from the main piRNA clusters of 42AB and 38C, and I had suggested calling them 'niche piRNA clusters' to denote they are very special and distinct to only be responsive to Drosophila Art4 knockdown.

In regards to the revision's changing of gene names, the convention for gene names is to use the previous name designation. Rather than calling the gene DART1, the conventional name of this gene in Flybase is Art1 (CG6554). There is the same problem with using the new name DART4 when in Flybase the gene is called Art4 (CG5358). Alternatively, the authors should clarify the re-naming up front and make it consistent with Drosophila genetics nomenclature, perhaps dArt1 or dArt4 would be more appropriate.

There is a meme for this I saw on Twitter, "But not all X are Y"

Author response:

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

Reviewer #1 (Public review):  

From my reading, this study aimed to achieve two things:  

(1) A neurally-informed account of how Pieron's and Fechner's laws can apply in concert at distinct processing levels.  

(2) A comprehensive map in time and space of all neural events intervening between stimulus and response in an immediately-reported perceptual decision.  

I believe that the authors achieved the first point, mainly owing to a clever contrast comparison paradigm, but with good help also from a new topographic parsing algorithm they created. With this, they found that the time intervening between an early initial sensory evoked potential and an "N2" type process associated with launching the decision process varies inversely with contrast according to Pieron's law. Meanwhile, the interval from that second event up to a neural event peaking just before response increases with contrast, fitting Fechner's law, and a very nice finding is that a diffusion model whose drift rates are scaled by Fechner's law, fit to RT, predicts the observed proportion of correct responses very well. These are all strengths of the study.   

We thank the reviewer for their comments that added context to the events we detected in relation to previous findings. We also believe that the change in the HMP algorithm suggested by the reviewer improved the precision of our analyses and the manuscript. We respond to the reviewer’s specific comments below.

(1) The second, generally stated aim above is, in the opinion of this reviewer, unconvincing and ill-defined. Presumably, the full sequence of neural events is massively task-dependent, and surely it is more in number than just three. Even the sensory evoked potential typically observed for average ERPs, even for passive viewing, would include a series of 3 or more components - C1, P1, N1, etc. So are some events being missed? Perhaps the authors are identifying key events that impressively demarcate Pieron- and Fechner-adherent sections of the RT, but they might want to temper the claim that they are finding ALL events. In addition, the propensity for topographic parsing algorithms to potentially lump together distinct processes that partially co-evolve should be acknowledged.  

We agree with the reviewer that the topographical solutions found by HMP will be dependent on the task and the quality and type of data. We address this point in the last section of the discussion (see also response to R3.5). We would also like to add that the events detected by HMP are, by construction, those that contribute to the RT and not necessarily all ERPs elicited by a stimulus.

In addition to the new last section of the discussion we also make these points clear in the revised manuscript at the discussion start: 

“By modeling the recorded single-trial EEG signal between stimulus onset and response as a sequence of multivariate events with varying by-trial peak times, we  aimed to detect recurrent events that contribute to the duration of the reaction time in the present perceptual decision-making task”.

Regarding the typical visual ERPs, in response to this comment but also comments R1.2, R1.3 and R2.1, we aimed for a more precise description of the topographies and thus reduced the width of the HMP expected events to 25ms. This ensures that we do not miss events shorter than the initial expectations of 50ms (see Appendix B of Weindel et al., 2024 and also response to  R1.3). This new estimation provides evidence for at least two of the visual ERPs that, based on their timings and topographies (in relation with the spatial frequency of the stimulus), we interpret as the N40 and the P100 (see response to R1.5 for the justification of this categorization). We provide a description and justification of the interpretations in the result section “Five trial-recurrent sequential events occur in the EEG during decisions” and the discussion section “Visual encoding time”.

(2) To take a salient example, the last neural event seems to blend the centroparietal positivity with a more frontal midline negativity, some of which would capture the CNV and some motor-execution related components that are more tightly time-locked to, of course, the response. If the authors plotted the traditional single-electrode ERP at the frontal focus and centroparietal focus separately, they are likely to see very different dynamics and contrast- and SAT-dependency. What does this mean for the validity of the multivariate method? If two or more components are being lumped into one neural event, wouldn't it mean that properties of one (e.g., frontal burstiness at response) are being misattributed to the other (centroparietal signal that also peaks but less sharply at response)?

Using the new HMP parameterization described above we show that the reviewer's intuition was correct. Using an expected pattern duration of 25ms the last event in the original manuscript splits in two events. The before-last event, now referred to the lateralized readiness potential (LRP) presents a strong lateralization (Figure 3) with an increased negativity over the motor cortex contralateral to the right hand. The effect of contrast is mostly on the last event that we interpret as the CPP (Figure 5). Despite the improved precision of the topographies of the identified events, it is however to be noted that some components will overlap. If the LRP is generated when a certain amount of evidence is accumulated (e.g. that the CPP crosses a certain value) then a time-based topography will necessarily include that CPP activity in addition to the lateralized potential. We discuss this in the section “Motor execution” of the discussion:

“Adding the abrupt onset of this potential, we believe that this event is the start of motor execution, engaged after a certain amount of evidence. The evidence for this interpretation is manifest in the fact that the event's topography shares some activity with the CPP event that follows, an expected result if the LRP is triggered at a certain amount of evidence, indexed by the CPP”.

(3) Also related to the method, why must the neural events all be 50 ms wide, and what happens if that is changed? Is it realistic that these neural events would be the same duration on every trial, even if their duration was a free parameter? This might be reasonable for sensory and motor components, but unlikely for cognitive.  

The HMP method is sensitive to the event's duration as shown in the manuscript about the method (Appendix B of Weindel et al., 2024). Nevertheless as long as the topography in the real data is longer than the expected one it shouldn't be missed (i.e. same goes for by-trial variations in the event width). For this reason we halved the expected event width of 50ms (introduced by the original HsMM-MVPA paper by Anderson and colleagues) in the revision. This new estimation with 25ms thus is much less likely to miss events as evidenced by the new visual and motor events. In the revised manuscript this is addressed at the start of the Results section:

“Contrary to previous applications (Anderson et al.,2016; Berberyan et al., 2021; Zhang et al., 2018; Krause et al., 2024) we assumed that the multivariate pattern was represented by a 25ms half-sine as our previous research showed that a shorter expected pattern width increases the likelihood of detecting cognitive events (see Appendix B of Weindel et al., 2024)”.

Regarding the event width as a free parameter this is both technically and statistically difficult to implement as the amount of computing capacity, flexibility and trade-offs among the HMP parameters would, given the current implementation, render the model unfit for most computers and statistically unidentifiable.

(4) In general, I wonder about the analytic advantage of the parsing method - the paradigm itself is so well-designed that the story may be clear from standard average event-related potential analysis, and this might sidestep the doubts around whether the algorithm is correctly parsing all neural events.  

Average ERP analysis suffers from an impossibility to differentiate between an effect of an experimental factor on the amplitude vs. on the timing of the underlying components (Luck, 2005). Furthermore the overlap of components across trials bluries the distinction between them. For both reasons we would not be able to reach the same level of certainty and precision using ERP analyses. Furthermore the relatively low number of trials per experimental cell (contrast level X SAT X participant = 6 trials) makes the analyses hard to perform on ERP which typically require more trials per modality. From the reviewer’s comment we understand that this point was not clear. We therefore discuss this in the revision, Section “Functional interpretation of the events” of the results:

“Nevertheless identifying neural dynamics on these ERPs centered on stimulus is complicated by the time variation of the underlying single-trial events (see probabilities displayed in Figure 3 for an illustration and Burle et al., 2008, for a discussion). The likely impact of contrast on both amplitude and time on the underlying single-trial event does not allow one to interpret the average ERP traces as showing an effect in one or the other dimension without strong assumptions (Luck, 2005)”.

(5) In particular, would the authors consider plotting CPP waveforms in the traditional way, across contrast levels? The elegant design is such that the C1 component (which has similar topography) will show up negative and early, giving way to the CPP, and these two components will show opposite amplitude variations (not just temporal intervals as is this paper's main focus), because the brighter the two gratings, the stronger the aggregate early sensory response but the weaker the decision evidence due to Fechner. I believe this would provide a simple, helpful corroborating analysis to back up the main functional interpretation in the paper.  

We agree with the suggestion and have introduced the representation on top of Figure 5 for sets of three electrodes in the occipital, posterior and frontal regions. The new panels clearly show an inversion of the contrast effect dependent on the time and locus of the electrodes. We discuss this in Section “Functional interpretation of the events” of the results:

“This representation shows that there is an inversion of the contrast effect with higher contrasts having a higher amplitude on the electrodes associated with visual potentials in the first couple of deciseconds (left panel of Figure 5A) while parietal and frontal electrodes shows a higher amplitude for lower contrasts in later portions of the ERPs (middle and right panel of Figure 5A)”.

To us, this crucially shows that we cannot achieve the same decomposition using traditional ERP analyses. In these plots it appears that while, as described by the reviewer, there is an inversion, the timing and amplitude of the changes due to contrast can hardly be interpreted.

(6) The first component is picking up on the C1 component (which is negative for these stimulus locations), not a "P100". Please consult any visual evoked potential study (e.g., Luck, Hillyard, etc). It is unexpected that this does not vary in latency with contrast - see, for example. Gebodh et al (2017, Brain Topography) - and there is little discussion of this. Could it be that nonlinear trends were not correctly tested for?  

We disagree with the reviewer on the interpretation of the ERP. The timing of the detected component is later than the one usually associated with a C1. Furthermore the central display does not create optimal conditions to detect a C1

We do agree that the topography raises the confusion but we believe that this is due to the spatial frequency of the stimulus that generates a high posterior positivity (see references in the following extract). The new HMP solution also now happens to show an effect of contrast on the P100 latencies, we believe this is due to the increased precision in the time location of the component. We discuss this in the “Visual encoding time” section of the discussion:

“The following event, the P100, is expressed around 70ms after the N40, its topography is congruent with reports for stimuli with low spatial frequencies as used in the current study (Kenemans et al., 2002, 2000; Proverbio et al., 1996). The timing of this P100 component is changed by the contrast of the stimulus in the direction expected by the Piéron law (Figure 4A)”. 

(7) There is very little analysis or discussion of the second stage linked to attention orientation - what would the role of attention orientation be in this task? Is it spatial attention directed to the higher contrast grating (and if so, should it lateralise accordingly?), or is it more of an alerting function the authors have in mind here?  

We agree that we were not specific enough on the interpretation of this attention stage. We now discuss our hypothesis in the section “Attention orientation” of the discussion:  

“We do however observe an asymmetry in the topographical map Figure 3. This asymmetry might point to an attentional bias with participants (or at least some participants) allocating attention to one side over the other in the same way as the N2pc component (Luck and Hillyard, 1994, Luck et al., 1997). Based on this collection of observations, we conclude that this third event represents an attention orientation process. In line with the finding of Philiastides et al. (2006), this attention orientation event might also relate to the allocation of resources. Other designs varying the expected cognitive load or spatial attention could help in further interpreting the functional role of this third event”.

We would like to add that it is unlikely that the asymmetry we mention in the discussion cannot stem from the redirection towards higher contrast as the experimental design balanced the side of presentation. We therefore believe that this is a behavioral bias rather than a bias toward the highest contrast stimulus as suggested by the reviewer. We hope that, while more could be tested and discussed, this discussion is sufficient given the current manuscript's goal.

Reviewer #2 (Public review):  

Summary:  

The authors decomposed response times into component processes and manipulated the duration of these processes in opposing directions by varying contrast, and overall by manipulating speed-accuracy tradeoffs. They identify different processes and their durations by identifying neural states in time and validate their functional significance by showing that their properties vary selectively as expected with the predicted effects of the contrast manipulation. They identify 3 processes: stimulus encoding, attention orienting, and decision. These map onto classical event-related potentials. The decision-making component matched the CPP, and its properties varied with contrast and predicted decision-accuracy, while also exhibiting a burst not characteristic of evidence accumulation.  

Strengths:  

The design of the experiment is remarkable and offers crucial insights. The analysis techniques are beyond state-of-the-art, and the analyses are well motivated and offer clear insights.  

Weaknesses:  

It is not clear to me that the results confirm that there are only 3 processes, since e.g., motor preparation and execution were not captured. While the authors discuss this, this is a clear weakness of the approach, as other components may also have been missed. It is also unclear to what extent topographies map onto processes, since, e.g., different combinations of sources can lead to the same scalp topography.  

We thank the reviewer for their kind words and for the attention they brought on the question of the missing motor preparation event. In light of this comment (and also R1.1, R3.3) the revised manuscript uses a finer grained approach for the multivariate event detection. This preciser estimation comes from the use of a shorter expected pattern in which the initial expectation of a 50ms half-sine was halved, therefore ensuring that we do not miss events shorter than the initial expectations (see Appendix B of Weindel et al., 2024 and also response to  R1.3). In the new solution the motor component that the reviewer expected is found as evidenced by the topography of the event, its lateralization and a time-to-response congruent with a response execution event. This is now described in the section “Motor execution” of the revised manuscript: 

“The before last event, identified as the LRP, shows a strong hemispheric asymmetry congruent with a right hand response. The peak of this event is approximately 100 ms before the response which is congruent with reports that the LRP peaks at the onset of electromyographical activity in the effector muscle (Burle et al., 2004), typically happening 100ms before the response in such decision-making tasks (Weindel et al., 2021). Furthermore, while its peak time is dependent on contrast, its expression in the EEG is less clearly related to the contrast manipulation than the following CPP event”.

Reviewer #3 (Public review):  

Summary:  

In this manuscript, the authors examine the processing stages involved in perceptual decision-making using a new approach to analysing EEG data, combined with a critical stimulus manipulation. This new EEG analysis method enables single-trial estimates of the timing and amplitude of transient changes in EEG time-series, recurrent across trials in a behavioural task. The authors find evidence for three events between stimulus onset and the response in a two-spatial-interval visual discrimination task. By analysing the timing and amplitude of these events in relation to behaviour and the stimulus manipulation, the authors interpret these events as related to separable processing stages for stimulus encoding, attention orientation, and decision (deliberation). This is largely consistent with previous findings from both event-related potentials (across trials) and single-trial estimates using decoding techniques and neural network approaches.  

Strengths:  

This work is not only important for the conceptual advance, but also in promoting this new analysis technique, which will likely prove useful in future research. For the broader picture, this work is an excellent example of the utility of neural measures for mental chronometry.  

We appreciate the very positive review and thank the reviewer for pointing out important weaknesses in our original manuscript and also providing resources to address them in the recommendations to authors. Below we comment on each identified weakness and how we addressed them.   

Weaknesses:  

(1) The manuscript would benefit from some conceptual clarifications, which are important for readers to understand this manuscript as a stand-alone work. This includes clearer definitions of Piéron's and Fechner's laws, and a fuller description of the EEG analysis technique.

We agree that the description of both laws were insufficient, we therefore added the following text in the last paragraph of the introduction:

“Piéron’s law predicts that the time to perceive the two stimuli (and thus the choice situation) should follow a negative power law with the stimulus intensity (Figure 1, green curve). In contradistinction, Fechner’s law states that the perceived difference between the two patches follows the logarithm of the absolute contrast of the two patches (Figure 1, yellow curve). As the task of our participants is to judge the contrast difference, Piéron’s law should predict the time at which the comparison starts (i.e. the stimuli become perceptible), while Fechner’s law should implement the comparison, and thus decision, difficulty”.

Regarding the EEG analysis technique we added a few elements at the start of the result:

“The hidden multivariate pattern model (HMP) implemented assumed that a task-related multivariate pattern event is represented by a half-sine whose timing varies from trial to trial based on a gamma distribution with a shape parameter of 2 and a scale, controlling the average latency of the event, free-to-vary per event (Weindel et al., 2024)”.

We also made the technique clearer at the start of the discussion:

“By modeling the recorded single-trial EEG signal between stimulus onset and response as a sequence of multivariate events with varying by-trial peak times, we aimed to detect recurrent events that contribute to the duration of the reaction time in the present perceptual decision-making task. In addition to the number of events, using this hidden multivariate pattern approach (Weindel et al., 2024) we estimated the trial-by-trial probability of each event’s peak, therefore accessing at which time sample each event was the most likely to occur”.

Additionally, we added a proper description in the method section (see the new first paragraph of the “Hidden multivariate pattern” subsection). 

(2) The manuscript, broadly, but the introduction especially, may be improved by clearly delineating the multiple aims of this project: examining the processes for decision-making, obtaining single-trial estimates of meaningful EEG-events, and whether central parietal positivity reflects ramping activity or steps averaged across trials.

