En lo personal, me parece interesante cómo la investigación social cuantitativa surge imitando el modelo de las ciencias naturales del siglo XIX. Entiendo que en ese momento era lógico querer darle a lo social la misma validez y objetividad que tenían disciplinas como la física

Yo pienso que lo que plantea McKay (2006) es muy cierto: la investigación sí ayuda a los docentes a ser mejores maestros, porque los obliga a cuestionar su práctica y buscar nuevas formas de enseñar. Sin embargo, siento que su visión es un poco limitada, porque reduce el valor de la investigación solo a mejorar la enseñanza. Para mí, investigar no solo sirve para encontrar respuestas u orientaciones, usino también para abrir la mente del docente, motivarlo a innovar y hasta transformar la manera en que se entiende l educación.

Los autores destacan las ventajas de cuestionarios y entrevistas como instrumentos de recolección de datos, pero no profundizan en sus limitaciones. La elección de instrumentos no debería centrarse solo en la accesibilidad o versatilidad, sino también en la calidad y pertinencia de los resultados que se esperan obtener.

Brown, paratacamites afirmo lo obvio en vez de considerar un posible sesgo que para ese punto podría ser más obvio, hablando de 2014, considero que para ese punto sería obvio, más que nada considerando que la sociedad en ese punto pasaba por situaciones humanas más complejas

Me parece interesante lo que menciona el autor Seidman (1998) acerca de la secuencia de las tres entrevistas. No obstante, no se lleva a cabo una recolección de datos ni cuantitativos ni cualitativos, por como menciona el autor, se queda en una "recomendación", desde esta perspectiva lo mencionado por el autor me parece un análisis sucinto con un potencial mayor.

Post #3 Con respecto a lo que menciona Flick (2014) Aunque la triangulación propuesta es un recurso metodológico enriquecedor, debe aplicarse con suma cautela, asegurando la coherencia en su diseño

2

Habla sobre una distinción más general y conocida de 2 autores diferentes en donde la metodología cuantitativa y cualitativa ya que el método de realizar el análisis de cada una de ellas es abierta y no numérica al contrario del otro. sin embargo, al principio dice que son muy similares

Aunque resaltan la validez de trabajar con muestras pequeñas en la investigación cualitativa, pueden tener limitaciones. Un número reducido de participantes puede introducir sesgos o dejar fuera perspectivas relevantes del fenómeno estudiado. Además, la falta de criterios claros y uniformes sobre el tamaño muestral en estudios cualitativos puede afectar y la comparabilidad entre investigaciones.

POST 3, PIENSO DIFERENTE A LORD RUTHERFORD YA QUE SI BIEN LOS DATOS CUANTITATIVOS SON HASTA CIERTA PARTE EXACTOS, NO QUIERE DECIR QUE LOS OTROS TIPOS DE INVESTIGACIÓN NO LO SEAN O QUE SEAN MENOS IMPORTANTES, YA QUE SIRVEN PARA DIFERENTES TIPOS DE INVESTIGACIONES Y TODAS SON BUENAS DEPENDEINDO DONDE LAS DESENVUELVAS

Critica 2.

De acuerdo con Mackey y Gass (2005) y sus recomendaciones de un análisis de datos, nos fomentan que es recomendable estar relacionados y creo es algo muy importante ya que se debe saber interpretar cuantitativamente cada dato

El hecho de que en 1995 apenas se hizo el diferenciador entre lo cuantitativo y cualitativo, es muy poco razonable, pero se entiende que no querían hacerlo, ya que en todo momento estaba sumergida por el area científica.

Los autores presentan la triangulación casi como una garantía de validez, cuando en la práctica no siempre es así. La convergencia de resultados puede fortalecer una investigación, pero también puede ocultar tensiones, contradicciones o aspectos únicos que cada método revela por separado. En este sentido, el énfasis excesivo en la validación podría limitar el potencial de la triangulación como herramienta para enriquecer la comprensión del fenómeno más allá de la mera confirmación de hipótesis.

Justificación: Tradicionalmente, la investigación, como actividad científica, ha sido considerada como una tarea exclusiva de grupos académicos de élite; sin embargo, los requerimientos de la sociedad y la Academia actual han originado una mayor necesidad de llevar a cabo estudios científicos y reportarlos en publicaciones académicas.

Rutherford tenía cierta razón al resaltar la importancia de medir en ciencia, pero su afirmación resulta parcial y no del todo correcta. Un conocimiento no es “pobre” por no ser numérico; simplemente responde a otra lógica. Lo cuantitativo aporta precisión y ser objetivo, mientras lo cualitativo aporta el sentido del porque

El artículo de Rodas Pacheco y , aunque útil como introducción, presenta varias limitaciones. En primer lugar, su tratamiento de la metodología es demasiado superficial y se limita a definiciones generales sin ofrecer ejemplos concretos de aplicación en investigaciones reales. Además, no desarrolla con suficiente detalle los procedimientos estadísticos en el enfoque cuantitativo ni las técnicas de codificación en el cualitativo, Finalmente, al ser un texto más descriptivo que analítico, corre el riesgo de repetir información ya ampliamente disponible en manuales básicos de metodología, sin aportar innovaciones significativas al campo.

Considero que es correcto como antecedente, pero la idea de Rutherford sé me hace muy pasada, el considerar que solamente lo cuantitativo tenga validez, te limita a un solo panorama nada crítico, para ser un genio en este proceso, considero que debe de tener una idea más abierta.

1

el autor hace referencia que el método científico está basado diciendo que cualquier conocimiento que no puede ser medido numéricamente era un conocimiento pobre y difiero en que las ciencias sociales antes de ser reconocidas ya podían ser sustentables.

Justificacion del tema: Tradicionalmente, la investigación, como actividad científica, ha sido considerada como una tarea exclusiva de grupos académicos de élite; sin embargo, los requerimientos de la sociedad y la Academia actual han originado una mayor necesidad de llevar a cabo estudios científicos y reportarlos en publicaciones académicas.

POST 1, DIFIERO TOTALMENTE EN LO QE SE MANIFIESTA YA QUE UNA PERSONA COMÚN PUEDE SER BUENA SI SE DEDICA A INVESTIGAR Y COCNOCER A PROFUNDIDAD DEL TEMA

Critica 1.

Al aplicar las entrevistas según Seidman (1998) establece que es bueno implementar 3 entrevistas para romper el hielo, yo creo que no necesariamente tengan q ser 3, sino una introducción con una buena comunicación tipo platica y después la entrevista que es la va a permitir al investigador poder involucrarse más que es como lo dice el autor y un desenlace muy breve dando a entender que se logró el objetivo de la entrevista

justificación de la investigación Alonso Martínez y Katia Amaya

Justificación: Recolección, análisis, exploración y representación de datos; al igual que la interpretación y validación de resultados.

Metodología: Una reseña histórica, y un análisis sucinto de los distintos métodos de investigación y los componentes de estos procesos, a saber: participantes; recolección, análisis, exploración y representación de datos; al igual que la interpretación y validación de resultados.

Post #1 Cuestiono la opinión que tenía Lourd Rutherford, ya que hoy en día, cualquiera que tenga una visión de hacer estudios científicos sin serlo necesariamente lo puede lograr.

Esta es la justificación de la investigación : Tradicionalmente, la investigación, como actividad científica, ha sido considerada como una tarea exclusiva de grupos académicos de élite; sin embargo, los requerimientos de la sociedad y la Academia actual han originado una mayor necesidad de llevar a cabo estudios científicos y reportarlos en publicaciones académicas.

recolección, análisis, exploración y representación de datos; al igual que la interpretación y validación de resultados.

Tradicionalmente, la investigación, como actividad científica, ha sido considerada como una tarea exclusiva de grupos académicos de élite; sin embargo, los requerimientos de la sociedad y la Academia actual han originado una mayor necesidad de llevar a cabo estudios científicos y reportarlos en publicaciones académicas.

Considero que es relevante que se aclare un hecho importante que la mayor parte de los investigadores se vuelven profesores, pero eso no significa que sean buenos enseñando, pienso que ese campo es poco explorado, pero que debería saberse si es mejor o peor.

Metodología

Una reseña histórica, y un análisis sucinto de los distintos métodos de investigación y los componentes de estos procesos, a saber: participantes; recolección, análisis, exploración y representación de datos; al igual que la interpretación y validación de resultados.

Esta es la justificación de la investigación. Tradicionalmente, la investigación, como actividad científica, ha sido considerada como una tarea exclusiva de grupos académicos de élite; sin embargo, los requerimientos de la sociedad y la Academia actual han originado una mayor necesidad de llevar a cabo estudios científicos y reportarlos en publicaciones académicas.

Justificación Tradicionalmente, la investigación, como actividad científica, ha sido considerada como una tarea exclusiva de grupos académicos de élite; sin embargo, los requerimientos de la sociedad y la Academia actual han originado una mayor necesidad de llevar a cabo estudios científicos y reportarlos en publicaciones académicas.

Justificación.

Tradicionalmente, la investigación, como actividad científica, ha sido considerada como una tarea exclusiva de grupos académicos de élite; sin embargo, los requerimientos de la sociedad y la Academia actual han originado una mayor necesidad de llevar a cabo estudios científicos y reportarlos en publicaciones académicas.

Esta es la justificación de la investigación

Esta es la justificacion Tradicionalmente, la investigación, como actividad científica, ha sido considerada como una tarea exclusiva de grupos académicos de élite; sin embargo, los requerimientos de la sociedad y la Academia actual han originado una mayor necesidad de llevar a cabo estudios científicos y reportarlos en publicaciones académicas.

Esta es la justificación del trabajo de investigación

Esta es la justificación del trabajo de insvestigación

Esta es la justificación del trabajo de investigación.

Metodología

Una reseña histórica, y un análisis sucinto de los distintos métodos de investigación y los componentes de estos procesos, a saber: participantes; recolección, análisis, exploración y representación de datos; al igual que la interpretación y validación de resultados.

Ezt töröljük ki!

• previous NAV+distribution value if it was since the last official NAV

Csak kivétel esetén töltendő, ha üresen marad, akkor a kerekítési szabály: díj összeg az alapdeviza az alap/sorozat devizájának kerekítése szerint van meghatározva.

Magyar szöveg: NAV-hoz kapcsolódó díjak kezelésének, módosításának célja az, hogy egy olyan keretrendszert alakítsunk ki, amiben az ügyfél tudja hozzárendelni a portfóliókhoz és az ahhoz kapcsolódó sorozatokhoz.

Reviewer #3 (Public review):

Summary

This paper investigates how disinformation affects reward learning processes in the context of a two-armed bandit task, where feedback is provided by agents with varying reliability (with lying probability explicitly instructed). They find that people learn more from credible sources, but also deviate systematically from optimal Bayesian learning: They learned from uninformative random feedback, learned more from positive feedback, and updated too quickly from fully credible feedback (especially following low-credibility feedback). Overall, this study highlights how misinformation could distort basic reward learning processes, without appeal to higher order social constructs like identity.

Strengths

• The experimental design is simple and well-controlled; in particular, it isolates basic learning processes by abstracting away from social context
• Modeling and statistics meet or exceed standards of rigor
• Limitations are acknowledged where appropriate, especially those regarding external validity
• The comparison model, Bayes with biased credibility estimates, is strong; deviations are much more compelling than e.g. a purely optimal model
• The conclusions are of substantial interest from both a theoretical and applied perspective

Weaknesses

The authors have addressed most of my concerns with the initial submission. However, in my view, evidence for the conclusion that less credible feedback yields a stronger positivity bias remains weak. This is due to two issues.

Absolute or relative positivity bias?

The conclusion of greater positivity bias for lower credible feedback (Fig 5) hinges on the specific way in which positivity bias is defined. Specifically, we only see the effect when normalizing the difference in sensitivity to positive vs. negative feedback by the sum. I appreciate that the authors present both and add the caveat whenever they mention the conclusion. However, without an argument that the relative definition is more appropriate, the fact of the matter is that the evidence is equivocal.

There is also a good reason to think that the absolute definition is more appropriate. As expected, participants learn more from credible feedback. Thus, normalizing by average learning (as in the relative definition) amounts to dividing the absolute difference by increasingly large numbers for more credible feedback. If there is a fixed absolute positivity bias (or something that looks like it), the relative bias will necessarily be lower for more credible feedback. In fact, the authors own results demonstrate this phenomenon (see below). A reduction in relative bias thus provides weak evidence for the claim.

It is interesting that the discovery study shows evidence of a drop in absolute bias. However, for me, this just raises questions. Why is there a difference? Was one a just a fluke? If so, which one?

Positivity bias or perseveration?

Positivity bias and perseveration will both predict a stronger relationship between positive (vs. negative) feedback and future choice. They can thus be confused for each other when inferred from choice data. This potentially calls into question all the results on positivity bias.

The authors clearly identify this concern in the text and go to considerable lengths to rule it out. However, the new results (in revision 1) show that a perseveration-only model can in fact account for the qualitative pattern in the human data (the CA parameters). This contradicts the current conclusion:

Critically, however, these analyses also confirmed that perseveration cannot account for our main finding of increased positivity bias, relative to the overall extent of CA, for low-credibility feedback.

Figure 24c shows that the credibility-CA model does in fact show stronger positivity bias for less credible feedback. The model distribution for credibility 1 is visibly lower than for credibilities 0.5 and 0.75.

The authors need to be clear that it is the magnitude of the effect that the perseveration-only model cannot account for. Furthermore, they should additionally clarify that this is true only for models fit to data; it is possible that the credibility-CA model could capture the full size of the effect with different parameters (which could fit best if the model was implemented slightly differently).

The authors could make the new analyses somewhat stronger by using parameters optimized to capture just the pattern in CA parameters (for example by MSE). This would show that the models are in principle incapable of capturing the effect. However, this would be a marginal improvement because the conclusion would still rest on a quantitative difference that depends on specific modeling assumptions.

New simulations clearly demonstrate the confound in relative bias

Figure 24 also speaks to the relative vs. absolute question. The model without positivity bias shows a slightly stronger absolute "positivity bias" for the most credible feedback, but a weaker relative bias. This is exactly in line with the logic laid out above. In standard bandit tasks, perseveration can be quite well-captured by a fixed absolute positivity bias, which is roughly what we see in the simulations (I'm not sure what to make of the slight increase; perhaps a useful lead for the authors). However, when we divide by average credit assignment, we now see a reduction. This clearly demonstrates that a reduction in relative bias can emerge without any true differences in positivity bias.

Given everything above, I think it is unlikely that the present data can provide even "solid" evidence for the claim that positivity bias is greater with less credible feedback. This confound could be quickly ruled out, however, by a study in which feedback is sometimes provided in the absence of a choice. This would empirically isolate positivity bias from choice-related effects, including perseveration.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public review):

This is a well-designed and very interesting study examining the impact of imprecise feedback on outcomes in decision-making. I think this is an important addition to the literature, and the results here, which provide a computational account of several decision-making biases, are insightful and interesting.

We thank the reviewer for highlighting the strengths of this work.

I do not believe I have substantive concerns related to the actual results presented; my concerns are more related to the framing of some of the work. My main concern is regarding the assertion that the results prove that non-normative and non-Bayesian learning is taking place. I agree with the authors that their results demonstrate that people will make decisions in ways that demonstrate deviations from what would be optimal for maximizing reward in their task under a strict application of Bayes' rule. I also agree that they have built reinforcement learning models that do a good job of accounting for the observed behavior. However, the Bayesian models included are rather simple, per the author's descriptions, applications of Bayes' rule with either fixed or learned credibility for the feedback agents. In contrast, several versions of the RL models are used, each modified to account for different possible biases. However, more complex Bayes-based models exist, notably active inference, but even the hierarchical Gaussian filter. These formalisms are able to accommodate more complex behavior, such as affect and habits, which might make them more competitive with RL models. I think it is entirely fair to say that these results demonstrate deviations from an idealized and strict Bayesian context; however, the equivalence here of Bayesian and normative is, I think, misleading or at least requires better justification/explanation. This is because a great deal of work has been done to show that Bayes optimal models can generate behavior or other outcomes that are clearly not optimal to an observer within a given context (consider hallucinations for example), but which make sense in the context of how the model is constructed as well as the priors and desired states the model is given.

As such, I would recommend that the language be adjusted to carefully define what is meant by normative and Bayesian and to recognize that work that is clearly Bayesian could potentially still be competitive with RL models if implemented to model this task. An even better approach would be to directly use one of these more complex modelling approaches, such as active inference, as the comparator to the RL models, though I would understand if the authors would want this to be a subject for future work.

We thank the reviewer for raising this crucial and insightful point regarding the framing of our results and the definitions of 'normative' and 'Bayesian' learning. Our primary aim in this work was to characterize specific behavioral signatures that demonstrate deviations from predictions generated by a strict, idealized Bayesian framework when learning from disinformation (which we term “biases”). We deliberately employed relatively simple Bayesian models as benchmarks to highlight these specific biases. We fully agree that more sophisticated Bayes-based models (as mentioned by the reviewer, or others) could potentially offer alternative mechanistic explanations for participant behavior. However, we currently do not have a strong notion about which Bayesian models can encompass our findings, and hence, we leave this important question for future work.

To enhance clarity within the current manuscript we now avoided the use of the term “normative” to refer to our Bayesian models, using the term “ideal” instead. We also define more clearly what exactly we mean by that notion when the idea model is described:

“This model is based on an idealized assumptions that during the feedback stage of each trial, the value of the chosen bandit is updated (based on feedback valence and credibility) according to Bayes rule reflecting perfect adherence to the instructed task structure (i.e., how true outcomes and feedback are generated).”

Moreover, we have added a few sentences in the discussion commenting on how more complex Bayesian models might account for our empirical findings:

“However, as hypothesized, when facing potential disinformation, we also find that individuals exhibit several important biases i.e., deviations from strictly idealized Bayesian strategies. Future studies should explore if and under what assumptions, about the task’s generative structure and/or learner’s priors and objectives, more complex Bayesian models (e.g., active inference (58)) might account for our empirical findings.”

Abstract:

The abstract is lacking in some detail about the experiments done, but this may be a limitation of the required word count. If word count is not an issue, I would recommend adding details of the experiments done and the results.

We thank the reviewer for their valuable suggestion. We have now included more details about the experiment in the abstract:

“In two experiments, participants completed a two-armed bandit task, where they repeatedly chose between two lotteries and received outcome-feedback from sources of varying credibility, who occasionally disseminated disinformation by lying about true choice outcome (e.g., reporting non reward when a reward was truly earned or vice versa).”

One comment is that there is an appeal to normative learning patterns, but this suggests that learning patterns have a fixed optimal nature, which may not be true in cases where the purpose of the learning (e.g. to confirm the feeling of safety of being in an in-group) may not be about learning accurately to maximize reward. This can be accommodated in a Bayesian framework by modelling priors and desired outcomes. As such, the central premise that biased learning is inherently non-normative or non-Bayesian, I think, would require more justification. This is true in the introduction as well.

Introduction:

As noted above, the conceptualization of Bayesian learning being equivalent to normative learning, I think requires further justification. Bayesian belief updating can be biased and non-optimal from an observer perspective, while being optimal within the agent doing the updating if the priors/desired outcomes are set up to advantage these "non-optimal" modes of decision making.

We appreciate the reviewer's thoughtful comment regarding the conceptualization of "normative" and "Bayesian" learning. We fully agree that the definition of "normative" is nuanced and can indeed depend on whether one considers reward-maximization or the underlying principles of belief updating. As explained above we now restrict our presentation to deviations from “ideal Bayes” learning patterns and we acknowledge the reviewer’s concern in a caveat in our discussion.

Results:

I wonder why the agent was presented before the choice, since the agent is only relevant to the feedback after the choice is made. I wonder if that might have induced any false association between the agent identity and the choice itself. This is by no means a critical point, but it would be interesting to get the authors' thoughts.

We thank the reviewer for raising this interesting point regarding the presentation of the agent before the choice. Our decision to present the agent at this stage was intentional, as our original experimental design aimed to explore the possible effects of "expected source credibility" on participants' choices (e.g., whether knowledge of feedback credibility will affect choice speed and accuracy). However, we found nothing that would be interesting to report.

The finding that positive feedback increases learning is one that has been shown before and depends on valence, as the authors note. They expanded their reinforcement learning model to include valence, but they did not modify the Bayesian model in a similar manner. This lack of a valence or recency effect might also explain the failure of the Bayesian models in the preceding section, where the contrast effect is discussed. It is not unreasonable to imagine that if humans do employ Bayesian reasoning that this reasoning system has had parameters tuned based on the real world, where recency of information does matter; affect has also been shown to be incorporable into Bayesian information processing (see the work by Hesp on affective charge and the large body of work by Ryan Smith). It may be that the Bayesian models chosen here require further complexity to capture the situation, just like some of the biases required updates to the RL models. This complexity, rather than being arbitrary, may be well justified by decision-making in the real world.

Thanks for these additional important ideas which speak more to the notion that more complex Bayesian frameworks may account for biases we report.

The methods mention several symptom scales- it would be interesting to have the results of these and any interesting correlations noted. It is possible that some of the individual variability here could be related to these symptoms, which could introduce precision parameter changes in a Bayesian context and things like reward sensitivity changes in an RL context.

We included these questionnaires for exploratory purposes, with the aim of generating informed hypotheses for future research into individual differences in learning. Given the preliminary nature of these analyses, we believe further research is required about this important topic.

Discussion:

(For discussion, not a specific comment on this paper): One wonders also about participants' beliefs about the experiment or the intent of the experimenters. I have often had participants tell me they were trying to "figure out" a task or find patterns even when this was not part of the experiment. This is not specific to this paper, but it may be relevant in the future to try and model participant beliefs about the experiment especially in the context of disinformation, when they might be primed to try and "figure things out".

We thank the reviewer for this important recommendation. We agree and this point is included in our caveat (cited above) that future research should address what assumptions about the generative task structure can allow Bayesian models to account for our empirical patterns.

As a general comment, in the active inference literature, there has been discussion of state-dependent actions, or "habits", which are learned in order to help agents more rapidly make decisions, based on previous learning. It is also possible that what is being observed is that these habits are at play, and that they represent the cognitive biases. This is likely especially true given, as the authors note, the high cognitive load of the task. It is true that this would mean that full-force Bayesian inference is not being used in each trial, or in each experience an agent might have in the world, but this is likely adaptive on the longer timescale of things, considering resource requirements. I think in this case you could argue that we have a departure from "normative" learning, but that is not necessarily a departure from any possible Bayesian framework, since these biases could potentially be modified by the agent or eschewed in favor of more expensive full-on Bayesian learning when warranted.<br /> Indeed, in their discussion on the strategy of amplifying credible news sources to drown out low-credibility sources, the authors hint at the possibility of longer-term strategies that may produce optimal outcomes in some contexts, but which were not necessarily appropriate to this task. As such, the performance on this task- and the consideration of true departure from Bayesian processing- should be considered in this wider context.

Another thing to consider is that Bayesian inference is occurring, but that priors present going in produce the biases, or these biases arise from another source, for example, factoring in epistemic value over rewards when the actual reward is not large. This again would be covered under an active inference approach, depending on how the priors are tuned. Indeed, given the benefit of social cohesion in an evolutionary perspective, some of these "biases" may be the result of adaptation. For example, it might be better to amplify people's good qualities and minimize their bad qualities in order to make it easier to interact with them; this entails a cost (in this case, not adequately learning from feedback and potentially losing out sometimes), but may fulfill a greater imperative (improved cooperation on things that matter). Given the right priors/desired states, this could still be a Bayes-optimal inference at a social level and, as such, may be ingrained as a habit that requires effort to break at the individual level during a task such as this.

We thank the reviewer for these insightful suggestions speaking further to the point about more complex Bayesian models.

The authors note that this task does not relate to "emotional engagement" or "deep, identity-related issues". While I agree that this is likely mostly true, it is also possible that just being told one is being lied to might elicit an emotional response that could bias responses, even if this is a weak response.

We agree with the reviewer that a task involving performance-based bonuses, and particularly one where participants are explicitly told they are being lied to, might elicit weak emotional response. However, our primary point is that the degree of these responses is expected to be substantially weaker than those typically observed in the broader disinformation literature, which frequently deals with highly salient political, social, or identity-related topics that inherently carry strong emotional and personal ties for participants, leading to much more pronounced affective engagement and potential biases. Our task deliberately avoids such issues thus minimizing the potential for significant emotion-driven biases. We have toned down the discussion accordingly:

“This occurs even when the decision at hand entails minimal emotional engagement or pertinence to deep, identity-related, issues.”

Reviewer #2 (Public review):

This valuable paper studies the problem of learning from feedback given by sources of varying credibility. The solid combination of experiment and computational modeling helps to pin down properties of learning, although some ambiguity remains in the interpretation of results.

Summary:

This paper studies the problem of learning from feedback given by sources of varying credibility. Two banditstyle experiments are conducted in which feedback is provided with uncertainty, but from known sources. Bayesian benchmarks are provided to assess normative facets of learning, and alternative credit assignment models are fit for comparison. Some aspects of normativity appear, in addition to deviations such as asymmetric updating from positive and negative outcomes.

Strengths:

The paper tackles an important topic, with a relatively clean cognitive perspective. The construction of the experiment enables the use of computational modeling. This helps to pinpoint quantitatively the properties of learning and formally evaluate their impact and importance. The analyses are generally sensible, and parameter recovery analyses help to provide some confidence in the model estimation and comparison.

We thank the reviewer for highlighting the strengths of this work.

Weaknesses:

(1) The approach in the paper overlaps somewhat with various papers, such as Diaconescu et al. (2014) and Schulz et al. (forthcoming), which also consider the Bayesian problem of learning and applying source credibility, in terms of theory and experiment. The authors should discuss how these papers are complementary, to better provide an integrative picture for readers.

Diaconescu, A. O., Mathys, C., Weber, L. A., Daunizeau, J., Kasper, L., Lomakina, E. I., ... & Stephan, K. E. (2014). Inferring the intentions of others by hierarchical Bayesian learning. PLoS computational biology, 10(9), e1003810.