For the sake of clarity we removed the question of the ramping activity vs steps in the introduction and focused on the processes in decision-making and their single-trial measurement as this is the main topic of the paper. Furthermore the references provided by the reviewer allowed us to write a more comprehensive review of previous studies and how the current study is in line with those. These changes are mainly manifested in these new sentences:

“As an example Philiastides et al. (2006) used a classifier on the EEG activity of several conditions to show that the strength of an early EEG component was proportional to the strength of the stimulus while a later component was related to decision difficulty and behavioral performance (see also Salvador et al., 2022; Philiastides and Sajda, 2006). Furthermore the authors interpreted that a third EEG component was indicative of the resource allocated to the upcoming decision given the perceived decision difficulty. In their study, they showed that it is possible to use single-trial information to separate cognitive processes within decision-making. Nevertheless, their method requires a decoding approach, which requires separate classifiers for each component of interest and restrains the detection of the components to those with decodable discriminating features (e.g. stimuli with strong neural generators such as face stimuli, see Philiastides et al., 2006)”.

(3) A fuller discussion of the limitations of the work, in particular, the absence of motor contributions to reaction time, would also be appreciated. 

As laid out in responses to comments R1.1 and R2 the new estimates now include evidence for a motor preparation component. We discuss this in the new “motor execution” paragraph in the discussion section. Additionally we discuss the limitation of the study and the method in the two last paragraphs of the discussion (in the new Section “Generalization and limitation”).

(4) At times, the novelty of the work is perhaps overstated. Rather, readers may appreciate a more comprehensive discussion of the distinctions between the current work and previous techniques to gauge single-trial estimates of decision-related activity, as well as previous findings concerning distinct processing stages in decision-making. Moreover, a discussion of how the events described in this study might generalise to different decision-making tasks in different contexts (for example, in auditory perception, or even value-based decision-making) would also be appreciated.  

We agree that the original text could be read as overstating. In addition to the changes linked to R3.2 we also now discuss the link with the previous studies in the before-last paragraph of the discussion before the conclusion in the new “Generalization and limitations” section:

“The present study showed what cognitive processes are contributing to the reaction time and estimated single-trial times of these processes for this specific perceptual decision-making task. The identified processes and topographies ought to be dependent on the task and even the stimuli (e.g. sensory events will change with the sensory modality). More complex designs might generate a higher number of cognitive processes (e.g. memory retrieval from a cue, Anderson et al., 2016) and so could more natural stimuli which might trigger other processes in the EEG (e.g. appraisal vs. choice as shown by Frömer et al., 2024). Nevertheless, the observation of early sensory vs. late decision EEG components is likely to generalize across many stimuli and tasks as it has been observed in other designs and methods (Philiastides et al., 2006; Salvador et al., 2022). To these studies we add that we can evaluate the trial-level contribution, as already done for specific processes (e.g. Si et al., 2020; Sturm et al., 2016), for the collection of events detected in the current study”.

Reviewing Editor Comments:  

As you will see, all three reviewers agree that the paper makes a valuable contribution and has many strengths. You will also see that they have provided a range of constructive comments highlighting potential issues with the interpretation of the outcomes of your signal decomposition method. In particular, all three reviewers point out that your results do not identify separate motor preparation signals, which we know must be operating on this type of task. The reviewers suggest further discussion of this issue and the potential limitations of your analysis approach, as well as suggesting some additional analyses that could be run to explore this further. While making these changes would undoubtedly enhance the paper and the final public reviews, I should note that my sense is that they are unlikely to change the reviewers' ratings of the significance of the findings and the strength of evidence in the final eLife assessment  

Reviewer #1 (Recommendations for the authors):  

(1) Abstract: "choice onset" is ill-defined and not the label most would give the start of the RT interval. Do you mean stimulus onset?  

We replaced with "choice onset" with "stimulus onset" in the abstract

(2) Similarly "choice elements" in the introduction seem to refer to sensory attributes/objects being decided about?  

We replaced "choice-elements" with "choice-relevant features of the stimuli"

(3) "how the RT emerges from these putative components" - it would be helpful to specify more what level of answer you're looking for, as one could simply answer "when they're done."  

We replaced with "how the variability in RTs emerges from these putative components"

(4) Line 61-62: I'm not sure this is a fully correct characterisation of Frömer et al. It was not similar in invoking a step function - it did not invoke any particular mechanism or function, and in that respect does not compare well to Latimer et al. Also, I believe it was the overlap of stimulus-locked components, not response-locked, that they argued could falsely generate accumulator-like buildup in the response-locked ERP.  

We indeed wrongly described Frömer et al. The sentence is now "In human EEG data, the classical observation of a slowly evolving centro-parietal positivity, scaling with evidence accumulation, was suggested to result from the overlap of time-varying stimulus-related activity in the response-locked event related potential"

(5) Line 78: Should this be single-trial *latency*?  

This referred to location in time but we agree that the term is confusing and thus replaced it with latencies.

(6) The caption of Figure 1 should state what is meant by the y-axis "time"  

We added the sentence "The y-axis refers the time predicted by each law given a contrast value (x-axis) and the chosen set of parameters." in the caption of Figure 1

(7) Line 107: Is this the correct description of Fechner's law? If the perceived difference follows the log of the physical difference, then a constant physical difference should mean a constant perceived difference. Perhaps a typo here.  

This was indeed a typo we replaced the corresponding part of the sentence with "the perceived difference between the two patches follows the logarithm of the absolute contrast of the two patches"

(8) Line 128: By scale, do you mean magnitude/amplitude?  

No, this refers to the parameter of a gamma distribution. To clarify we edited the sentence:  "based on a gamma distribution with a shape parameter of 2 and a scale parameter, controlling the average latency of the event, free-to-vary per event"

(9) The caption of Figure 3 is insufficient to make sense of the top panel. What does the inter-event interval mean, and why is it important to show? What is the "response" event?  

We agree that the top panel was insufficiently described. To keep the length of the paper short and because of the relatively low amount of information provided by these panels we replaced them for a figure only showing the average topographies as well as the asymmetry tests for each event.

(10) Figure 4: caption should say what the top vs bottom row represents (presumably, accuracy vs speed emphasis?), and what the individual dots represent, given the caption says these are "trial and participant averaged". A legend should be provided for the rightmost panels.  

We agree and therefore edited Figure 4. The beginning of the caption mentioned by the reviewer now reads: “A) The panels represent the average duration between events for each contrast level, averaged across participants and trials (stimulus and response respectively as first and last events) for accuracy (top) and speed instructions (bottom).”. Additionally we added legends for the SAT instructions and the model fits.

(11) Line 189: argued for a decision-making role of what?  

Stafford and Gurney (2004) proposed that Pieron’s law could reflect a non-linear transformation from sensory input to action outcomes, which they argued reflected a response mechanism. We (Van Maanen et al., 2012) specified this result by showing that a Bayesian Observer Model in which evidence for two alternative options was accumulated following Bayes Rule indeed predicted a power relation between the difference in sensory input of the two alternatives, and mean RT. However, the current data suggest that such an explanation cannot be the full story, as also noted by R3. To clarify this point we replaced the comment by the following sentence:

“Note that this observation is not necessarily incongruent with theoretical work that argued that Piéron’s law could also be a result of a response selection mechanism (Stafford and Gurney, 2004; Van Maanen et al., 2012; Palmer et al., 2005). It could be that differences in stimulus intensity between the two options also contribute to a Piéron-like relationship in the later intervals, that is convoluted with Fechner’s law (see Donkin and Van Maanen, 2014 for a similar argument). Unfortunately, our data do not allow us to discriminate between a pure logarithmic growth function and one that is mediated by a decreasing power function”.

(12) Table 2: There is an SAT effect even on the first interval, which is quite remarkable and could be discussed more - does this mean that the C1 component occurs earlier under speed pressure? This would be the first such finding.  

The original event we qualified as a P100 was sensitive to SAT but the earliest event is now the N40 and isn’t statistically sensitive to speed pressure in this data. We believe that the fact that the P100 is still sensitive to SAT is not a surprise and therefore do not outline it.

(13) Line 221: "decrease of activation when contrast (and thus difficulty) increases" - is this shown somewhere in the paper?  

The whole section for this analysis was rewritten (see comment below)

(14) I find the analysis of Figure 5 interesting, but the interpretation odd. What is found is that the peak of the decision signal aligns with the response, consistent with previous work, but the authors choose to interpret this as the decision signal "occurring as a short-lived burst." Where is the quantitative analysis of its duration across trials? It can at least be visually appraised in the surface plot, and this shows that the signal has a stimulus-locked onset and, apart from the slowest RTs, remains present and for the most part building, until response. What about this is burst-like? A peak is not a burst.  

This was the residue of a previous version of the paper where an analysis reported that no evidence accumulation trace was found. But after proper simulations this analysis turned out to be false because of a poor statistical test. Thus we removed this paragraph in the revised manuscript and Figure 5 has now been extended to include surface plots for all the events.

Reviewer #2 (Recommendations for the authors):  

Overall, I really enjoyed reading this paper. However, in some places the approach is a bit opaque or the results are difficult to follow. As I read the paper, I noted:  

Did you do a simple DDM, or did you do a collapsing bound for speed?  

The fitted DDM was an adaptation of the proportional rate diffusion model. We make this clearer at the end of the introduction: "Given that Fechner’s law is expected to capture decision difficulty we connected this law to the classical diffusion decision models by replacing the rate of accumulation with Fechner’s law in the proportional rate diffusion model of Palmer et al.(2005).”

It is confusing that the order of intervals in the text doesn't match the order in the table. It might be better to say what events the interval is between rather than assuming that the reader reconstructs.  

We agree and adapted the order in both the text and the table. The table is now also more explicit (e.g. RT instead of S-R)

Otherwise, I do wonder to what extent the method is able to differentiate processes that yield similar scalp topographies and find it a bit concerning that no motor component was identified.  

We believe that the new version with the LRP/CPP is a demonstration that the method can handle similar topographies. The method can handle events with close topographies as long as they are separate in time, however if they are not sequential to one another the method cannot capture both events. We now discuss this, in relation with the C1/P100 overlap, in the discussion section “Visual encoding time”:

“Nevertheless this event, seemingly overlapping with the P100 even at the trial level (Figure 5C), cannot be recovered by the method we applied. The fact that the P100 was recovered instead of the C1 could indicate that only the timing of the P100 contributes to the RT (see Section 3 of Weindel et al., 2024)”.

And we more generally address the question of overlap in the new section “Generalization and limitation”.

Reviewer #3 (Recommendations for the authors):  

Major Comments:  

(1) If we agree on one thing, it is that motor processes contribute to response time. Line 364: "In the case of decision-making, these discrete neural events are visual encoding, attention-orientation, and decision commitment, and their latency make up the reaction time." Does the third event, "decision commitment", capture both central parietal positivity (decision deliberation) and motor components? If so, how can the authors attribute the effects to decision deliberation as opposed to motor preparation?  

Thanks to the suggestions also in the public part. This main problem is now addressed as we do capture both a motor component and a decision commitment.

Line 351 suggests that the third event may contain two components.  

This was indeed our initial, badly written, hypothesis. Nevertheless the new solution again addresses this problem.

The time series in Figure 6 shows an additional peak that is not evident in the simulated ramp of Appendix 1.  

This was probably due to the overlap of both the CPP and the LRP. It is now much clearer that the CPP looks mostly like a ramp while the LRP looks much more like a burst-like/peaked activity. We make this clear in the “Decision event” paragraph of the discussion section:

“Regarding the build-up of this component, the CPP is seen as originating from single-trial ramping EEG activities but other work (Latimer et al., 2015; Zoltowski et al., 2019) have found support for a discrete event at the trial-level. The ERPs on the trial-by-trial centered event in Figure 5 show support for both accounts. As outlined above, the LRP is indeed a short burst-like activity but the build-up of the CPP between high vs low contrast diverges much earlier than its peak”.

Previous analyses (Weindel et al., 2024) found motor-related activity from central parietal topographies close to the response by comparing the difference in single-trial events on left- vs right-hand response trials. The authors suggest at line 315 that the use of only the right hand for responding prevented them from identifying a motor event.  

The use of only the right hand should have made the event more identifiable because the topography would be consistent across trials (rather than inverting on left vs right hand response trials).  

The reviewer is correct, in the original manuscript we didn’t test for lateralization, but the comment of the reviewer gave us the idea to explicitly test for the asymmetry (Figure 3). This test now clearly shows what would be expected for a motor event with a strong negativity over the left motor cortex.

The authors state on line 422 that the EEG data were truncated at the time of the response.  

Could this have prevented the authors from identifying a motor event that might overlap with the timing of the response?  

We thank the reviewer for this suggestion. This would have been a possibility but the problem is that adding samples after the response also adds the post-response processes (error monitoring, button release, stimulus disappearance, etc.). While increasing the samples after the response is definitely something that we need to inspect, we think that the separation we achieved in this revision doesn’t call for this supplementary analysis.

The largest effects of contrast on the third event amplitude appear around the peak as opposed to the ramp. If the peak is caused by the motor component, how does this affect the conclusions that this third event shows a decision-deliberation parietal processes as opposed to a motor process (a number of studies suggest a causal role for motor processes in decision-making e.g. Purcell et al., 2010 Psych Rev; Jun et al., 2021 Nat Neuro; Donner et al., 2009 Curr Bio).  

This result now changed and it does look like the peak capturing most of the effect is no longer true. We do however think that there might be some link to theories of motor-related accumulation. We therefore added this to the discussion in the Motor execution section:

“Based on all these observations, it is therefore very likely that this LRP event signs the first passage of a two-step decision process as suggested by recent decision-making models (Servant et al., 2021; Verdonck et al., 2021; Balsdon et al., 2023)”.

I would suggest further investigation into the motor component (perhaps by extending the time window of analysed EEG to a few hundred ms after the response) and at least some discussion of the potential contribution of motor processes, in relation to the previous literature.  

We believe that the absence of a motor component is sufficiently addressed in the revised manuscript and in the responses to the other comments.    

(2) What do we learn from this work? Readers would appreciate more attention to previous findings and a clearer outline of how this work differs. Two points stand out, outlined below. I believe the authors can address these potential complaints in the introduction and discussion, and perhaps provide some clarification in the presentation of the results.  

In the introduction, the authors state that "... to date, no study has been able to provide single-trial evidence of multiple EEG components involved in decision-making..." (line 64). Many readers would disagree with this. For example, Philiastides, Ratcliff, & Sadja (2006) use a single-trial analysis to unravel early and late EEG components relating to decision difficulty and accuracy (across different perceptual decisions), which could be related to the components in the current work. Other, network-based single-trial EEG analyses (e.g., Si et al., 2020, NeuroImage, Sturn et al., 2016 J Neurosci Methods) could also be related to the current component approach. Yet other approaches have used inverse encoding models to examine EEG components related to separable decision processes within trials (e.g., Salvador et al., 2022, Nat Comms). The results of the current work are consistent with this previous work - the two components from Philiastides et al., 2006 can be mapped onto the components in the current work, and Salvador et al., 2022 also uncover stimulus- and decision-deliberation related components.  

We completely agree with the reviewer that the link to previous work was insufficient. We now include all references that the reviewer points out both in the introduction (see response R3.2) and in the discussion (see response R3.4). We wish to thank the reviewer for bringing these papers to our attention as they are important for the manuscript.

The authors relate their components to ERPs. This prompts the question of whether we would get the same results with ERP analyses (and, on the whole, the results of the current work are consistent with conclusions based on ERP analyses, with the exception of the missing motor component). It's nice that this analysis is single-trial, but many of the follow-up analyses are based on grouping by condition anyway. Even the single-trial analysis presented in Figure 4 could be obtained by median splits (given the hypotheses propose opposite directions of effects, except for the linear model). 

We do not agree with the reviewer in the sense that classical ERP analyses would require much more data-points. The performance of the method is here to use the information shared across all contrast levels to be able to model the processing time of a single contrast level (6 trials per participant). Furthermore, as stated in the response to R1.4 and R1.5, the aim of the paper is to have the time of information processing components which cannot be achieved with classical ERPs without strong, and likely false, assumptions.

Medium Comments:  

(1) The presentation of Piéron's law for the behavioural analysis is confusing. First, both laws should be clearly defined for readers who may be unfamiliar with this work. I found the proposal that Piéron's law predicts decreasing RT for increasing pedestal contrast in a contrast discrimination paradigm task surprising, especially given the last author's previous work. For example, Donkin and van Maanen (2014) write "However, the commonality ofPiéron's Law across so many paradigms has lead researchers (e.g., Stafford & Gurney, 2004; Van Maanen et al., 2012) to propose that Piéron's Law is unrelated to stimulus scaling, but is a result of the architecture of the response selection (or decision making) process." The pedestal contrast is unrelated to the difficulty of the contrast discrimination task (except for the consideration of Fechner's law). Instead, Piéron's law would apply to the subjective difference in contrast in this task, as opposed to the pedestal contrast. The EEG results are consistent with these intuitions about Piéron's law (or more generally, that contrast is accumulated over time, so a later EEG component for lower pedestal contrast makes sense): pedestal contrast should lead to faster detection, but not necessarily faster discrimination. Perhaps, given the complexity of the manuscript as a whole, the predictions for the behavioural results could be simplified?  

We agree that the initial version was confusing. We now clarified the presentation of Piéron's law at the end of the introduction (see also response to R2).

Once Fechner's law is applied, decision difficulty increases with increasing contrast, so Piéron's law on the decision-relevant intensity (perceived difference in contrast) would also predict increasing RT with increasing pedestal contrast. It is unlikely that the data are of sufficient resolution to distinguish a log function from a power of a log function, but perhaps the claim on line 189 could be weakened (the EEG results demonstrate Piéron's law for detection, but do not provide evidence against Piéron's law in discrimination decisions).  

This is an excellent observation, thank you for bringing it to our attention. Indeed, the data support the notion that Pieron’s law is related to detection, but do not rule out that it is also related to decision or discrimination. In earlier work, we (Donkin & Van Maanen, 2014) addressed this question as well, and reached a similar conclusion. After fitting evidence accumulation models to data, we found no linear relationship between drift rates and stimulus difficulty, as would have been the case if Pieron's law could be fully explained by the decision process (as -indirectly- argued by Stafford & Gurney, 2004; Van Maanen et al., 2012). The fact that we observed evidence for a non-linear relationship between drift rates and stimulus difficulty led us to the same conclusion, that Pieron’s law could be reflected in both discrimination and decision processes. We added the following comment to the discussion about the functional locus of Pieron's law to clarify this point:

“Note that this observation is not necessarily incongruent with theoretical work that argued that Piéron’s law could also be a result of a response selection mechanism (Stafford and Gurney, 2004; Van Maanen et al., 2012; Palmer et al., 2005). It could be that differences in stimulus intensity between the two options also contribute to a Piéron like relationship in the later intervals, that is convoluted with Fechner’s law (see Donkin and Van Maanen, 2014, for a similar argument). Unfortunately, our data do not allow us to discriminate between a pure logarithmic growth function and one that is mediated by a decreasing power function”.

(2) Appendix 1 shows that the event detection of the HMP method will also pick up on ramping activity. The description of the problem in the introduction is that event-like activity could look like ramping when averaged across trials. To address this problem, the authors should simulate events (with some reasonable dispersion in timing such that they look like ramping when averaged) and show that the HMP method would not pull out something that looked like ramping. In other words, the evidence for ramping in this work is not affected by the previously identified confounds.  

We agree that this demonstration was necessary and thus added the suggested simulation to Appendix 1. As can be seen in the Figure 1 of the appendix, when we simulate a half-sine the average ERP based on the timing of the event looks like a half-sine.

(3) Some readers may be interested in a fuller discussion of the failure of the Fechner diffusion model in the speed condition.  

We are unsure which failure the reviewer refers to but assumed it was in relation to the behavioral results and thus added: 

It is unlikely that neither Piéron nor Fechner law impact the RT in the speed condition. Instead this result is likely due to the composite nature of the RT where both laws co-exist in the RT but cancel each other out due to their opposite prediction.

Minor Comments:  

(1) "By-trial" is used throughout. Normally, it is "trial-by-trial" or "single-trial" or "trial-wise".

We replaced all occurrences of “by-trial”  with the three terms suggested were appropriate.

(2) Line 22: "The sum of the times required for the completion of each of these precessing steps is the reaction time (RT)." The total time required. Processing.  

Corrected for both.

(3) Line 26/27: "Despite being an almost two century old problem (von Helmholtz, 2021)." Perhaps the citation with the original year would make this point clearer.  

We agree and replaced the citation.

(4) Line 73: "accounted by estimating". Accounted for by estimating.  

Corrected.

(5) Line 77 "provides an estimation on the." Of the.  

Corrected.

(6) Line 86: "The task of the participants was to answer which of two sinusoidal gratings." The picture looks like Gabor's? Is there a 2d Gaussian filter on top of the grating? Clarify in the methods, too.  

We incorrectly described the stimuli as those were indeed just Gabor’s. This is now corrected both in the main text and the method section.

(7) Figure 1 legend: "The Fechner diffusion law" Fechner's law or your Fechner diffusion model?  

Law was incorrect so we changed to model as suggested.

(8) Line 115: "further allows to connects the..." Allows connecting the.  

Corrected.

(9) Line 123: "lower than 100 ms or higher than..." Faster/slower.  

Corrected.

(10) Line 131: "To test what law." Which law.?  

Corrected to model.

(11) Figure 2 legend: "Left: Mean RT (dot) and average fit (line) over trials and participants for each contrast level used." The fit is over trials and participants? Each dot is? Average trials for each contrast level in each participant?  

This sentence was corrected to “Mean RT (dot) for each contrast level and averaged predictions of the individual fits (line) with Accuracy (Top) and Speed (Bottom) instructions.”.

(12) Line 231: "A comprehensive analysis of contrast effect on". The effect of contrast on.  

This title was changed to “functional interpretation of the events”.

(13) Line 23: "the three HMP event with". Three HMP events.

The sentence no longer exists in the revised manuscript.

(14) Line 270: "Secondly, we computed the Pearson correlation coefficient between the contrast averaged proportion of correct." Pearson is for continuous variables. Proportion correct is not continuous. Use Spearman, Kendall, or compute d'.  

The reviewer rightly pointed out our error, we corrected this by computing Spearman correlation.

(15)  Line 377: "trial 𝑛 + 1 was randomly sampled from a uniform distribution between 0.5 and 1.25 seconds." It's just confusing why post-response activity in Figure 5 does look so consistent. Throughout methods: "model was fitted" should be "was fit", and line 448, "were split".  

We do not have a specific hypothesis of why the post-response activity in the previous Figure 5 was so consistent. Maybe the Gaussian window (same as in other manuscripts with a similar figure, e.g. O’Connell et al. 2012) generated this consistency. We also corrected the errors mentioned in the methods.

(16) The linear mixed models paragraph is a bit confusing. Can it clearly state which data/ table is being referred to and then explain the model? "The general linear mixed model on proportion of correct responses was performed using a logit link. The linear mixed models were performed on the raw milliseconds scale for the interval durations and on the standardized values for the electrode match." We go directly from proportion correct to raw milliseconds...  

The confusion was indeed due to the initial inclusion of a general linear mixed model on proportion correct which was removed as it was not very informative. The new revision should be clearer on the linear mixed models (see first sentence of subsection ‘linear mixed models' in the method section).

(17) A fuller description of the HMP model would be appreciated.  

We agree that this was necessary and added the description of the HMP model in the corresponding method section “Hidden multivariate pattern” in addition to a more comprehensive presentation of HMP in the first paragraph of the Result and Discussion sections.

(18) Line 458: "Fechner's law (Fechner, 1860) states that the perceived difference (𝑝) between the two patches follows the logarithm of the difference in physical intensity between..." ratio of physical intensity.  

Corrected.

(19) P is defined in equations 2 and 4. I would include the beta in equation 4, like in equation 2, then remove the beta from equations 3 and 5 (makes it more readable). I would also just include the delta in equation 2, state that in this case, c1 = c+delta/2 or whatever.  

This indeed makes the equation more readable so we applied the suggestions for equations 2, 3, 4 and 5. The delta was not added in equation 2 but instead in the text that follows:

“Where 𝐶1 = 𝐶0 + 𝛿, again with a modality and individual specific adjustment slope (𝛽).” 

(20) The appendix suggests comparing the amplitudes with those in Figure 3, but the colour bar legend is missing, so the reader can only assume the same scale is used?  

We added the color bar as it was indeed missing. Note though that the previous version displayed the estimation for the simulated data while this plot in the revised manuscript shows the solution on real data obtained after downsampling the data (and therefore look for a larger pattern as in the main text). We believe that this representation is more useful given that the solution for the downsampled data is no longer the same as the one in the main text (due to the difference in pattern width).

Santé Mentale et Addictions : De l'Intime au Populationnel

Résumé Exécutif

Ce document de synthèse analyse les thèmes centraux de la leçon inaugurale de Maria Melchior, épidémiologiste et titulaire de la chaire Santé Publique 2025-2026 au Collège de France.

La santé mentale, désignée grande cause nationale pour 2025 et 2026, est présentée comme un défi majeur qui nécessite une double approche : une compréhension empathique de la souffrance intime et une analyse rigoureuse des dynamiques populationnelles.

L'épidémiologie offre un regard distancié mais essentiel pour quantifier l'ampleur du phénomène, identifier les facteurs de risque et éclairer les politiques publiques.

Les données révèlent une prévalence élevée en France : un adulte sur dix souffre de dépression ou d'anxiété, et une part significative de la population, y compris les jeunes, est touchée par des conduites addictives (tabac, alcool, cannabis, mais aussi jeux et internet).

Un constat central est celui des inégalités sociales "massives" qui se manifestent dès l'enfance, creusant un fossé entre les populations défavorisées, plus à risque et ayant moins accès aux soins, et les plus privilégiées.

L'étude de la santé mentale se heurte à des défis de taille, notamment une forte stigmatisation persistante dans la société et des difficultés métrologiques dues à l'absence de marqueurs biologiques objectifs.

La stratégie de santé publique la plus efficace, selon le "paradoxe de la prévention" de Geoffrey Rose, ne consiste pas uniquement à cibler les individus les plus à risque, mais à améliorer la santé mentale de l'ensemble de la population en agissant sur les déterminants sociaux.

Le concept d' "universalisme proportionné" affine cette approche en combinant des actions universelles avec un soutien renforcé pour les groupes les plus vulnérables.

En conclusion, l'amélioration de la santé mentale collective passe par des interventions qui dépassent le système de soins pour s'attaquer aux racines du mal-être : l'isolement, les inégalités sociales, et les conditions de vie et de travail.

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1. Le Double Regard sur la Santé Mentale : Intime et Populationnel

L'analyse de la santé mentale exige une articulation constante entre la souffrance individuelle et les dynamiques collectives. L'épidémiologie, bien que centrée sur l'étude des populations, ne peut ignorer la dimension subjective et intime du mal-être psychique.

L'Impératif de l'Empathie : L'Intime Derrière les Chiffres

Maria Melchior insiste sur la nécessité de ne jamais oublier que "derrière les concepts, les théories et les chiffres, il y a de vraies personnes et des histoires singulières".

Cette prise de conscience, issue d'une expérience personnelle durant ses études de psychologie, souligne que toute démarche de recherche sur la santé mentale doit conserver une forme d'empathie et s'interroger sur le vécu des personnes concernées.

S'intéresser à la santé mentale, même à grande échelle, requiert d'imaginer une personne réelle et ce qui se passe en elle.

L'Approche Épidémiologique : Monter en Généralité

L'épidémiologie se distingue par sa démarche observationnelle et intégrative.

Elle ne se limite pas aux mécanismes biologiques, mais englobe une large gamme de facteurs de risque : psychologiques, médicaux, comportementaux, sociaux et économiques.

• Objectif : Identifier les facteurs qui augmentent ou diminuent le risque de troubles psychiques et d'addictions à l'échelle d'une population.

• Méthode : Mettre en place des enquêtes de grande ampleur pour dégager des tendances concernant les variations de risque dans le temps, l'espace et entre les sous-groupes.

• Finalité : Passer de situations particulières à des points communs pour "monter en généralité" et identifier les forces qui régissent les comportements humains. Les chiffres produits peuvent ainsi éclairer les politiques publiques et, en retour, aider à mieux saisir des situations individuelles.

2. Panorama de la Santé Mentale et des Addictions en France

Les grandes enquêtes épidémiologiques menées en France, notamment par Santé publique France et l'Observatoire français des drogues et des tendances addictives (OFDT), permettent de dresser un tableau précis de la prévalence des troubles psychiques et des addictions.

Population Cible

Trouble / Addiction

Statistique Clé et Source

Adultes

Épisode dépressif caractérisé

1 personne sur 10 (Baromètre SPF, 2021)

États anxieux

1 personne sur 10 (Baromètre SPF, 2021)

Consommation d'alcool à risque

Plus d'1 personne sur 5

Consommation de cannabis (année)

1 personne sur 10

Tabagisme quotidien

1 personne sur 4 (taux en baisse)

Toute population

Addiction comportementale (jeux d'argent)

1 personne sur 10 a un comportement problématique (OFDT, 2023)

Adolescents

Risque de dépression (modéré à sévère)

14 % des collégiens, 15 % des lycéens

(17 ans)

Usage excessif des réseaux sociaux

1 jeune sur 5 (ESCAPADE, 2017)

(17 ans)

Jeux d'argent et de hasard (année)

1/3 des jeunes de 17 ans, bien qu'interdit aux mineurs (ESCAPADE)

Enfants

Trouble probable de la santé mentale

13 % des enfants (Étude Enabee, 2002)

Les addictions comportementales, notamment liées à l'usage d'internet (réseaux sociaux, jeux vidéo) et aux jeux d'argent en ligne, sont un phénomène en hausse, particulièrement chez les jeunes.

3. Facteurs de Risque et Inégalités Sociales Massives

L'épidémiologie permet d'identifier des groupes plus vulnérables et des facteurs de risque spécifiques.

• Différences de genre : Les filles et les femmes présentent des niveaux plus élevés de dépression et d'anxiété, tandis que les garçons et les hommes sont plus touchés par les troubles du comportement, l'hyperactivité/inattention et les conduites addictives.

• Inégalités sociales : Qualifiées de "massives", elles apparaissent dès l'enfance et se creusent avec le temps. Les enfants issus des familles et des quartiers les plus défavorisés ont les risques les plus élevés tout en ayant l'accès aux soins le plus faible.

Un rapport de la Cour des comptes de 2023 illustre cette disparité : le recours aux soins en pédopsychiatrie est deux fois plus élevé à Paris qu'en Seine-Saint-Denis.

• Facteurs environnementaux : De nouvelles recherches explorent l'impact de facteurs comme l'absence d'espaces verts ou l'exposition aux nuisances sonores sur la santé mentale.

4. Les Défis de l'Étude de la Santé Mentale

Étudier la santé mentale présente des obstacles uniques, tant sur le plan social qu'éthique et méthodologique.

La Stigmatisation et la Peur

Les troubles psychiques continuent de faire peur et d'être associés à des représentations négatives.

• Dangerosité perçue : 74 % des personnes interrogées en 2014 estimaient que les "malades mentaux" sont dangereux.

• Discrimination : Dans un sondage de 2023, 80 % des personnes estiment qu'avoir un trouble psychique réduit les opportunités de trouver un emploi ou un logement, et 63 % pensent que les personnes concernées sont moins bien traitées dans le système éducatif ou au travail.

Les Enjeux Éthiques de la Recherche

La nature intime de la santé mentale suscite des questionnements éthiques fréquents dans la recherche.

La crainte principale est que poser des questions sur la souffrance psychique, et notamment sur les pensées suicidaires, pourrait inciter à un passage à l'acte.

Cependant, la science invalide cette crainte :

"De méta-analyses [...] montrent qu'interroger des personnes [...] sur leurs pensées ou sur leurs intentions suicidaires non seulement n'entraîne pas de passage à l'acte mais n'est pas non plus perçu de manière négative et pourrait même parfois être associé à une légère diminution des comportements suicidaires."

L'Exemple de la Cohorte Tempo

L'étude de cohorte Tempo, qui suit plus de 1000 personnes depuis l'enfance jusqu'à l'âge adulte, illustre la faisabilité et la richesse de la recherche longitudinale en santé mentale.

• Originalité : C'est l'une des rares études au monde à disposer de données sur trois générations (les participants, leurs parents via la cohorte Gazel, et bientôt leurs propres enfants), permettant d'étudier la transmission intergénérationnelle.

• Résultats clés :

◦ Le trouble de l'hyperactivité/inattention (TDAH) de l'enfance persiste sur près de 30 ans et est associé à des conduites addictives, des difficultés scolaires et un risque de chômage accru.   

◦ La consommation de cannabis à l'adolescence a des effets délétères sur le parcours scolaire et professionnel 20 ans plus tard.   

◦ La consommation ponctuelle importante d'alcool à l'adolescence prédit un trouble de l'usage à l'âge adulte dans 25 % des cas.