Schulz, L., Schulz, E., Bhui, R., & Dayan, P. Mechanisms of Mistrust: A Bayesian Account of Misinformation Learning. https://doi.org/10.31234/osf.io/8egxh

We thank the reviewers for pointing us to this relevant work. We have updated the introduction, mentioning these precedents in the literature and highlighting our specific contributions:

“To address these questions, we adopt a novel approach within the disinformation literature by exploiting a Reinforcement Learning (RL) experimental framework (36). While RL has guided disinformation research in recent years (37–41), our approach is novel in using one of its most popular tasks: the “bandit task”.”

We also explain in the discussion how these papers relate to the current study:

“Unlike previous studies wherein participants had to infer source credibility from experience (30,37,72), we took an explicit-instruction approach, allowing us to precisely assess source-credibility impact on learning, without confounding it with errors in learning about the sources themselves. More broadly, our work connects with prior research on observational learning, which examined how individuals learn from the actions or advice of social partners (72–75). This body of work has demonstrated that individuals integrate learning from their private experiences with learning based on others’ actions or advice—whether by inferring the value others attribute to different options or by mimicking their behavior (57,76). However, our task differs significantly from traditional observational learning. Firstly, our feedback agents interpret outcomes rather than demonstrating or recommending actions (30,37,72).”

(2) It isn't completely clear what the "cross-fitting" procedure accomplishes. Can this be discussed further?

We thank the reviewer for requesting further clarification on the cross-fitting procedure. Our study utilizes two distinct model families: Bayesian models and CA models. The credit assignment parameters from the CA models can be treated as “data/behavioural features” corresponding to how choice feedback affects choice-propensities. The cross fitting-approach allows us in effect to examine whether these propensity features are predicted from our Bayesian models. To the extent they are not, we can conclude empirical behavior is “biased”.

Thus, in our cross-fitting procedure we compare the CA model parameters extracted from participant data (empirical features) with those that would be expected if our Bayesian agents performed the task. Specifically, we first fit participant behavior with our Bayesian models, then simulate this model using the best-fitted parameters and fit those simulations with our CA models. This generates a set of CA parameters that would be predicted if participants behavior is reduced to a Bayesian account. By comparing these predicted Bayesian CA parameters with the actual CA parameters obtained from human participants, the cross-fitting procedure allows us to quantitatively demonstrate that the observed participant parameters are indeed statistically significant deviations from normative Bayesian processing. This provides a robust validation that the biases we identify are not artifacts of the CA model's structure but true departures from normative learning.

We also note that Reviewer 3 suggested an intuitive way to think about the CA parameters—as analogous to logistic regression coefficients in a “sophisticated regression” of choice on (recencyweighted) choice-feedback. We find this suggestion potentially helpful for readers. Under this interpretation, the purpose of the cross-fitting method can be seen simply as estimating the regression coefficients that would be predicted by our Bayesian agents, and comparing those to the empirical coefficients.

In our manuscript we now explain this issues more clearly by explaining how our model is analogous to a logistic regression:

“The probability to choose a bandit (say A over B) in this family of models is a logistic function of the contrast choice-propensities between these two bandits. One interpretation of this model is as a “sophisticated” logistic regression, where the CA parameters take the role of “regression coefficients” corresponding to the change in log odds of repeating the just-taken action in future trials based on the feedback (+/- CA for positive or negative feedback, respectively; the model also includes gradual perseveration which allows for constant log-odd changes that are not affected by choice feedback) . The forgetting rate captures the extent to which the effect of each trial on future choices diminishes with time. The Q-values are thus exponentially decaying sums of logistic choice propensities based on the types of feedback a bandit received.”

We also explain our cross-fitting procedure in more detail:

“To further characterise deviations between behaviour and our Bayesian learning models, we used a “crossfitting” method. Treating CA parameters as data-features of interest (i.e., feedback dependent changes in choice propensity), our goal was to examine if and how empirical features differ from features extracted from simulations of our Bayesian learning models. Towards that goal, we simulated synthetic data based on Bayesian agents (using participants’ best fitting parameters), but fitted these data using the CA-models, obtaining what we term “Bayesian-CA parameters” (Fig. 2d; Methods). A comparison of these BayesianCA parameters, with empirical-CA parameters obtained by fitting CA models to empirical data, allowed us to uncover patterns consistent with, or deviating from, ideal-Bayesian value-based inference. Under the sophisticated logistic-regression interpretation of the CA-model family the cross-fitting method comprises a comparison between empirical regression coefficients (i.e., empirical CA parameters) and regression coefficient based on simulations of Bayesian models (Bayesian CA parameters).”

(3) The Credibility-CA model seems to fit the same as the free-credibility Bayesian model in the first experiment and barely better in the second experiment. Why not use a more standard model comparison metric like the Bayesian Information Criterion (BIC)? Even if there are advantages to the bootstrap method (which should be described if so), the BIC would help for comparability between papers.

We thank the reviewer for this important comment regarding our model comparison approach. We acknowledge that classical information criteria like AIC and BIC are widely used in RL studies. However, we argue our method for model-comparison is superior.

We conducted a model recovery analysis demonstrating a significant limitation of using AIC or BIC for model-comparison in our data. Both these methods are strongly biased in favor of the Bayesian models. Our PBCM method, on the other hand, is both unbiased and more accurate. We believe this is because “off the shelf” methods like AIC and BIC rely on strong assumptions (such as asymptotic sample size and trial-independence) that are not necessarily met in our tasks (Data is finite; Trials in RL tasks depend on previous trials). PBCM avoids such assumptions to obtain comparison criteria specifically tailored to the structure and size of our empirical data. We have now mentioned this fact in the results section of the main text:

“We considered using AIC and BIC, which apply “off-the shelf” penalties for model-complexity. However, these methods do not adapt to features like finite sample size (relying instead on asymptotic assumption) or temporal dependence (as is common in reinforcement learning experiments). In contrast, the parametric bootstrap cross-fitting method replaces these fixed penalties with empirical, data-driven criteria for modelselection. Indeed, model-recovery simulations confirmed that whereas AIC and BIC were heavily biased in favour of the Bayesian models, the bootstrap method provided excellent model-recovery (See Fig. S20).”

We have also included such model recovery in the SI document:

(4) As suggested in the discussion, the updating based on random feedback could be due to the interleaving of trials. If one is used to learning from the source on most trials, the occasional random trial may be hard to resist updating from. The exact interleaving structure should also be clarified (I assume different sources were shown for each bandit pair). This would also relate to work on RL and working memory: Collins, A. G., & Frank, M. J. (2012). How much of reinforcement learning is working memory, not reinforcement learning? A behavioral, computational, and neurogenetic analysis. European Journal of Neuroscience, 35(7), 10241035.

We thank the reviewer for this point. The specific interleaved structure of the agents is described in the main text:

“Each agent provided feedback for 5 trials for each bandit pair (with the agent order interleaved within the bandit pair).”

As well as in the methods section:

“Feedback agents were randomly interleaved across trials subject to the constraint that each agent appeared on 5-trials for each bandit pair.”

We also thank the reviewer for mentioning the relevant work on working memory. We have now added it to our discussion point:

“In our main study, we show that participants revised their beliefs based on entirely non-credible feedback, whereas an ideal Bayesian strategy dictates such feedback should be ignored. This finding resonates with the “continued-influence effect” whereby misleading information continues to influence an individual's beliefs even after it has been retracted (59,60). One possible explanation is that some participants failed to infer that feedback from the 1-star agent was statistically void of information content, essentially random (e.g., the group-level credibility of this agent was estimated by our free-credibility Bayesian model as higher than 50%). Participants were instructed that this feedback would be “a lie” 50% of the time but were not explicitly told that this meant it was random and should therefore be disregarded. Notably, however, there was no corresponding evidence random feedback affected behaviour in our discovery study. It is possible that an individual’s ability to filter out random information might have been limited due to a high cognitive load induced by our main study task, which required participants to track the values of three bandit pairs and juggle between three interleaved feedback agents (whereas in our discovery study each experimental block featured a single bandit pair). Future studies should explore more systematically how the ability to filter random feedback depends on cognitive load (61).”

(5) Why does the choice-repetition regression include "only trials for which the last same-pair trial featured the 3-star agent and in which the context trial featured a different bandit pair"? This could be stated more plainly.

We thank the reviewer for this question. When we previously submitted our manuscript, we thought that finding enhanced credit-assignment for fully credible feedback following potential disinformation from a different context would constitute a striking demonstration of our “contrast effect”. However, upon reexamining this finding we found out we had a coding error (affecting how trials were filtered). We have now rerun and corrected this analysis. We have assessed the contrast effect for both "same-context" trials (where the contextual trial featured the same bandit pair as the learning trial) and "different-context" trials (where the contextual trial featured a different bandit pair). Our re-analysis reveals a selective significant contrast effect in the samecontext condition, but no significant effect in the different-context condition. We have updated the main text to reflect these corrected findings and provide a clearer explanation of the analysis:

“A comparison of empirical and Bayesian credit-assignment parameters revealed a further deviation from ideal Bayesian learning: participants showed an exaggerated credit-assignment for the 3-star agent compared with Bayesian models [Wilcoxon signed-rank test, instructed-credibility Bayesian model (median difference=0.74, z=11.14); free-credibility Bayesian model (median difference=0.62, z=10.71), all p’s<0.001] (Fig. 3a). One explanation for enhanced learning for the 3-star agents is a contrast effect, whereby credible information looms larger against a backdrop of non-credible information. To test this hypothesis, we examined whether the impact of feedback from the 3-star agent is modulated by the credibility of the agent in the trial immediately preceding it. More specifically, we reasoned that the impact of a 3-star agent would be amplified by a “low credibility context” (i.e., when it is preceded by a low credibility trial). In a binomial mixed effects model, we regressed choice-repetition on feedback valence from the last trial featuring the same bandit pair (i.e., the learning trial) and the feedback agent on the trial immediately preceding that last trial (i.e., the contextual credibility; see Methods for model-specification). This analysis included only learning trials featuring the 3-star agent, and context trials featuring the same bandit pair as the learning trial (Fig. 4a). We found that feedback valence interacted with contextual credibility (F(2,2086)=11.47, p<0.001) such that the feedback-effect (from the 3-star agent) decreased as a function of the preceding context-credibility (3-star context vs. 2-star context: b= -0.29, F(1,2086)=4.06, p=0.044; 2star context vs. 1-star context: b=-0.41, t(2086)=-2.94, p=0.003; and 3-star context vs. 1-star context: b=0.69, t(2086)=-4.74, p<0.001) (Fig. 4b). This contrast effect was not predicted by simulations of our main models of interest (Fig. 4c). No effect was found when focussing on contextual trials featuring a bandit pair different than the one in the learning trial (see SI 3.5). Thus, these results support an interpretation that credible feedback exerts a greater impact on participants’ learning when it follows non-credible feedback, in the same learning context.”

We have modified the discussion accordingly as well:

“A striking finding in our study was that for a fully credible feedback agent, credit assignment was exaggerated (i.e., higher than predicted by our Bayesian models). Furthermore, the effect of fully credible feedback on choice was further boosted when it was preceded by a low-credibility context related to current learning. We interpret this in terms of a “contrast effect”, whereby veridical information looms larger against a backdrop of disinformation (21). One upshot is that exaggerated learning might entail a risk of jumping to premature conclusions based on limited credible evidence (e.g., a strong conclusion that a vaccine is produces significant side-effect risks based on weak credible information, following non-credible information about the same vaccine). An intriguing possibility, that could be tested in future studies, is that participants strategically amplify the extent of learning from credible feedback to dilute the impact of learning from noncredible feedback. For example, a person scrolling through a social media feed, encountering copious amounts of disinformation, might amplify the weight they assign to credible feedback in order to dilute effects of ‘fake news’. Ironically, these results also suggest that public campaigns might be more effective when embedding their messages in low-credibility contexts , which may boost their impact.”

And we have included some additional analyses in the SI document:

“3.5 Contrast effects for contexts featuring a different bandit

Given that we observed a contrast effect when both the learning and the immediately preceding "context trial” involved the same pair of bandits, we next investigated whether this effect persisted when the context trial featured a different bandit pair – a situation where the context would be irrelevant to the current learning. Again, we used in a binomial mixed effects model, regressing choice-repetition on feedback valence in the learning trial and the feedback agent in the context trial. This analysis included only learning trials featuring the 3-star agent, and context trials featuring a different bandit pair than the learning trial (Fig. S22a). We found no significant evidence of an interaction between feedback valence and contextual credibility (F(2,2364)=0.21, p=0.81) (Fig. S22b). This null result was consistent with the range of outcomes predicted by our main computational models (Fig. S22c).

We aimed to formally compare the influence of two types of contextual trials: those featuring the same bandit pair as the learning trial versus those featuring a different pair. To achieve this, we extended our mixedeffects model by incorporating a new predictor variable, "CONTEXT_TYPE" which coded whether the contextual trial involved the same bandit pair (coded as -0.5) or a different bandit pair (+0.5) compared to the learning trial. The Wilkinson notation for this expanded mixed-effects model is:

𝑅𝐸𝑃𝐸𝐴𝑇 ~ 𝐶𝑂𝑁𝑇𝐸𝑋𝑇_𝑇𝑌𝑃𝐸 ∗ 𝐹𝐸𝐸𝐷𝐵𝐴𝐶𝐾 ∗ (𝐶𝑂𝑁𝑇𝐸𝑋𝑇_2-star + 𝐶𝑂𝑁𝑇𝐸𝑋𝑇_3-star) + 𝐵𝐸𝑇𝑇𝐸𝑅 + (1|𝑝𝑎𝑟𝑡𝑖𝑐𝑖𝑝𝑎𝑛𝑡)

This expanded model revealed a significant three-way interaction between feedback valence, contextual credibility, and context type (F(2,4451) = 7.71, p<0.001). Interpreting this interaction, we found a 2-way interaction between context-source and feedback valence when the context was the same (F(2,4451) = 12.03, p<0.001), but not when context was different (F(2,4451) = 0.23, p = 0.79). Further interpreting the double feedback-valence * context-source interaction (for the same context) we obtained the same conclusions as reported in the main text.”

(6) Why apply the "Truth-CA" model and not the Bayesian variant that it was motivated by?

Thanks for this very useful suggestion. We are unsure if we fully understand the question. The Truth-CA model was not motivated by a new Bayesian model. Our Bayesian models were simply used to make the point that participants may partially discriminate between truthful and untruthful feedback (for a given source). This led to the idea that perhaps more credit is assigned for truth (than lie) trials, which is what we found using our Truth-CA model. Note we show that our Bayesian models cannot account for this modulation.

We have now improved our "Truth-CA" model. Previously, our Truth-CA model considered whether feedback on each trial was true or not based on realized latent true outcomes. However, it is possible that the very same feedback would have had an opposite truth-status if the latent true outcome was different (recall true outcomes are stochastic). This injects noise into the trial classification in our previous model. To avoid this, in our new model feedback is modulated by the probability the reported feedback is true (marginalized over stochasticity of true outcome).

We have described this new model in the methods section:

“Additionally, we formulated a “Truth-CA” model, which worked as our Credibility-CA model, but incorporated a free truth-bonus parameter (TB). This parameter modulates the extent of credit assignment for each agent based on the posterior probability of feedback being true (given the credibility of the feedback agent, and the true reward probability of the chosen bandit). The chosen bandit was updated as follows:

𝑄 ← (1 – 𝑓_Q) ∗ 𝑄 + [𝐶𝐴(𝑎𝑔𝑒𝑛𝑡) + 𝑇𝐵 ∗ (𝑃(𝑡𝑟𝑢𝑡ℎ) − 0.5)] ∗ 𝐹

where P(truth) is the posterior probability of the feedback being true in the current trial (for exact calculation of P(truth) see “Methods: Bayesian estimation of posterior belief that feedback is true”).”

All relevant results have been updated accordingly in the main text:

“To formally address whether feedback truthfulness modulates credit assignment, we fitted a new variant of the CA model (the “Truth-CA” model) to the data. This variant works as our Credibility-CA model but incorporated a truth-bonus parameter (TB) which increases the degree of credit assignment for feedback as a function of the experimenter-determined likelihood the feedback is true (which is read from the curves in Fig 6a when x is taken to be the true probability the bandit is rewarding). Specifically, after receiving feedback, the Q-value of the chosen option is updated according to the following rule: 𝑄 ← (1 – 𝑓_Q) ∗ 𝑄 + [𝐶𝐴(𝑎𝑔𝑒𝑛𝑡) + 𝑇𝐵 ∗ (𝑃(𝑡𝑟𝑢𝑡ℎ) − 0.5)] ∗ 𝐹 where 𝑇𝐵 is the free parameter representing the truth bonus, and 𝑃(𝑡𝑟𝑢𝑡ℎ) is the probability the received feedback being true (from the experimenter’s perspective). We acknowledge that this model falls short of providing a mechanistically plausible description of the credit assignment process, because participants have no access to the experimenter’s truthfulness likelihoods (as the true bandit reward probabilities are unknown to them). Nonetheless, we use this ‘oracle model’ as a measurement tool to glean rough estimates for the extent to which credit assignment Is boosted as a function of its truthfulness likelihood. Fitting this Truth-CA model to participants' behaviour revealed a significant positive truth-bonus (mean=0.21, t(203)=3.12, p=0.002), suggesting that participants indeed assign greater weight to feedback that is likely to be true (Fig. 6c; see SI 3.3.1 for detailed ML parameter results). Notably, simulations using our other models (Methods) consistently predicted smaller truth biases (compared to the empirical bias) (Fig. 6d). Moreover, truth bias was still detected even in a more flexible model that allowed for both a positivity bias and truth-bias (see SI 3.7). The upshot is that participants are biased to assign higher credit based on feedback that is more likely to be true in a manner that is inconsistent with out Bayesian models and above and beyond the previously identified positivity biases.“

Finally, the Supplementary Information for the discovery study has also been revised to feature this analysis:

“We next assessed whether participants infer whether the feedback they received on each trial was true or false and adjust their credit assignment based on this inference. We again used the “Truth-CA” model to obtain estimates for the truth bonus (TB), the increase in credit assignment as a function of the posterior probability of feedback being true. As in our main study, the fitted truth bias parameter was significantly positive, indicating that participants assign greater weight to feedback they believe is likely to be true (Fig, S4a; see SI 3.3.1 for detailed ML parameter results). Strikingly, model-simulations (Methods) predicted a lower truth bonus than the one observed in participants (Fig. S4b).”

(7) "Overall, the results from this study support the exact same conclusions (See SI section 1.2) but with one difference. In the discovery study, we found no evidence for learning based on 50%-credibility feedback when examining either the feedback effect on choice repetition or CA in the credibility-CA model (SI 1.2.3)" - this seems like a very salient difference, when the paper reports the feedback effect as a primary finding of interest, though I understand there remains a valence-based difference.

We agree with the reviewer and thank them for this suggestion. We now state explicitly throughout the manuscript that this finding was obtained only in one of our two studies. In the section “Discovery study” of the results we state explicitly this finding was not found in the discovery study:

“However, we found no evidence for learning based on 50%-credibility feedback when examining either the feedback effect on choice repetition or CA in the credibility-CA model (SI 1.2.3).”

We also note that related to another concern from R3 (that perseveration may masquerade as positivity bias) we conducted additional analyses (detailed in SI 3.6.2). These analyses revealed that the observed positivity bias for the 1-star agent in the discovery study falls within the range predicted by simple choice-perseveration. Consequently, we have removed the suggestion that participants still learn from the random agent in the discovery study. Furthermore, we have modified the discussion section to include a possible explanation for this discrepancy between the two studies:

“Notably, however, there was no corresponding evidence random feedback affected behaviour in our discovery study. It is possible that an individual’s ability to filter out random information might have been limited due to a high cognitive load induced by our main study task, which required participants to track the values of three bandit pairs and juggle between three interleaved feedback agents (whereas in our discovery study each experimental block featured a single bandit pair). Future studies should explore more systematically how the ability to filter random feedback depends on cognitive load (61).”

(8) "Participants were instructed that this feedback would be "a lie 50% of the time but were not explicitly told that this meant it was random and should therefore be disregarded." - I agree that this is a possible explanation for updating from the random source. It is a meaningful caveat.

Thank you for this thought. While this can be seen as a caveat—since we don’t know what would have happened with explicit instructions—we also believe it is interesting from another perspective. In many real-life situations, individuals may have all the necessary information to infer that the feedback they receive is uninformative, yet still fail to do so, especially when they are not explicitly told to ignore it.

In future work, we plan to examine how behaviour changes when participants are given more explicit instructions—for example, that the 50%-credibility agent provides purely random feedback.

(9) "Future studies should investigate conditions that enhance an ability to discard disinformation, such as providing explicit instructions to ignore misleading feedback, manipulations that increase the time available for evaluating information, or interventions that strengthen source memory." - there is work on some of this in the misinformation literature that should be cited, such as the "continued influence effect". For example: Johnson, H. M., & Seifert, C. M. (1994). Sources of the continued influence effect: When misinformation in memory affects later inferences. Journal of experimental psychology: Learning, memory, and cognition, 20(6), 1420.

We thank the reviewer for pointing us towards the relevant literature. We have now included citations about the “continued influence effect” of misinformation in the discussion:

“In our main study, we show that participants revised their beliefs based on entirely non-credible feedback, whereas an ideal Bayesian strategy dictates such feedback should be ignored. This finding resonates with the “continued-influence effect” whereby misleading information continues to influence an individual's beliefs even after it has been retracted (59,60).”

(10) Are the authors arguing that choice-confirmation bias may be at play? Work on choice-confirmation bias generally includes counterfactual feedback, which is not present here.

We agree with the reviewer that a definitive test for choice-confirmation bias typically requires counterfactual feedback, which is not present in our current task. In our discussion, we indeed suggest that the positivity bias we observe may stem from a form of choice-confirmation, drawing on the extensive literature on this bias in reinforcement learning (Lefebvre et al., 2017; Palminteri et al., 2017; Palminteri & Lebreton, 2022). However, we fully acknowledge that this link is a hypothesis and that explicitly testing for choice-confirmation bias would necessitate a future study specifically incorporating counterfactual feedback. We have included a clarification of this point in the discussion:

“Previous reinforcement learning studies, report greater credit-assignment based on positive compared to negative feedback, albeit only in the context of veridical feedback (43,44,62). Here, supporting our a-priori hypothesis we show that this positivity bias is amplified for information of low and intermediate credibility (in absolute terms in the discovery study, and relative to the overall extent of CA in both studies) . Of note, previous literature has interpreted enhanced learning for positive outcomes in reinforcement learning as indicative of a confirmation bias (42,44). For example, positive feedback may confirm, to a greater extent than negative feedback one’s choice as superior (e.g., “I chose the better of the two options”). Leveraging the framework of motivated cognition (35), we posited that feedback of uncertain veracity (e.g., low credibility) amplifies this bias by incentivising individuals to self-servingly accept positive feedback as true (because it confers positive, desirable outcomes), and explain away undesirable, choice-disconfirming, negative feedback as false. This could imply an amplified confirmation bias on social media, where content from sources of uncertain credibility, such as unknown or unverified users, is more easily interpreted in a self-serving manner, disproportionately reinforcing existing beliefs (63). In turn, this could contribute to an exacerbation of the negative social outcomes previously linked to confirmation bias such as polarization (64,65), the formation of ‘echo chambers’ (19), and the persistence of misbelief regarding contemporary issues of importance such as vaccination (66,67) and climate change (68–71). We note however, that further studies are required to determine whether positivity bias in our task is indeed a form of confirmation bias.”

Reviewer #3 (Public review):

Summary

This paper investigates how disinformation affects reward learning processes in the context of a two-armed bandit task, where feedback is provided by agents with varying reliability (with lying probability explicitly instructed). They find that people learn more from credible sources, but also deviate systematically from optimal Bayesian learning: They learned from uninformative random feedback, learned more from positive feedback, and updated too quickly from fully credible feedback (especially following low-credibility feedback). Overall, this study highlights how misinformation could distort basic reward learning processes, without appeal to higher-order social constructs like identity.

Strengths

(1) The experimental design is simple and well-controlled; in particular, it isolates basic learning processes by abstracting away from social context.

(2) Modeling and statistics meet or exceed the standards of rigor.

(3) Limitations are acknowledged where appropriate, especially those regarding external validity.

(4) The comparison model, Bayes with biased credibility estimates, is strong; deviations are much more compelling than e.g., a purely optimal model.

(5) The conclusions are interesting, in particular the finding that positivity bias is stronger when learning from less reliable feedback (although I am somewhat uncertain about the validity of this conclusion)

We deeply thank the reviewer for highlighting the strengths of this work.

Weaknesses

(1) Absolute or relative positivity bias?

In my view, the biggest weakness in the paper is that the conclusion of greater positivity bias for lower credible feedback (Figure 5) hinges on the specific way in which positivity bias is defined. Specifically, we only see the effect when normalizing the difference in sensitivity to positive vs. negative feedback by the sum. I appreciate that the authors present both and add the caveat whenever they mention the conclusion (with the crucial exception of the abstract). However, what we really need here is an argument that the relative definition is the right way to define asymmetry....

Unfortunately, my intuition is that the absolute difference is a better measure. I understand that the relative version is common in the RL literature; however previous studies have used standard TD models, whereas the current model updates based on the raw reward. The role of the CA parameter is thus importantly different from a traditional learning rate - in particular, it's more like a logistic regression coefficient (as described below) because it scales the feedback but not the decay. Under this interpretation, a difference in positivity bias across credibility conditions corresponds to a three-way interaction between the exponentially weighted sum of previous feedback of a given type (e.g., positive from the 75% credible agent), feedback positivity, and condition (dummy coded). This interaction corresponds to the nonnormalized, absolute difference.

Importantly, I'm not terribly confident in this argument, but it does suggest that we need a compelling argument for the relative definition.

We thank the reviewer for raising this important point about the definition of positivity bias, and for their thoughtful discussion on the absolute versus relative measures. We believe that the relative valence bias offers a distinct and valuable perspective on positivity bias. Conceptually, this measure describes positivity bias in a manner akin to a “percentage difference” relative to the overall level of learning which allows us to control for the overall decreases in the overall amount of credit assignment as feedback becomes less credible. We are unsure if one measure is better or more correct than the other and we believe that reporting both measures enriches the understanding of positivity bias and allows for a more comprehensive characterization of this phenomenon (as long as these measures are interpreted carefully). We have stated the significance of the relative measure in the results section:

“Following previous research, we quantified positivity bias in 2 ways: 1) as the absolute difference between credit-assignment based on positive or negative feedback, and 2) as the same difference but relative to the overall extent of learning. We note that the second, relative, definition, is more akin to “percentage change” measurements providing a control for the overall lower levels of credit-assignment for less credible agent.”

We also wish to point out that in our discovery study we had some evidence for amplification of positivity bias in absolute sense.

(2) Positivity bias or perseveration?

A key challenge in interpreting many of the results is dissociating perseveration from other learning biases. In particular, a positivity bias (Figure 5) and perseveration will both predict a stronger correlation between positive feedback and future choice. Crucially, the authors do include a perseveration term, so one would hope that perseveration effects have been controlled for and that the CA parameters reflect true positivity biases. However, with finite data, we cannot be sure that the variance will be correctly allocated to each parameter (c.f. collinearity in regressions). The fact that CA- is fit to be negative for many participants (a pattern shown more strongly in the discovery study) is suggestive that this might be happening. A priori, the idea that you would ever increase your value estimate after negative feedback is highly implausible, which suggests that the parameter might be capturing variance besides that it is intended to capture.

The best way to resolve this uncertainty would involve running a new study in which feedback was sometimes provided in the absence of a choice - this would isolate positivity bias. Short of that, perhaps one could fit a version of the Bayesian model that also includes perseveration. If the authors can show that this model cannot capture the pattern in Figure 5, that would be fairly convincing.

We thank the reviewer for this very insightful and crucial point regarding the potential confound between positivity bias and perseveration. We entirely agree that distinguishing these effects can be challenging. To rigorously address this concern and ascertain that our observed positivity bias, particularly its inflation for low-credibility feedback, is not merely an artifact of perseveration, we conducted additional analyses as suggested.

First, following the reviewer’s suggestion we simulated our Bayesian models, including a perseveration term, for both our main and discovery studies. Crucially, none of these simulations predicted the specific pattern of inflated positivity bias for low-credibility feedback that we identified in participants.

Additionally, taking a “devil’s advocate” approach, we tested whether our credibility-CA model (which includes perseveration but not a feedback valence bias) can predict our positivity bias findings. Thus, we simulated 100 datasets using our Credibility-CA model (based on empirical best-fitting parameters). We then fitted each of these simulated datasets using our CredibilityValence CA model. By examining the distribution of results across these synthetic datasets fits and comparing them to the actual results from participants, we found that while perseveration could indeed lead (as the reviewer suspected) to an artifactual positivity bias, it could not predict the magnitude of the observed inflation of positivity bias for low-credibility feedback (whether measured in absolute or relative terms).

Based on these comprehensive analyses, we are confident that our main results concerning the modulation of a valence bias as a function of source-credibility cannot be accounted by simple choice-perseveration. We have briefly explained these analyses in the main results section:

“Previous research has suggested that positivity bias may spuriously arise from pure choice-perseveration (i.e., a tendency to repeat previous choices regardless of outcome) (49,50). While our models included a perseveration-component, this control may not be preferent. Therefore, in additional control analyses, we generated synthetic datasets using models including choice-perseveration but devoid of feedback-valence bias, and fitted them with our credibility-valence model (see SI 3.6.1). These analyses confirmed that perseveration can masquerade as an apparent positivity bias. Critically, however, these analyses also confirmed that perseveration cannot account for our main finding of increased positivity bias, relative to the overall extent of CA, for low-credibility feedback.”

Additionally, we have added a detailed description of these additional analyses and their findings to the Supplementary Information document:

“3.6 Positivity bias results cannot be explained by a pure perseveration

3.6.1 Main study

Previous research has suggested it may be challenging to dissociate between a feedback-valence positivity bias and perseveration (i.e., a tendency to repeat previous choices regardless of outcome). While our Credit Assignment (CA) models already include a perseveration mechanism to account for this, this control may not be perfect. We thus conducted several tests to examine if our positivity-bias related results could be accounted for by perseveration.

First we examined whether our Bayesian-models, augmented by a perseveration mechanism (as in our CA model) can generate predictions similar to our empirical results. We repeated our cross-fitting procedure to these extended Bayesian models. To briefly recap, this involved fitting participant behavior with them, generating synthetic datasets based on the resulting maximum likelihood (ML) parameters, and then fitting these simulated datasets with our Credibility-Valence CA model (which is designed to detect positivity bias). This test revealed that adding perseveration to our Bayesian models did not predict a positivity bias in learning. In absolute terms there was a small negativity bias (instructed-credibility Bayesian: b=−0.19, F(1,1218)=17.78, p<0.001, Fig. S23a-b; free-credibility Bayesian: b=−0.17, F(1,1218)=13.74, p<0.001, Fig. S23d-e). In relative terms we detected no valence related bias (instructed-credibility Bayesian: b=−0.034, F(1,609)=0.45, p=0.50, Fig. S22c; free-credibility Bayesian: b=−0.04, F(1,609)=0.51, p=0.47, Fig. S23f). More critically, these simulations also did not predict a change in the level of positivity bias as a function of feedback credibility, neither at an absolute level (instructed-credibility Bayesian: F(2,1218)=0.024, p=0.98, Fig. S23b; free-credibility Bayesian: F(2,1218)=0.008, p=0.99, Fig. S23e), nor at a relative level (instructedcredibility Bayesian: F(2,609)=1.57, p=0.21, Fig. S23c; free-credibility Bayesian: F(2,609)=0.13, p=0.88, Fig. S23f). The upshot is that our positivity-bias findings cannot be accounted for by our Bayesian models even when these are augmented with perseveration.

However, it is still possible that empirical CA parameters from our credibility-valence model (reported in main text Fig. 5) were distorted, absorbing variance from a perseveration. To address this, we took a “devil's advocate” approach testing the assumption that CA parameters are not truly affected by feedback valance and that there is only perseveration in our data. Towards that goal, we simulated data using our CredibilityCA model (which includes perseveration but does not contain a valence bias in its learning mechanism) and then fitted these synthetic datasets using our Credibility-Valence CA model to see if the observed positivity bias could be explained by perseveration alone. Specifically, we generated 101 “group-level” synthetic datasets (each including one simulation for each participant, based on their empirical ML parameters), and fitted each dataset with our Credibility-Valence CA model. We then analysed the resulting ML parameters in each dataset using the same mixed-effects models as described in the main text, examining the distribution of effects of interest across these simulated datasets. Comparing these simulation results to the data from participants revealed a nuanced picture. While the positivity bias observed in participants is within the range predicted by a pure perseveration account when measured in absolute terms (Fig. S24a), it is much higher than predicted by pure perseveration when measured relative to the overall level of learning (Fig. S24c). More importantly, the inflation in positivity bias for lower credibility feedback is substantially higher in participants than what would be predicted by a pure perseveration account, a finding that holds true for both absolute (Fig. S24b) and relative (Fig. S24d) measures.”

“3.6.2 Discovery study

We then replicated these analyses in our discovery study to confirm our findings. We again checked whether extended versions of the Bayesian models (including perseveration) predicted the positivity bias results observed. Our cross-fitting procedure showed that the instructed-credibility Bayesian model with perseveration did predict a positivity bias for all credibility levels in this discovery study, both when measured in absolute terms [50% credibility (b=1.74,t(824)=6.15), 70% credibility (b=2.00,F(1,824)=49.98), 85% credibility (b=1.81,F(1,824)=40.78), 100% credibility (b=2.42,F(1,824)=72.50), all p's<0.001], and in relative terms [50% credibility (b=0.25,t(412)=3.44), 70% credibility (b=0.31,F(1,412)=17.72), 85% credibility (b=0.34,F(1,412)=21.06), 100% credibility (b=0.42,F(1,412)=31.24), all p's<0.001]. However, importantly, these simulations did not predict a change in the level of positivity bias as a function of feedback credibility, neither at an absolute level (F(3,412)=1.43,p=0.24), nor at a relative level (F(3,412)=2.06,p=0.13) (Fig. S25a-c). In contrast, simulations of the free-credibility Bayesian model (with perseveration) predicted a slight negativity bias when measured in absolute terms (b=−0.35,F(1,824)=5.14,p=0.024), and no valence bias when measured relative to the overall degree of learning (b=0.05,F(1,412)=0.55,p=0.46). Crucially, this model also did not predict a change in the level of positivity bias as a function of feedback credibility, neither at an absolute level (F(3,824)=0.27,p=0.77), nor at a relative level (F(3,412)=0.76,p=0.47) (Fig. S25d-f).

As in our main study, we next assessed whether our Credibility-CA model (which includes perseveration but no valence bias) predicted the positivity bias results observed in participants in the discovery study. This analysis revealed that the average positivity bias in participants is higher than predicted by a pure perseveration account, both when measured in absolute terms (Fig. S26a) and in relative terms (Fig. S26c). Specifically, only the aVBI for the 70% credibility agent was above what a perseveration account would predict, while the rVBI for all agents except the completely credible one exceeded that threshold. Furthermore, the inflation in positivity bias for lower credibility feedback (compared to the 100% credibility agent) is significantly higher in participants than would be predicted by a pure perseveration account, in both absolute (Fig. S26b) and relative (Fig. S26d) terms.

Together, these results show that the general positivity bias observed in participants could be predicted by an instructed-credibility Bayesian model with perseveration, or by a CA model with perseveration. Moreover, we find that these two models can predict a positivity bias for the 50% credibility agent, raising a concern that our positivity bias findings for this source may be an artefact of not-fully controlled for perseveration. However, the credibility modulation of this positivity bias, where the bias is amplified for lower credibility feedback, is consistently not predicted by perseveration alone, regardless of whether perseveration is incorporated into a Bayesian or a CA model. This finding suggests that participants are genuinely modulating their learning based on feedback credibility, and that this modulation is not merely an artifact of choice perseveration.”

(3) Veracity detection or positivity bias?

The "True feedback elicits greater learning" effect (Figure 6) may be simply a re-description of the positivity bias shown in Figure 5. This figure shows that people have higher CA for trials where the feedback was in fact accurate. But assuming that people tend to choose more rewarding options, true-feedback cases will tend to also be positive-feedback cases. Accordingly, a positivity bias would yield this effect, even if people are not at all sensitive to trial-level feedback veracity. Of course, the reverse logic also applies, such that the "positivity bias" could actually reflect discounting of feedback that is less likely to be true. This idea has been proposed before as an explanation for confirmation bias (see Pilgrim et al, 2024 https://doi.org/10.1016/j.cognition.2023.105693and much previous work cited therein). The authors should discuss the ambiguity between the "positivity bias" and "true feedback" effects within the context of this literature....

Before addressing these excellent comments, we first note that we have now improved our "TruthCA" model. Previously, our Truth-CA model considered whether feedback on each trial was true or not based on realized latent true outcomes. However, it is possible that the very same feedback would have had an opposite truth-status if the latent true outcome was different (recall true outcomes are stochastic). This injects noise into the trial classification in our former model. To avoid this, in our new model feedback is modulated by the probability the reported feedback is true (marginalized over stochasticity of true outcome). Please note in our responses below that we conducted extensive analysis to confirm that positivity bias doesn’t in fact predict the truthbias we detect using our truth biased model

We have described this new model in the methods section:

“Additionally, we formulated a “Truth-CA” model, which worked as our Credibility-CA model, but incorporated a free truth-bonus parameter (TB). This parameter modulates the extent of credit assignment for each agent based on the posterior probability of feedback being true (given the credibility of the feedback agent, and the true reward probability of the chosen bandit). The chosen bandit was updated as follows:

𝑄 ← (1 – 𝑓_Q) ∗ 𝑄 + [𝐶𝐴(𝑎𝑔𝑒𝑛𝑡) + 𝑇𝐵 ∗ (𝑃(𝑡𝑟𝑢𝑡ℎ) − 0.5)] ∗ 𝐹

where P(truth) is the posterior probability of the feedback being true in the current trial (for exact calculation of P(truth) see “Methods: Bayesian estimation of posterior belief that feedback is true”).”

All relevant results have been updated accordingly in the main text:

To formally address whether feedback truthfulness modulates credit assignment, we fitted a new variant of the CA model (the “Truth-CA” model) to the data. This variant works as our Credibility-CA model, but incorporated a truth-bonus parameter (TB) which increases the degree of credit assignment for feedback as a function of the experimenter-determined likelihood the feedback is true (which is read from the curves in Fig 6a when x is taken to be the true probability the bandit is rewarding). Specifically, after receiving feedback, the Q-value of the chosen option is updated according to the following rule:

𝑄 ← (1 – 𝑓_Q) ∗ 𝑄 + [𝐶𝐴(𝑎𝑔𝑒𝑛𝑡) + 𝑇𝐵 ∗ (𝑃(𝑡𝑟𝑢𝑡ℎ) − 0.5)] ∗ 𝐹

where 𝑇𝐵 is the free parameter representing the truth bonus, and 𝑃(𝑡𝑟𝑢𝑡ℎ) is the probability the received feedback being true (from the experimenter’s perspective). We acknowledge that this model falls short of providing a mechanistically plausible description of the credit assignment process, because participants have no access to the experimenter’s truthfulness likelihoods (as the true bandit reward probabilities are unknown to them). Nonetheless, we use this ‘oracle model’ as a measurement tool to glean rough estimates for the extent to which credit assignment Is boosted as a function of its truthfulness likelihood.

Fitting this Truth-CA model to participants' behaviour revealed a significant positive truth-bonus (mean=0.21, t(203)=3.12, p=0.002), suggesting that participants indeed assign greater weight to feedback that is likely to be true (Fig. 6c; see SI 3.3.1 for detailed ML parameter results). Notably, simulations using our other models (Methods) consistently predicted smaller truth biases (compared to the empirical bias) (Fig. 6d). Moreover, truth bias was still detected even in a more flexible model that allowed for both a positivity bias and truth-bias (see SI 3.7). The upshot is that participants are biased to assign higher credit based on feedback that is more likely to be true in a manner that is inconsistent with out Bayesian models and above and beyond the previously identified positivity biases.”

Finally, the Supplementary Information for the discovery study has also been revised to feature this analysis:

“We next assessed whether participants infer whether the feedback they received on each trial was true or false and adjust their credit assignment based on this inference. We again used the “Truth-CA” model to obtain estimates for the truth bonus (TB), the increase in credit assignment as a function of the posterior probability of feedback being true. As in our main study, the fitted truth bias parameter was significantly positive, indicating that participants assign greater weight to feedback they believe is likely to be true (Fig, S4a; see SI 3.3.1 for detailed ML parameter results). Strikingly, model-simulations (Methods) predicted a lower truth bonus than the one observed in participants (Fig. S4b).”

Additionally, we thank the reviewer for pointing us to the relevant work by Pilgrim et al. (2024). We agree that the relationship between "true feedback" and "positivity bias" effects is nuanced, and their potential overlap warrants careful consideration. Note our analyses suggest that this is not solely the case. Firstly, simulations of our Credibility-Valence CA model predict only a small "truth bonus" effect, which is notably smaller than what we observed in participants. Secondly, we formulated an extension of our "Truth-CA" model that includes a valence bias in credit assignment. If our truth bonus results were merely an artifact of positivity bias, this extended model should absorb that variance, producing a null truth bonus parameter. However, fitting this model to participant data still revealed a significant positive truth bonus, which again exceeds the range predicted by simulations of our Credibility CA model:

“3.7 Truth inference is still detected when controlling for valence bias

Given that participants frequently select bandits that are, on average, mostly rewarding, it is reasonable to assume that positive feedback is more likely to be objectively true than negative feedback. This raises a question if the "truth inference" effect we observed in participants might simply be an alternative description of a positivity bias in learning. To directly test this idea, we extended our Truth-CA model to explicitly account for a valence bias in credit assignment. This extended model features separate CA parameters for positive and negative feedback for each agent. When we fitted this new model to participant behavior, it still revealed a significant truth bonus in both the main study (Wilkoxon’s signrank test: median = 0.09, z(202)=2.12, p=0.034; Fig. S27a) and the discovery study (median = 3.52, z(102)=7.86, p<0.001; Fig. S27c). Moreover, in the main study, this truth bonus remained significantly higher than what was predicted by all the alternative models, with the exception of the instructed-credibility bayesian model (Fig. S27b). In the discovery study, the truth bonus was significantly higher than what was predicted by all the alternative models (Fig. S27d).”

Together, these findings suggest that our truth inference results are not simply a re-description of a positivity bias.

Conversely, we acknowledge the reviewer's point that our positivity bias results could potentially stem from a more general truth inference mechanism. We believe that this possibility should be addressed in a future study where participants rate their belief that received feedback is true (rather than a lie).We have extended our discussion to clarify this possibility and to include the suggested citation:

“Our findings show that individuals increase their credit assignment for feedback in proportion to the perceived probability that the feedback is true, even after controlling for source credibility and feedback valence. Strikingly, this learning bias was not predicted by any of our Bayesian or credit-assignment (CA) models. Notably, our evidence for this bias is based on a “oracle model” that incorporates the probability of feedback truthfulness from the experimenter's perspective, rather than the participant’s. This raises an important open question: how do individuals form beliefs about feedback truthfulness, and how do these beliefs influence credit assignment? Future research should address this by eliciting trial-by-trial beliefs about feedback truthfulness. Doing so would also allow for testing the intriguing possibility that an exaggerated positivity bias for non-credible sources reflects, to some extent, a truth-based discounting of negative feedback—i.e., participants may judge such feedback as less likely to be true. However, it is important to note that the positivity bias observed for fully credible sources (here and in other literature) cannot be attributed to a truth bias—unless participants were, against instructions, distrustful of that source.”

The authors get close to this in the discussion, but they characterize their results as differing from the predictions of rational models, the opposite of my intuition. They write:

“Alternative "informational" (motivation-independent) accounts of positivity and confirmation bias predict a contrasting trend (i.e., reduced bias in low- and medium credibility conditions) because in these contexts it is more ambiguous whether feedback confirms one's choice or outcome expectations, as compared to a full-credibility condition.”

I don't follow the reasoning here at all. It seems to me that the possibility for bias will increase with ambiguity (or perhaps will be maximal at intermediate levels). In the extreme case, when feedback is fully reliable, it is impossible to rationally discount it (illustrated in Figure 6A). The authors should clarify their argument or revise their conclusion here.

We apologize for the lack of clarity in our previous explanation. We removed the sentence you cited (it was intended to make a different point which we now consider non-essential). Our current narration is consistent with the point you are making.

(4) Disinformation or less information?

Zooming out, from a computational/functional perspective, the reliability of feedback is very similar to reward stochasticity (the difference is that reward stochasticity decreases the importance/value of learning in addition to its difficulty). I imagine that many of the effects reported here would be reproduced in that setting. To my surprise, I couldn't quickly find a study asking that precise question, but if the authors know of such work, it would be very useful to draw comparisons. To put a finer point on it, this study does not isolate which (if any) of these effects are specific to disinformation, rather than simply less information. I don't think the authors need to rigorously address this in the current study, but it would be a helpful discussion point.

We thank the reviewer for highlighting the parallel (and difference) between feedback reliability and reward stochasticity. However, we have not found any comparable results in the literature. We also note that our discussion includes a paragraph addressing the locus of our effects making the point that more studies are necessary to determine whether our findings are due to disinformation per se or sources being less informative. While this paragraph was included in the previous version it led us to infer our Discussion was too long and we therefore shortened it considerably:

“An important question arises as to the psychological locus of the biases we uncovered. Because we were interested in how individuals process disinformation—deliberately false or misleading information intended to deceive or manipulate—we framed the feedback agents in our study as deceptive, who would occasionally “lie” about the true choice outcome. However, statistically (though not necessarily psychologically), these agents are equivalent to agents who mix truth-telling with random “guessing” or “noise” where inaccuracies may arise from factors such as occasionally lacking access to true outcomes, simple laziness, or mistakes, rather than an intent to deceive. This raises the question of whether the biases we observed are driven by the perception of potential disinformation as deceitful per se or simply as deviating from the truth. Future studies could address this question by directly comparing learning from statistically equivalent sources framed as either lying or noisy. Unlike previous studies wherein participants had to infer source credibility from experience (30,37,72), we took an explicit-instruction approach, allowing us to precisely assess source-credibility impact on learning, without confounding it with errors in learning about the sources themselves. More broadly, our work connects with prior research on observational learning, which examined how individuals learn from the actions or advice of social partners (72–75). This body of work has demonstrated that individuals integrate learning from their private experiences with learning based on others’ actions or advice—whether by inferring the value others attribute to different options or by mimicking their behavior (57,76). However, our task differs significantly from traditional observational learning. Firstly, our feedback agents interpret outcomes rather than demonstrating or recommending actions (30,37,72). Secondly, participants in our study lack private experiences unmediated by feedback sources. Finally, unlike most observational learning paradigms, we systematically address scenarios with deliberately misleading social partners. Future studies could bridge this by incorporating deceptive social partners into observational learning, offering a chance to develop unified models of how individuals integrate social information when credibility is paramount for decision-making.”

(5) Over-reliance on analyzing model parameters

Most of the results rely on interpreting model parameters, specifically, the "credit assignment" (CA) parameter. Exacerbating this, many key conclusions rest on a comparison of the CA parameters fit to human data vs. those fit to simulations from a Bayesian model. I've never seen anything like this, and the authors don't justify or even motivate this analysis choice. As a general rule, analyses of model parameters are less convincing than behavioral results because they inevitably depend on arbitrary modeling assumptions that cannot be fully supported. I imagine that most or even all of the results presented here would have behavioral analogues. The paper would benefit greatly from the inclusion of such results. It would also be helpful to provide a description of the model in the main text that makes it very clear what exactly the CA parameter is capturing (see next point).

We thank the reviewer for this important suggestion which we address together with the following point.

(6) RL or regression?

I was initially very confused by the "RL" model because it doesn't update based on the TD error. Consequently, the "Q values" can go beyond the range of possible reward (SI Figure 5). These values are therefore not Q values, which are defined as expectations of future reward ("action values"). Instead, they reflect choice propensities, which are sometimes notated $h$ in the RL literature. This misuse of notation is unfortunately quite common in psychology, so I won't ask the authors to change the variable. However, they should clarify when introducing the model that the Q values are not action values in the technical sense. If there is precedent for this update rule, it should be cited.

Although the change is subtle, it suggests a very different interpretation of the model.

Specifically, I think the "RL model" is better understood as a sophisticated logistic regression, rather than a model of value learning. Ignoring the decay term, the CA term is simply the change in log odds of repeating the just-taken action in future trials (the change is negated for negative feedback). The PERS term is the same, but ignoring feedback. The decay captures that the effect of each trial on future choices diminishes with time. Importantly, however, we can re-parameterize the model such that the choice at each trial is a logistic regression where the independent variables are an exponentially decaying sum of feedback of each type (e.g., positive-cred50, positive-cred75, ... negative-cred100). The CA parameters are simply coefficients in this logistic regression.

Critically, this is not meant to "deflate" the model. Instead, it clarifies that the CA parameter is actually not such an assumption-laden model estimate. It is really quite similar to a regression coefficient, something that is usually considered "model agnostic". It also recasts the non-standard "cross-fitting" approach as a very standard comparison of regression coefficients for model simulations vs. human data. Finally, using different CA parameters for true vs false feedback is no longer a strange and implausible model assumption; it's just another (perfectly valid) regression. This may be a personal thing, but after adopting this view, I found all the results much easier to understand.

We thank the reviewer for their insightful and illuminating comments, particularly concerning the interpretation of our model parameters and the nature of our Credit assignment model. We believe your interpretation of the model is accurate and we now narrate it to readers in the hope that our modelling will become clearer and more intuitively. We also present to readers how these recasts our “cross-fitting” approach in the way you suggested (we return to this point below).

Broadly, while we agree that modelling results depend on underlying assumptions, we believe that “model-agnostic” approaches also have important limitations—especially in reinforcement learning (RL), where choices are shaped by histories of past events, which such approaches often fail to fully account for. As students of RL, we are frequently struck by how careful modelling demonstrates that seemingly meaningful “model-agnostic” patterns can emerge as artefacts of unaccounted-for variables. We also note that the term “model-agnostic” is difficult to define—after all, even regression models rely on assumptions, and some computational models make richer or more transparent assumptions than others. Ideally, we aim to support our findings using converging methods wherever possible.

We want to clarify that many of our reported findings indeed stem from straightforward behavioral analyses (e.g., simple regressions of choice-repetition), which do not rely on complex modeling assumptions. The two key results that primarily depend on the analysis of model parameters are our findings related to positivity bias and truth inference.

Regarding the positivity bias, identifying truly model-agnostic behavioral signatures, distinct from effects like choice-perseveration, has historically been a significant challenge in the literature. Classical research on this bias rests on the interpretation of model parameters (Lefebvre et al., 2017; Palminteri et al., 2017), or at least on the use of models to assess what an “unbiased learner” baseline should look like (Palminteri & Lebreton, 2022). Some researchers have suggested possible regressions incorporating history effects to detect positivity bias from choicerepetition behavior, but these regressions (as our model) rely on subtle assumptions about forgetting and history effects (Toyama et al., 2019). Specifically, in our case, this issue is also demonstrated by analysis we conducted related to the previous point the reviewer made (about perseveration masquerading as positivity bias). We believe that dissociating clearly positivity bias from perseveration is an important challenge for the field going forward.

For our truth inference results, obtaining purely behavioral signatures is similarly challenging due to the intricate interdependencies (the reviewer has identified in previous points) between agent credibility, feedback valence, feedback truthfulness, and choice accuracy within our task design.

Finally, we agree with the reviewer that regression coefficients are often interpreted as a “modelagnostic” pattern. From this perspective even our findings regarding positivity and truth bias are not a case of over-reliance on complex model assumptions but are rather a way to expose deviations between empirical “sophisticated” regression coefficients and coefficients predicted from Bayesian models.