5. La Mesure en Santé Mentale : De la Subjectivité à la Catégorisation

L'un des plus grands défis de l'épidémiologie psychiatrique est la mesure des troubles.

L'Absence de "Gold Standard" Biologique

Contrairement à de nombreuses maladies, il n'existe pas de test biologique (sanguin, cérébral) pour diagnostiquer un trouble psychique.

L'évaluation repose entièrement sur la parole et le comportement rapportés par les personnes, ce qui introduit une part d'incertitude.

L'Évolution des Classifications (DSM/CIM)

Pour standardiser l'évaluation, des classifications ont été développées.

• Historique : Les premières nosographies (Pinel, Kraepelin) se concentraient sur les pathologies les plus sévères observées en asile.

• Le tournant du DSM : La nécessité d'évaluer les conscrits américains lors des guerres mondiales a accéléré le développement de manuels standardisés.

Une révolution a eu lieu dans les années 1970 sous l'égide de Robert Spitzer : le Diagnostic and Statistical Manual (DSM) est passé d'une approche basée sur les causes psychanalytiques (difficiles à observer) à une définition basée sur des symptômes observables et leurs répercussions sur la vie des personnes.

• Conséquence : Cette approche a rendu possible la création de questionnaires standardisés, pierre angulaire de l'épidémiologie psychiatrique moderne.

Définir le "Normal" et le "Pathologique"

Selon la réflexion du philosophe Georges Canguilhem, un état n'est pas pathologique simplement parce qu'il est statistiquement rare ou jugé négativement par la société (l'exemple de l'homosexualité, autrefois listée comme un trouble mental, en est une illustration frappante).

La définition moderne d'un état pathologique se centre sur la souffrance psychique exprimée par la personne et l'impact négatif des symptômes sur sa vie.

6. La Perspective de Santé Publique : Stratégies et Paradoxes

La santé publique considère que les caractéristiques d'une population influencent en retour la santé de chaque individu qui la compose.

Le Paradoxe de la Prévention et l'Universalisme Proportionné

• Le Paradoxe de Geoffrey Rose : Les maladies et leurs facteurs de risque se distribuent sur un continuum dans la population.

Par conséquent, la stratégie de prévention la plus efficace ne consiste pas à cibler uniquement les quelques individus à très haut risque, mais à décaler légèrement la distribution de l'ensemble de la population.

Autrement dit, une petite amélioration de la santé mentale de tous a un impact collectif plus grand qu'une grande amélioration pour quelques-uns.

• L'Universalisme Proportionné de Michael Marmot : Cette approche moderne combine la vision populationnelle de Rose avec une attention particulière pour les plus vulnérables.

Il s'agit de mettre en place des actions universelles bénéfiques à tous, tout en modulant l'intensité de l'aide en fonction des besoins. Le programme Improva de promotion de la santé mentale dans les collèges en est un exemple.

L'Importance des Symptômes "Intermédiaires"

Le fardeau sociétal le plus lourd n'est pas le fait des cas les plus sévères (qui sont peu nombreux), mais de la masse de personnes présentant des symptômes intermédiaires ou "infracliniques".

Même sans correspondre à un diagnostic formel, ces symptômes causent de la souffrance et altèrent significativement la qualité de vie, la capacité à travailler ou à nouer des liens.

7. Conclusion et Perspectives d'Action

Pour améliorer la santé mentale de la population, il est impératif d'agir sur ses déterminants, qui se situent en grande partie en dehors du système de santé.

• Agir sur les déterminants sociaux : Suivant les travaux d'Émile Durkheim sur l'isolement et de Lisa Berkman sur les réseaux sociaux, il est crucial d'améliorer la densité et la qualité des liens relationnels.

Cela passe par une action sur leurs causes profondes : les inégalités sociales, les conditions de travail, l'accès au logement et les politiques de protection des familles.

• La Grande Cause Nationale 2025-2026 : Cet engagement politique vise à améliorer les perceptions collectives des troubles psychiques pour faciliter l'accès aux soins et réduire la stigmatisation.

• Améliorer la littératie en santé mentale : La diffusion à grande échelle des connaissances issues de la recherche épidémiologique est fondamentale pour que chacun puisse mieux reconnaître les signes de mal-être (chez soi ou chez les autres) et accepter les personnes qui souffrent.

Document d'information : Enjeux et Perspectives de la Transition Climatique et Énergétique

Résumé Exécutif

Ce document synthétise les analyses et les perspectives issues de la Journée du Climat organisée à Le Mans Université, dix ans après les Accords de Paris.

Il met en lumière une réalité complexe : si des progrès notables ont été accomplis, les grands objectifs climatiques mondiaux demeurent hors d'atteinte.

Les émissions de CO2 continuent d'augmenter à l'échelle planétaire, et la consommation d'énergies fossiles atteint des niveaux records, principalement en raison de la croissance des marchés asiatiques.

Dans ce contexte, la France représente un cas singulier, avec un mix électrique déjà largement décarboné grâce au nucléaire et aux énergies renouvelables.

Cependant, le pays fait face à un paradoxe majeur : alors que la consommation réelle d'électricité est en baisse depuis 2017, la politique énergétique nationale prévoit une augmentation massive de la capacité de production. Cette divergence crée un risque de surproduction, de perturbation du marché et de tensions sur le réseau électrique et le parc nucléaire.

La transition énergétique induit également de nouvelles dépendances stratégiques, notamment vis-à-vis des minéraux critiques pour les batteries, les panneaux solaires et les éoliennes, dont le raffinage est massivement contrôlé par la Chine.

La technologie des batteries, pilier de la décarbonation des transports et du stockage des énergies renouvelables, est au cœur de ces enjeux.

L'Europe peine à établir une chaîne de valeur souveraine, comme en témoigne l'échec de projets d'envergure.

Des innovations de rupture, telles que les batteries sodium-ion développées en France, et l'intégration de diagnostics avancés ("batteries intelligentes") offrent des perspectives prometteuses pour améliorer la durabilité et la performance.

Enfin, l'efficacité de la transition repose sur son ancrage territorial.

Les stratégies doivent intégrer les services écosystémiques (comme le carbone bleu), encourager l'implication citoyenne (via les communautés énergétiques) et repenser la gouvernance.

Les approches descendantes, qu'il s'agisse de réglementations européennes ou des négociations climatiques mondiales (COP), montrent leurs limites en peinant à intégrer les réalités et les aspirations locales, soulignant l'impératif d'une concertation plus juste et inclusive.

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1. La Transition Énergétique : Entre Progrès et Réalités Incontournables

La transition énergétique constitue le défi central de la lutte contre le changement climatique.

L'analyse présentée par Marc Fontecave, Professeur au Collège de France, dresse un tableau lucide de la situation, soulignant les écarts entre les ambitions affichées et les dynamiques réelles.

1.1. Un Bilan Mondial Contrasté et des Objectifs Hors d'Atteinte

La première observation est sans appel : les objectifs fixés lors des Accords de Paris ne seront pas atteints.

• Objectifs manqués : L'ambition de limiter le réchauffement à 1,5°C d'ici 2100 et d'atteindre la neutralité carbone en 2050 est désormais considérée comme "relativement inatteignable".

• Hausse des émissions : Les émissions mondiales de CO2 continuent leur progression.

Le rythme d'augmentation en 2024 est comparable à celui des dix années précédentes. Cette hausse est principalement tirée par les marchés asiatiques en croissance rapide, notamment l'Inde.

• Dépendance fossile record : Loin de diminuer, la consommation mondiale de charbon, de pétrole et de gaz naturel n'a jamais été aussi élevée.

Les projections indiquent une augmentation continue des capacités mondiales de charbon et une demande record pour le pétrole en 2025.

• Un fossé persistant : Un écart se creuse entre les connaissances scientifiques, les déclarations politiques et les actions concrètes.

Bien que l'Europe et la France voient leurs émissions territoriales diminuer, ce chiffre doit être nuancé.

Pour la France, une part importante de cette baisse est attribuée à une désindustrialisation continue.

L'empreinte carbone du pays, qui inclut les émissions liées aux importations, ne baisse pratiquement pas.

1.2. La Singularité du Cas Français

La France se distingue par une situation énergétique particulière qui en fait un cas d'étude à part.

• Forte électrification : Avec 25-27 % d'électricité dans sa consommation énergétique totale, la France est l'un des pays les plus électrifiés au monde.

• Électricité très décarbonée : La production électrique française est à 95 % bas-carbone, ce qui place la dépendance du pays aux énergies fossiles juste en dessous de 60 %, une performance bien meilleure que la moyenne mondiale.

• Facture fossile : Cette dépendance représente néanmoins une facture considérable, s'élevant en moyenne à 60 milliards d'euros par an pour l'importation d'hydrocarbures.

Les trois piliers de la transition énergétique pour la France sont :

1. La diminution de la consommation : Tous les scénarios, y compris la feuille de route gouvernementale, prévoient une baisse drastique de la consommation d'énergie, de 1500 TWh actuellement à moins de 1000 TWh.

2. L'électrification des usages : Pour sortir des fossiles, il est nécessaire d'électrifier massivement les transports (véhicules électriques), le chauffage (pompes à chaleur) et l'industrie (fours électriques, hydrogène vert).

L'électrification directe est privilégiée pour son efficacité énergétique supérieure.

3. Le recours au carbone et à la chaleur non fossiles : Pour les usages non électrifiables, des alternatives comme la biomasse (bois, biocarburants), la géothermie et les biogaz sont nécessaires, bien qu'elles présentent des limites (gisements, compétition avec l'alimentaire, empreinte carbone).

1.3. Le Paradoxe de la Consommation et de la Production Électrique

L'analyse de la production et de la consommation électrique en France révèle une divergence préoccupante.

État des lieux de la production électrique française (Données 2024)

Indicateur

Valeur

Commentaire

Production totale

~540 TWh

La France est le premier pays exportateur d'électricité en Europe.

Part du nucléaire

~360 TWh

Socle du mix, assurant près de 70 % de la production.

Production bas-carbone

95 %

Niveau le plus élevé depuis 1950.

Part des fossiles

3,6 %

Niveau le plus bas depuis 1950.

Intensité carbone

21 g CO2/kWh

Parmi les plus basses du monde (vs. ~360 g CO2/kWh en Allemagne).

La politique nucléaire a connu un changement majeur, passant d'un projet de fermeture de réacteurs à la décision d'en construire 14 nouveaux (6 confirmés, 8 en option).

La capacité des réacteurs français à moduler leur production ("pilotabilité") est un atout stratégique pour équilibrer le réseau.

Le paradoxe identifié est le suivant :

• Une consommation en baisse : Contrairement aux projections, la consommation d'électricité en France diminue depuis 2017 pour atteindre en 2024 son niveau de 2004.

Cette baisse s'explique par l'efficacité énergétique, les prix élevés, la sobriété, la désindustrialisation et une électrification des usages plus lente que prévu.

• Une production planifiée en forte hausse : La feuille de route du gouvernement, basée sur des scénarios de consommation désormais obsolètes (projections RTE 2021/2023), prévoit une augmentation de la production de près de 200 TWh, principalement via l'éolien et le solaire.

• Les risques associés : Cette décorrélation pourrait mener à une surproduction structurelle, perturbant gravement le marché, nécessitant une modulation excessive et techniquement risquée du parc nucléaire, et créant des tensions sur les réseaux électriques.

De nouveaux scénarios de consommation revus à la baisse par RTE sont attendus pour corriger cette trajectoire.

1.4. Nouvelles Dépendances et Impératifs de Recherche

La transition énergétique, si elle réduit la dépendance aux fossiles, en crée de nouvelles.

• Dépendance aux minéraux : La production de batteries, d'éoliennes et de panneaux solaires nécessite une quantité croissante de ressources minérales (graphite, lithium, cobalt, cuivre, etc.).

Le raffinage de ces matériaux est très largement dominé par la Chine, créant une nouvelle dépendance géopolitique.

• Maturité technologique : De nombreuses technologies clés ne sont pas encore matures et nécessitent des efforts de recherche et d'innovation considérables.

Cela inclut la production d'hydrogène vert, le recyclage des matériaux, l'amélioration des rendements photovoltaïques, le développement de mines responsables, la décarbonation de l'industrie lourde (acier), la valorisation de la biomasse, le nucléaire de 4ème génération, la modernisation des réseaux et le stockage d'énergie.

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2. Le Stockage Électrochimique : Pilier Technologique de la Décarbonation

Les batteries sont au cœur de la transition, essentielles pour la mobilité électrique et pour stabiliser les réseaux face à l'intermittence des énergies renouvelables.

La conférence de Jean-Marie Tarascon, Professeur au Collège de France, a mis en évidence les avancées, les défis et les innovations de ce secteur stratégique.

2.1. L'Ascension des Batteries et le Défi de la Souveraineté Européenne

Le stockage électrochimique est en passe de devenir la forme dominante de stockage d'énergie, dépassant le stockage hydroélectrique.

• Marchés en plein essor : La demande est tirée par trois secteurs majeurs : le véhicule électrique (50 % des ventes mondiales prévues en 2030), le stockage stationnaire pour les énergies renouvelables, et les drones.

• Les Gigafactories : Pour répondre à cette demande, des usines de très grande capacité se construisent dans le monde.

L'Europe, avec plus de 20 projets dont 6 en France, tente d'acquérir sa souveraineté, visant 19 % de la production mondiale en 2029.

• Le manque de chaîne de valeur : L'Europe reste massivement dépendante, important 98 % des machines d'assemblage et une part similaire des matériaux.

L'échec du projet suédois Northvolt, qui visait une intégration verticale complète sans maîtriser toute la chaîne de valeur, illustre cette fragilité. La proposition de créer un "Airbus des batteries" pour fédérer les compétences se heurte aux réticences des acteurs à collaborer.

2.2. Innovations et Matériaux d'Avenir

La recherche scientifique est la clé pour surmonter les dépendances et améliorer les performances.

• Du NMC au LFP : Dans le lithium-ion, la technologie dominante des véhicules électriques évolue.

Les matériaux NMC (Nickel-Manganèse-Cobalt) à haute densité énergétique cèdent du terrain aux matériaux LFP (Lithium-Fer-Phosphate), qui sont moins chers, plus sûrs et ne contiennent pas de cobalt.

Cependant, la production de LFP est contrôlée à 88 % par la Chine.

• La technologie Sodium-ion : Portée en France par la start-up Tiamat, cette technologie représente une alternative stratégique.

Le sodium est 10 000 fois plus abondant que le lithium.

Bien que moins denses en énergie, les batteries sodium-ion offrent une puissance supérieure, une durée de vie exceptionnelle (jusqu'à 17 000 cycles) et un coût potentiellement plus faible.

Elles sont idéales pour le stockage stationnaire (ex: data centers) et la mobilité légère.

• Vers le tout-solide et les batteries intelligentes :

La recherche s'oriente vers les batteries "tout-solide", qui remplacent l'électrolyte liquide par un solide pour plus de sécurité et de densité énergétique, bien que des défis d'interface persistent.

Une autre innovation majeure est l'intégration de capteurs (fibres optiques) au cœur des batteries pour en suivre l'état de santé en temps réel (température, pression, chimie).

Ce "passeport de santé" permettra d'optimiser leur usage, de faciliter leur seconde vie et de développer des systèmes d'auto-réparation.

2.3. Enjeux de Durabilité : Écocompatibilité et Recyclage

La durabilité des batteries est un enjeu aussi important que leur performance.

• Pression sur les ressources :

Un véhicule électrique utilise six fois plus de minéraux qu'un véhicule thermique.

La demande en lithium, cobalt et nickel pourrait dépasser la production d'ici 2030.

L'exploitation de nouvelles ressources, y compris en Europe (comme le lithium en France), et surtout le développement du recyclage ("mine urbaine") sont impératifs.

• Réglementation européenne : L'UE met en place un cadre strict imposant la déclaration de l'empreinte carbone, des taux de matériaux recyclés obligatoires (dès 2030) et un passeport électronique pour chaque batterie.

• Recherche sur le recyclage : Les méthodes actuelles (pyrométallurgie, hydrométallurgie) sont énergivores.

L'un des objectifs de la recherche est de concevoir des batteries "de type Lego", facilement démontables pour un recyclage ciblé de leurs composants.

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3. L'Ancrage Territorial : Clé de Voûte d'une Transition Juste et Efficace

La réussite de la transition climatique ne peut être décrétée d'en haut ; elle doit s'incarner dans les territoires, en tenant compte de leurs spécificités géographiques, sociales et économiques.

3.1. Des Territoires aux Stratégies Plurielles

Les approches locales varient considérablement, reflétant la diversité des enjeux.

• Plans Climat-Air-Énergie Territoriaux (PCAET) : L'analyse des PCAET dans l'Ouest de la France montre un foisonnement d'initiatives.

Si l'atténuation (mitigation) est un axe commun, les notions d'adaptation et de résilience sont traitées de manière inégale, la résilience étant plus prégnante dans les territoires littoraux directement menacés.

• Rôle des écosystèmes : Les écosystèmes locaux sont des alliés pour la neutralité carbone.

Les zones humides littorales, par exemple, stockent massivement du carbone ("carbone bleu") tout en fournissant d'autres services essentiels comme la protection contre les inondations.

• Controverses du "Rewilding" : Les stratégies de restauration, comme le réensauvagement, peuvent générer des conflits.

Laisser des écosystèmes évoluer librement ou réintroduire de grands animaux se heurte aux paysages culturels et agricoles européens, créant des tensions sur les usages et des chocs de valeurs.

Le succès de telles approches dépend fondamentalement de l'inclusion et de la concertation avec les populations locales.

3.2. L'Énergie Citoyenne et les Nouvelles Mobilités

L'implication des citoyens est un levier puissant pour accélérer la transition.

• Communautés énergétiques citoyennes : Des collectifs de citoyens émergent pour produire et consommer localement de l'énergie renouvelable.

Ces initiatives favorisent l'appropriation locale des enjeux, contribuent à la justice énergétique et permettent de lutter contre la précarité.

L'Ouest de la France est une région particulièrement dynamique, accueillant près d'un quart des projets citoyens nationaux.

• Décarboner les mobilités : Le secteur des transports représente 31 % des émissions de CO2 en France, les trajets domicile-travail en voiture comptant pour une part significative (13 % du total national).

Comprendre les facteurs (individuels, contextuels, normes sociales) qui influencent le choix du mode de transport est essentiel pour concevoir des politiques publiques efficaces favorisant les mobilités douces.

3.3. Gouvernance Globale et Concertation : Les Limites du Modèle Actuel

L'articulation entre les décisions locales, nationales et internationales reste un point de friction majeur.

• Approches descendantes : Des réglementations comme celle de l'UE sur la déforestation importée, bien qu'intentionnées, peuvent être perçues comme unilatérales et impérialistes par les pays producteurs, qui se tournent vers d'autres marchés moins regardants.

De même, dans certains pays comme Haïti, les plans climatiques sont souvent impulsés par des acteurs internationaux et déconnectés des réalités du terrain.

• Le défi des COP : Les négociations climatiques mondiales, comme la COP30 au Brésil, peinent à intégrer de manière authentique la voix des populations locales et des peuples autochtones.

Leurs préoccupations sont souvent diluées dans un langage diplomatique visant le consensus, ce qui conduit à une forme de décision à deux vitesses et pousse les groupes non entendus à s'auto-organiser en marge des processus officiels.

L'enjeu est de traduire les aspirations des territoires en politiques internationales concrètes et justes.

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 are grateful to the reviewers for their thoughtful and constructive evaluations of our manuscript. Their comments helped us clarify key aspects of the study and strengthen both the presentation and interpretation of our findings. The central goal of this work is to dissect how the opposing activities of GATA4 and CTCF coordinate chromatin topology and transcriptional timing during human cardiomyogenesis. The reviewers’ feedback has allowed us to refine this message and better contextualize our results within the broader framework of chromatin regulation and cardiac development.

In response to the reviews, in our preliminary revision we have already implemented substantial improvements to the manuscript, including additional analyses, clearer data visualization, and revisions to the text to avoid overinterpretation. These refinements enhance the robustness of our conclusions without altering the overall scope of the study. A small number of additional analyses and experiments are ongoing and will be added to the full revision, as detailed below.

We believe that the revised manuscript, together with the planned updates, fully addresses the reviewers’ concerns and substantially strengthens the contribution of this work to the field.

Reviewer 1 – Point 1:

In the datasets you are examining, what are the relative percentages in each of the four groups relating compartmentalization change to expression change (A→B, expression up; A→B, down; B→A, up; B→A, down)?

We quantified compartment–expression relationships using Hi-C and bulk RNA-seq from H9 ESCs and CMs. The percentages for each category are shown below and incorporated into updated Figure S2H.

Group

Downregulated in CM

Upregulated in CM

A-to-A

11.92%

8.44%

A-to-B

18.20%

2.79%

B-to-A

7.96%

18.07%

B-to-B

14.36%

6.44%

A chi-squared test comparing observed vs. expected distributions (based on gene density across bins) confirmed a strong association between compartment dynamics and transcriptional behavior. B-to-A genes are significantly enriched among genes upregulated in CMs, while A-to-B genes are enriched among those downregulated (updated Figure S2H).

We next assessed with GSEA how these gene classes respond to GATA4 and CTCF knockdown. In 2D CMs, GATA4 knockdown reduces expression of CM-upregulated B-to-A genes and increases expression of CM-downregulated A-to-B genes, whereas CTCF knockdown produces the opposite pattern (updated Figure 2F).

Applying the same analysis to cardioid bulk RNA-seq (updated Figure 4E) revealed the strongest effects in SHF-RV organoids, consistent with monolayer data. In SHF-A organoids, only GATA4 knockdown had a measurable impact on CM-upregulated B-to-A and CM-downregulated A-to-B genes. Because the subsets of CM-downregulated B-to-A and CM-upregulated A-to-B genes were very small and showed no consistent trends, Figure 4 focuses on the two informative categories only. The full classification is provided in Reviewer Figure 1 below.

(The figure cannot be rendered in this text-only format)

Reviewer Figure 1. GSEA for CM-upregulated B-to-A and CM-downregulated A-to-B genes. p-values by Adaptive Monte-Carlo Permutation test.

Reviewer 1 – Point 2

This phrase in the abstract is imprecise: ‘whereas premature CTCF depletion accelerates yet confounds cardiomyocyte maturation.’

---

The abstract has been revised to: “whereas premature CTCF depletion accelerates yet alters cardiomyocyte maturation.” (lines 29-30).

Reviewer 1 – Point 3

Regarding this statement: "Disruption of [3D chromatin architecture] has been linked to genetic dilated cardiomyopathy (DCM) caused by lamin A/C mutations8,9, and mutations in chromatin regulators are strongly enriched in de novo congenital heart defects (CHD)10, underscoring their pathogenic relevance11." The first studies to implicate chromatin structural changes in heart disease, including the role of CTCF in that process, were PMID: 28802249, a model of acquired, rather than genetic, disease.

We added the following sentence to the paragraph introducing CTCF: “Moreover, depletion of CTCF in the adult cardiomyocytes leads to heart failure28,29.” (line 72)

Reviewer 1 – Point 4

Can you quantify this statement: ‘the compartment switch coincided with progressive reduction of promoter–gene body interactions’?

We quantified promoter–gene body contacts by calculating the area under the curve (AUC) of the virtual 4C signal derived from H9 Hi-C data across differentiation. As a result of this analysis we added the following sentence: “Quantitatively, interactions between the TTN promoter and its gene body decreased by ~55% from the pluripotent stage to day 80 cardiomyocytes.” (lines 89-91).

Reviewer 1 – Point 5

Regarding this statement: "six regions became less accessible in CMs, correlating with ChIP-seq signal for the ubiquitous architectural protein CTCF." I don't see 6 ATAC peaks in either TTN trace in Figure 1A.

We corrected the text as it follows: “TTN experienced clear changes in chromatin accessibility during CM differentiation: ATAC-seq identified two CM-specific peaks that correlated with ChIP-seq signal for the cardiac pioneer TF GATA4 at the two promoters, one driving full length titin and the other the shorter cronos isoform. In contrast, two regions became less accessible in CMs, correlating with two of the six ChIP-seq peaks for the ubiquitous architectural protein CTCF” (lines 93-97). We attribute the differences between ChIP-seq and ATAC-seq profiles to methodological sensitivity and/or biological variability between datasets generated in different laboratories and cell batches.

Reviewer 1 – Point 6

Western blots need molecular weight markers.

We edited the relevant panels accordingly (updated Figures 1E and 2B).

Reviewer 1 – Point 7

Regarding this statement: "The decrease in CTCF protein levels may explain its selective detachment from TTN during cardiomyogenesis." At face value, these findings suggest the opposite: i.e. that a massive downregulation of CTCF at protein level should affect its binding across the genome, which is not tested and is hard to evaluate between ChIP-seq studies from different groups and from different developmental timeframes.

We revised the text to avoid implying selective detachment and performed a genome-wide analysis of CTCF occupancy using ENCODE ChIP-seq datasets generated by the same laboratory with matched protocols in hESCs and hESC-derived CMs. This analysis shows that 43.2% of CTCF sites present in ESCs are lost in CMs, whereas only 5.7% are gained, confirming a broad reduction in CTCF binding during differentiation. These results are now included in__ updated Figure 1B__.

Reviewer 1 – Point 8a

A couple thoughts on the FISH experiments in Figure 2. A claim of 'impaired B-A transition' would be more convincing if you show, by FISH, that the relative distance of TTN from lamin B increases with differentiation.

Although prior work from us and others has established that TTN transitions from the nuclear periphery in hESCs to a more internal position during cardiomyogenesis (Poleshko et al. 2017; Bertero et al. 2019a), we are reproducing this trajectory in WTC11 hiPSCs as part of the FISH experiments for the full revision.

__Reviewer 1 – Point 8b __

In the [FISH] images: are you showing a total projection of all z planes? One assumes the quantitation is relative to a 3D reconstruction in which the lamin B signal is restricted to the periphery. Have you shown this? __

Quantification was performed on full 3D reconstructions from Z-stacks, as detailed in the Methods (lines 721-727). While the original submission displayed maximum-intensity projections, updated Figure 2D and Figure S2E now show representative single optical sections, which more clearly highlight the spatial relationship between the TTN locus and the nuclear lamina.

Reviewer 1 – Point 8c

Lastly, these data are very interesting and important, provoking reexamination of your interpretation of the results in Figure 1. Figure 1 was interpreted to show that less CTCF binding led to decreased lamina (and thus B compartment) association during development. Figure 2 shows that depleting CTCF does not change association of TTN with lamina.

Our interpretation is that by day 25 of hiPSC-CM differentiation the TTN locus may have reached its maximal radial repositioning even in control cells, limiting the ability to detect earlier effects of CTCF depletion. To test whether CTCF knockdown accelerates lamina detachment at earlier stages, we are repeating the FISH analysis for the inducible CTCF knockdown line at multiple time points during differentiation.

Reviewer 1 – Point 9

A thought about this statement: "Altogether, these results suggest that GATA4 and CTCF function as positive and negative regulators of B-to-A compartment switching, likely acting through global and local chromatin remodeling, respectively." GATA4 induces TTN expression and its knockdown prevents TTN expression-the evidence that GATA4 affects compartmentalization is unclear. By activating the gene, GATA4 may shift TTN to B classification.

Our current data do not allow us to disentangle whether GATA4-driven transcriptional activation precedes or follows the B-to-A compartment shift. We have therefore removed the mechanistic speculation from this sentence to avoid overinterpretation. Nevertheless, the analyses in updated Figure 2F, discussed in the response to Reviewer 1 - Point 1, show that GATA4 knockdown preferentially reduces expression of CM-upregulated B-to-A genes, while CTCF knockdown has the opposite effect, supporting the conclusion that both factors influence the transcriptional programs associated with B-to-A transitions.

Reviewer 1 – Point 10

__I'm not sure what I am looking at in Figure 3C. Are those traces integration of interactions over a defined window? "Each [mutant is] clearly different from WT" is not obvious from the presentation. The histograms are plotting AUC of what? Interactions of those peaks with the mutated region? I genuinely appreciate how laborious this experiment must have been and encourage you to explain better what you are showing. __

We revised the main text to avoid overstating the differences (“clearly” “in a similar manner”, line 192) and expanded the l__egends of updated Figures 3C–D__ to clarify what is being shown: “(C) 4C-seq in hiPSCs using the promoter-proximal region of TTN as viewpoint. The top panel shows raw interaction profiles. The lower panels plot pairwise differences between conditions to reveal subtle changes. A schematic indicating the 4C viewpoint is included for clarity. Right inset: zoom of the CBS4–5 region. Mean of n = 3 cultures. (D) AUC of the differential 4C-seq signal for defined intervals (panel C). p-values by one-sample t-test against μ = 0.”. We also added a visual cue in updated Figure 3C indicating the 4C viewpoint to facilitate interpretation.

Reviewer 1 – Point 11

Again acknowledging how challenging these experiments are: when you mutant a locus, you change CTCF binding but you also change the DNA. Thus, attributing the changes in interactions to presence/absence of CTCF binding is difficult, because the DNA substrate itself has changed. Perhaps you are presenting all of this as a negative result, given the modest effect on transcription, which is as important as a positive result, given the assumptions usually made about such things. But the results are not clearly described and your interpretation seems to go between implying the structural change causative and being agnostic.

We recognize that deleting a genomic region can affect both CTCF binding and the DNA substrate itself. For this reason, we implemented two parallel genome-editing strategies:

(1) a straightforward Cas9-mediated deletion of ~100 bp centered on each CBS, and

(2) a more precise HDR approach replacing only the 20 bp core CTCF motif.

Because the HDR strategy succeeded, all downstream analyses were carried out on these minimal edits, which substantially limit disruption of other transcription factor motifs and reduce the likelihood of sequence-dependent polymer effects unrelated to CTCF.

Nevertheless, to avoid implying unwarranted causality in the absence of more conclusive evidence, we added a paragraph to the Discussion outlining these limitations, including the sentence: “Our study also reflects general challenges in separating chromatin-architectural and transcriptional mechanisms. Although the CBS edits were restricted to the core CTCF motifs, additional sequence-dependent effects cannot be fully excluded, and we therefore interpret the resulting changes as consistent with—but not exclusively due to—loss of CTCF binding.” (lines 365-368)

Reviewer 1 - Point 12.

Figure 4C: since you have RNA-seq data, a much more objective way to present these data would be to show all data (again, A-B, up; A-B, down; B-A, up; B-A, down) and the effects of CTCF or GATA4. Regardless, you can still focus on the cardiac specific genes. But my guess is if you examine all genes, the pattern you show in panel C will not be present in the majority of cases. Furthermore, if this hypothesis is wrong, such an analysis will allow you to identify other genes affected by the mechanisms you describe and your analysis will test whether these mechanisms are in fact conserved at different loci.

As outlined in our response to Point 1, we extended the analysis to all genes undergoing compartment changes and incorporated this into the cardioid RNA-seq dataset. This revealed a clear and consistent relationship between GATA4 or CTCF knockdown and the expression of B-to-A and A-to-B gene classes (updated Figure 4E).

Reviewer 2 - Point 1.1

1. CTCF regulation at TTN locus:

(1) Figure 1A: The claim of the authors about convergent CTCF sites and transcriptional activation of TTN is quite simplistic. This claim is only valid when we know where cohesin is loaded. If cohesin is loaded at then intragenic GATA4 binding site, then the only important CTCF sites is at the promoter of TTN. I suggest that the authors read few more publications which may help the authors to better understand how cohesin and CTCF team up to regulate transcription, such as Hsieh et al., Nature Genetics, 2022; Liu et al., Nature Genetics, 2021; Rinzema et al., Nature Structural and Molecular Biology, 2022.

__Suggestion: The authors should add cohesin (RAD21/SMC1A) and NIPBL ChIP-seq for better interpretation. __

In line with the reviewer’s insightful suggestion, we integrated cohesin ChIP-seq data into updated Figure 1A. Specifically, we added a RAD21 ChIP-seq track from hESCs, which provides direct evidence of cohesin occupancy across the TTN locus. RAD21 binding closely parallels CTCF binding at five sites within the gene body, supporting a model in which promoter-proximal CTCF anchors cohesin to stabilize repressive loops at this locus. This analysis substantially strengthens the mechanistic framework and is consistent with the studies recommended by the reviewer, which we have now cited (lines 68 and 104).

Reviewer 2 - Point 1.2. (2) Figure 3B: If delta2CBS only has heterozygenous deletion of CBS6, why we would expect the binding will be weaken to 50%. However, the CTCF binding is reduced to around 1/10 in the ChIP-qPCR. How do the authors explain this?

Sequencing of the Δ2CBS line shows that one CBS6 allele carries the intended EcoRI replacement, while the second allele contains a 2-bp deletion within the core CTCF motif (Figure S3C). Remarkably, this small deletion is sufficient to abolish CTCF binding, resulting in complete loss of occupancy at CBS6 despite heterozygosity. We clarified this in the text as follows: “CTCF ChIP-qPCR in hiPSCs confirmed complete loss of CTCF binding at the targeted sites, including CBS6 in the Δ2CBS line, indicating that the 2-bp deletion sufficed to disrupt CTCF binding while occupancy at other CBSs remained unaffected.” (lines 187–189).