We have now described the main learning rule of our model in the main text to ensure that the meaning of the CA parameters is clearer for readers:

“Next, we formulated a family of non-Bayesian computational RL models. Importantly, these models can flexibly express non-Bayesian learning patterns and, as we show in following sections, can serve to identify learning biases deviating from an idealized Bayesian strategy. Here, an assumption is that during feedback, the choice propensity for the chosen bandit (which here is represented by a point estimate, “Q value“, rather than a distribution) either increases or decreases (for positive or negative feedback, respectively) according to a magnitude quantified by the free “Credit-Assignment (CA)” model parameters (47):

𝑄(𝑐ℎ𝑜𝑠𝑒𝑛) ← (1 – 𝑓_Q) ∗ 𝑄(𝑐ℎ𝑜𝑠𝑒𝑛) + 𝐶𝐴(𝑎𝑔𝑒𝑛𝑡, 𝑣𝑎𝑙𝑒𝑛𝑐𝑒) ∗ 𝐹

where F is the feedback received from the agents (coded as 1 for reward feedback and -1 for non-reward feedback), while fQ (∈[0,1]) is the free parameter representing the forgetting rate of the Q-value (Fig. 2a, bottom panel; Fig. S5b; Methods). The probability to choose a bandit (say A over B) in this family of models is a logistic function of the contrast choice-propensities between these two bandits. One interpretation of this model is as a “sophisticated” logistic regression, where the CA parameters take the role of “regression coefficients” corresponding to the change in log odds of repeating the just-taken action in future trials based on the feedback (+/- CA for positive or negative feedback, respectively; the model also includes gradual perseveration which allows for constant log-odd changes that are not affected by choice feedback; see “Methods: RL models”) . The forgetting rate captures the extent to which the effect of each trial on future choices diminishes with time. The Q-values are thus exponentially decaying sums of logistic choice propensities based on the types of feedback a bandit received.”

We also explain the implications of this perspective for our cross-fitting procedure:

“To further characterise deviations between behaviour and our Bayesian learning models, we used a “crossfitting” method. Treating CA parameters as data-features of interest (i.e., feedback dependent changes in choice propensity), our goal was to examine if and how empirical features differ from features extracted from simulations of our Bayesian learning models. Towards that goal, we simulated synthetic data based on Bayesian agents (using participants’ best fitting parameters), but fitted these data using the CA-models, obtaining what we term “Bayesian-CA parameters” (Fig. 2d; Methods). A comparison of these BayesianCA parameters, with empirical-CA parameters obtained by fitting CA models to empirical data, allowed us to uncover patterns consistent with, or deviating from, ideal-Bayesian value-based inference. Under the sophisticated logistic-regression interpretation of the CA-model family the cross-fitting method comprises a comparison between empirical regression coefficients (i.e., empirical CA parameters) and regression coefficient based on simulations of Bayesian models (Bayesian CA parameters). Using this approach, we found that both the instructed-credibility and free-credibility Bayesian models predicted increased BayesianCA parameters as a function of agent credibility (Fig. 3c; see SI 3.1.1.2 Tables S8 and S9). However, an in-depth comparison between Bayesian and empirical CA parameters revealed discrepancies from ideal Bayesian learning, which we describe in the following sections.”

Recommendations for the authors:

Reviewer #3 (Recommendations for the authors):

(1) Keep terms consistent, e.g., follow-up vs. main; hallmark vs. traditional.

We have now changed the text to keep terms consistent.

(2) CA model is like a learning rate; but it's based on the raw reward, not the TD error - this seems strange.

We thank the reviewer for this comment. We understand that the use of a CA model instead of a TD error model may seem unusual at first glance. However, the CA model offers an important advantage: it more easily accommodates what we term "negative learning rates". This means that some participants may treat certain agents (especially the random one) as consistently deceitful, leading them to effectively increase/reduce choice tendencies following negative/positive feedback. A CA model handles this naturally by allowing negative CA parameters as a simple extension of positive ones. In contrast, adapting a TD error model to account for this is more complex. For instance, attempting to introduce a "negative learning rate" makes the RW model behave in a non-stable manner (e.g., Q values become <0 or >1). At the initial stages of our project, we explored different approaches to dealing with this issue and we found the CA model provides the best approach. For these reasons, we decided to proceed with our CA model.

Additionally, we used the CA model in previous studies (e.g., Moran, Dayan & Dolan (2021)) where we included (in SI) a detailed discussion of the similarities and difference between creditassignment and Rescorla-Wagner models

(3) Why was the follow-up study not pre-registered?

We appreciate the reviewer's comment regarding preregistration, which we should have done. Unfortunately, this is now “water under the bridge” but going forward we hope to pre-register increasing parts of our work.

(4) Other work looking at reward stochasticity?

As noted in point 4 of the main weaknesses, previous work on reward stochasticity primarily focused on explaining the increase/decrease in learning and its mechanistic bases under varying stochasticity levels. In our study, we uniquely characterize several specific learning biases that are modulated by source credibility, a topic not extensively explored within the existing reward stochasticity framework, as far as we know.

(5) Equation 1 is different from the one in the figure?

The reviewer is completely correct. The figure provides a simplified visual representation, primarily focusing on the feedback-based update of the Q-value, and for simplicity, it omits the forgetting term present in the full Equation 1. To ensure complete clarity and prevent any misunderstanding, we have now incorporated a more detailed explanation of the model, including the complete Equation 1 and its components, directly within the main text. This comprehensive description will ensure that readers are fully aware of how the model operates.

“Next, we formulated a family of non-Bayesian computational RL models. Importantly, these models can flexibly express non-Bayesian learning patterns and, as we show in following sections, can serve to identify learning biases deviating from an idealized Bayesian strategy. Here, an assumption is that during feedback, the choice propensity for the chosen bandit (which here is represented by a point estimate, “Q value“, rather than a distribution) either increases or decreases (for positive or negative feedback, respectively) according to a magnitude quantified by the free “Credit-Assignment (CA)” model parameters (47):

𝑄(𝑐ℎ𝑜𝑠𝑒𝑛) ← (1 – 𝑓_Q) ∗ 𝑄(𝑐ℎ𝑜𝑠𝑒𝑛) + 𝐶𝐴(𝑎𝑔𝑒𝑛𝑡, 𝑣𝑎𝑙𝑒𝑛𝑐𝑒) ∗ 𝐹

where F is the feedback received from the agents (coded as 1 for reward feedback and -1 for non-reward feedback), while fQ (∈[0,1]) is the free parameter representing the forgetting rate of the Q-value (Fig. 2a, bottom panel; Fig. S5b; Methods).”

(6) Please describe/plot the distribution of all fitted parameters in the supplement. I would include the mean and SD in the main text (methods) as well.

Following the reviewer’s suggestions, we have included in the Supplementary Document tables displaying the mean and SD of fitted parameters from participants for our main models of interest. We have also plotted the distributions of such parameters. Both for the main study:

(7) "A novel approach within the disinformation literature by exploiting a Reinforcement Learning (RL) experimental framework".

The idea of applying RL to disinformation is not new. Please tone down novelty claims. It would be nice to cite/discuss some of this work as well.

https://arxiv.org/abs/2106.05402?utm_source=chatgpt.com https://www.scirp.org/pdf/jbbs_2022110415273931.pdf https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4173312

We thank the reviewer for pointing us towards relevant literature. We have now toned down the sentence in the introduction and cited the references provided:

“To address these questions, we adopt a novel approach within the disinformation literature by exploiting a Reinforcement Learning (RL) experimental framework (36). While RL has guided disinformation research in recent years (37–40), our approach is novel in using one of its most popular tasks: the “bandit task”.”

(8) Figure 3a - The figures should be in the order that they're referenced (3 is referenced before 2).

We generally try to stick to this important rule but, in this case, we believe that our ordering serves better the narrative and hope the reviewer will excuse this small violation.

(9) "Additionally, we found a positive feedback-effect for the 3-star agent"

What is the analysis here? To avoid confusion with the "positive feedback" effect, consider using "positive effect of feedback". The dash wasn't sufficient to avoid confusion in my case.

We have now updated the terms in the text to avoid confusion.

(10) The discovery study revealed even stronger results supporting a conclusion that the credibility-CA model was superior to both Bayesian models for most subjects

This is very subjective, but I'll just mention that my "cherry-picking" flag was raised by this sentence. Are you only mentioning cases where the discovery study was consistent with the main study? Upon a closer read, I think the answer is most likely "no", but you might consider adopting a more systematic (perhaps even explicit) policy on when and how you reference the discovery study to avoid creating this impression in a more casual reader.

We thank the reviewer for this valuable suggestion. To prevent any impression of "cherry-picking", we have removed specific references to the discovery study from the main body of the text. Instead, all discussions regarding the convergence and divergence of results between the two studies are now in the dedicated section focusing on the discovery study:

“The discovery study (n=104) used a disinformation task structurally similar to that used in our main study, but with three notable differences: 1) it included 4 feedback agents, with credibilities of 50%, 70%, 85% and 100%, represented by 1, 2, 3, and 4 stars, respectively; 2) each experimental block consisted of a single bandit pair, presented over 16 trials (with 4 trials for each feedback agent); and 3) in certain blocks, unbeknownst to participants, the two bandits within a pair were equally rewarding (see SI section 1.1). Overall, this study's results supported similar conclusions as our main study (see SI section 1.2) with a few differences. We found convergent support for increased learning from more credible sources (SI 1.2.1), superior fit for the CA model over Bayesian models (SI 1.2.2) and increased learning from feedback inferred to be true (SI 1.2.6). Additionally, we found an inflation of positivity bias for low-credibility both when measured relative to the overall level of credit assignment (as in our main study), or in absolute terms (unlike in our main study) (Fig. S3; SI 1.2.5). Moreover, choice-perseveration could not predict an amplification of positivity bias for low-credibility sources (see SI 3.6.2). However, we found no evidence for learning based on 50%-credibility feedback when examining either the feedback effect on choice repetition or CA in the credibility-CA model (SI 1.2.3).”

(11) An in-depth comparison between Bayesian and empirical CA parameters revealed discrepancies from normative Bayesian learning.

Consider saying where this in-depth comparison can be found (based on my reading, I think you're referring to the next section?

We have now modified the sentence for better clarity:

“However, an in-depth comparison between Bayesian and empirical CA parameters revealed discrepancies from ideal Bayesian learning, which we describe in the following sections.”

(12) "which essentially provides feedback" Perhaps you meant "random feedback"?

We have modified the text as suggested by the reviewer.

<(13) Essentially random

Why "essentially"? Isn't it just literally random?

We have modified the text as suggested by the reviewer.

(14) Both Bayesian models predicted an attenuated credit-assignment for the 3-star agent

Attenuated relative to what? I wouldn't use this word if you mean weaker than what we see in the human data. Instead, I would say people show an exaggerated credit-assignment, since Bayes is the normative baseline.

We changed the text according to the reviewer’s suggestion:

“A comparison of empirical and Bayesian credit-assignment parameters revealed a further deviation from ideal Bayesian learning: participants showed an exaggerated credit-assignment for the 3-star agent compared with Bayesian models.”

(15) "there was no difference between 2-star and 3-star agent contexts (b=0.051, F(1,2419)=0.39, p=0.53)"

You cannot confirm the null hypothesis! Instead, you can write "The difference between 2-star and 3-star agent contexts was not significant". Although even with this language, you should be careful that your conclusions don't rest on the lack of a difference (the next sentence is somewhat ambiguous on this point).

Additionally, the reported b coefs do not match the figure, which if anything, suggests a larger drop from 0.75 (2-star) to 1 (3-star). Is this a mixed vs fixed effects thing? It would be helpful to provide an explanation here.

We thank the reviewer for this question. When we previously submitted our manuscript, we thought that finding enhanced credit-assignment for fully credible feedback following potential disinformation from a DIFFERENT context would constitute a striking demonstration of our “contrast effect”. However, upon reexamining this finding we found out we had a coding error (affecting how trials were filtered). We have now rerun and corrected this analysis. We have assessed the contrast effect for both "same-context" trials (where the contextual trial featured the same bandit pair as the learning trial) and "different-context" trials (where the contextual trial featured a different bandit pair). Our re-analysis reveals a selective significant contrast effect in the same-context condition, but no significant effect in the different-context condition. We have updated the main text to reflect these corrected findings and provide a clearer explanation of the analysis:

“A comparison of empirical and Bayesian credit-assignment parameters revealed a further deviation from ideal Bayesian learning: participants showed an exaggerated credit-assignment for the 3-star agent compared with Bayesian models [Wilcoxon signed-rank test, instructed-credibility Bayesian model (median difference=0.74, z=11.14); free-credibility Bayesian model (median difference=0.62, z=10.71), all p’s<0.001] (Fig. 3a). One explanation for enhanced learning for the 3-star agents is a contrast effect, whereby credible information looms larger against a backdrop of non-credible information. To test this hypothesis, we examined whether the impact of feedback from the 3-star agent is modulated by the credibility of the agent in the trial immediately preceding it. More specifically, we reasoned that the impact of a 3-star agent would be amplified by a “low credibility context” (i.e., when it is preceded by a low credibility trial). In a binomial mixed effects model, we regressed choice-repetition on feedback valence from the last trial featuring the same bandit pair (i.e., the learning trial) and the feedback agent on the trial immediately preceding that last trial (i.e., the contextual credibility; see Methods for model-specification). This analysis included only learning trials featuring the 3-star agent, and context trials featuring the same bandit pair as the learning trial (Fig. 4a). We found that feedback valence interacted with contextual credibility (F(2,2086)=11.47, p<0.001) such that the feedback-effect (from the 3-star agent) decreased as a function of the preceding context-credibility (3-star context vs. 2-star context: b= -0.29, F(1,2086)=4.06, p=0.044; 2star context vs. 1-star context: b=-0.41, t(2086)=-2.94, p=0.003; and 3-star context vs. 1-star context: b=0.69, t(2086)=-4.74, p<0.001) (Fig. 4b). This contrast effect was not predicted by simulations of our main models of interest (Fig. 4c). No effect was found when focussing on contextual trials featuring a bandit pair different than the one in the learning trial (see SI 3.5). Thus, these results support an interpretation that credible feedback exerts a greater impact on participants’ learning when it follows non-credible feedback, in the same learning context.”

We have modified the discussion accordingly as well:

“A striking finding in our study was that for a fully credible feedback agent, credit assignment was exaggerated (i.e., higher than predicted by our Bayesian models). Furthermore, the effect of fully credible feedback on choice was further boosted when it was preceded by a low-credibility context related to current learning. We interpret this in terms of a “contrast effect”, whereby veridical information looms larger against a backdrop of disinformation (21). One upshot is that exaggerated learning might entail a risk of jumping to premature conclusions based on limited credible evidence (e.g., a strong conclusion that a vaccine produces significant side-effect risks based on weak credible information, following non-credible information about the same vaccine). An intriguing possibility, that could be tested in future studies, is that participants strategically amplify the extent of learning from credible feedback to dilute the impact of learning from noncredible feedback. For example, a person scrolling through a social media feed, encountering copious amounts of disinformation, might amplify the weight they assign to credible feedback in order to dilute effects of ‘fake news’. Ironically, these results also suggest that public campaigns might be more effective when embedding their messages in low-credibility contexts, which may boost their impact.”

And we have included some additional analyses in the SI document:

“3.5 Contrast effects for contexts featuring a different bandit Given that we observed a contrast effect when both the learning and the immediately preceding "context trial” involved the same pair of bandits, we next investigated whether this effect persisted when the context trial featured a different bandit pair – a situation where the context would be irrelevant to the current learning. Again, we used in a binomial mixed effects model, regressing choice-repetition on feedback valence in the learning trial and the feedback agent in the context trial. This analysis included only learning trials featuring the 3-star agent, and context trials featuring a different bandit pair than the learning trial (Fig. S22a). We found no significant evidence of an interaction between feedback valence and contextual credibility (F(2,2364)=0.21, p=0.81) (Fig. S22b). This null result was consistent with the range of outcomes predicted by our main computational models (Fig. S22c).”

We aimed to formally compare the influence of two types of contextual trials: those featuring the same bandit pair as the learning trial versus those featuring a different pair. To achieve this, we extended our mixedeffects model by incorporating a new predictor variable, "CONTEXT_TYPE" which coded whether the contextual trial involved the same bandit pair (coded as -0.5) or a different bandit pair (+0.5) compared to the learning trial. The Wilkinson notation for this expanded mixed-effects model is:

𝑅𝐸𝑃𝐸𝐴𝑇 ~ 𝐶𝑂𝑁𝑇𝐸𝑋𝑇_𝑇𝑌𝑃𝐸 ∗ 𝐹𝐸𝐸𝐷𝐵𝐴𝐶𝐾 ∗ (𝐶 𝐶𝑂𝑁𝑇𝐸𝑋𝑇_2-star + 𝐶𝑂𝑁𝑇𝐸𝑋𝑇_3-star) + 𝐵𝐸𝑇𝑇𝐸𝑅 + (1|𝑝𝑎𝑟𝑡𝑖𝑐𝑖𝑝𝑎𝑛𝑡)

This expanded model revealed a significant three-way interaction between feedback valence, contextual credibility, and context type (F(2,4451) = 7.71, p<0.001). Interpreting this interaction, we found a 2-way interaction between context-source and feedback valence when the context was the same (F(2,4451) = 12.03, p<0.001), but not when context was different (F(2,4451) = 0.23, p = 0.79). Further interpreting the double feedback-valence * context-source interaction (for the same context) we obtained the same conclusions as reported in the main text.”

(16) "Strikingly, model-simulations (Methods) showed this pattern is not predicted by any of our other models"

Why doesn't the Bayesian model predict this?

Thanks for the comment. Overall, Bayesian models do predict a slight truth inference effect (see Figure 6d). However, these effects are not as strong as the ones observed in participants, suggesting that our results go beyond what would be predicted by a Bayesian model.

Conceptually, it's important to note that the Bayesian model can infer (after controlling for source credibility and feedback valence) whether feedback is truthful based solely on prior beliefs about the chosen bandit. Using this inferred truth to amplify the weight of truthful feedback would effectively amount to “bootstrapping on one’s own beliefs.” This is most clearly illustrated with the 50% agent: if one believes that a chosen bandit yields rewards 70% of the time, then positive feedback is more likely to be truthful than negative feedback. However, a Bayesian observer would also recognize that, given the agent’s overall unreliability, such feedback should be ignored regardless.

(17) "A striking finding in our study was that for a fully credible feedback agent, credit assignment was exaggerated (i.e., higher than predicted by a Bayesian strategy)".

"Since we did not find any significant interactions between BETTER and the other regressors, we decided to omit it from the model formulation".

Was this decision made after seeing the data? If so, please report the original analysis as well.

We have included the BETTER regressor again, and we have re-run the analyses. We now report the results of such regression. We have also changed the methods section accordingly:

“We used a different mixed-effects binomial regression model to test whether value learning from the 3-star agent was modulated by contextual credibility. We focused this analysis on instances where the previous trial with the same bandit pair featured the 3-star agent. We regressed the variable REPEAT, which indicated whether the current trial repeated the choice from the previous trial featuring the same bandit-pair (repeated choice=1, non-repeated choice=0). We included the following regressors: FEEDBACK coding the valence of feedback in the previous trial with the same bandit pair (positive=0.5, negative=-0.5), CONTEXT2-star indicating whether the trial immediately preceding the previous trial with the same bandit pair (context trial) featured the 2-star agent (feedback from 2-star agent=1, otherwise=0), and CONTEXT3star indicating whether the trial immediately preceding the previous trial with the same bandit pair featured the 3-star agent. We also included a regressor (BETTER) coding whether the bandit chosen in the learning trial was the better -mostly rewarding- or the worse -mostly unrewarding- bandit within the pair. We included in this analysis only current trials where the context trial featured a different bandit pair. The model in Wilkinson’s notation was:

𝑅𝐸𝑃𝐸𝐴𝑇~ 𝐹𝐸𝐸𝐷𝐵𝐴𝐶𝐾 ∗ (𝐶𝑂𝑁𝑇𝐸𝑋𝑇_2-star + 𝐶𝑂𝑁𝑇𝐸𝑋𝑇_3-star) + 𝐵𝐸𝑇𝑇𝐸𝑅 + (1|𝑝𝑎𝑟𝑡𝑖𝑐𝑖𝑝𝑎𝑛𝑡) ( 13 )

In figure 4c, we independently calculate the repeat probability difference for the better (mostly rewarding) and worse (mostly non-rewarding) bandits and averaged across them. This calculation was done at the participants level, and finally averaged across participants.”

Document de Briefing : "Savoir ou périr" et les défis de l'éducation en France

Source: Extraits de "Rentrée scolaire : savoir ou réussite, pourquoi l’école tourne à l’envers (avec Bernard Lahire)"

Ce document de briefing synthétise les thèmes principaux, les idées essentielles et les faits importants tirés de l'entretien avec le sociologue Bernard Lahire, en se concentrant sur son ouvrage "Savoir ou périr" et ses réflexions sur le système éducatif français.

1. Le Savoir comme Condition de Survie Collective

Bernard Lahire insiste sur une idée fondamentale : le savoir n'est pas une simple affaire culturelle ou académique, mais une condition intrinsèque à la survie collective de l'humanité.

Il souligne que depuis l'aube de l'humanité, la transmission des expériences et des connaissances a été vitale pour l'adaptation des nouveaux venus et le développement des sociétés.

Citation clé : "on se rend pas compte que le savoir depuis le début de l'expérience de l'humanité euh c'est une des conditions de la survie collective"

Exemple historique : Lahire cite l'échec d'une expédition écossaise en Antarctique en 1845, dont aucun membre n'a survécu faute de savoirs adaptés à l'environnement hostile, contrairement aux Inuits qui y prospéraient.

Application contemporaine : La crise du coronavirus a mis en lumière l'urgence de la recherche et du savoir. Les investissements dans la recherche sur les coronavirus 10-15 ans auparavant, qui avaient été coupés, auraient pu accélérer la réponse.

"la recherche est directement euh concernée par les processus d'adaptation et que si on n'a pas ces savoirs et ben on est mal parti collectivement en fait".

Conséquence : L'oubli de cette vérité fondamentale nous rend "hors sol" et vulnérables aux défis futurs.

2. Le Système Scolaire "Tourne à l'Envers" : L'Obsession de l'Évaluation

Malgré l'importance vitale du savoir, Lahire dénonce un paradoxe français (et plus largement institutionnel) : l'école, censée être un lieu d'apprentissage, est devenue une institution "pilotée par l'évaluation", ce qui la fait "tourner à l'envers".

Dérive institutionnelle : Les institutions, créées avec un objectif précis, finissent souvent par dévier de leur mission initiale. L'école en est un exemple où l'évaluation a pris le pas sur l'apprentissage.

Bâchotage et surcharge des programmes : Lahire critique le "bâchotage" et la "surcharge des programmes", une problématique déjà soulevée par Marc Bloch en 1943. "on remplace le goût de la connaissance par le goût du succès".

Témoignages de grands scientifiques : Il s'appuie sur les expériences de personnalités comme Einstein, qui était "dégoûté de la physique" à force d'ingurgiter des choses par cœur, ou Grothendieck, qui critiquait ses collègues "trop dociles" et manquant d'ambition intellectuelle profonde.

La peur de la faute : Une spécificité française est la "peur de la faute", qui inhibe l'apprentissage des langues étrangères et contredit l'esprit scientifique, où l'erreur est une étape vers la découverte.

"on a tous peur de la faute je sais pas où on l'a attrapé mais évidemment que c'est à l'école que ça s'est passé".

Effets négatifs de la compétition et du stress : La peur et la compétition sont contre-productives pour l'apprentissage et la recherche.

Lahire témoigne de sa propre "boule au ventre" pendant sa scolarité et cite Laurent Lafforgue, lauréat de la médaille Fields, qui n'a presque rien publié pendant 10 ans, soulignant l'importance de laisser du temps aux chercheurs sans pression évaluative excessive.

La nécessité du retour, pas uniquement de l'évaluation : Les élèves ont besoin de retours, d'encouragements et de guidance (comme des tuteurs pour une plante), mais pas d'une évaluation constante et stressante.

3. Les Inégalités Sociales et la Reproduction : "Les enfants ne vivent pas dans le même monde"

Au-delà des problèmes pédagogiques, Lahire met en lumière l'impact profond des inégalités sociales et de la reproduction sociale sur les parcours scolaires, en s'appuyant sur son ouvrage "Enfance de classe".

Différences d'expériences dès le plus jeune âge : L'idée que "les enfants vivent au même moment dans la même société mais pas dans le même monde" illustre que, dès 5-6 ans, des enfants de milieux différents ont déjà des passés, des interactions et des horizons de possibles radicalement distincts.

"l'horizon n'est pas du tout le même les possibilités ne sont pas les mêmes".

Le mythe du "quand on veut on peut" : Lahire réfute fermement cette idée, la qualifiant de "régression scientifique".

Il souligne le "poids très très lourd des déterminismes sociaux d'origine".

Altricialité secondaire et dépendance aux adultes : La longue période de dépendance des enfants vis-à-vis des adultes (altricialité secondaire) a des conséquences majeures.

Les caractéristiques des parents (capitaux culturels, intérêt pour la pédagogie) influencent fortement la capacité des enfants à s'adapter à l'école.

Le rêve républicain de l'égalité : L'égalité n'est pas une réalité, mais un "horizon".

Les sociétés sont inégalitaires, mais l'État et les collectivités ont la responsabilité d'infléchir ces processus en offrant des opportunités culturelles et éducatives à ceux qui en sont le plus éloignés.

L'importance de l'ouverture culturelle : Les activités culturelles (théâtre, musées) sont cruciales pour "donner une chance" aux enfants de milieux défavorisés de s'approprier ces codes et de lutter contre l'autocensure. Sans cela, de nombreux élèves se projettent vers des "études courtes" faute de "background culturel".

4. Le Parcours Personnel de Bernard Lahire et la Critique Politique

Lahire, lui-même issu d'un "milieu ouvrier", a souffert du système scolaire mais a réussi grâce à un ensemble de facteurs (soutien familial, enseignants, chance), réfutant l'idée de sa seule "agentivité".

Réussite non individuelle : Sa réussite est le produit de "toutes les relations que [j'ai] eues avec toutes ces personnes et ces institutions".

Critique de la politique à court terme : Lahire exprime sa "tristesse" et son "dégoût" face à une politique perçue comme "décevante", "hors sol" et focalisée sur des querelles partisanes, plutôt que sur les défis à long terme (comme les enjeux climatiques ou éducatifs) qui nécessitent une vision sur "10000 ans".

Critique des décideurs ignorants des réalités éducatives : Il déplore la nomination de ministres de l'Éducation qui "connaissent très mal leur dossier", citant l'exemple d'Élisabeth Borne affirmant que le projet professionnel devait s'exprimer dès la maternelle.

5. Conséquences Budgétaires et Menaces sur l'Éducation

La situation est aggravée par les contraintes budgétaires, qui menacent directement les initiatives visant à lutter contre les inégalités.

Impact des coupes budgétaires : Les plans d'économie prévus pourraient entraîner une baisse des dotations pour les départements, impactant directement les collèges REP et les activités culturelles essentielles pour ces élèves. "il y a peut-être des activités culturelles qui vont devoir être annulées notamment pour louer un car".

En résumé, Bernard Lahire alerte sur un système éducatif qui a perdu de vue l'essence du savoir, obsédé par l'évaluation et incapable de compenser efficacement les inégalités sociales profondes, le tout aggravé par des décisions politiques court-termistes et un manque de compréhension des enjeux éducatifs.

This work has been published in GigaByte Journal under a CC-BY 4.0 license (https://doi.org/10.46471/gigabyte.161), and has published the reviews under the same license.

Reviewer 1. Gang Wang

Is the language of sufficient quality? Yes. There are many details in the article, such as citation format, spelling, etc. [Supplementary Table 3a, 3b, 3c) → (Supplementary Table 3a, 3b, 3c) The citation format of the article also needs to be adjusted according to the journal requirements.

Is there sufficient detail in the methods and data-processing steps to allow reproduction? No. A previous reviewer mentioned that RagTag could be used to improve the quality of genome assembly. I suggest you seriously consider this.

Is there sufficient information for others to reuse this dataset or integrate it with other data? No

Overall Comments: The article is logically clear and the analysis is complete. The description of both sample collection and sequencing is relatively clear. At the same time, the analysis process shown in Figure 1 is also very reasonable. However, as described by the previous reviewer, I suggest that you remove the high-quality level. There are many details in the article, such as citation format, spelling, etc. [Supplementary Table 3a, 3b, 3c) → (Supplementary Table 3a, 3b, 3c) The citation format of the article also needs to be adjusted according to the journal requirements. Figure 2, the letters of a and b are too different, please unify them. Figure 4 is completely unclear, please increase the font size. A previous reviewer mentioned that RagTag could be used to improve the quality of genome assembly. I suggest you seriously consider this. Re-review: The authors used FCS-GX to exclude contaminating sequences in the genome, so I agree that this paper should be published.

Reviewer 2. Agustin Ariel Baricalla

Are all data available and do they match the descriptions in the paper? No. Matching data: NCBI project with access to the NCBI-SRA deposited raw data. Nonmatching data: Oxford Nanopore data: The authors reply to a previously submitted manuscript arguing that this data was not used, but Fig. 1 refers to Nanopore Minion data. The manuscript body and the additional data section do not include the Quast and BUSCO reports or their corresponding plots.

Are the data and metadata consistent with relevant minimum information or reporting standards? See GigaDB checklists for examples http://gigadb.org/site/guide No. GigaByte suggests a checklist including the genome, CDS, and proteins in FASTA format, as well as the annotations in GFF format; however, these items are not available for evaluation.

Is there sufficient detail in the methods and data-processing steps to allow reproduction? Yes. The FastP step for raw data processing is mentioned in the results section but is not detailed in the methods section.

Is there sufficient data validation and statistical analyses of data quality? No. The authors have not included the BUSCO results. The OrthoDB database for 'passeriformes_odb12' contains over 10,000 curated genes, representing approximately 50-60% of the total genes in a typical passeriform genome. Therefore, the BUSCO report for the new assembly should be provided. The author mentioned that "The gene completeness for Passer was assessed through Benchmarking Universal Single-Copy Orthologs ( Busco version 5.5.0 ) [26] by using the orthologous genes in the Gallus gallus [ chicken] genome" but BUSCO uses the OrthoDB datasets to run, I do not understand what this phrase refers to.

Is there sufficient information for others to reuse this dataset or integrate it with other data? Yes. All the procedures are consistent and the programs or pipelines are well-known and well documented in the bioinformatic and genomic fields.

Additional Comments: The inclusion of the mitochondrial genome represents a significant improvement in this manuscript. I recommend presenting all nuclear results together first, followed by a separate and clear description of the mitochondrial analysis and findings to enhance clarity. The data is interesting for analyzing the genetic dynamics behind Passer domesticus adaptation and evolution and can show differences between the previous genomes available from a European reference sample but this is not presented in this work. As of this revision, the NCBI's Passer domesticus genome includes two European reference genomes, both classified with 'chromosome-like' status (NCBI: GCF_036417665.1 and GCA_001700915.1). These genomes can be utilized in two distinct ways: (1) performing a 'genome-guided assembly' with MASURCA, using one of these genomes alongside the Illumina data, or (2) conducting genome scaffolding by employing one of these genomes as a reference and the assembled genome from raw reads as a query, using tools like RagTag or the chromosome scaffolder available in MASURCA. Both approaches could potentially lead to improvements in scaffold number and contiguity metrics, such as N50, N90, and the largest scaffold.

Re-review: The authors have subtly improved the original version previously presented, but have not managed to surpass the minimum standards established by the publisher to be published by the journal. Easily achievable changes have been requested to complement the analysis previously made and have been ignored. Requests have not been answered, graphics that generate confusion between them and the text presented have not been fixed, and no relevant improvement between the previous and current versions has been shown.

Hacer diagnóstico diferencia con TDAH o desnutrición

para contrastar lo dicho por varios autores

resaltar la voz experta en el tema

para refutar alguna postura

para corroborar lo que dice

lmfao you forgot the 90s don't ya

Update 2021

https://hyp.is/go?url=https%3A%2F%2Fpubmed.ncbi.nlm.nih.gov%2F33017170%2F&group=world

Nuevo verosímil para Barthes: destrucción del signo de manera regresiva, para cuestionar la representación desde el estudio de lo bello y las experiencias artísticas desde una perspectiva que no se basa en principios religiosos o espirituales, sino en la razón, la experiencia humana y el mundo terrenal.

La supremacía de la Historia, de lo real, lo verdadero sobre lo verosímil, de la imitación de la poesía.

Esto parece un testimonio de la importancia que se le da a las historias "basadas en un hecho real".

Sensación de inestabilidad o desorientación que produce el texto en el lector al desestabilizar el sentido único y monolítico. Este vértigo surge al confrontar un texto que es lo opuesto a un tratado de un solo significado, ya que lo no codificado, lo que queda fuera del discurso normativo, genera esta sensación de profundidad y multiplicidad de interpretaciones.

Finalidad estética de la descripción en Flaubert

Crítica platónica al artista como una pseudohacedor.

Lo verosímil en los descriptivo se encuentra en una función estética.

Pregunta central

Descripción

Oposición entre la estructura general del relato que es predictiva y la descripción que es puramente sumatoria y no va creando una elección de consecuencia sobre el relato como la primera.

estructura desordenada de la notación insignificante de las descripciones

Descripciones detalladas en la narrativa que, aunque parecen ser meros registros de lo real, en realidad son selecciones deliberadas que construyen el efecto de realidad, ya que el texto remplaza lo real con lo escrito y crea una ilusión de transparencia

Omisiones del análisis estructural sobre lo que se considera relleno de valor funcional indirecto

* Should al principio de la oración funciona como un if. * should he ever = si llegara. * Put down = ejecutar (para personas), sacrificar (para animales).

Update 2022

https://hyp.is/go?url=https%3A%2F%2Fwww.bmj.com%2Fcontent%2F370%2Fbmj.m2980&group=world

Update 2022

https://hyp.is/go?url=https%3A%2F%2Fwww.bmj.com%2Fcontent%2F369%2Fbmj.m1328&group=world

Update 2020

https://hyp.is/go?url=https%3A%2F%2Fwww.bmj.com%2Fcontent%2F369%2Fbmj.m1328&group=world

Update 2022

https://hyp.is/go?url=https%3A%2F%2Fwww.bmj.com%2Fcontent%2F377%2Fbmj-2022-069989&group=world

Update 2023

https://hyp.is/go?url=https%3A%2F%2Fwww.bmj.com%2Fcontent%2F373%2Fbmj.n949&group=world

https://hyp.is/go?url=https%3A%2F%2Fwww.bmj.com%2Fcontent%2F370%2Fbmj.m3320&group=world

Update

https://hyp.is/go?url=https%3A%2F%2Fwww.bmj.com%2Fcontent%2F384%2Fbmj.p2929&group=world

Update 2024

Acá aparece el lenguaje como generador del mundo. Además de ejercer el poder del lenguaje en la realidad y crear esa gramática mítica que refiere Tolkien necesaria para la subcreación.

La renovación por parte de la fantasía desde esa subcreación tiene que ver con deshacer esa fatiga de la realidad y comodidad visual de su cotidianidad.

La creación fantástica requiere esfuerzo: diferencia con la irrealidad del sueño.

la subcreación en la literatura como una manifestación más elevada por crear un mundo secundario separado del mundo primario y que se hace más poderosa. Parece hacer eco de la máxima de Mallarmé: "hay que elevar la página a la potencia del cielo estrellado".

Noción de lo fantástico.

La mitología como subcreación más que representación. Se trata más de la posibilidad de moldear el mundo que de volver a los temas míticos de origen.

El adjetivo como manifestación de una gramática mítica. Genera un poder de abstracción que se ejerce sobre el mundo exterior a nuestra mente y crear nuevas formas que llevan una fantasía de lo real. Acá es desde donde sitúa Tolkien la idea del hombre (ser humano) como subcreador. El poder de la fantasía es hacer efectiva la voluntad de la visión fantástica.

Contexto de la definición. Características para entender los cuentos de hadas.

Desplazamiento de la noción de poiesis en la obra de arte: de aletheia al operari.

Poiesis en la época moderna

Praxis para Aristóteles

Diferencias: praxis como acto, poiesis como revelación (cercano al concepto de aletheia).

Según esto toda poiesis es acto.

🧠 “La razón se confunde frente al prodigio del amor”

El emperador admite que el amor desafía la lógica. Lo que parece un fenómeno biológico se transforma en un misterio que no puede explicarse con pura razón.

🫀 La carne propia vs. la carne ajena

Nuestro propio cuerpo nos interesa de manera mínima: lo lavamos, lo alimentamos, lo cuidamos para evitar dolor.

Pero, en el amor, el cuerpo ajeno se convierte en objeto de obsesión, de deseo apasionado.

✨ El enigma del deseo

¿Por qué?

Porque ese cuerpo está animado por una individualidad distinta a la nuestra (tiene un alma propia).

Porque exhibe ciertos trazos de belleza, aunque la belleza es tan subjetiva que ni los mejores jueces coinciden en qué la define.

🗝️ Sentido profundo

El texto muestra cómo el amor erosiona la frontera entre lo racional y lo irracional:

La carne deja de ser mera materia y se convierte en misterio encarnado en el otro.

El deseo no surge de la carne en sí, sino del hecho de que esa carne es otredad viviente.

La belleza funciona como chispa, pero es arbitraria y variable.

👉 En resumen: Adriano reconoce el amor como una paradoja —una fuerza que convierte lo banal (un cuerpo) en lo más precioso, por la simple diferencia de que no es el nuestro.

⚰️ Despojamiento como la muerte

El inicio compara el acto del amor (o de la entrega espiritual) con la muerte: desnudez absoluta, pérdida de todo poder, abandono del yo.

🙇 Humildad más que derrota o plegaria

En la derrota uno es humillado por otro.

En la plegaria uno se humilla ante los dioses.

Pero en el amor, la humildad es todavía más radical: es voluntaria. Uno se vacía por decisión propia.

🔄 El retorno de lo humano

Después de ese instante de anulación, vuelve la vida con toda su carga:

Negativas: límites, rechazos.

Responsabilidades: lo que uno debe asumir.

Dones: lo que se recibe y se da.

Tristes confesiones: la vulnerabilidad.

Frágiles ternuras: la delicadeza que sobrevive en lo cotidiano.

🧭 Sentido

El pasaje muestra cómo el amor o la entrega íntima tiene un movimiento doble:

Vaciamiento absoluto (como la muerte).

Retorno a lo complejo y humano (responsabilidad, ternura, confesiones).

Es casi una dialéctica: el amor como aniquilación momentánea seguida por la reconstrucción del tejido vital.

⚰️ Despojamiento como la muerte

El inicio compara el acto del amor (o de la entrega espiritual) con la muerte: desnudez absoluta, pérdida de todo poder, abandono del yo.

🙇 Humildad más que derrota o plegaria

En la derrota uno es humillado por otro.

En la plegaria uno se humilla ante los dioses.

Pero en el amor, la humildad es todavía más radical: es voluntaria. Uno se vacía por decisión propia.

🔄 El retorno de lo humano

Después de ese instante de anulación, vuelve la vida con toda su carga:

Negativas: límites, rechazos.

Responsabilidades: lo que uno debe asumir.

Dones: lo que se recibe y se da.

Tristes confesiones: la vulnerabilidad.

Frágiles ternuras: la delicadeza que sobrevive en lo cotidiano.

🧭 Sentido

El pasaje muestra cómo el amor o la entrega íntima tiene un movimiento doble:

Vaciamiento absoluto (como la muerte).

Retorno a lo complejo y humano (responsabilidad, ternura, confesiones).

Es casi una dialéctica: el amor como aniquilación momentánea seguida por la reconstrucción del tejido vital.

🍷 El bebedor y la razón

Beber no implica necesariamente perder la cabeza: uno puede tomar vino y seguir siendo dueño de sí.

Aquí el vino representa el placer controlado, el exceso manejable.

❤️ El amante y la razón

Pero en el amor, quien se mantiene demasiado lúcido no se entrega del todo.

“No obedece del todo a su dios” = no honra plenamente a Eros.

Amar exige perder algo de control, dejar que la pasión gobierne.

🗝️ Sentido profundo

Yourcenar (a través de Adriano) plantea un contraste:

El vino → placer que se puede modular.

El amor → fuerza divina que pide rendición.

Amar con cálculo frío es casi una contradicción: el dios exige sacrificio de la razón.

👉 Es una idea que viene de la tradición griega: Platón en el Fedro habla de la manía erótica (locura divina del amor) como una forma superior de verdad.

🌿 Diana de los bosques

Adriano no habla de la diosa literalmente, sino como símbolo de la caza y de un contacto intenso con la naturaleza. Diana es la divinidad de los bosques, los animales salvajes y la luna. Relacionarse con ella es relacionarse con lo salvaje, lo femenino y lo misterioso.

🐗 La caza del jabalí

En la adolescencia, la caza era rito de paso.

Para Adriano, matar un jabalí no era solo deporte, sino un primer contacto con el mando (dirigir a los cazadores, perros, organizar) y con el peligro real (un animal que podía matarlo).

La experiencia mezcla erotismo (“ser amado”), violencia y poder.

⚔️ Formación del carácter

Se entregaba con “furor”, es decir, con pasión casi dionisíaca.

Sus excesos provocaron que el propio Trajano —su tutor y futuro padre adoptivo— lo reprendiera.

En clave literaria, esto muestra a un joven impulsivo, con sed de riesgo, que debía ser domesticado para convertirse en emperador.

Clima en las finanzas

Sacar

Agregar espacio

Agregar espacio

sacar el punto después de la N a todos los cargos

sacar el punto después de la N

Quando está salvando, ele já considera que o a gente está disponível para receber novos cards (o limite de card é considerado contando cards ativos).

Author response:

Reviewer #1 (Public review):

Summary:

This work by Govorunova et al. identified three naturally blue-shifted channelrhodopsins (ChRs) from ancyromonads, namely AnsACR, FtACR, and NlCCR. The phylogenetic analysis places the ancyromonad ChRs in a distinct branch, highlighting their unique evolutionary origin and potential for novel applications in optogenetics. Further characterization revealed the spectral sensitivity, ionic selectivity, and kinetics of the newly discovered AnsACR, FtACR, and NlCCR. This study also offers valuable insights into the molecular mechanism underlying the function of these ChRs, including the roles of specific residues in the retinal-binding pocket. Finally, this study validated the functionality of these ChRs in both mouse brain slices (for AnsACR and FtACR) and in vivo in Caenorhabditis elegans (for AnsACR), demonstrating the versatility of these tools across different experimental systems.

In summary, this work provides a potentially valuable addition to the optogenetic toolkit by identifying and characterizing novel blue-shifted ChRs with unique properties.

Strengths:

This study provides a thorough characterization of the biophysical properties of the ChRs and demonstrates the versatility of these tools in different ex vivo and in vivo experimental systems. The mutagenesis experiments also revealed the roles of key residues in the photoactive site that can affect the spectral and kinetic properties of the channel.

We thank the Reviewer for his/her positive evaluation of our work.

Weaknesses:

While the novel ChRs identified in this work are spectrally blue-shifted, there still seems to be some spectral overlap with other optogenetic tools. The authors should provide more evidence to support the claim that they can be used for multiplex optogenetics and help potential end-users assess if they can be used together with other commonly applied ChRs. Additionally, further engineering or combination with other tools may be required to achieve truly orthogonal control in multiplexed experiments.

To demonstrate the usefulness of ancyromonad ChRs for multiplex optogenetics as a proof of principle, we co-expressed AnsACR with the red-shifted cation-conducting ChR Chrimson and measured net photocurrent generated by this combination as a function of the wavelength. We found that it is hyperpolarizing in the blue region of the spectrum, and depolarizing at the red region. In the revision, we added a new panel (Figure 1D) showing these results and the following paragraph to the main text:

“To test the possibility of using AnsACR in multiplex optogenetics, we co-expressed it with the red-shifted CCR Chrimson (Klapoetke et al., 2014) fused to an EYFP tag in HEK293 cells. We measured the action spectrum of the net photocurrents with 4 mM Cl^- in the pipette, matching the conditions in the neuronal cytoplasm (Doyon, Vinay et al. 2016). Figure 1D, black shows that the direction of photocurrents was hyperpolarizing upon illumination with λ<500 nm and depolarizing at longer wavelengths. A shoulder near 520 nm revealed a FRET contribution from EYFP (Govorunova, Sineshchekov et al. 2020), which was also observed upon expression of the Chrimson construct alone (Figure 1D, red)”.

In the C. elegans experiments, partial recovery of pharyngeal pumping was observed after prolonged illumination, indicating potential adaptation. This suggests that the effectiveness of these ChRs may be limited by cellular adaptation mechanisms, which could be a drawback in long-term experiments. A thorough discussion of this challenge in the application of optogenetics tools would prove very valuable to the readership.

We added the following paragraph to the revised Discussion:

“One possible explanation of the partial recovery of pharyngeal pumping that we observed after 15-s illumination, even at the highest tested irradiance, is continued attenuation of photocurrent during prolonged illumination (desensitization). However, the rate of AnsACR desensitization (Figure 1 – figure supplement 4A and Figure 1 – figure supplement 5A) is much faster than the rate of the pumping recovery, reducing the likelihood that desensitization is driving this phenomenon. Another possible reason for the observed adaptation is an increase in the cytoplasmic Cl^- concentration owing to AnsACR activity and hence a breakdown of the Cl^- gradient on the neuronal membrane. The C. elegans pharynx is innervated by 20 neurons, 10 of which are cholinergic (Pereira, Kratsios et al. 2015). A pair of MC neurons is the most important for regulation of pharyngeal pumping, but other pharyngeal cholinergic neurons, including I1, M2, and M4, also play a role (Trojanowski, Padovan-Merhar et al. 2014). Moreover, the pharyngeal muscles generate autonomous contractions in the presence of acetylcholine tonically released from the pharyngeal neurons (Trojanowski, Raizen et al. 2016). Given this complexity, further elucidation of pharyngeal pumping adaptation mechanisms is beyond the scope of this study.”

Reviewer #2 (Public review):

Summary:

Govorunova et al present three new anion opsins that have potential applications in silencing neurons. They identify new opsins by scanning numerous databases for sequence homology to known opsins, focusing on anion opsins. The three opsins identified are uncommonly fast, potent, and are able to silence neuronal activity. The authors characterize numerous parameters of the opsins.

Strengths:

This paper follows the tradition of the Spudich lab, presenting and rigorously characterizing potentially valuable opsins. Furthermore, they explore several mutations of the identified opsin that may make these opsins even more useful for the broader community. The opsins AnsACR and FtACR are particularly notable, having extraordinarily fast onset kinetics that could have utility in many domains. Furthermore, the authors show that AnsACR is usable in multiphoton experiments having a peak photocurrent in a commonly used wavelength. Overall, the author's detailed measurements and characterization make for an important resource, both presenting new opsins that may be important for future experiments, and providing characterizations to expand our understanding of opsin biophysics in general.

We thank the Reviewer for his/her positive evaluation of our work.

Weaknesses:

First, while the authors frequently reference GtACR1, a well-used anion opsin, there is no side-by-side data comparing these new opsins to the existing state-of-the-art. Such comparisons are very useful to adopt new opsins.

GtACR1 exhibits the peak sensitivity at 515 nm and therefore is poorly suited for combination with red-shifted CCRs or fluorescent sensors, unlike blue-light-absorbing ancyromonad ACRs. Nevertheless, we conducted side-by-side comparison of ancyromonad ChRs, GtACR1 and GtACR2, the latter of which has the spectral maximum at 470 nm. The results are shown in the new Figures 1E and F, and the new multipanel Figure 1 – figure supplement 4 added in the revision. We also added the following text, describing these results, to the revised Results section:

“Figures 1E and F show the dependence of the peak photocurrent amplitude and reciprocal peak time, respectively, on the photon flux density for ancyromonad ChRs and GtACRs. The current amplitude saturated earlier than the time-to-peak for all tested ChRs. Figure 1 – figure supplement 4A-E shows normalized photocurrent traces recorded at different photon densities. Quantitation of desensitization at the end of 1-s illumination revealed a complex light dependence (Figure 1, Figure Supplement 4F). Figure 1 – figure supplement 5 shows normalized photocurrent traces recorded in response to a 5-s light pulse of the maximal available intensity and the magnitude of desensitization at its end.”

Next, multiphoton optogenetics is a promising emerging field in neuroscience, and I appreciate that the authors began to evaluate this approach with these opsins. However, a few additional comparisons are needed to establish the user viability of this approach, principally the photocurrent evoked using the 2p process, for given power densities. Comparison across the presented opsins and GtACR1 would allow readers to asses if these opsins are meaningfully activated by 2P.

We carried out additional 2P experiments in ancyromonad ChRs, GtACR1 and GtACR2 and added their results to a new main-text Figure 6 and Figure 6 – figure supplement 1. We added the new section describing these results, “Two-photon excitation”, to the main text in the revision:

“To determine the 2P activation range of AnsACR, FtACR, and NlCCR, we conducted raster scanning using a conventional 2P laser, varying the excitation wavelength between 800 and 1,080 nm (Figure 6 – figure supplement 1). All three ChRs generated detectable photocurrents with action spectra showing maximal responses at ~925 nm for AnsACR, 945 nm for FtACR, and 890 nm for NlCCR (Figure 6A). These wavelengths fall within the excitation range of common Ti:Sapphire lasers, which are widely used in neuroscience laboratories and can be tuned between ~700 nm and 1,020-1,300 nm. To assess desensitization, cells expressing AnsACR, FtACR, or NlCCR were illuminated at the respective peak wavelength of each ChR at 15 mW for 5 seconds. GtACR1 and GtACR2, previously used in 2P experiments (Forli, Vecchia et al. 2018, Mardinly, Oldenburg et al. 2018), were included for comparison. The normalized photocurrent traces recorded under these conditions are shown in Figure 6B-F. The absolute amplitudes of 2P photocurrents at the peak time and at the end of illumination are shown in Figure 6G and H, respectively. All five tested variants exhibited comparable levels of desensitization at the end of illumination (Figure 6I).”

Reviewer #3 (Public review):

Summary:

The authors aimed to develop Channelrhodopsins (ChRs), light-gated ion channels, with high potency and blue action spectra for use in multicolor (multiplex) optogenetics applications. To achieve this, they performed a bioinformatics analysis to identify ChR homologues in several protist species, focusing on ChRs from ancyromonads, which exhibited the highest photocurrents and the most blue-shifted action spectra among the tested candidates. Within the ancyromonad clade, the authors identified two new anion-conducting ChRs and one cation-conducting ChR. These were characterized in detail using a combination of manual and automated patch-clamp electrophysiology, absorption spectroscopy, and flash photolysis. The authors also explored sequence features that may explain the blue-shifted action spectra and differences in ion selectivity among closely related ChRs.

Strengths:

A key strength of this study is the high-quality experimental data, which were obtained using well-established techniques such as manual patch-clamp and absorption spectroscopy, complemented by modern automated patch-clamp approaches. These data convincingly support most of the claims. The newly characterized ChRs expand the optogenetics toolkit and will be of significant interest to researchers working with microbial rhodopsins, those developing new optogenetic tools, as well as neuro- and cardioscientists employing optogenetic methods.

We thank the Reviewer for his/her positive evaluation of our work.

Weaknesses:

This study does not exhibit major methodological weaknesses. The primary limitation of the study is that it includes only a limited number of comparisons to known ChRs, which makes it difficult to assess whether these newly discovered tools offer significant advantages over currently available options.

We conducted side-by-side comparison of ancyromonad ChRs and GtACRs, wildly used for optical inhibition of neuronal activity. The results are shown in the new Figures 1E and F, and the new multipanel Figure 1 – figure supplement 4 and Figure 1 – figure supplement 5 added in the revision. We also added the following text, describing these results, to the revised Results section:

“Figures 1E and F show the dependence of the peak photocurrent amplitude and reciprocal peak time, respectively, on the photon flux density for ancyromonad ChRs and GtACRs. The current amplitude saturated earlier than the time-to-peak for all tested ChRs. Figure 1 – figure supplement 4A-E shows normalized photocurrent traces recorded at different photon densities. Quantitation of desensitization at the end of 1-s illumination revealed a complex light dependence (Figure 1, Figure Supplement 4F). Figure 1 – figure supplement 5 shows normalized photocurrent traces recorded in response to a 5-s light pulse of the maximal available intensity and the magnitude of desensitization at its end.”

Additionally, although the study aims to present ChRs suitable for multiplex optogenetics, the new ChRs were not tested in combination with other tools. A key requirement for multiplexed applications is not just spectral separation of the blue-shifted ChR from the red-shifted tool of interest but also sufficient sensitivity and potency under low blue-light conditions to avoid cross-activation of the respective red-shifted tool. Future work directly comparing these new ChRs with existing tools in optogenetic applications and further evaluating their multiplexing potential would help clarify their impact.