Reviewer 2 - Point 1.3a (3) Figure 3C: There are two problems with the 4C experiments: (a) The changes are really mild. In fact, none of the p-values in Figure 3D are significant.

The effect of deleting CBS1 is indeed modest, consistent with reports that individual CTCF binding sites often show functional redundancy (i.e., Rodríguez-Carballo et al. 2017; Barutcu et al. 2018; Kang et al. 2021). Nevertheless, our 4C-seq experiments have reproducibly shown the same directional trend across biological replicates. To increase statistical power and more rigorously assess the robustness of this effect, we are generating additional 4C replicates as part of the full revision.

Reviewer 2 - Point 1.3b [In the 4C experiments] (b) The authors should also consider a model that CTCF directly serves as a repressor. In this way, 3D genome may not be involved. B-A switch is simply caused by the activation of the locus.

We now explicitly acknowledge this possibility in the Discussion. The revised text states: “Moreover, our data cannot unambiguously separate CTCF’s architectural role from potential direct repressive activity. Both mechanisms could contribute to the observed effects, and our findings likely reflect the combined influence of CTCF on chromatin topology and gene regulation.” (lines 368–371).

Reviewer 2 - Point 2.1a 2. __(CTCF) detachment: The authors mentioned few times "detachment". In the context of this manuscript, the authors indicate detachment from nuclear lamina. However, the authors haven't provide convincing evidence about this. __

In the two instances where we used the term “detachment,” we intended it to refer exclusively to reduced CTCF binding to DNA, not to lamina repositioning. To avoid ambiguity, we have replaced “detachment” with “reduced binding” in both locations (lines 123 and 329). We do not use this term to describe TTN–lamina positioning.

Reviewer 2 - Point 2.1b (1) Figure 1D: I doubt whether such changes of CTCF protein abundance will lead to LAD detachment. Suggest the authors read van Schaik et al., Genome Biology, 2022. With the full depletion of CTCF, the effects on LADs are still very restricted.

We agree that the observed correlation between reduced CTCF levels and the relocation of TTN away from a LAD does not establish causality. As outlined in our response to Reviewer 1 – Point 8c, we are performing additional FISH experiments at earlier differentiation stages in the CTCF inducible knockdown line to directly assess whether partial CTCF depletion is sufficient to alter the timing of TTN–lamina separation.

Reviewer 2 - Point 2.2 (2) Figure 2D: Lamin B1 should be mostly at nuclear periphery. I have few questions: (1) is the antibody specific? (2) do these cells carry mutation in LMNB1 gene? (3) is the staining actually LMNA?

As also clarified in response to Reviewer 1 – Point 8b, the original images displayed maximum-intensity projections of Z-stacks, which obscured the peripheral distribution of LMNB1. We have updated Figure 2D and Figure S2E to show representative individual optical sections, which more clearly display the expected peripheral LMNB1 signal. We also confirm that the antibody used is specific for LMNB1 and previously validated (Bertero et al. 2019b), and that the WTC11-derived lines used in this study carry no mutation in LMNB1.

Reviewer 2 - Point 3

3. Opposite functions of GATA4 and CTCF: These data in Figure 5E-H argues the opposite role of GATA4 and CTCF in transcriptional regulation. Would it be that CTCF KD just affected cell proliferation, which is actually known for many cell types, rather than affect CM differentiation process? If this is the reason, inversed correlation between CTCF KD and GATA4 KD in Figure 4D could also be explained by opposite effects on cell cycle.

We directly evaluated this possibility. In FHF–LV cardioids, cell cycle profiling in Figure 6C and Figure S6C (now S7C) showed that CTCF knockdown does not alter the distribution of CMs across G1/S/G2–M phases, in contrast to the marked increase in proliferation observed with GATA4 knockdown.

Because this comment referred specifically to the SHF data, we also analyzed mitotic gene expression in the SHF–RV bulk RNA-seq dataset using GSEA. CTCF knockdown did not significantly enrich any cell cycle–related gene sets, whereas GATA4 knockdown produced a strong enrichment for mitotic cell cycle terms, in line with FHF-LV data (Reviewer Figure 2).

These results are summarized in updated Figure S5C, reporting also the results of the broader GSEA analysis, and together indicate that the transcriptional divergence between CTCF and GATA4 knockdown is not simply explained by opposing effects on proliferation.

(The figure cannot be rendered in this text-only format)

Reviewer Figure 2. GSEA for mitotic cell cycle in SHF-RV after inducible knockdown of CTCF (left) or GATA4 (right). p-values by Adaptive Monte-Carlo Permutation test.

Reviewer 2 - Point 4 4. In discussion, the authors suggested that CTCF is a local chromatin remodeller. In my view, association with local chromatin compaction doesn't qualify CTCF as a chromatin remodeler. To my knowledge, CTCF does not have an enzymatic domain, then how does it remodel chromatin?

Our intended meaning was that CTCF shapes 3D chromatin architecture through its role in organizing intergenic looping, not that it remodels chromatin enzymatically. To avoid confusion, we have removed the original sentence from the Discussion.

Reviewer 2 - Point 5. 5. Some conclusions are drawn based on insignificant p-values, e.g. Figure 2F, Figure 3D, etc. The authors should be careful about their conclusion, and tone down their statement for the observations have borderline significance.

The conclusions based on bulk RNA-seq have been revised in response to Reviewer 1 – Point 1 (updated Figure 2F). By subsetting B-to-A and A-to-B genes according to their expression dynamics, this analysis now yields clearer and statistically significant differences between conditions.

Regarding the 4C-seq data, as acknowledged in Reviewer 2 – Point 3a, the observed effects are modest. We are generating additional biological replicates to increase statistical power. In the meantime, we have adjusted the text to avoid overstating these findings. The revised manuscript now states: “While the difference did not reach significance, these trends suggest …” (lines 199–200).

Reviewer 2 - Minor comment 1. Minor comments: 1. Figure 1A: (1) I suggest to label two promoters in the gene model. It's unclear in the figure in the current version; (2) I was a bit confused with the way how the authors labeled CTCF directionality. I thought there are a lot of promoters. Why didn't they use triangles?

We updated Figure 1A to label both TTN promoters and indicate their orientation. For CTCF sites, we now clearly display the motif direction and core binding region as determined by FIMO analysis of the CTCF ChIP-seq peaks, improving consistency and interpretability.

Reviewer 2 - Minor comment 2. 2. Figure 2C: I think the drastical reduction of titin-mEGFP levels is only due to the way how the authors analyze their FACS data. Can the author quantify on median fluorescence intensity?

The gating strategy for titin-mEGFP⁺ cells was defined using a reporter-negative control, and cells lacking TNNT2 expression showed no detectable titin-mEGFP signal, confirming the specificity of the gate. To complement this analysis, we also quantified the median fluorescence intensity (MFI) of titin-mEGFP⁺ cells. The MFI analysis corroborates the original findings, showing a significant decrease in GATA4 knockdown and an increase in CTCF knockdown (updated Figure S2D).

__Reviewer 2 - Minor comment 3. 3. Figure S2G: P value should be -log10, I assume. Please label it accurately. __

We appreciate the reviewer pointing out this labeling error. In the revised manuscript, this panel has been removed to accommodate the updated compartment–expression analysis now presented in updated Figure 2H (see response to Reviewer 1 – Point 1), and the issue is no longer applicable.

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Poleshko A, Shah PP, Gupta M, Babu A, Morley MP, Manderfield LJ, Ifkovits JL, Calderon D, Aghajanian H, Sierra-Pagán JE, et al. 2017. Genome-Nuclear Lamina Interactions Regulate Cardiac Stem Cell Lineage Restriction. Cell 171: 573–587.

Rodríguez-Carballo E, Lopez-Delisle L, Zhan Y, Fabre PJ, Beccari L, El-Idrissi I, Huynh THN, Ozadam H, Dekker J, Duboule D. 2017. The HoxD cluster is a dynamic and resilient TAD boundary controlling the segregation of antagonistic regulatory landscapes. Genes Dev 31: 2264–2281.

Il me semble aussi que les fichiers HTML qui constituent le ePub doivent avoir une syntaxe plus rigoureuse que ce que permet HTML 5, c'est-à-dire qu'il faut que ça respect impérativement les standards de balisage de XML comme le fait de devoir tout le temps fermer les balises ouvertes.

Mais il y a aussi l'inverse : les utilisateurs influencent la conception d'outils technologiques. Si certains concepteurs écoutent leurs internautes et prennent en compte les habitudes de consommation, cela agit dans la manière dont ces technologies sont crées.

L'auteur dans cet extrait souligne un fait intéressant. Nous avons tendance a percevoir le numérique comme une vague qui bouleverse les relations interpersonnelles telle que nous les connaissions. En un sens cela est vrai car grâce ( ou a cause) des sites et appli de rencontre nous pouvons chercher l'amour en ligne sans devoir faire appel a une tierce personne. Dans les faits il y a effectivement un grand changement. Mais si on fait la part des choses on se rend compte que aujourd'hui ou il y a un siècle, l'amour connait les même tourments. C'est simplement que nous y somme désormais plus exposés.

Manque "a" entre les deux mots.

Author response:

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

Reviewer #1:

(1) The authors state that more is known about glial reactivation than cell-cycle re-entry. They are confusing many points here. More gene networks that require cell-cycle re-entry are known. Some of the genes listed for "reactivation" are, in fact, required for cell cycle re-entry/proliferation. And the authors confuse gliosis vs glial reactivation.

We thank the reviewer for this important and constructive comment. We fully agree that clearly distinguishing between the concepts of glial reactivation, glial proliferation, gliosis, and neurogenesis is essential to avoid conceptual confusion in our study.

Injury-induced retinal regeneration in zebrafish:

Glial reactivation refers to the initial response of quiescent Müller glia (MG) to injury, characterized by morphological changes and upregulation of reactive markers (e.g., gfap, ascl1a, lin28a) and activation of signaling pathways such as Notch, Jak/Stat, and Wnt (Lahne et al., 2020; Pollak et al., 2013; Sifuentes et al., 2016; Yao et al., 2016).

Glial proliferation refers to the clonal expansion of these MG-derived progenitor cells, which undergo rapid cell-cycle re-entry and amplify to generate sufficient progenitors for regeneration (Iribarne and Hyde, 2022; Lee et al., 2024; Wan and Goldman, 2016)

Gliosis vs neurogenesis represents a divergent fate decision following proliferation. In zebrafish, MG-derived progenitor cells differentiate into retinal neurons that can replace those damaged or lost due to retinal injury. In contrast, mammalian MG tend to undergo an initial gliotic surge and rapidly revert to a quiescent state, exhibiting gliosis and glial scarring (Thomas et al., 2016; Yin et al., 2024). Thus, we totally agreed that gliosis cannot be confused with glial reactivation because glial reactivation is the very first step of glial injury responses, whereas gliogensis is the very last glial response to the injury.

We agree with the reviewer that many genes typically described as “reactivation markers” (e.g., ascl1a, lin28a, sox2, mycb, mych) are also essential regulators of cell-cycle re-entry (Gorsuch et al., 2017; Hamon et al., 2019; Lee et al., 2024; Lourenço et al., 2021; Pollak et al., 2013; Thomas et al., 2016). Because the glial reactivation is a leading event for glial proliferation, the regulators of glial reactivation are expected to be responsible for glial proliferation as well.

In our study, we focused on the states preceding glial proliferation to understand the mechanism underlying injury-induced glial cell-cycle re-entry. We defined these transitional states and the subsequent proliferative MG states based on single-cell RNA-seq trajectory analysis. (revised lines: 41-58)

(2) A major weakness of the approach is testing cone ablation and regeneration in early larval animals. For example, cones are ablated starting the day that they are born. MG that are responding are also very young, less than 48 hrs old. It is also unclear whether the immune response of microglia is a mature response. All of these assays would be of higher significance if they were performed in the context of a mature, fully differentiated, adult retina. All analysis in the paper is negatively affected by this biological variable.

We thank the reviewer for raising this important point regarding the developmental stage of the retina in our model system. We have carefully considered this concern and now provide additional clarification and justification, as follows:

(1) The glial responses in larval and adult retina:

Previous studies have demonstrated that injury-induced glial responses are largely conserved in larval and adult zebrafish retina, including reactive gliosis marked by gfap upregulation and proliferation(Meyers et al., 2012; Sarich et al., 2025). In our study, G/R cones were ablated beginning at 5 dpf using metronidazole (MTZ), and we observed robust induction of PCNA⁺ MG in the inner nuclear layer, consistent with injury-induced proliferation (Figure 1E). These findings align with previous studies showing that key features of MG regenerative responses are conserved across larval and adult stages.

(2) The microglial responses in larval and adult retina:

Retinal microglia functionally mature at 5 dpf in the zebrafish retina (Mazzolini et al., 2020; Svahn et al., 2013), and prior studies have demonstrated that microglia in larval and adult zebrafish exhibit similar responses to injury, including migration, morphological activation, and phagocytosis(Nagashima and Hitchcock, 2021; White et al., 2017). In our experiments using Tg(mpeg1: GFP) larvae, we observed clear microglial recruitment to the outer nuclear layer (ONL) following cone ablation (Figure 1E and Figure 1-figure supplement 1A), supporting the functional competence of larval microglia in injury-induced immune responses

(3) The contribution using larval animals to study the regeneration program:

We agree that regeneration studies in the adult retina can provide important biological insights, particularly in a fully differentiated tissue environment. Accordingly, we have acknowledged this limitation in our revised manuscript “limitations of this study” section (revised lines 534-540: “1. Our study focuses on larval zebrafish, in which the core features of MG and immune responses are conserved compared to the adult. However, we acknowledge that the adult retina—with its fully matured differentiated retina and immune response—provides irreplaceable biological insight. Nevertheless, larval models offer a powerful platform to uncover conserved regenerative mechanisms and serve as a valuable complement for identifying age-dependent differences in MG-mediated regeneration.”) and have stated our intention to extend future analyses to adult zebrafish, especially to explore age-dependent differences in redox signaling and MG proliferation. At the same time, we believe that the larval model offers unique advantages for uncovering fundamental, conserved mechanisms of regeneration and enables characterization of age-dependent regulatory differences. Thus, our study in larval animals serves as a complementary and informative platform for understanding both the conserved and developmental stage-specific features of injury-induced regeneration.

(4) Related to the above point, the clonal analysis of cxcl18b+ MG is complicated by the fact that new MG are still being born in the CMZ (as are new cones for that matter).

We thank the reviewer for raising this important point regarding potential contributions from CMZ-derived progenitors to the lineage-traced cxcl18b⁺ MG clones. To address this concern, we have implemented evidence to rule out a CMZ origin for the clones analyzed:

Spatial restriction of clones: All clones included in our analysis were located exclusively within the central and dorsal retina, as shown in Figure 2H. From the spatial distribution of reactive MG populations across the retina, we observed a patterned organization in which the vast majority of proliferating MG arose from local mature MG–derived progenitors, rather than from peripheral CMZ-derived progenitors. However, we acknowledge that we cannot entirely exclude the possibility that CMZ-derived progenitors contribute to injury-induced MG proliferation, particularly in the peripheral retina.

We have clarified this point in the revised Methods section (revised lines 756–762: “Clone analysis of cxcl18b⁺ lineage-traced MG was restricted to cells located in the central and dorsal region of the zebrafish retina after G/R cone ablation in Figure 2, Figure 6, and their figure supplement. This spatial restriction strongly suggests that the proliferative MG originate from local mature MG, although we cannot completely rule out the possibility that CMZ-derived progenitors contribute to the generation of proliferative MG in the peripheral retina.”) and updated the corresponding figure legends.

(4) A near identical study was already done by Hoang et al., 2020, in adult zebrafish, a more relevant biological timepoint. Did the authors check this published RNA-seq database for their gene(s) of interest?

We thank the reviewer for pointing out the relevance of the study by Hoang et al., 2020, which characterized the transcriptional dynamics of MG reactivation in the adult zebrafish retina. We agree that comparisons with their single-cell RNA-seq dataset are important to confirm the conservation of our findings in larval vs adult zebrafish.

To this end, we examined the adult zebrafish MG dataset reported by Hoang et al., and confirmed that cxcl18b is also present and enriched in their analysis, particularly in activated MG populations under various injury paradigms:

(1) cxcl18b is listed as a differentially expressed gene (DEG) in Supplementary Table ST2, enriched in GFP⁺ MG following injury. It is also significantly upregulated in both NMDA-induced and light damage conditions, as shown in Supplementary Table ST3.