As a proof of principle, we co-expressed AnsACR with the red-shifted cation-conducting CCR Chrimson and demonstrated that the net photocurrent generated by this combination is hyperpolarizing in the blue region of the spectrum, and depolarizing at the red region. In the revision, we added a new panel (Figure 1D) showing these results and the following paragraph to the main text:

“To test the possibility of using AnsACR in multiplex optogenetics, we co-expressed it with the red-shifted CCR Chrimson (Klapoetke et al., 2014) fused to an EYFP tag in HEK293 cells. We measured the action spectrum of the net photocurrents with 4 mM Cl^- in the pipette, matching the conditions in the neuronal cytoplasm (Doyon, Vinay et al. 2016). Figure 1D, black shows that the direction of photocurrents was hyperpolarizing upon illumination with λ<500 nm and depolarizing at longer wavelengths. A shoulder near 520 nm revealed a FRET contribution from EYFP (Govorunova, Sineshchekov et al. 2020), which was also observed upon expression of the Chrimson construct alone (Figure 1D, red)”.

Reviewing Editor Comments:

The reviewers suggest that direct comparison to GtACR1 is the most important step to make this work more useful to the community.

We followed the Reviewers’ recommendations and carried out side-by-side comparison of ancyromonad ChRs and GtACR1 as well as GtACR2 (Figure 1E and F, Figure 1 – figure supplement 4, Figure 1 – figure supplement 5, and Figure 6). Note, however, that GtACR1’s spectral maximum is at 515 nm, which makes it poorly suitable for blue light excitation. Also, ChRs are known to perform very differently in different cell types and upon expression of their genes in different vector backbones, so our results cannot be generalized for all experimental systems. Each ChR user needs to select the most appropriate tool for his/her purpose by testing several candidates in his/her own experimental setting.

Reviewer #1 (Recommendations for the authors):

(1) The figure legend for Figure 2D-I appears to be incomplete. Please provide a detailed explanation of the panels.

In the revision, we have expanded the legend of Figure 2 to explain all individual panels.

(2) The meaning of the Vr shift (Y-axis in Figure 2H-I) should be clarified in the main text to aid reader understanding.

In the revision, we added the phrase “which indicated higher relative permeability to NO₃ than to Cl^-“ to explain the meaning of the Vr shift upon replacement of Cl^- with NO₃-.

(3) Adding statistical analysis for the peak and end photocurrent values in Figure 2D-F would strengthen the claim that there is minimal change in relative permeability during illumination.

In the revision, we added the V_r values for the peak photocurrent to Figure 2H-I, which already contained the V_r values for the end photocurrent, and carried out a statistical analysis of their comparison. The following sentence was added to the text in the revision:

“The V_r values of the peak current and that at the end of illumination were not significantly different by the two-tailed Wilcoxon signed-rank test (Fig. 2G), indicating no change in the relative permeability during illumination.”

(4) Figure 4H and I seem out of place in Figure 4, as the title suggests a focus on wild-proteins and AnsACR mutants. The authors could consider moving these panels to Figure 3 for better alignment with the content.

As noted below, we changed the panel order in Figure 4 upon the Reviewer’s request. In particular, former Figure 4I is Figure 4C in the revision, and former Figure 4H is now panel C in Figure 3 – figure supplement 1 in the revision. We rearranged the corresponding section of the text (highlighted yellow in the manuscript).

(5) The characterization section could be strengthened by including data on the pH sensitivity of FtACR, which is currently missing from the main figures.

Upon the Reviewer’s request, we carried out pH titration of FtACR absorbance and added the results as Figure 4B in the revision.

(6) The logic in Figure 4A-G appears somewhat disjointed. For example, Figure 4A shows pH sensitivity for WT AnsACR and the G86E mutant, while Figure 4 B-D shifts to WT AnsACR and the D226N mutant, and Figure 4E returns to the G86E mutant. Reorganizing or clarifying the flow would improve readability.

We followed the Reviewer’s advice and changed the panel order in Figure 4. In the revised version, the upper row (panels A-C) shows the pH titration data of the three WTs, the middle row (panels D-F) shows analysis of the AnsACR_D226N mutant, and the lower row (panels G-I) shows analysis of the AnsACR_G88E mutant. We also rearranged accordingly the description of these panels in the text.

(7) In Figure 5A, "NIACR" should likely be corrected to "NlCCR".

We corrected the typo in the revision.

(8) The statistical significance in Figure 6C and D is somewhat confusing. Clarifying which groups are being compared and using consistent symbols would improve interoperability.

In the revision, we improved the figure panels and legend to clarify that the comparisons are between the dark and light stimulation groups within the same current injection.

(9) The authors pointed out that at rest or when a small negative current was injected, the neurons expressing Cl- permeable ChRs could generate a single action potential at the beginning of photostimulation, as has been reported before. The authors could help by further discussing if and how this phenomenon would affect the applicability of such tools.

We mentioned in the revised Discussion section that activation of ACRs in the axons could depolarize the axons and trigger synaptic transmission at the onset of light stimulation, and this undesired excitatory effect need to be taken into consideration when using ACRs.

Reviewer #2 (Recommendations for the authors):

Govorunova et al present three new anion opsins that have potential applications in silencing neurons. This paper follows the tradition of the Spudich lab, presenting and rigorously characterizing potentially valuable opsins. Furthermore, they explore several mutations of the identified opsin that may make these opsins even more useful for the broader community. In general, I feel positively about this manuscript. It presents new potentially useful opsins and provides characterization that would enable its use. I have a few recommendations below, mostly centered around side-by-side comparisons to existing opsins.

(1) My primary concern is that while there is a reference to GtACR1, a highly used opsin first described by this team, they do not present any of this data side by side.

When evaluating opsins to use, it is important to compare them to the existing state of the art. As a potential user, I need to know where these opsins differ. Citing other papers does not solve this as, even within the same lab, subtle methodological differences or data plotting decisions can obscure important differences.

As we explained in the response to the public comments, we carried out side-by-side comparison of ancyromonad ChRs and GtACRs as requested by the Reviewer. The results are shown in the new Figures 1E and F, and the new multipanel Figure 1 – figure supplement 4 and Figure 1 – figure supplement 5, added in the revision. However, we would like to emphasize a limited usefulness of such comparative analysis, as ChRs are known to perform very differently in different cell types and upon expression of their genes in different vector backbones, so our results cannot be generalized for all experimental systems. Each ChR user needs to select the most appropriate tool for his/her purpose by testing several candidates in his/her own experimental setting.

(2) Multiphoton optogenetics is an emerging field of optogenetics, and it is admirable that the authors address it here. The authors should present more 2p characterization, so that it can be established if these new opsins are viable for use with 2P methods, the way GtACR1 is. The following would be very useful for 2P characterization:

Photocurrents for a given power density, compared to GtACR1 and GtACR2.

The new Figure 6 (B-F) added in the revision shows photocurrent traces recorded from the three ancyromonad ChRs and  two GtACRs upon 2P excitation of a given power density.

Comparing NICCR and FtACR's wavelength specificity and photocurrent. If these opsins are too weak to create reasonable 2P spectra, this difference should be discussed.

The new Figure 6A shows the 2P action spectra of all three ancyromonad ChRs.

A Trace and calculated photocurrent kinetics to compare 1P and 2P. This need not be the flash-based absorption characterization of Figure 3, but a side-by-side photocurrent as in Figure 2.

As mentioned above, photocurrent traces recorded from ancyromonad ChRs and GtACRs upon 2P excitation are shown in the new Figure 6 (B-F). However, direct comparison of the 2P data with the 1P data is not possible, as we used laser scanning illumination for the former and wild-field illumination for the latter.

Characterization of desensitization. As the authors mention, many opsins undergo desensitization, presenting the ratio of peak photocurrent vs that at multiple time points (probably up to a few seconds) would provide evidence for how effectively these constructs could be used in different scenarios.

We conducted a detailed analysis of desensitization under both 1P and 2P excitation. The new Figure 1 – figure supplement 4 and Figure 1 – figure supplement 5 show the data obtained under 1P excitation, and the new Figure 6 shows the data for 2P conditions.

I have to admit, that by the end of the paper, I was getting confused as to which of the three original constructs had which property, and how that was changing with each mutation. I would suggest that a table summarizing each opsin and mutation with its onset and offset kinetics, peak wavelength, photocurrent, and ion selectivity would greatly increase the ability to select and use opsins in the future.

In the revision, we added a table of the spectroscopic properties of all tested mutants as Supplementary File 2. This study did not aim to analyze other parameters listed by the Reviewer. We added the following sentence referring to this table to the main text:

“Supplementary File 2 contains the λ values of the half-maximal amplitude of the long-wavelength slope of the spectrum, which can be estimated more accurately from the action spectra than the λ of the maximum.”

It may be out of the scope of this manuscript, but if a soma localization sequence can be shown to remove the 'axonal spiking' (as described in line 441), this would be a significant addition to the paper.

Our previous study (Messier et al., 2018, doi: 10.7554/eLife.38506) showed that a soma localization sequence can reduce, but not eliminate, the axonal spiking. We plan to test these new ACRs with the trafficking motifs in the future.

NICCR appears to have the best photocurrents of all tested opsins in this paper. It seems odd that it was omitted from the mouse cortical neurons experiments.

We have not included analysis of NlCCR behavior in neurons because we are preparing a separate manuscript on this ChR.

Figure 6 would benefit from more gradation in the light powers used to silence and would benefit from comparison to GtACR. I suggest using a fixed current with a series of illumination intensities to see which of the three opsins (or GtACR) is most effective at silencing. At present, it looks binary, and a user cannot evaluate if any of these opsins would be better than what is already available.

In the revision, we added the data comparing the light sensitivity of AnsACR and FtACR with previously identified GtACR1 and GtACR2 (new Figure 1E and F) to help users compare these ACRs. Although they are less sensitive to light comparing to GtACR1 and GtACR2, they could still be activated by commercially available light sources if the expression levels are similar. Less sensitive ACRs may have less unwanted activation when using with other optogenetic tools.

Reviewer #3 (Recommendations for the authors):

Suggested Improvements to Experiments, Data, or Analyses:

(1) Line 25: "significantly exceeding those by previously known tools" and Line 408: "NlCCR is the most blue-shifted among ancyromonad ChRs and generates larger photocurrents than the earlier known CCRs with a similar absorption maximum." As noted in the public review, this statement applies only to a very specific subgroup of ChRs with spectral maxima below 450 nm. If the goal was to claim that NlCCR is a superior tool among a broader range of blue-light-activated ChRs, direct comparisons with state-of-the-art ChRs such as ChR2 T159C (Berndt et al., 2011), CatCh (Kleinlogel et al., 2014), CoChR (Klapoetke et al., 2014), CoChR-3M (Ganjawala et al., 2019), or XXM 2.0 (Ding et al., 2022) would be beneficial. If the goal was to demonstrate superiority among tools with spectra below 450 nm, I suggest explicitly stating this in the paper.

The Reviewer correctly inferred that we emphasized the superiority of NlCCR among tools with similar spectral maxima, not all blue-light-activated ChRs available for neuronal photoexcitation, most of which exhibit absorption maxima at longer wavelengths. To clarify this, we added “with similar spectral maxima” to the sentence in the original Line 25. The sentence in Line 408 already contains this clarification: “with a similar absorption maximum”.

(2) Lines 111-113: "The absorption spectra of the purified proteins were slightly blue-shifted from the respective photocurrent action spectra (Figure 1D), likely due to the presence of non-electrogenic cis-retinal-bound forms." I would be skeptical of this statement. The spectral shifts in NlCCR and AnsACR are small and may fall within the range of experimental error. The shift in FtACR is more apparent; however, if two forms coexist in purified protein, this should be reflected as two Gaussian peaks in the absorption spectrum (or at least as a broader total peak reflecting two states with close maxima and similar populations). On the contrary, the action spectrum appears to have two peaks, one potentially below 465 nm. Generally, neither spectrum appears significantly broader than a typical microbial rhodopsin spectrum. This question could be clarified by quantifying the widths of the absorption and action spectra or by overlaying them on the same axis. In my opinion, the two spectra seem very similar, and just appearance of the "bump" in the action spectum shifts the apparent maximum of the action spectrum to the red. If there were two states, then they should both be electrogenic, and the slight difference in spectra might be explained by something else (e.g. by a slight difference in the quantum yields of the two states).

As the Reviewer suggested, in the revision we added a new figure (Figure 1 – figure supplement 2), showing the overlay of the absorption and action spectra of each ancyromonad ChR. This figure shows that the absorption spectra are wider than the action spectra (especially in AnsACR and FtACR), which confirms our interpretation (contribution of the non-electrogenic blue-shifted cis-retinal-bound forms to the absorption spectrum). Note that the presence of such forms explaining a blue shift of the absorption spectrum has been experimentally verified in HcKCR1 (doi: 10.1016/j.cell.2023.08.009; 10.1038/s41467-025-56491-9). Therefore, we revised the text as follows:

“The absorption spectra of the purified proteins (Figure 1C) were slightly blue-shifted from the respective photocurrent action spectra (Figure 1 – figure supplement 3), likely due to the presence of non-electrogenic cis-retinal-bound forms. The presence of such forms, explaining the discrepancy between the absorption and the action spectra, was verified by HPLC in KCRs (Tajima et al. 2023, Morizumi et al., 2025).”

(3) Lines 135-136: "The SyncroPatch enables unbiased estimation of the photocurrent amplitude because the cells are drawn into the wells without considering their tag fluorescence." While SyncroPatch does allow unbiased selection of patched cells, it does not account for the fraction of transfected cells. Without a method to exclude non-transfected cells, which are always present in transient transfections, the comparison of photocurrents may be affected by the proportion of untransfected cells, which could vary between constructs. To clarify whether the statistically significant difference in the Kolmogorov-Smirnov test could indicate that the fraction of transfected cells after 48-72h differs between constructs, I suggest analyzing only transfected cells or reporting fractions of transfected cells by each construct.

The Reviewer correctly states that non-transfected cells are always present in transiently transfected cell populations. However, his/her suggestion to “exclude non-transfected cells” is not feasible in the absence of a criterion for such exclusion. As it is evident from our data, transient transfection results in a continuum of the amplitude values, and it is not possible to distinguish a small photocurrent from no photocurrent, considering the noise level. We would like, however, to emphasize that not excluding any cells provides an estimate of the overall potency of each ChR variant, which depends on both the fraction of transfected cells and their photocurrents. This approach mimics the conditions of in vivo experiments, when non-expressing cells also cannot be excluded.

(4) Line 176: "AnsACR and FtACR photocurrents exhibited biphasic rise." The fastest characteristic time is very close to the typical resolution of a patch-clamp experiment (RC = 50 μs for a 10 pF cell with a 5 MΩ series resistance). Thus, I am skeptical that the faster time constant of the biphasic opening represents a protein-specific characteristic time. It may not be fully resolved by patch-clamp and could simply result from low-pass filtering of a specific cell. I suggest clarifying this for the reader.

The Reviewer is right that the patch clamp setup acts as a lowpass filter. Earlier, we directly measured its time resolution (~15 μs) by recording the ultrafast (occurring on the ps time scale) charge movements related to the trans-cis isomerization (doi: 10.1111/php.12558). However, the lowpass filter of the setup can only slow the entire signal, but cannot lead to the appearance of a separate kinetic component (i.e. a monophasic process cannot become biphasic). Therefore, we believe that the biphasic photocurrent rise reflects biphasic channel opening rather than a measurement artifact. Two phases in the channel opening have also been detected in GtACR1 (doi: 10.1073/pnas.1513602112) and CrChR2 (10.1073/pnas.1818707116).

(5) Line 516: "The forward LED current was 900 mA." It would be more informative to report the light intensity rather than the forward current, as many readers may not be familiar with the specific light output of the used LED modules at this forward current.

We have added the light intensity value in the revision:

“The forward LED current was 900 mA (which corresponded to the irradiance of ~2 mW mm^-2)…”

(6) Lines 402-403: "The NlCCR ... contains a neutral residue in the counterion position (Asp85 in BR), which is typical of all ACRs. Yet, NlCCR does not conduct anions, instead showing permeability to Na+." This is not atypical for CCRs and has been demonstrated in previous works of the authors (CtCCR in Govorunova et al. 2021, ChvCCR1 in Govorunova et al. 2022). What is unique is the absence of negatively charged residues in TM2, as noted later in the current study. However, the absence of negatively charged residues in TM2 appears to be rare for ACRs as well. Not as a strong point of criticism, but to enhance clarity, I suggest analyzing the frequency of carboxylate residues in TM2 of ACRs to determine whether the unique finding is relevant to ion selectivity or to another property.

The Reviewer is correct that some CCRs lack a carboxylate residue in the D85 position, so this feature alone cannot be considered as a differentiating criterion. However, the complete absence of glutamates in TM2 is not rare in ACRs and is found, for example, in HfACR1 and CarACR2. We have discussed this issue in our earlier review (doi: 10.3389/fncel.2021.800313) and do not think that repeating this discussion in this manuscript is appropriate.

Recommendations for Writing and Presentation:

(1) Some figures contain incomplete or missing labels:

Figure 2: Panels D to I lack labels.

In the revision, we have expanded the legend of Figure 2 to explain all individual panels.

Figure 3 - Figure Supplement 1: Missing explanations for each panel.

In the revision, we changed the order of panes and explained all individual panels in the legend.

Figure 5 - Figure Supplement 1: Missing explanations for each panel.

No further explanation for individual panels in this Figure is needed because all panels show the action spectra of various mutants, the names of which are provided in the panels themselves. Repeating this information in the figure legend would be redundant.

(2) In Figure 2, "sem" is written in lowercase, whereas "SEM" is capitalized in other figures. Standardizing the format would improve consistency.

In the revision, we changed the font of the SEM abbreviation to the uppercase in all instances.

(3) Line 20: "spectrally separated molecules must be found in nature." There is no proof that they cannot be developed synthetically; rather, it is just difficult. I suggest softening this statement, as the findings of this study, together with others, will probably allow designing molecules with specified spectral properties in the future.

In the revision, we changed the cited sentence to the following:

“Multiplex optogenetic applications require spectrally separated molecules, which are difficult to engineer without disrupting channel function”.

(4) Line 216-219: "Acidification increased the amplitude of the fast current ~10-fold (Figure 4F) and shifted its Vr ~100 mV (Figure 3 - figure supplement 1D), as expected of passive proton transport. The number of charges transferred during the fast peak current was >2,000 times smaller than during the channel opening, from which we concluded that the fast current reflects the movement of the RSB proton." The claim about passive transport of the RSB proton should be clarified, as typically, passive transport is not limited to exactly one proton per photocycle, and the authors observe the increase in the fast photocurrents upon acidification.

We thank the Reviewer for pointing out the confusing character of our description. To clarify the matter, we added a new photocurrent trace to Figure 4I in the revision recorded from AnsACR_G86E at 0 mV and pH 7.4. We have rewritten the corresponding section of Results as follows:

“Its rise and decay τ corresponded to the rise and decay τ of the fast positive current recorded from AnsACR_G86E at 0 mV and neutral pH, superimposed on the fast negative current reflecting the chromophore isomerization (Figure 4I, upper black trace). We interpret this positive current as an intramolecular proton transfer to the mutagenetically introduced primary acceptor (Glu86), which was suppressed by negative voltage (Figure 4I, lower black trace). Acidification increased the amplitude of the fast negative current ~10-fold (Figure 4I, black arrow) and shifted its V_r ~100 mV to more depolarized values (Figure 4 – figure supplement 2A). This can be explained by passive inward movement of the RSB proton along the large electrochemical gradient.”

Minor Corrections:

(1) Line 204: Missing bracket in "phases in the WT (Figure 4D."

The quoted sentence was deleted during the revision.

(2) Line 288: Typo-"This Ala is conserved" should probably be "This Met is conserved."

We mean here the Ala four residues downstream from the first Ala. To avoid confusion, we changed the cited sentence to the following:

“The Ala corresponding to BR’s Gly122 is also found in AnsACR and NlCCR (Figure 5A)…”

(3) Lines 702-704: Missing Addgene plasmid IDs in "(plasmids #XXX and #YYY, respectively)."

In the revision, we added the missing plasmid IDs.

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

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Reply to the reviewers

Note to all Reviewers:

We would like to thank all the reviewers for their time and insightful feedback. In response to the comments and points raised, we have performed major revisions to our manuscript. We have expanded our analysis on the role of TP53 loss of function in BM activation (Figure 3), investigating human LUAD datasets as well as murine LUAD models. We show that TP53 pathway is significantly negatively correlated with BM, and that loss of TP53 leads to the acquisition of the basal-like phenotype regardless of the type of driver oncogene present (KRAS/EGFR). Furthermore, we added a new figure (Figure 7), where we demonstrate that type I interferon can promote BM activation in LUAD harboring TP53 mutations but not in those with wild type TP53. With this, we propose a mechanism of action of how a subset of LUAD tumors (TP53-mut) upregulate BM, become more aggressive and resistant to therapies.

Finally, we have made the manuscript clearer and transparent by improving the presentation of plots, as well as including source data files and Rmarkdown files for reproducibility.

Reviewer 1:

Major comments

R1-Comment 1: The authors did not submit with the manuscript all the results that they have obtained from their analysis, on which they based their claims. I suggest that the authors submit a SourceData file in Excel format. This file should contain the values and the relevant information for each of the plots presented in the main and supplementary figures. For example, in case of box plots, the five-number summary should be provided. Further, the p-values and the test used for their calculations should be also mentioned. The file could be organized in a way that the data and relevant information for each figure panel are presented in separated data sheets in order that the reader can easily navigate through the file and find the information for each figure panel fast. Similarly, the authors should provide access to the scripts that they have developed or adapted from published scripts to perform the analysis of the datasets and obtain the results presented in the manuscript. The access to the scripts used in the manuscript is important to reproduce analysis. The scripts can be deposited at github, for example.

Reply: We thank the reviewer for their advice in making the presentation of our results and methods more transparent and reproducible. We have now provided the source data file (supplementary file 2), which contains relevant data for each figure. We have also uploaded Rmarkdown files to github and R Markdown HTML reports are compiled in Supplementary File 3, this shows how the analyses were performed and how each figure generated. All datasets required to reproduce the analyses and figures have also been added to Zenodo (10.5281/zenodo.16964654) and will be published when the article is in press.

R1-Comment 2: The results and their interpretations are mainly done based on in silico analysis from publicly available transcriptomic datasets. The confirmation of the results obtained by the in silico analysis is limited to the last figure, in which the authors show results obtained by multiplexed immunohistochemistry and histo-cytometry of tissue microarrays from a curated cohort of FFPE samples. The relevance of the results obtained by the in silico analysis may increase if the authors could present results either in a conditional (lung-specific) Kras mutant mouse model, or patient-derived xenograft (PDX) mouse model of lung cancer. The PDX mouse model will be more suitable in case that access to genetically modified mouse models is not given and/or the time for the experiments is limited. In both cases, the hyperactivation of the small GTPase KRAS should expand the BM gene expression signature in the mouse lung in a Sox9-dependent manner, thereby leading to lung tumors. Further, Sox9 loss-of-function experiments should reduce the BM gene expression signature and favor the ALV gene expression signature. These results would strongly support the interpretation of the in silico results by the authors in the present manuscript, and would significantly increase the impact of the manuscript in the scientific field of lung cancer.

Reply: We thank the reviewer for their insightful feedback on how to improve the impact of our study through further functional validation of in silico findings. To address this comment, we have performed additional analyses, including data and experiments from both murine and human LUAD model systems to elucidate a novel mechanism of BM activation in LUAD. We appreciate the reviewer’s suggestion to pursue analysis of Sox9 involvement in regulating BM activation and agree that both KRAS and SOX9 activation are likely to be involved in at least some elements of the process of disease progression we described in this manuscript. Indeed, previous studies have completed the experiments suggested, demonstrating Sox9 knock-out reduced Kras driven tumour progression and morphological grade in vivo (PMID: 37258742 and 34021911); and was associated with loss of AT2 lineage identity (PMID: 37468622).

Our analysis of human LUAD using scRNA-seq data has demonstrated that this differentiation spectrum in fact extends beyond loss of lineage fidelity and in a subset of cells leads to transdifferentiation to a basal-like cell state. In our revised manuscript, we have more clearly elucidated the role of KRAS and TP53 in these two events during LUAD progression, demonstrating that while oncogenic KRAS (and likely downstream SOX9 activation) can lead to the loss of lineage commitment in LUAD cells, mutations in TP53 are required for acquisition of the basal-like phenotype. We have also expanded on this mechanism identifying a novel role for type-1 interferon signaling in the presence of TP53 loss-of-function as a mechanism that can lead to BM activation and acquisition of a basal-like cell state in LUAD. The data related to these analyses are now presented in figures 3 and 7.

In accordance with the 3Rs principles for ethical use of animals in research we have taken advantage of publicly available data from previous experiments analyzing conditional (lung-specific) Kras mutant mouse model to validate our in-silico findings. This confirmed our in silico analysis of human LUAD, demonstrating an important role for TP53 loss of function in regulating BM activation (presented in Figure 3E&F and Figure S3F&G)

We also showed that the type I interferon signaling is capable of driving BM activation in LUAD but only in the context of TP53 loss-of-function. These experiments were performed using 3D organotypic cultures of H441 cells (human adenocarcinoma cell line with mutant TP53) and A549 cells (human adenocarcinoma cell line with wild-type TP53). These 3D cultures were treated with IFN-alpha, both BM and basal-like marker upregulation (MKI67, CDC20, TOP2A, S100A9, S100A2, SOX9 and KRT17) was observed only in LUAD cells carrying a mutation in TP53. These data are now presented in Figure S7D.

R1-Comment 3: In general, the description of the results in the corresponding section of the manuscript can be improved to facilitate the understanding of the results presented.

As an example, the figure 1B is described on page 13 as follows: "...we first used a publicly available microarray dataset [9] to identify genes differentially expressed between epithelial cells engaged in BM (embryonic day 14 [E14]) or ALV (embryonic day 19 [E19]) (Figure 1B and TABLE S1)." By looking at the plot in figure 1B, this description is not sufficient to understand what the authors present in this figure panel, not even after reading the corresponding figure legend.

Reply: We thank the reviewer for their advice on making our manuscript clearer. Throughout the manuscript we have now edited the result descriptions, we have also provided further detail to the methods sections, figure legends and axes labels to enhance clarity and facilitate understanding of the analyses performed.

In the example cited we have edited the sections referenced above as follows:

“To test this hypothesis, we identified genes that were differentially expressed in epithelial cells engaged in active BM (corresponding to embryonic day 14) vs active ALV (corresponding to embryonic day 19), using a publicly available microarray dataset.”

We have also changed the Y axis label of Figure 1B to: “log2(FC E19 [ALV] – E14[BM])”.

The description in the figure legend has also been modified to provide more context: “Dot plot showing the identification of genes differentially expressed by epithelial cells during murine developmental-BM (embryonic day 14) and ALV (embryonic day 19) [1]. Genes with the highest Fold Change of expression between day 14 (BM) and 19 (ALV) of murine lung development are coloured green or red, respectively. These genes were used to generate ALV/BM signatures [9]”

R1-Comment 5: Another example is the description of the figure 3B on page 16: "This showed low levels of BM activation in tumour cells from residual disease (RD) that was significantly increased in samples with recurrent progressive disease (PD) (Figure 3B)." By looking figure 3B and the corresponding figure legend, one cannot find the group "residual disease (RD)".

__Reply: __We thank the reviewer for their diligent reading and have now corrected the figures to provide clearer labelling of axes and maintain consistency throughout. In the example cited, we have corrected the axis label to Residual disease (RD) and partial response (PR).

R1-Comment 6: Another example is the description of the figure 3C and 3D on page 16: "Single-cell analysis showed that both ALV-BM- and ALV-BM+ LUAD cells were increased in samples from recurrent progressive disease (Figure 3C,D)." By looking at figure 3D and the corresponding legend, I do not find the explanation of "TRUE" and "FALSE". The same is for figures 3J and 3M.

Reply: For this example (Figure 3 in the original manuscript is now figure 4), TRUE/FALSE labels have been replaced by PR (partial response) and PD (progressive disease) in panel D; replaced by “Responder (R)” or “Non-responder (NR)” in panels J&M.

R1-Comment 7: Other figure panels were also poorly described in the results section and in the corresponding legends. Further, the presentation of the results in the main and supplementary figures has to be improved. For example, labeling of the Y-axis in the figures 1H to 1J, 2C, 2D, 2G, 2H, 3B, 3C, 3J, 3L, etc. has to be improved. As a point of reference, I would suggest checking how other authors present similar results in life science journals. These deficiencies in the presentation and description of the results make it difficult for the readers to understand the manuscript.

Reply: These axes labels have been changed throughout to provide more information. “BM” changed to “BM (ssGSEA score)” or “BM (module score)” and “ALV” changed to “ALV (ssGSEA score)” or “ALV (module score)” for figures 1H, 1I, 1J, S1H-L, 2C, 2D, 3B, 3F, S3E, S3F, S3G, 4B, 4C, 4J, 4L, S4C, S4D, S5A, 6A, S6A, S6B, ssGSEA score was applied to bulk RNAseq samples, and modules scores were calculated for single cells.

Additionally:

S2A, S2B – OS label changed to Survival probability/OS probability.

S4H – y axis label changed to PDL1 (RPPA).

S3B – y axis label changed to “Tumour mutational burden (mut/mB).

S3C – y axis label changed to “Tobacco smoking (SBS mutational signature)”.

4F – y axis label changed to “DFS (proportion)”.

4H – y axis label changed to “PFS (proportion)”.

R1-R1-Comment 8: The authors write on page 18 "Despite AT2 cells being well described as the cell of origin for LUAD, this population was significantly less abundant in LUAD samples compared to control, demonstrating a high degree of transcriptomic plasticity within LUAD epithelium (Figure 4D)." How can the authors show that these results are not produced by the process of integration of the four scRNA-seq NSCLC datasets, the implementation of a specific machine learning classifier for the cell type-classification, or the manually filtration to exclude doublets? For example, will the authors achieve the same (or similar) results using a different machine learning classifier? If yes, please include the results in the manuscript.

Reply: The integration was performed using the method described by Stuart et al. (PMID: 31178118), implemented in the Seurat package. The term “machine learning classifier” has now been replaced by “label transfer” to clarify the method used and avoid confusion. Label transfer was only used to identify major cell types in the datasets used, i.e. the whole epithelial population. Doublet removal was performed as follows (and described in the methods section): epithelial cells were clustered using the shared nearest neighbor (SNN) modularity optimization algorithm implemented by the FindClusters function in the Seurat R package, based on 30 principal components and setting the resolution parameter to 0.1. This clustering solution identified multiple small clusters with divergent expression profiles to the majority of cells that were initially classified as epithelial (in the label transfer analysis). Manual examination of the marker genes for these small clusters showed they were characterized by expression of epithelial genes alongside canonical markers for either B cells (CD79A), macrophages (CD68, SPP1, APOE, CD14, MARCO) or Tcells/NK cells (CD3D, NKG7, CXCR4). These cells were therefore classed as heterotypic doublets and excluded from further analysis. All other cell types from the integrated datasets were analyzed in the same way, and no further epithelial clusters (that were not small clusters of doublets) were identified.

Further clustering to identify epithelial subpopulations was performed on the integrated dataset and the results presented from this analysis represent the clustering solution that ensures all subpopulations were identified across datasets to mitigate any potential batch-effects not resolved by the integration process. Furthermore, our results showing that LUAD cells exhibit a high degree of transcriptomic plasticity were also confirmed by the lineage fidelity analysis (Figure 5G&I), which demonstrates this observation is not dependent on a single clustering, integration or machine learning algorithm. This observation is also supported by other studies that have described loss of lineage commitment during LUAD tumorigenesis, where tumour cells become transcriptionally and phenotypically distinct from healthy AT2 cells.

Reviewer 1:

Minor comments:

__R1-Comment 9: __Please introduce the abbreviation for alveogenesis the first time that is used in the abstract, as it was done for branching morphogenesis.

__Reply: __Abbreviation for alveogenesis has now been added to the abstract.

R1-Comment 10: On page 18 the author write: "Consistent with the analyses presented above, pseudo bulk expression profiles for each sample showed that ALV and BM scores were significantly negatively correlated (r = -0.68, p = 4.1e-09)." Where are these results shown? I was not able to find these results. If they are not in the current version of the manuscript, please include the results

Reply: Scatter plot showing the negative correlation has now been added as Figure S5A.

__R1-Comment 11: __The authors should submit a supplementary table containing a list of the different data sets that were used for this manuscript. The table should include accession numbers and links to the different depositories, in which the data sets can be found. This will improve the overview of the datasets used in the study, as well as facilitate the finding of the datasets by the readers.

Reply: The list of all datasets used in this study, together with accession numbers and links are now in Supplementary file1.

R1-Comment 12: In figure 1G, change the color for FALSE in the legend.

Reply: Color for FALSE changed in Figure 1G and Figure S1E.

R1-Comment 13: Provide the complete list of mutated genes for Figure S2C.

Reply: Figure S2C has been replaced by figures 3C (top mutated genes in LUAD-BM) and S3A (top mutated genes in LUAD-ALV).

Reviewer 1 (Significance (required)):

__R1-Comment 14: __Conceptually, Bienkowska KJ et al. propose that LUAD tumors undergo reversion from an alveogenic to branching morphogenic phenotype during disease progression, generating inflamed or basal-like cell states that are variably persistent following TKI or ICB treatments. This concept is in line with reports using murine models of Kras-driven LUAD. In addition, there are parallels with findings in idiopathic pulmonary fibrosis (IPF, another hyperproliferative lung disease), in which KRT5-/KRT17+ basaloid cells were transiently found, like the basal-like phenotype that Bienkowska KJ identified in human LUAD. In other words, the concept proposed by the authors is novel and in line with previous publication in LC and IPF.

Response: We are glad the reviewer found our results novel and appreciated how they provide a linkage of previously defined mechanisms seen in murine developmental models to human cancer progression, and how they may be relevant for other diseases such as IPF.

__R1-Comment 15: __The in silico analysis of publicly available transcriptomic datasets presented by Bienkowska KJ et al. is original and comprehensive. It is an interesting contribution to the cancer research field. However, the impact of their findings to this scientific field will significantly increase if the authors could confirm the interpretation of their results using other experimental systems in addition to the one used in the las figure. For example, the experiments that I suggested in point 2., using either conditional Kras transgenic mice or a PDX mouse model for lung cancer will not only confirm the concpet proposed by the authors, but it will also provide further mechanistic insides related to this model at cellular and molecular level.

Response: We thank the reviewer for describing our analysis as original and comprehensive and their suggestion to develop the manuscript further with additional mechanistic analyses. We have comprehensively examined the mechanisms responsible for regulating BM activation using a combination of in vivo models and 3D organotypic cultures, elucidating a novel role for type-1 interferon signaling in the presence of TP53 loss-of-function as a mechanism that can lead to BM activation and acquisition of a basal-like cell state in LUAD. For further information regarding these additions to the revised manuscript, we direct the reviewer to the response provided to R1-comment 2 (above).

__R1-Comment 16: __Overall, the manuscript by Bienkowska KJ et al. addresses topics that are relevant to the field of lung cancer, the leading cause of cancer-related deaths worldwide. The bioinformatic methods implemented are cutting-edge. However, the text of the manuscript and the presentation of the results in the figures have to be improved to better exploit the potential of their findings. In addition, further experiments should be performed to confirm (and perhaps complement) the interpretation of their findings. I hope that my comments support the authors to improve the manuscript to reach the standard of manuscripts recently published at renowned journals in Review COMMONS. I recommend a major revision of the manuscript before publication.

__Reply: __We are pleased to read that the reviewer found the methods implemented by us to be cutting-edge, and that they recognized the relevance of this topic to the lung cancer field.

We thank the reviewer for their comments, which have helped us to significantly improve our manuscript.

We have made changes to how we present our data (as described in responses above) and performed further analyses to support our original findings. We have also now performed further in silico and functional analyses to expand and complement our original findings.

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

R2-Comment 1: __The study is novel and interesting, but the mechanisms how the dysregulation of developmental program was driven by specific oncogene and how to link these signatures to therapy were also not clear. __

__Reply: __We are pleased that the reviewer finds our study to be novel and interesting. We appreciate the reviewer pointing out the need to clarify the role of specific oncogenes to BM activation and response to therapies.

We have now added further analyses and edited the text to examine and explain how the ALV and BM signatures are driven by different oncogenes (Figure 3 and results section “TP53 loss of function is required for BM activation”), which showed that common oncogenic drives (e.g. KRAS and EGFR) can drive reduced ALV signature expression but TP53 mutations (or deletion in murine models) was critical for driving BM activation. Implications for therapy response are shown in (Figure 4). We have shown that BM activation is a key determinant of tyrosine kinase inhibitor (TKI) resistance in LUAD, representing a frequently activated off-target mechanism of resistance that supersedes the presence of an actionable oncogenic driver in terms of response rates; and that the BM signature also identified patients that, although positive for immune checkpoint blockade (ICB) response biomarkers, will likely fail to respond to this treatment. In the manuscript we have now thoroughly revised these sections of the results to clarify the details associated with these conclusions (results sections: “BM activation is associated with targeted-therapy resistance in lung adenocarcinomas” and “BM activation predicts poor response to immune-checkpoint blockade”).

We have also added further data to the manuscript elucidating the molecular mechanisms regulating BM activation (Figure 7), which has identified an important role for aberrant type-I interferon signaling in the context of mutant TP53.

Reviewer #2 (Significance (Required)):

__R2-Comment 2: __The authors in this manuscript aimed to examine the role of developmental programmes, alveogenesis and branching morphogenesis (BM), in regulating phenotypic diversity in NSCLC. They demonstrated that developmental programmes (ALV and BM) frequently become

dysregulated in NSCLC, with BM activation identifying aggressive LUAD that were resistant

to multiple therapies, including TKIs and ICB. They found that BM activation in LUAD was associated with TP53 pathway mutations and required AT2 cells to lose their alveolar identity, acquiring a basallike state. The study is very intriguing, and the findings may pave a link to the disease progression and therapy resistance in LUAD.

__Response: __We are pleased the reviewer found the study intriguing and with the potential to better understand LUAD progression and resistance to therapies.

__R2-Comment 3: __The current results presented, although comprehensively presented, is still many an association study, the mechanisms how these dysregulations of developmental programmes driven by the driver oncogenes or carcinogens are still unknown.

Response: We thank the reviewer for challenging us to further examine the molecular mechanisms underpinning our initial observations. As described above (see response to Reviewer #1 comment 2), we have performed additional in silico and mechanistic experimental analyses, which identified a novel role for type-I IFN signaling and TP53 loss-of-function in the activation of the BM program in LUAD. We hope these additions have enhanced the significance of the manuscript presented.

__R2-Comment 4: __The NSCLC is a heterogeneous disease, LUAD and LUSC are two different diseases in terms of oncogenesis, driver mutations and response to treatment. The manuscript may either just focused on LUAD or describe results carefully to include both LUAD and LUSC. For example, in the result of abstract, only LUAD was described, there was no mention of LUSC.

__Response: __We agree with the reviewer that NSCLC is a heterogeneous and complex disease. Indeed, this was in part what motivated us to investigate the role played by developmental processes in these distinct oncogenic processes. Our analyses showed that LUSC tumors were generally high for the BM signature (Figure 1I), which likely contributed to why this signature did not stratify survival rates for LUSC (Figure S2B). As a result, we opted to focus on LUAD as we found that BM activation was predictive of disease progression and survival in this NSCLC subtype. However, we did not completely remove LUSC from our manuscript to examine the degree to which LUAD tumors upregulating BM become “LUSC-like” and evaluate whether histological transformations occurred in LUAD cases with BM activation (as described in Figure 5 and the “BM activation in LUAD is associated with a basal-like phenotype” results section).

We have also now added a description of results from both LUAD and LUSC analyses to the abstract to clarify these points.

__R2-Comment 5: __The most common driver mutation of LUAD was EGFR, the authors also try to link the BM activation link to TKI resistance. I assumed that the TKIs most of the patients used were EGFR TKI, but the study did not examine the role of EGFR in the dysregulation of developmental programmes.

__Response: __We would like to thank the reviewer for highlighting an important aspect of how our work fits with current clinical practice in LUAD management. Our analyses were carried out over multiple cohorts that include different patient demographics, which have varied prevalence for specific oncogenic driver mutations (with EGFR mutations typically being more prevalent in Asian cohorts and KRAS mutations generally being the most common oncogenic driver in Western cohorts). To examine these two common oncogenic drivers impact on BM activation, we now include a direct analysis of BM level in cases harboring these mutations (Figure S3D-E). This showed that that irrespective of oncogenic driver mutations TP53 loss of function was associated with increased BM. Our new analysis of KRAS driven mouse models has also showed that KRAS activation is sufficient to induce reduced expression of the ALV signature but failed to elicit increased BM activation. Given our analysis of human tumours showed that EGFR mutant LUAD cases with wild-type TP53 had low levels of BM activation (Figure S3D), we have no reason to suspect that EGFR mutations alone would be sufficient to elicit BM activation.

__R2-Comment 6: __The TKI resistance was very complicated, not just EGFR T790M, the results and discussion regarding the activation of BM and TKI resistance seems not adequate. The mouse model used by Dr. Chang was mainly KRAS driven mouse lung cancer model (mice carrying RosatdT, Sox2EGFP, ShhCre, Sox9CKO, Fgfr2CKO, RosamTmG, Sox9CreER, Nkx2.1CreER, and KrasLSL-G12D alleles). It is not clear whether the EGFR driven (the most common driver of LUAD) mouse model also has same genetic signature. At least, the authors should describe or discuss these discrepancies.

__Response: __We thank the reviewer for their comments and advice on making our manuscript clearer. We have now revised the description of BM activation and TKI resistance in the results section (titled “BM activation is associated with targeted-therapy resistance in lung adenocarcinomas”).

La fundación de unas 500 colonias griegas fue clave para la expansión del mundo helénico. alrededor del 40% de los griegos vivía en estos nuevos territorios, lo que fortaleció el comercio, difundió la cultura griega y amplió su influencia política y económica.

El comercio mejoró porque los barcos mercantes permitían transportar más productos de forma segura y el camino Diolkos en Corinto facilitaba acortar rutas evitando rodear toda la península.

este recurso era tan valioso para los griegos que era el único que controlaba el estado, servía para poder alimentar a la población, sobre todo en épocas de sequia

Acá se demuestra lo importante que fue el comercio para los griegos, no solo para lo económico, sino también en lo cultural.

Debido a su ubicación geográfica que conectaba a Atenas con Europa, Asia y áfrica, este se volvió un punto de encuentro clave para el comercio.

3000 a.C. hasta el 1200 a.C. Fue un periodo marcado por el uso de bronce para herramientas y armas, y la exportacion de aceite de oliva, vino, ceramica y productos artesanales. El crecimiento economico facilito la acumulacion de riqueza y el desarrollo de una clase mercantil.

This report came up in conversation. Compares 20yrs of EU digital policy to everything open. -[ ] lees report digital commons na 20jr EU digipol #geonovumtb

Briefing sur l'impact de l'IA sur l'enseignement sur France Culture

Ce document de briefing analyse les thèmes principaux, les idées essentielles et les faits importants issus des discussions sur l'intégration de l'intelligence artificielle dans l'éducation.

1. L'IA dans l'enseignement : omniprésence, défis et opportunités

L'IA, en particulier les IA génératives grand public, est déjà massivement présente dans les pratiques des élèves et, dans une moindre mesure, des enseignants.

Selon le ministère de l'Éducation nationale, 80% des élèves et 20% des professeurs utilisent déjà l'IA.

• Usages des élèves : Les élèves utilisent l'IA pour corriger des textes, reformuler des cours, mieux comprendre des notions, trouver des définitions de mots, et parfois pour tricher.

Un élève témoigne : "Je lui demande quand j'ai besoin d'aide pour faire les dissertations de philosophie ou de français. (...) il te mâche un petit peu le travail."

Cependant, il y a aussi une conscience des limites : "parfois il donne des informations fausses et donc faut quand même vérifier si c'est ça ou pas."

Un exemple concret de triche est cité : une élève utilisant une IA lors d'un devoir surveillé pour obtenir les solutions à l'oreille.

• Problèmes cognitifs : Christophe Caillot, professeur d'histoire, souligne que les IA génératives sont "extrêmement problématiques au point de vue cognitif" car elles agissent comme des "courts circuits dans les apprentissages".

Il explique qu'apprendre est un "chemin, un parcours qu'on doit faire assez long chemin d'embûe" et que l'IA "nous empêche d'accéder au savoir aux apprentissages".

• Perte de sens de l'apprentissage : L'usage de l'IA est vu comme "dévalorisant" les apprentissages et posant un "problème anthropologique" en expliquant "qu'on peut se passer d'apprendre dans la vie", ce qui revient à "se passer de ce qui fait un peu le sel de la vie".

• Rôle des IA conçues pour l'éducation : Orian Ledroit, directrice générale d'EdTech France, distingue les IA grand public (comme ChatGPT) des "autres IA qui sont aussi dans les classes mais qui ont été conçus à des fins éducatives et qui n'ont pas ni les mêmes impacts ni les mêmes modèles technologiques et qui sont (...) utiles à des apprentissages qui sont plus stimulants plus personnalisés".

Elle mentionne des études montrant des effets positifs et négatifs selon le contexte, l'accompagnement et les utilisateurs, notamment sur la motivation à apprendre (réduction de la peur de l'échec).

• Manque de recul et de formation : Jada Pistili, docteure en philosophie spécialisée dans l'éthique de l'IA, souligne le manque de recul scientifique pour évaluer les impacts à long terme de ces technologies.

"On est tous un peu des cobaye en fait," dit-elle, insistant sur l'approche "mitigée" nécessaire. Elle mentionne également le manque de formation des enseignants.

2. Le débat sur la personnalisation et le remplacement des enseignants

• L'argument de la personnalisation (EdTech) : Orian Ledroit défend que l'IA peut "donner les moyens aux enseignants (...) d'identifier encore mieux peut-être encore plus facilement les fragilités d'un élève ou au contraire les facilités de l'autre" et ainsi permettre une "différenciation" ou "personnalisation" de l'apprentissage.

Elle affirme que l'IA ne vise pas à remplacer les enseignants mais à "soutenir leur pratique pédagogique".

• La critique de la personnalisation (Syndicat enseignant) : Christophe Caillot rejette l'argument de la personnalisation comme un "argument marketing", expliquant qu'une IA, n'étant pas une personne, "ne peut pas personnaliser".

Il compare les IA éducatives actuelles aux "teaching machines" de Skinner des années 50, qui n'ont pas abouti à une révolution.

Pour lui, la solution aux difficultés des enseignants réside dans l'embauche de personnel humain ("des enseignants, des CPE des AESH des AED et cetera des humains en fait") plutôt que dans l'adoption d'IA qui posent des "problèmes écologiques monstrueux" et sont "fondées sur le pillage des données".

• La peur du remplacement : Le "spectre du remplacement" est une inquiétude majeure chez les enseignants, comme en témoigne la forte participation aux formations syndicales sur le sujet.

Christophe Caillot cite des "expérimentations déjà aux États-Unis en Angleterre en Corée du Sud de classe voire d'école sans prof", y voyant le véritable objectif de ces entreprises : "si ces tech elles veulent exister (...) elles ont besoin à un moment que de prendre la place des enseignants il y a pas d'autres solutions."

• L'enseignant augmenté : Orian Ledroit évoque l'idée d'"augmenter l'enseignant" en automatisant les tâches chronophages qui "ne relèvent pas de la pratique pédagogique", comme la gestion des emplois du temps, la notation, la correction des copies et la préparation des cours.

Christophe Caillot y voit une vision "transhumaniste" qui suggère que les enseignants actuels sont "insuffisants".

3. IA, service public et modèle économique

• Articulation public-privé : Orian Ledroit rappelle que le secteur de l'éducation travaille déjà avec des entreprises privées (ex: manuels scolaires) et que "quand on développe un outil d'intelligence artificielle à des fins éducatives on fait le pari de on investit on fait de la recherche et développement".

L'État, selon elle, prend moins de risques.

Elle insiste sur le fait que les logiciels éducatifs doivent être "conformes à un cadre qui est défini par l'État et notamment qui prévoit le fait d'être conforme à toutes les réglementations européennes en matière de données personnelles RGPD et cetera".

• Critique de la marchandisation de l'éducation : Christophe Caillot dénonce le fait que la tech considère l'éducation comme un "marché parmi d'autres", ce qui est en contradiction avec la vision de l'école comme "service public" et "bien commun".

Il estime que la "recherche du profit rentre en concurrence s'affronte avec la défense du bien commun", citant l'exemple de la privatisation de l'eau.

• Transparence et biais : Jada Pistili suggère que l'Open Source pourrait être une solution pour la "transparence" des systèmes d'IA, notamment concernant les "données d'entraînement" et les "biais" qui en découlent.

Orian Ledroit affirme que les IA éducatives, développées avec des pédagogues, intègrent la correction des biais dès la conception, contrairement aux IA génératives grand public.

Christophe Caillot contredit cette affirmation en citant l'exemple de ChatGPT produisant des interprétations biaisées de la laïcité française.

• Double discours : Jada Pistili observe une "forme d'hypocrisie" et un "double mesure et double poids" dans les politiques publiques : on interdit aux élèves d'utiliser l'IA pour les devoirs, mais on dote les enseignants d'outils basés sur l'IA, ce qui crée un manque de sens pour les élèves.

Elle insiste sur la nécessité d'un "vrai programme, une vraie pédagogie" pour la formation à l'IA des enseignants, afin de ne pas "dénigrer un peu la figure de l'enseignant".

4. Bilan et perspectives

Le débat révèle une tension fondamentale entre le potentiel de l'IA à "bouleverser l'éducation" et les inquiétudes profondes quant à ses implications cognitives, éthiques et sociétales.

• La rapidité du changement : Jada Pistili conclut que "tout va un peu trop vite" et que la société "tâtonne" face à une technologie qui "va transformer plein de domaines".
• L'autonomie des élèves : Christophe Caillot insiste sur la nécessité de "maintenir la capacité de nos élèves des futures générations à ne pas utiliser les IA" et à "maintenir son autonomie par rapport à ses soi-disants outils", plutôt que de les former à les utiliser "de manière névrotique".
• La crise de l'école : Orian Ledroit souligne les défis actuels de l'école publique (reproduction des inégalités, baisse du niveau, manque de profs) et voit l'IA comme un moyen "pragmatique et concret de s'appuyer sur des outils qui ont fait leurs preuves" pour y répondre, en attendant des investissements massifs.

En somme, l'intégration de l'IA dans l'éducation est une réalité complexe, perçue tantôt comme une solution prometteuse pour une personnalisation de l'apprentissage et un allègement des tâches des enseignants, tantôt comme une menace pour le sens de l'apprentissage, l'autonomie des élèves et la nature du service public d'éducation.

Le manque de recul, de formation et de transparence, ainsi que la question de la marchandisation du savoir, sont au cœur des préoccupations.

“misconception…to think of the economy as an engine with spending as its gas pedal.” This emphasizes the author’s critique of oversimplified macroeconomic models that treat the economy like a machine, ignoring complexity and human behavior.

financial intermediaries…enable specialization” and “as that trust ebbs and flows, the financial sector can amplify fluctuations.” This shows the author’s point that finance is crucial for specialization but is sensitive to trust, which can magnify economic ups and downs.

the pricesystem guides the economy toward sustainable use of resources” and “individuals who attempt to override the pricesystem…can easily miscalculate the costs.” This emphasizes that the author argues the price system naturally encourages sustainability, while personal or government interference can backfire.

“attacks the misconception…that the economy can be analyzed like a machine” and “fails to take account of the many nonmechanistic aspects of the economy.” This shows the author’s critique of treating economics purely like engineering, emphasizing that human behavior and social factors make the economy more complex than a machine.

He knows that his breakfast depends upon workers on the coffee plantations…utensils made of aluminum, china, steel, and glass.” This emphasizes the global interconnection of labor and resources—showing how everyday items rely on a complex, international network of production and trade.

how many ship-builders, sailors, sail-makers, rope-makers, must have been employed…What a variety of labour too is necessary in order to produce the tools of the meanest of those workmen!” Note that this illustrates the vast network of specialized labor required even for basic production, showing the complexity and interdependence of economies.

the produce of the joint labour of a great multitude of workmen.” Note that even simple goods rely on the coordinated work of many people, emphasizing the importance of specialization and trade in everyday life.