(2) In Supplementary Table ST5, cxcl18b is identified as a classifier of activated MG, with classification power scores of 0.552 (NMDA), 0.632 (light damage), and 0.574 (TNFα + γ-secretase inhibitor treatment), indicating its consistent expression across multiple injury models.

(3). In their pseudotime analysis (Figure 4C and Supplementary Table ST8), cxcl18b is specifically expressed in Module 5, which is expressed earlier along the trajectory than ascl1a. This temporal pattern of cxcl18b preceding ascl1a expression is consistent with our trajectory analysis in larval MG (Figure 1H), further supporting its role as an early marker of the transitional state before proliferation.

These findings underscore the robustness and biological relevance of cxcl18b as a conserved marker of injury-responsive MG in both larval and adult zebrafish. Our data expand upon the prior work by specifically characterizing a cxcl18b-defined transitional MG state preceding cell-cycle re-entry, thereby offering additional insights into the temporal staging of MG activation during regeneration.

(5) KD of cxcl18b did not affect MG proliferation or any other defined outcome. And yet the authors continually state such phrases as "microglia-mediated inflammation is critical for activating the cxcl18b-defined transitional states that drive MG proliferation." This is false. Cxcl18b does not drive MG proliferation at all.

We thank the reviewer for raising this concern. We agree with the reviewer and have revised this statement as "These findings suggest that microglia-mediated inflammation may contribute to the activation of cxcl18b-defined transitional states that precede MG proliferation, although a causal relationship remains to be established." (revised lines 251-253).

(6) A technical concern is that intravitreal injections are not routinely performed in larval fish.

We appreciate the reviewer’s technical concern regarding the use of intravitreal injections in larval zebrafish. In our study, we performed intraocular injection according to previously established methods (Alvarez et al., 2009; Giannaccini et al., 2018; Rosa et al., 2023). This approach involves carefully delivering a small volume of viral suspension into the intraocular space by a glass micropipette. To address this concern, we will revise the Materials and Methods section to clearly describe the injection procedure and will cite the relevant references accordingly.

Reviewer #2:

(1) The authors note a peak of PCNA+ Muller glia at 72 hours post injury. This is somewhat surprising as the MG would be expected to generate progenitor cells that would continue proliferating and stain with PCNA. Indeed, only a handful of PCNA+ cells are seen in the INL/ONL layer in Figure 1E2 with few clusters of progenitors present. It would be helpful to stain with a Muller glia marker to confirm these PCNA+ cells are Muller glia. It's also curious that almost all the PCNA+ cells are in the dorsal retina, even though G/R cone loss extends across both dorsal and ventral retina. Is this typical for cone ablation models in larval zebrafish?

We thank the reviewer for their insightful comment regarding the spatial distribution and identity of PCNA⁺ cells following injury.

In our study, we observed that the injury-induced proliferating cells (PCNA⁺) were predominantly located in the central and dorsal regions of the retina at 72 hours post-injury (hpi) (Figure 1E). To verify the identity of these proliferating cells, we performed additional immunostaining using BLBP, and confirmed that the majority of PCNA⁺ cells also express BLBP (Figure 1–figure supplement 1B in our revised Data), these results supporting their MG origin.

The regional bias of MG proliferation towards the central and dorsal retina is consistent with previous findings. Notably, (Krylov et al., 2023) demonstrated that MG exhibit region-specific heterogeneity in their regenerative responses to photoreceptor ablation. Their study identified proliferative MG subpopulations predominantly in the central (fgf24-expressing) and dorsal (efnb2a-expressing) domains, whereas ventral MG showed limited proliferative capacity (Krylov et al., 2023). These observations provide a plausible explanation for the spatially restricted PCNA⁺ MG population observed in our model following cone ablation.

(2) In Line 148: What is meant by "most original MG states" in this context? Original meaning novel? Or original meaning the earliest state MG adopted following injury? The language here is confusing.

We thank the reviewer for pointing out the ambiguous phrasing in our original manuscript. The term “most original MG states” was imprecise and misleading, as it could be interpreted as referring to the quiescent state of MG. In our context, we intended to describe the earliest transitional states in MG respond to injury, as they begin to exit quiescence and enter reactive characteristics. These early transitional MG populations co-express quiescent markers such as cx43 and early reactive markers gfap, as shown in Figure 1H.

To avoid confusion and improve conceptual clarity, we have revised the manuscript by replacing “most original MG states” with “early transitional MG state” (revised line 154) and have added a clearer explanation in the corresponding Results section to define this population more accurately.

(3) Perhaps provide a better image in Figure 2A of the cxcl18b at 48 hpi and 72 hpi. The current images appear virtually identical, with very little cxcl18b expression observed, especially compared to the 24 hpi. This is in contrast to the Tg(cxcl18b:GFP) transgenic line shown in Figure 2D, which indicates either much higher expression in proliferating cells at 48 hpi or the stability of GFP protein. Can the authors provide guidance on the accurate temporal expression of cxcl18b? Does expression peak rapidly at 24 hpi and then rapidly decline or is there persistence of expression to 48-72 hpi?

We appreciate the reviewer’s careful observation regarding the apparent similarity of cxcl18b expression at 48 hpi and 72 hpi in the in situ hybridization (ISH) images (Figure 2A), and the differences compared to the Tg(cxcl18b: GFP) reporter line shown in Figure 2D.

(1) The similarity of ISH images at the 48 hpi and 72 hpi (Figure 2A):

The cxcl18b mRNA signal peaked at 24 hpi, suggesting a rapid transcriptional response after retina injury. By 48 hpi, cxcl18b expression had already declined substantially, and by 72 hpi, the signal was further reduced to near-background levels. This temporal expression pattern explains why the ISH images at 48 hpi and 72 hpi appear nearly identical and much weaker compared to 24 hpi.

(2) The discrepancy between ISH and GFP reporter signal (Figure 2D):

The Tg(cxcl18b: GFP) reporter line shows persistent GFP expression beyond the transcriptional window of cxcl18b mRNA. This may be due to the prolonged stay of GFP protein, which remains detectable even after the endogenous transcription of cxcl18b has diminished. This explanation is also noted in the manuscript (revised lines 198–200). As a result, GFP⁺ MG cells are still visible at 48–72 hpi, and some of them co-label with PCNA.

These findings are consistent with our Pseudotime analysis based on scRNA-seq data (Figure 1H), which shows that cxcl18b expression precedes the induction of proliferative markers such as pcna and ascl1a.

(4) Line 198: The establishment of the Tg(cxcl18b:Cre-vhmc:mcherry::ef1a:loxP-dsRed-loxP-EGFP::lws2:nfsb-mCherry) is considerable but the nomenclature doesn't properly fit. Is the mCherry fused with Cre and driven by the cxcl18b promoter? What is the vhmc component? Finally, while this may provide the ability to clonally track cxcl18b-expressing MG, it does not address the prior question of what is the actual temporal expression of cxcl18b? If anything, this only addresses whether proliferating MG expressed cxcl18b at some point in their history, but does not indicate that cxcl18b expression co-exists in proliferating cells. The most convincing evidence is in Supplemental Figure 2B.

The "vmhc" component refers to the ventricular myosin heavy chain promoter, commonly used to label atrial cardiomyocytes (Jin et al., 2009). We cloned the vmhc upstream region containing its promoter and fusing with mCherry for selection during transgenic fish line construction.

Clone analysis using the Tg(cxcl18b: Cre-vmhc: mCherry::ef1a: loxP-DsRed-loxP-EGFP::lws2: nfsb-mCherry) further indicates that cxcl18b-defined the transitional state is the essential routing for MG proliferation. We have clarified in the revised text that this lineage tracing indicates a “history of injury-induced cxcl18b expression” rather than its ongoing expression during proliferation (revised line 205).

(5) Line 203: The data shown in Figure 2F do not indicate that these MG are cxcl18b+. Rather, the data are consistent with the interpretation that these MG expressed Cre at some prior stage and now express GFP from the ef1a promoter rather than DsRed. Whether these MG continue to express cxcl18b at the time these fish were collected is not addressed by these data. It is not accurate to conclude that these cells are cxcl18b+.

We thank the reviewer for pointing out this important issue. We agreed that the GFP⁺ MG shown in Figure 2F represents cells that have previously expressed cxcl18b and thus belong to the cxcl18b-expressing cell lineage, but this does not indicate that they continue to express cxcl18b at the time of sample collection. Performing clonal analysis using the Cre-loxp system, the GFP signal reflects historical cxcl18b promoter activity rather than ongoing transcription. We have revised the relevant sentence in our manuscript to clarify this point and now refer to these GFP⁺ cells as "cxcl18b lineage-traced MG" rather than "cxcl18b⁺ MG" to avoid any misinterpretation (revised line 207).

(6) Line 213: The statement that proliferative MG mostly originated from cxcl18b+ MG transitional states is a conclusion that does appear fully supported by the data. Whether those MG continue to express cxcl18b remains unanswered by the data in Figure 2 and would likely be inconsistent with the single-cell data in Figure 1.

We thank the reviewer for this valuable comment. We agree that the original statement on Line 213 regarding the lineage relationship between cxcl18b⁺ transitional MG and proliferative MG required clarification.

(1) The cxcl18b expression dynamics:

Our single-cell RNA-seq and ISH analyses consistently show that cxcl18b expression peaks as early as 24 hpi and declines rapidly, with significantly reduced expression by 48 and 72 hpi. These findings suggest that cxcl18b marks an early transitional MG state, rather than being maintained in proliferative MG. Indeed, in our scRNA-seq pseudotime trajectory analysis (Figure 1H), cxcl18b expression is highest in early transitional MG clusters (Clusters 1) and downregulated as cells progress toward proliferative states (Clusters 3/6), supporting a model in which cxcl18b is downregulated before cell-cycle re-entry.

(2) Prolonged stability of GFP protein:

The GFP signal observed in Tg(cxcl18b: GFP) retinas at 72 hpi may be because of the prolonged stability of GFP protein, rather than sustained cxcl18b transcription. The actual expression dynamics of cxcl18b are more directly reflected by our in situ hybridization and single-cell RNA-seq data, both showing a rapid decline after its early peak at 24 hpi. This explanation is also noted in the manuscript (revised lines 196–197).

(7) Line 246: The use of Dexamethasone to block inflammation is a widely used approach. However, dexamethasone is a broad-spectrum anti-inflammatory molecule that works through glucocorticoid signaling that may involve more than microglia. The observation that microglia recruitment and cxcl18a expression are both reduced is correlative but does not prove causation. Thus, the data are not sufficient to conclude that microglia-mediated inflammation is critical for activating cxcl18b expression. Indeed, data in Figure 1 indicate that cxcl18b expression occurs prior to microglia migration to the ONL.

We thank the reviewer for this thoughtful and important comment. We fully acknowledge that dexamethasone is a broad-spectrum anti-inflammatory agent that acts via glucocorticoid receptor signaling and may influence multiple immune and non-immune pathways beyond microglia.

In our study, dexamethasone treatment led to a reduction in both microglial recruitment and the number of cxcl18b⁺ MG at 72 hpi, suggesting a potential association between inflammation and cxcl18b activation. However, we agree that this observation remains correlative and is not sufficient to establish a direct link between microglia activity and cxcl18b induction. Our time-course analysis indicates that cxcl18b expression peaks at 24 hpi, preceding robust microglial accumulation in the ONL, further highlighting the need to clarify the temporal dynamics and cellular sources of inflammatory cues.

To address this question more conclusively, selective ablation of microglia during cone injury would be necessary. However, implementing such an approach would require a complex intersection of three transgenic lines—Tg(mpeg1: nfsB-mCherry) for microglia ablation, Tg(lws2: nfsB-mCherry) for cone ablation, and Tg(cxcl18b: GFP) for reporting—posing substantial genetic and experimental challenges.

We have revised the Results section accordingly to state: “These findings suggest that microglia-mediated inflammation may contribute to the activation of cxcl18b-defined transitional states that precede MG proliferation, although a causal relationship remains to be established.” (revised lines 251–253). We also added a new paragraph in the “Result: Clonal analysis reveals injury-induced MG proliferation via cxcl18b-defined transitional states associated with inflammation” as “While dexamethasone suppressed both microglial recruitment and cxcl18b⁺ MG generation, its broad anti-inflammatory action precludes definitive conclusions about microglial causality. Dissecting this relationship would require concurrent ablation of microglia and cone photoreceptors using a triple-transgenic strategy, which is beyond the scope of the current study. Targeted approaches will be necessary to resolve the specific role of microglia in initiating cxcl18b expression.” (revised lines 251–258) to explicitly acknowledge this limitation and the need for future studies using microglia-specific ablation models to resolve the mechanism.

(8) Could the authors clarify the basis of investigating NO signaling, given the relative expression of the genes by either cxcl18b+ MG or uninjured MG? Based on the expression illustrated in Supplemental Figure 4A, there is almost no expression of nos1 or nos2b in any MG. The authors are encouraged to revisit the earlier single-cell data sets to identify those cells that express components of NO signaling to determine the source(s) of NO that could be impacting the Muller glia.

We thank the reviewer for raising these important points.

Nitric oxide (NO) signaling has been implicated in the regeneration of multiple zebrafish tissues, including the heart (Rochon et al., 2020; Yu et al., 2024), spinal cord (Bradley et al., 2010), and fin (Matrone et al., 2021). Based on these findings, we hypothesized that NO signaling might also contribute to retinal regeneration.

As described in the manuscript, we compiled a redox-related gene list and systematically screened their roles in injury-induced MG proliferation using CRISPR-Cas9-mediated gene disruption. Among the candidates, disruption of nos genes significantly reduced the number of PCNA⁺ MG cells following G/R cone ablation (Figure 4), prompting us to further investigate the role of NO signaling.

(9) Line 319-320: this sentence appears to be missing text as "while no influenced across the nos mutants and gsnor mutants" does not make sense.

We appreciate the reviewer’s observation and agree that the original sentence was unclear. We have revised the sentence in the manuscript as follows:

“In contrast, no significant change in MG proliferation was observed in nos1, nos2a, or gsnor mutants compared to wild type (Figures 4F–4I)” (revised lines 326-328).

(10) Line 326-328: The text should be rewritten as the current meaning would suggest there was no significant loss of photoreceptors in the nos2b mutants. That is incorrect. Rather, there was no significant difference between WT and the nos2b mutants in the number of photoreceptors lost at 72 hpi following MTZ treatment. Both groups lost photoreceptors, but the number lost in nos2b hets and homozygotes was the same as WT.

We agree with the suggestion and have revised our manuscript. We have revised the sentence in the manuscript as follows:

“We observed no significant difference in the loss of cone photoreceptor at 72 hpi between nos2b mutants and WT, indicating that the reduced MG proliferation observed in nos2b mutants is independent of the injury (WT: 45 ± 8 remaining cones, n = 24; nos2b⁺/⁻: 49 ± 12, n = 20; nos2b⁻/⁻: 46 ± 9, n = 20; mean ± SEM) (Figure 4K).” (revised lines 331-335).

(11) There is concern over the inconsistencies with some of the data. In Figure 4, Supplement 1A, the single-cell data found virtually no expression of nos2b in either uninjured MG or cxcl18b+ MG. In contrast, the authors find nos2b expression by RT-PCR in the cxcl18b:GFP+ MG. The in situ expression of nos2b in Figure 5 - Supplement 1 is not persuasive. The red puncta are seen in a single cxcl18b:GFP+ cell but also in the plexiform layer and is other non cxcl18b:GFP+ cells.

We appreciate the concern regarding the apparent inconsistencies in nos2b expression across different datasets. We provide the following explanations:

(1) Low expression of nos2b in scRNA-seq data:

We propose a potential explanation: Nitric oxide (NO) signaling is known to exert its biological functions in a dose-dependent manner and is tightly regulated post-transcriptionally, especially in inducible nitric oxide synthase (iNOS) (Bogdan, 2001; Nathan and Xie, 1994; Thomas et al., 2008). Thus, even modest changes in nos2b expression may exert meaningful biological effects without producing strong transcriptional signals detectable by scRNA-seq, which could fall below the detection threshold of scRNA-seq methods. Supporting this idea, our functional assay (Figure 4J) reveals a clear concentration-dependent effect of NO on MG proliferation, consistent with the biological relevance of Nos2b activity despite its low transcript abundance.

(2) Regarding the in situ hybridization data:

We used both commercially available in situ hybridization probes from (HCR^TM) and RNAscope^TM (data not shown) to detect nos2b transcripts. While the nos2b signal was observed in other retinal cell types, including cells in the plexiform layer, our primary study was focused on examining its expression within the cxcl18b⁺ MG lineage.

(3) Regarding RT-PCR detection of nos2b in cxcl18b: GFP⁺ MG:

To enhance detection sensitivity, we enriched cxcl18b: GFP⁺ MG by FACS at 72 hpi and performed cDNA amplification before RT-PCR. This approach allowed the detection of low-abundance transcripts such as nos2b. It is also important to note that RT-PCR reflects fold changes in expression compared to MG to other retina cell type. The subtle but biologically upregulated of nos2b expression may not be readily captured by in situ hybridization or scRNA-seq.

(12) Line 356 - there is a disagreement over the interpretation of the current data. The statement that nos2b was specifically expressed in cxcl18b+ transitional MG states is not entirely accurate. This conclusion is based on expression of GFP from a cxcl18b promoter, which may reflect persistence of the GFP protein and not evidence of cxcl18b expression. Even assuming that the nos2b in situ hybridization and RT-PCR data are correct, the data would indicate that nos2b is expressed in proliferating MG that are derived from the cxcl18b+ transitional states. The single-cell trajectory analysis in Figure 2 indicates that cxcl18b is not co-expressed with PCNA. Furthermore, the single-cell data in Figure 4, Supplement 1, indicates no expression of nos2b in cxcl18b+ MG. The authors need to reconcile these seemingly contradictory pieces of data.

We thank the reviewer for this thoughtful and important comment. We agree that clarification is needed to accurately interpret the relationship between cxcl18b, nos2b, and MG proliferation, particularly considering the different temporal and technical contexts of our datasets.

(1) Lineage labeling and interpretation of GFP expression:

We acknowledge that in the Tg(cxcl18b: Cre-vhmc: mcherry::ef1a: loxP-dsRed-loxP-EGFP::lws2: nfsb-mCherry) line, GFP expression reflects historical activity of the cxcl18b promoter, rather than ongoing transcription. This GFP signal, due to its prolonged stay, may persist beyond the time window of endogenous cxcl18b expression. Accordingly, we have revised the manuscript to replace “cxcl18b⁺ MG” with “cxcl18b⁺ lineage-traced MG” throughout the relevant sections to prevent potential misinterpretation.

(2) Functional experiments support a lineage relationship between cxcl18b⁺ states and nos2b activity:

To further investigate the regulatory relationship between cxcl18b and nos2b, we conducted NO scavenging experiments using C-PTIO in the Tg(cxcl18b: GFP) background. We observed that the generation of cxcl18b: GFP⁺ MG after injury was not affected by NO depletion, indicating that cxcl18b activation precedes NO signaling (data not shown). However, PCNA⁺ MG was significantly reduced under the same treatment, suggesting that NO signaling is not required for cxcl18b⁺ transitional state formation, but is necessary for proliferation. Together with our MG-specific nos2b knockout data, these results support a model in which nos2b-derived NO acts downstream of the cxcl18b⁺ transitional state to promote MG cell-cycle re-entry.

(3) The scRNA-seq data with nos2b expression:

We agree with the reviewer that our scRNA-seq dataset shows minimal overlap between cxcl18b and pcna expression, which is consistent with our interpretation that cxcl18b expression marks a transitional phase before cell-cycle entry. Furthermore, nos2b transcripts were not robustly detected in cxcl18b⁺ MG clusters in our scRNA dataset. This discrepancy may be caused by technical limitations of scRNA-seq in capturing low-abundance or transient transcripts such as nos2b, as discussed in response to comment #11.

(13) The data in Figure 7 are interesting and suggest a link between NO signaling and notch activity. The use of the C-PTIO NO scavenger is not specific to MG, which limits the conclusions related to autocrine NO signaling in cxcl18b+ MG.

We acknowledge that the use of C-PTIO cannot distinguish between NO signaling within MG and paracrine effects from other retinal cells. Currently, technical limitations prevent MG-specific NO depletion. We have discussed this limitation accordingly in our revised “Limitations of this study” section (revised lines 540-545: “2. While our data suggest that injury-induced NO suppresses Notch signaling activation and promotes MG proliferation, the use of a general NO scavenger (C-PTIO) does not allow us to determine whether this regulation occurs in an autocrine or paracrine manner. The specific role of NO signaling within cxcl18b⁺ MG requires further validation using MG-specific NO depletion.”)

(14) Line 446-448. As mentioned before, the data do not support a causative link between microglia recruitment and cxcl18b induction. More specifically, dexamethasone is a broad-spectrum anti-inflammatory drug that blocks microglia activation and recruitment. Critically, the authors demonstrate that expression of cxcl18b occurs prior to microglia recruitment (see Figure 1, Supplement 1). Thus, the statement that cxcl18b induction depends on microglia recruitment is not accurate.

We thank the reviewer for reiterating this important point. We fully agree that the current data do not support a direct causal relationship between microglia recruitment and cxcl18b induction. As also addressed in our response to Comment 7, dexamethasone, as a broad-spectrum anti-inflammatory agent, cannot distinguish microglia-specific effects from those of other immune components. We have revised the text in revised lines 251–258 to clarify that microglia-mediated inflammation is associated with—but not required for—activation of cxcl18b-defined transitional MG states.

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corporalidad, virtualidad, performatividad

aca estaria bueno que el comerciante en vez de mencionar a esclavos que no se venda, diga que tiene animales en jaulas, ya que la esclava fea que tiene alli abajo es un sobrante sin importancia, nada mas, de este modo, cuando el prota baja abajo ve animales, mucho animales raros y nunca visto, y continua avanzando hsta finamente, al final, en la sombra bien al fondo, casi como si fuese basura, estaba una jaula con la esclava

HyperPost Presentation setup via IPFS Desktop on ThinkPad still experimental but got it to work

dweb-hyperpost slides

Analyse de l'Engagement Politique : Concepts, Paradoxes et Contexte

Résumé Exécutif

Ce document de synthèse analyse en profondeur les multiples facettes de l'engagement politique en s'appuyant sur les perspectives de la sociologie et de la science politique.

L'analyse révèle quatre axes majeurs.

Premièrement, une distinction conceptuelle fondamentale est établie entre la participation politique, qui inclut des actes peu coûteux comme le vote, et l'engagement, qui désigne des formes d'action plus intenses, publiques et risquées.

L'engagement se décline sur un continuum allant du simple sympathisant au militant permanent, avec des profils variés tels que les "militants par conscience" et les "bénéficiaires directs" de la lutte.

Deuxièmement, le document explore le paradoxe de l'action collective, tel que formulé par Mancur Olson.

Ce paradoxe explique pourquoi des individus rationnels peuvent s'abstenir de participer à une action collective même s'ils en partagent les objectifs, à cause de la tentation du "passager clandestin".

Les solutions à ce paradoxe résident dans les incitations sélectives et, de manière plus sociologique, dans les rétributions symboliques de l'engagement (reconnaissance, plaisir militant, fidélité à ses valeurs) théorisées par Daniel Gaxie.

Troisièmement, l'analyse aborde l'importance du contexte à travers la notion de Structure des Opportunités Politiques (SOP).

Ce concept macro-analytique soutient que le succès et les formes d'un mouvement social (pacifiques ou disruptives) dépendent de l'ouverture ou de la fermeture du système politique.

Bien qu'utile pour comprendre des dynamiques historiques comme le mouvement des droits civiques aux États-Unis, ce concept fait l'objet de critiques importantes pour son statisme et sa vision simplifiée des interactions entre l'État et les mouvements sociaux.

Enfin, le document souligne le rôle crucial des variables socio-démographiques et des socialisations individuelles.

L'engagement est fortement corrélé au capital culturel et à la "disponibilité biographique".

L'analyse met en lumière l'importance des émotions, notamment le "choc moral", en précisant que la capacité à ressentir une indignation face à une situation est elle-même socialement construite.

L'étude de cas du "Freedom Summer" de 1964 démontre de manière saisissante que l'engagement intense a des conséquences biographiques profondes et durables sur la trajectoire de vie des militants.

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1. Définir l'Engagement Politique : Au-delà de la Simple Participation

Une première perplexité soulevée par l'analyse concerne la définition même de l'engagement politique.

Le terme, tel qu'il est parfois utilisé, tend à regrouper toutes les formes d'activité politique, y compris les moins exigeantes.

Cependant, la recherche en sociologie politique opère une distinction cruciale entre la participation et l'engagement.

1.1. Participation vs. Engagement : Une Question d'Intensité et de Risque

La participation est la catégorie la plus large, englobant toutes les formes de contribution aux affaires de la cité.

Le vote, l'inscription sur les listes électorales ou la réponse à un sondage sont des formes minimales et peu coûteuses de participation.

Elles sont souvent individuelles, secrètes (comme le vote dans l'isoloir) et n'engagent l'individu que de manière très limitée.

L'engagement, en revanche, désigne des formes de participation plus intenses, exigeantes et coûteuses en temps, en énergie et parfois en ressources.

Il se caractérise par deux dimensions clés :

• L'exposition publique : S'engager, c'est s'exposer publiquement, que ce soit en manifestant, en signant une pétition nominative ou en prenant la parole pour une cause.

• La prise de risque : Cette exposition publique peut entraîner des rétorsions, des controverses, des sanctions professionnelles ou même des risques physiques (violences policières, par exemple).

La figure de l'intellectuel engagé, comme les signataires du Manifeste des 121 contre la guerre d'Algérie, illustre cette prise de risque.

L'engagement s'inscrit donc dans une démarche où l'individu accepte un coût personnel potentiellement élevé en échange de la défense d'une cause collective.

1.2. Le Continuum de l'Intensité de l'Engagement

L'engagement peut être vu comme un continuum avec différents degrés d'implication.

• Le sympathisant : Il soutient une cause ou une organisation de l'extérieur, sans adhésion formelle.

Sa participation est souvent ponctuelle, comme le fait de se joindre à une manifestation pour montrer son soutien.

• L'adhérent : Il formalise son soutien en prenant sa carte dans un parti, un syndicat ou une association.

Cet acte implique souvent une contribution financière (cotisation) et marque une identification plus forte. L'adhérent peut dire "nous" en parlant de l'organisation, mais son implication active peut rester limitée.

• Le militant : Il est véritablement partie prenante des activités de l'organisation.

Il consacre du temps et de l'énergie de manière régulière, défend activement les positions du groupe, participe aux actions et s'identifie fortement à ses couleurs.

Au sein même du militantisme, les auteurs McCarthy et Zald distinguent plusieurs statuts au sein des "organisations de mouvement social".

Statut

Description

Volontaires

Militants bénévoles qui participent sur leur temps libre, sans rémunération. Ils constituent la base de nombreuses organisations.

Permanents

Militants salariés par l'organisation pour assurer son fonctionnement quotidien.

Leur statut peut parfois créer des tensions avec les bénévoles.

Cadres (Porte-parole)

Personnes qui incarnent et représentent l'organisation publiquement (président, secrétaire général).

Ils négocient avec les autorités et s'expriment dans les médias.

Leur sélection et leur légitimité sont des enjeux cruciaux au sein des collectifs.

1.3. Profils de Militants et Logiques d'Engagement

Une autre distinction importante est celle proposée par McCarthy et Zald entre :

• Les bénéficiaires : Ce sont les personnes directement concernées par la lutte et qui en retireront un bénéfice personnel et immédiat en cas de succès (ex: les sans-papiers luttant pour leur régularisation).

• Les militants par conscience : Ce sont des personnes qui soutiennent la cause par conviction, sans attendre de bénéfice direct pour leur situation personnelle (ex: des citoyens français soutenant les sans-papiers).

Cette distinction est essentielle car les logiques d'engagement et les objectifs peuvent différer entre ces deux groupes, créant parfois des tensions au sein d'un même mouvement.

1.4. L'Évolution de l'Engagement Partisan

La thèse d'un déclin de l'engagement, souvent associée à la baisse du nombre d'adhérents dans les partis politiques, est nuancée.

Une hypothèse plus fructueuse est que les partis politiques dominants n'ont plus besoin de militants comme par le passé.

Transformés en "machines électorales" peuplées de professionnels de la politique, ils peuvent externaliser des tâches autrefois militantes (collage d'affiches, communication) à des entreprises spécialisées.

De plus, des mécanismes comme les primaires ouvertes ont réduit le rôle des militants dans la sélection des candidats.

Ce phénomène n'entraîne pas la fin de l'envie de s'engager, mais plutôt un report de l'engagement vers d'autres espaces, comme le secteur associatif ou les mouvements sociaux, perçus comme plus concrets et désintéressés par des militants déçus de la vie partisane.

2. Le Paradoxe de l'Action Collective

L'un des défis théoriques majeurs pour comprendre l'engagement est d'expliquer pourquoi des actions collectives émergent, alors même que la rationalité individuelle pourrait y faire obstacle.

2.1. Le Modèle de Mancur Olson

L'économiste Mancur Olson, dans son ouvrage Logique de l'action collective (1965), a rompu avec les théories antérieures qui postulaient l'irrationalité des foules (Gustave Le Bon) ou expliquaient la révolte par des facteurs psychologiques comme la "frustration relative" (Ted Gurr). Olson part du postulat d'un acteur rationnel et calculateur.

Le paradoxe qu'il met en évidence est le suivant :

1. Une action collective vise à obtenir un bien collectif, c'est-à-dire un avantage qui profitera à tous les membres d'un groupe, qu'ils aient participé à l'action ou non (ex: une augmentation de salaire pour tous les employés d'une entreprise).

2. Participer à l'action a un coût individuel (ex: perte de salaire pendant une grève, temps consacré, risques encourus).

3. L'acteur rationnel sera donc tenté d'adopter la stratégie du "passager clandestin" (free rider) : ne pas payer le coût de l'action tout en espérant bénéficier de ses retombées si les autres se mobilisent.

Si tout le monde suit ce calcul, l'action collective n'a jamais lieu, même si elle serait bénéfique pour tous.

2.2. Solutions au Paradoxe : Incitations Sélectives et Rétributions

Pour Olson, la solution au paradoxe réside dans les incitations sélectives : des bénéfices (ou des coûts) qui s'appliquent uniquement à ceux qui participent (ou ne participent pas) à l'action.

• Incitations sélectives négatives (coûts) : Rendre la non-participation plus coûteuse que la participation. Exemples : la pression sociale, la stigmatisation des "jaunes" lors d'une grève, voire les menaces physiques d'un piquet de grève.

• Incitations sélectives positives (bénéfices) : Offrir des avantages individuels réservés aux participants.

Olson évoque même des "incitations sélectives érotiques" (le plaisir de rencontrer des gens, de nouer des relations).

Le politiste Daniel Gaxie a sociologisé cette approche en développant le concept de rétributions de l'engagement.

Ces gratifications, qui motivent et soutiennent le militantisme, peuvent être de plusieurs natures :

• Matérielles : Obtention d'un logement social, d'un emploi via le réseau de l'organisation.

• Symboliques : Acquisition de responsabilités, de notoriété, de reconnaissance.

Le fait de passer dans les médias ou d'être le porte-parole d'une lutte est une gratification symbolique puissante.

• Identitaires et morales : Le plaisir d'agir en conformité avec ses valeurs, de "pouvoir se regarder dans la glace".

• Affectives et sociales : Le plaisir de la sociabilité militante, de partager des moments forts avec des camarades, de se sentir membre d'un collectif.

Ces rétributions expliquent pourquoi des "militants par conscience" ne sont pas totalement désintéressés : ils trouvent un intérêt (au sens sociologique) dans leur engagement.

Cette analyse, couplée aux critiques d'Albert Hirschman (qui note que le coût et le bénéfice de l'action peuvent se confondre, comme la fierté tirée d'une lutte difficile), permet de dépasser la vision purement utilitariste d'Olson.

3. Le Rôle du Contexte : La Structure des Opportunités Politiques (SOP)

Si le modèle d'Olson se concentre sur l'individu (micro), l'approche par la Structure des Opportunités Politiques (SOP) se place à un niveau macro-structurel pour analyser l'influence du contexte politique sur les mouvements sociaux.

3.1. Définition et Exemple Fondateur

La SOP désigne l'ensemble des éléments du contexte politique qui facilitent ou entravent l'émergence et le succès d'un mouvement social.

Le travail de Doug McAdam sur le mouvement pour les droits civiques aux États-Unis est l'exemple fondateur.

McAdam montre que les organisations noires existaient déjà dans les années 1930 mais piétinaient.

Leur succès dans les années 1950-60 s'explique par une ouverture de la SOP, due à plusieurs facteurs :

• Économiques : La crise du coton dans le Sud et la migration des Noirs vers les industries du Nord.

• Sociaux : Une "libération cognitive" où les Noirs, découvrant un racisme moins institutionnalisé dans le Nord, réalisent que la ségrégation n'est pas une fatalité.