As the economy becomes more specialized and the production becomes more roundabout, the financial sector takes on more significance.” Note that higher capital intensity and longer production processes increase the need for financial systems to support early-stage workers and investments.

“Most of the people whose work enables the farmer to harvest wheat have no idea that they are part of the wheat production process.” Note that complex production involves many unseen contributors, illustrating the concept of “roundabout” production in the Austrian school.

“Workers with powerful tools are more productive. It is easier to excavate a foundation with a bulldozer than with a spoon.” Note that more specialized work requires better tools (capital), which increases efficiency and output.

Improvements in transportation have connected the world’s regions more closely, promoting greater specialization”. Note that better transport enables wider trade networks, which increases economic efficiency and interdependence.

“The more you specialize, the more you need to trade to obtain what you want”. Note that this emphasizes the link between specialization and trade—economic interdependence grows as individuals focus on specific tasks.

specialization is subtle, deep, and highly dependent on context” and the examples before it. Note that this shows how the value of specialized skills depends on the broader economic and technological environment—changes in industry or technology can increase or decrease the importance of a person’s work.

materials that had to be mined and transported” and “transportation equipment itself had to be manufactured”. Note that this emphasizes the interconnectedness of production—how even simple goods rely on a vast network of labor, materials, and technology.

“what you would need to do to make your cereal completely from scratch” and “If you use tools…you would have to make those tools yourself.” Note that the passage illustrates how modern life relies on complex production processes and specialized skills, showing how dependent we are on the broader economy.

“almost everything you consume is something you could not possibly produce” and “human society…without specialization and trade”. Note that the author emphasizes the essential role of cooperation, trade, and specialization in supporting daily life and societal progress.

The MIT approach suppresses that question…” and “macro-economics… claim to know how to manage the overall levels of output and employment”. Note that the author is criticizing the overconfidence of economists, especially in macroeconomics, and how MIT-style training discourages healthy skepticism about what can truly be known or controlled.

“I have come to believe… that economists have lost the art of critical thinking.” This emphasizes the author’s critique of modern economics, particularly how MIT’s influence after WWII shifted the field toward less critical, more formulaic thinking, signaling a departure from questioning underlying assumptions.

'Smith identified the productivity gains from large-scale factory production… division of labour” and “free-market forces… and the pursuit of self-interest would best stimulate innovation and growth.” This shows how Adam Smith laid the groundwork for capitalism and the idea of the “invisible hand,” but his focus was more on class dynamics and supporting productive capitalists than purely individual self-interest.

“real-world behaviour of humans is not remotely consistent with the assumption of blind, individualistic greed.” This shows how experiments (like the 50–50 split being most common) challenge neoclassical economic theory, proving people value fairness and social norms over pure self-interest.

capitalism is only a very recent system compared to the long history of human economies. Note that humans have always worked to meet needs, but capitalism (about 250 years old) is just one stage among many and will likely be replaced by new ways of organizing work in the future. This helps put capitalism in perspective as temporary, not permanent.

Economists avoid the word capitalism

Naming is important - understanding system we live in.

focus of book = capitalism

UN’s Human Development Index (HDI) = GDP + health + education.

Allocate efficiency = maximizing usefulness of existing resources through exchange, ignoring fairness or distribution.

Neoclassical economics assumes scarcity as permanent and resources as given.

stanford emphasizes that everyday observation gives real insight into economic life, without relying on statistics.

connects personal finances to inflation and real income.

Budgeting shows how individuals experience the economy through income and expenses.

¿Por qué importa? Porque con orquestación ya logramos resultados “wow”, pero para que sean editables, colaborativos y sostenibles (no demos de una sola vez), necesitamos: estándares para intercambiar contenidos, traspasos sin fricción y trazabilidad clara. Así el salto desde cosas torpes como el viejo clip de Will Smith comiendo spaghetti a piezas cinematográficas no solo se ve mejor, se produce mejor.

Poder “pasar la posta” de un paso al siguiente sin hacks: un click para enviar tomas, mantener versionado, conservar la línea de tiempo y permitir round-trips (volver atrás, editar y regresar sin romper nada). Esto implica conectores, APIs y plantillas de proyecto coherentes entre generador → editor → render.

Que las apps hablen el mismo idioma. Ejemplos: un formato estándar para la escena (geometría, materiales, cámaras, luces, capas) y otro para el movimiento (keyframes, curvas, esqueletos). Así, si generas un personaje en la herramienta A, lo animas en la B y lo compones en la C, no pierdes nada al exportar/importar (piensa en cosas tipo USD/glTF para escena o BVH/retargeting para movimiento).

Coraz częściej przewiduje się również dodatkowe miejsce na moduły dopuszkowe dla..

delete

to informacja dla spółdzielni - delete

W przypadku mieszkań kluczową rolę przy planowaniu instalacji elektrycznej odgrywa materiał, z którego wykonany jest budynek. Jeżeli mamy do czynienia z blokiem z tzw. wielkiej płyty, podejście do rozprowadzenia przewodów musi być inne niż w nowym budownictwie. Najczęściej najlepszym rozwiązaniem okazuje się poprowadzenie instalacji po ścianach działowych, w podłodze lub w suficie podwieszanym – to nie tylko praktyczne, ale też korzystne cenowo dla inwestora.

Warto jednak pamiętać, że dobrze zaprojektowana instalacja to nie tylko oszczędność na etapie wykonania, ale też wygoda i bezpieczeństwo na lata. Dlatego już na samym początku warto przemyśleć liczbę gniazd, punktów świetlnych oraz ewentualne miejsce na dodatkowe przewody (np. do internetu, automatyki czy klimatyzacji), aby uniknąć kosztownych przeróbek w przyszłości.

nie ten dział, usunąć

przenieść na do następnego wersu

Oświetlenie schodów można zrealizować na kilka eleganckich sposobów. Najczęściej stosuje się lampki schodowe montowane w ścianie nad stopniami, które dyskretnie podkreślają każdy bieg. Popularnym rozwiązaniem są także taśmy LED umieszczone pod stopniami, dające efekt lekkiego podświetlenia. Alternatywnie, paski LED instalowane w profilach w ścianie tworzą nowoczesną i estetyczną linię światła. Takie rozwiązania nie tylko zwiększają bezpieczeństwo, ale również podnoszą walory wizualne wnętrza.

Jeśli w łazience przewidujesz jakiekolwiek gniazdko – np. do podłączenia lustra z podświetleniem LED czy innych urządzeń – zadbaj o to, by było ono chronione wyłącznikiem różnicowoprądowym RCD 30 mA typu A.

Blue - critical observations

Briefing Document: "La Rentrée 2025 pour les élèves de seconde, première et terminale" au Lycée Louis Vincent Ce document synthétise les informations clés et les thèmes principaux abordés lors du YouTube live de rentrée scolaire 2025-2026 du Lycée Louis Vincent.

Date de l'événement: YouTube live de la rentrée scolaire 2025-2026 Intervenants Principaux:

Olivier Palaise, Proviseur du Lycée Louis Vincent Alexianne Bonasso, Proviseure adjointe (BTS et 1ère) Lauren Fortini, Proviseure adjointe (Terminales et examens) Véronique Lefèvre, CPE (Internat) Janny Deico, Président du Conseil des Parents d'Élèves de Moselle (FCPE Moselle) Lionel René, Directeur Délégué sur les formations technologiques et industrielles Sylvie Bontempli, Secrétariat pédagogique 1. Présentation Générale du Lycée Louis Vincent Le Lycée Louis Vincent est un établissement historique, ouvert en 1920, construit par les Allemands, et qualifié d' "impérial" en raison de son envergure. Initialement un lycée technique avec 80% de formations industrielles, il est aujourd'hui un lycée général et technologique, majoritairement général (80% d'enseignement général).

Effectifs: Le lycée compte entre 1500 et 1600 élèves chaque année, avec environ 1580 élèves cette année. 485-486 élèves de seconde Environ 450 élèves de première Environ 450 élèves de terminale Formations BTS et Classes Préparatoires (TSI1, TSI2) Spécificité: Le lycée est réputé pour sa rigueur dans son fonctionnement. Accès: L'entrée principale pour les élèves se fait par le portail métallique bleu de la rue Toule. Les entrées et sorties sont contrôlées, les sacs vérifiés, et les élèves doivent présenter leur livret d'accueil avec photo. Historique Technologique: Le lycée célèbre les 200 ans des formations industrielles de Metz cette année, soulignant son héritage technique et son adaptation aux enjeux actuels (STI2D, STL). 2. Organisation de la Rentrée et Informations Pratiques La rentrée est échelonnée pour les différents niveaux afin de faciliter l'accueil:

Horaires de Rentrée:Secondes: 8h00 Premières: 9h00 Terminales: 9h30 Techniciens supérieurs / Classes prépa TSI1, TSI2: 10h00 / 8h30 Internat: Les internes sont attendus le dimanche soir (sauf rares exceptions le lundi matin). Une réunion pour les parents d'internes est prévue le dimanche soir à 20h30 en salle d'honneur. Pour l'internat d'excellence, l'accueil des parents est à partir de 16h, avec une réunion à 16h30 en présence de l'adjoint du commandant de la caserne CRS. Affichage des classes: Les classes seront affichées dans la cour. Il est noté que des "fuites" informatiques permettent parfois aux élèves et parents de connaître les classes à l'avance. Premières Réunions de Parents:Terminales: 8 septembre à 18h en salle d'honneur Premières Générales: Mercredi 10 septembre à 18h en salle d'honneur Premières et Terminales Technologiques: Vendredi 12 septembre à 18h en salle d'honneur Secondes: Réunion générale en salle d'honneur à 18h, suivie d'une répartition par classe avec les professeurs principaux à 18h30 pour discuter des attentes du lycée. Réunion d'explication de fonctionnement du lycée (ouverte à tous les parents): Vendredi 5 septembre à 18h en salle d'honneur. Emplois du temps: Les emplois du temps standard sont opérationnels dès le lundi 14h. Les parents sont invités à les consulter régulièrement via Pronote. Casiers: Attribution limitée, réservée aux demandes particulières (difficultés à porter des charges lourdes, problèmes médicaux). 3. Le Projet Lycée 4.0 et le Numérique La Région Grand Est met en œuvre le projet "Lycée 4.0", un projet pédagogique sur l'informatique et le numérique.

Distribution d'ordinateurs: Tous les nouveaux élèves (secondes, et autres classes s'ils ne proviennent pas d'un lycée de la région) recevront un ordinateur portable offert par la Région. La distribution aura lieu dès le lundi de la rentrée pour les secondes. Il est recommandé de ne pas ouvrir l'ordinateur immédiatement pour éviter tout dommage et de conserver le carton d'emballage pour la garantie. L'ordinateur est prêté pour les trois années (seconde, première, terminale) et pourra être conservé à l'issue de la 3ème année, sauf si l'élève quitte la Région Grand Est. Usage de l'ordinateur:Le lycée est entièrement équipé de Wifi. Les manuels scolaires sont numériques et fournis gratuitement par la Région (licences incluses). L'installation des logiciels et manuels sera encadrée par les professeurs de SNT (Sciences Numériques et Technologiques) durant la première semaine. Mise en garde: Il est fortement déconseillé d'installer des jeux sur l'ordinateur, car "c'est pas un ordinateur qui est prévu pour jouer, c'est un ordinateur qui est prévu pour les manuels scolaires pour aller faire des recherches pour internet et cetera". Tests de Positionnement: Les ordinateurs devront être opérationnels dès la deuxième semaine pour les tests de positionnement. Autres ressources numériques:Compte EduConnect: Permet l'accès à "Mon Bureau Numérique", aux notes (Pronote), au cahier de texte, aux procédures d'orientation et aux bourses. Les comptes EduConnect sont les mêmes que ceux utilisés au collège. Il est annoncé que les classes et emplois du temps seront accessibles via "Mon Bureau Numérique" dès le lendemain de la réunion. Compte Wifi Grand Est: Sera distribué aux élèves à la rentrée. Office de Microsoft: Fourni gratuitement avec des comptes spécifiques. Application Pronote et ScolenGo: Pronote sera la référence pour les emplois du temps. Les informations du cahier de texte seront sur Mon Bureau Numérique (ScolenGo). Les notes seront également sur Pronote. 4. Résultats Scolaires et Exigences Le lycée affiche de "très bons voire excellents" résultats, fruit du travail des élèves et des équipes éducatives.

Résultats BTS (session 2025):Métiers de la Chimie: 100% de réussite (en augmentation constante depuis 4 ans). SIRA: 67% CPI: 91% CPRP: 80% CRSA: 90% Moyenne des BTS: environ 88% de réussite. La classe prépa technologique (STI2D, STL SPCL) permet l'intégration en écoles d'ingénieurs. Résultats Baccalauréat (session juin 2025):Baccalauréat Général: Supérieur à 95% de réussite, 74% de mentions. Filières Technologiques (STI2D, STL): Supérieur à 95% de réussite. STI2D: 57% de mentions. STL: 66% de mentions. Parcoursup: Tous les élèves ont obtenu une réponse sur Parcoursup. Calculatrices: Une calculatrice spécifique et programmable est requise pour les épreuves de baccalauréat (mathématiques, physique). Une procédure de commande groupée est mise en place via les professeurs de mathématiques, avec un lien disponible sur le site du lycée jusqu'au 10 septembre. Épreuve Anticipée de Mathématiques (EAM) en Première: Nouveauté cette année, cette épreuve aura lieu en juin (2h écrite, avec une partie orale). Les sujets sont adaptés selon que l'élève suit la spécialité mathématiques ou l'enseignement mathématique obligatoire. Exigence et Bienveillance: Le lycée insiste sur la "certaine exigence au niveau travail [et] au niveau cadre de vie scolaire". Respect des adultes, pas de cris dans les couloirs. Téléphones portables: Interdits d'utilisation dans les bâtiments, sauf autorisation d'un adulte. L'ordinateur portable sera privilégié en classe. Absences et Retards: Une rigueur est demandée. Les retards dus aux transports scolaires sont à anticiper. Les absences doivent être justifiées, mais une vigilance est demandée aux parents sur les motifs réels. "on est exigeant tout en étant bienveillant". Ouverture du Lycée: Du lundi au vendredi de 7h30 à 18h30. Les cours commencent à 8h. Samedi Matin: Le lycée est ouvert 24 samedis par an pour "devoirs de rattrapage, rattrapage de devoirs, colle, etc." En cas de non-respect du règlement (ex: utilisation du portable dans les couloirs), des "colles" de 3h le samedi matin sont appliquées. 5. Soutien Scolaire et Orientation Accompagnement Personnalisé (AP): Des séances (environ 27 par an) sont proposées en mathématiques, physique et français (ou philosophie en terminale) en seconde, première et terminale. Ces aides ciblent des groupes d'environ 5 élèves. Préparation aux examens: Des exercices de simulation, notamment pour le "Grand Oral" (avec sollicitation des parents pour faire partie des jurys, ex: avocats). Accompagnement à l'Orientation: Réalisé par les professeurs principaux en seconde, première et terminale, axé sur la méthodologie et les informations Parcoursup. Psy-EN: Il est fortement recommandé aux élèves de terminale de prendre rendez-vous avec les psychologues de l'Éducation Nationale ("psy-EN") dès le premier trimestre, car leur planning est très chargé en fin d'année. Parcoursup: Présenté comme un "outil" et non comme la source de la complexité de l'orientation. Les élèves sont encouragés à créer leurs comptes Parcoursup dès la seconde et à explorer les formations. Pix: Certification d'usage du numérique, obligatoire pour toutes les terminales avant le baccalauréat, utile pour Parcoursup. SNU (Service National Universel): Les élèves de seconde peuvent y participer et cela peut remplacer les stages de seconde. 6. Restauration Scolaire et Aides Financières Accès Cantine: Possible dès la rentrée pour les demi-pensionnaires inscrits. Les élèves externes pourront également manger, mais la procédure administrative sera "un peu plus complexe". Tarifs: Complexité des tarifs en raison des aides régionales. Internat: L'hébergement est "gratuit" (coût de 10€, mais équivaut à une aide de 1200€), mais les repas sont payants (environ 1400€ pour l'internat complet). Demi-pension: Coût global d'environ 650€ à l'année pour un élève standard. Aides Régionales (ARS): Une aide de 20 centimes par repas est proposée aux familles non boursières mais dont les revenus sont juste au-dessus du seuil, ce qui représente environ 100€ de réduction annuelle. Fonds Sociaux: Disponibles pour les familles en difficulté (pré-bac). S'adresser aux professeurs, CPE, proviseures adjointes, gestionnaires, ou au secrétariat élève. Une adresse mail dédiée: fondsocial@ellvmes.fr. 7. Communication et Événements Info Parents: Toutes les informations sont régulièrement envoyées par mail via ce canal. Projet d'établissement et Plan d'évaluation: Documents communiqués aux parents pour présenter les objectifs et le fonctionnement des évaluations. Journée du Patrimoine: 20 septembre, occasion de visiter l'établissement (ateliers, vue depuis le clocher). Fête de la Science: Octobre. 8. Représentants de Parents d'Élèves L'importance de la participation des parents est fortement soulignée, à tous les niveaux: conseils de classe et conseil d'administration. Les élections se feront par voie numérique (Pronote).

Deux associations présentes: FCPE et PEEP. Réunions d'information pour les associations:PEEP: Mardi 9 septembre à 18h30 en salle d'honneur. FCPE: Jeudi 11 septembre à 18h30 en salle d'honneur. Rôle des Parents Élus:Accompagner les enfants dans leur parcours scolaire. Faire valoir les droits et représenter les parents et les enfants. Donner un avis sur les décisions pédagogiques et financières. Rôle de médiateur. FCPE (Janny Deico, Président FCPE Moselle): Association de 75 ans, défend les valeurs d'une "école publique gratuite, inclusive et laïque". Propose des formations aux parents élus pour les aider à intervenir efficacement. Participation aux Conseils de Classe: Il est essentiel d'avoir des parents formés et de représenter tous les enfants. Deux parents par conseil de classe sont nécessaires (84 parents pour 42 classes). 9. Infrastructures et Projets Abri Vélo Connecté: Un nouvel abri vélo autonome et connecté a été installé, équipé de panneaux solaires photovoltaïques pour recharger les vélos électriques et trottinettes. Il s'intègre à l'architecture en bois et permet de suivre la production et consommation d'énergie, promouvant la mobilité douce et le développement durable (STID2D). Il comprend également un espace personnel pour les élèves avec tables en bois pour recharger téléphones et ordinateurs. Travaux futurs: Des travaux sont prévus devant l'établissement dans le cadre du projet "Métis", visant à créer un espace piéton plus grand, moins de parkings et de voitures. L'ancien site de l'hôpital Bonsecours a déjà été transformé en appartements. Dangérosité du carrefour: Vigilance demandée aux élèves concernant le carrefour devant le lycée, très fréquenté. Interdiction de fumer: Il est interdit de fumer aux abords de l'établissement, y compris sur la placette de la rue Toule. Visite virtuelle: Une vidéo montre les locaux intérieurs (escalier monumental, loge, bureaux de la vie scolaire, CPE, secrétariat élèves, proviseures adjointes, salle des professeurs, CDI, salle de permanence, salle d'honneur, couloirs des salles de classe, bureau des Psy-EN, service informatique). Il est précisé que le lycée est entièrement accessible aux PMR (ascenseur). 10. Conclusion Le Lycée Louis Vincent se présente comme un établissement à la fois historique et moderne, axé sur la réussite de chaque élève, l'exigence bienveillante, l'innovation numérique et la collaboration avec les familles. Les équipes se tiennent à disposition pour accompagner les élèves et les parents tout au long de l'année scolaire.

Review coordinated by Life Science Editors Foundation Reviewed by: Dr. Angela Andersen, Life Science Editors Foundation Potential Conflicts of Interest: None

PUNCHLINE This preprint uncovers how embryonic oxygen levels act as a regulatory switch controlling limb development timing in mammals. Using mouse and chick embryos, the authors demonstrate that hindlimb initiation in mammals is delayed due to hypoxia-induced expression of NFKB transcription factors (cRel, Rela) and Hif1a, which repress the limb-initiating gene Tbx4. In contrast, chick embryos in normoxia activate fore- and hindlimbs simultaneously. This oxygen-dependent repression is lifted as placental oxygenation increases, triggering hindlimb EMT and Tbx4 expression. Notably, limb heterochrony is not due to cis-regulatory evolution, but instead arises from differential trans-acting factor expression. The findings reframe heterochrony as an environmentally cued developmental program in placental mammals.

BACKGROUND Heterochrony—alteration in developmental timing—has long been recognized in limb evolution. While birds initiate fore- and hindlimb development simultaneously, mammals typically exhibit delayed hindlimb formation. This developmental delay has been hypothesized to reflect an "energy trade-off" during early embryogenesis. Yet, the molecular mechanisms linking the environment to timing remain unexplored. Zhu et al. provide the first mechanistic insight by identifying oxygen levels and NFKB signaling as modulators of limb timing in mammals.

KEY QUESTION ADDRESSED What molecular and environmental factors underlie the delay in hindlimb development in mammalian embryos compared to avian species?

SUMMARY Using mouse and chick embryos, the authors first demonstrate that mammalian hindlimb development is delayed starting from the earliest stage of limb bud formation—specifically, the epithelial-to-mesenchymal transition (EMT). This delay correlates with delayed expression of the hindlimb-specifying transcription factor Tbx4, but not its upstream activators (Pitx1, Isl1) or its forelimb counterpart (Tbx5). Surprisingly, enhancer-swap experiments rule out differences in Tbx4 cis-regulatory elements as the cause. Instead, bulk RNA-seq and functional screens reveal cRel, a member of the NFKB family, as a repressor of Tbx4 in early mouse hindlimb buds. Further experiments show that cRel and Rela are upregulated by hypoxia, and their expression is suppressed as the embryo transitions to normoxia via placental oxygenation. Culturing mouse embryos in normoxic conditions prematurely induces Tbx4 expression and EMT in the hindlimb. Knockout and overexpression experiments with cRel, Rela, and Hif1a confirm a hypoxia–NFKB–Hif1a–Tbx4 regulatory axis. This mechanism links maternal oxygen levels to developmental timing and may be an adaptive feature of viviparous mammals.

KEY RESULTS Hindlimb EMT and Tbx4 expression are delayed in mice but not chicks * → In mouse embryos, hindlimb EMT is delayed by ~18 hours relative to the forelimb (Figure 1A–B) * → In chick embryos, forelimb and hindlimb EMT occur nearly simultaneously * → Expression of Tbx4 (hindlimb) and Tbx5 (forelimb) correlates with EMT timing in both mouse and chick embryos (Figure 1C–I)

Early limb patterning signals are not delayed (mouse and chick) * → BMP, Wnt, and RA signaling are active in both forelimb and hindlimb fields in mouse and chick embryos at the same developmental stage (Figure 2A–B) * → Expression of upstream transcription factors Pitx1, Isl1, and Hoxb9 occurs on time in both limb fields in mouse and chick embryos (Figure 2C–F)

Tbx4 enhancer function is conserved across species * → The mouse HLEA/HLEB and chick Tbx4-Rec1 enhancers drive equivalent spatial and temporal expression when introduced into either mouse or chick embryos (Figure 3C–E, H) * → CRISPR/dCas9-KRAB repression of Tbx4-Rec1 in chick embryos reduces Tbx4 expression (Figure 3F–I) * cRel and Rela repress Tbx4 in mouse hindlimbs * → Bulk RNA-seq of early hindlimb buds from mouse embryos reveals cRel as a candidate repressor of Tbx4 (Figure 4A–C) * → Electroporation of cRel and Rela into chick hindlimb buds reduces Tbx4 expression and limb bud size (Figure 4D) * → In cRel knockout mouse embryos, Tbx4 expression is elevated and EMT occurs earlier than in controls (Figure 4E–H)

Oxygen regulates hindlimb timing in mouse embryos * → In mouse embryos, early hypoxia is evidenced by nuclear Hif1a accumulation in hindlimb mesenchyme (Figure 5A–C) * → Culturing mouse embryos under normoxic conditions leads to precocious Tbx4 and Pitx1 expression in hindlimbs (Figure 5E–G) * → EMT is also accelerated under normoxia in mouse hindlimbs (Figure 5H–I) * → qPCR on lateral plate mesoderm (LPM)-derived cells from mouse embryos shows cRel is upregulated in hypoxic vs. normoxic conditions (Figure 5J)

cRel and Hif1a functionally interact in mouse embryos * → In Hif1a knockout mouse embryos, Tbx4 expression is elevated and hindlimb EMT is precocious—mimicking the cRel knockout phenotype (Figure S9I–J) * → Manipulating cRel expression alters Hif1a levels in mouse embryonic cells (Figure S10) * → scRNA-seq from mouse LPM derivatives confirms upregulation of Tbx4, Pitx1, and Hox9 under normoxia (Figure S6E)

STRENGTHS * Identifies a molecular mechanism linking environmental oxygen levels to developmental timing * Demonstrates that heterochrony arises from trans-acting regulatory inputs, not enhancer evolution * Uses a broad and rigorous toolkit: enhancer reporters, genetic knockouts, hypoxia assays, ex utero culture, single-cell and bulk RNA-seq * Highlights the adaptability of developmental programs to viviparous life history * Conceptually reframes heterochrony as plastic and environmentally modulated

FUTURE WORK & EXPERIMENTAL DIRECTIONS * Characterize direct chromatin binding of cRel and Hif1a at Tbx4 enhancers * Examine other NFKB targets in the LPM that might contribute to limb timing * Explore whether similar timing mechanisms are conserved in other mammalian species, including humans * Investigate how oxygen levels interface with metabolic and mitochondrial signaling during early development * Test whether early normoxia affects other embryonic heterochronies beyond limb formation * Directly test whether hypoxia modulates limb timing in birds. Although oxygen manipulation in chick embryos is technically challenging, comparative data would clarify whether the hypoxia–NFKB–Tbx4 axis is a placental adaptation or part of a broader vertebrate timing program.

AUTHORSHIP NOTE This review was drafted with the assistance of ChatGPT (OpenAI) to organize and articulate key insights. Dr. Angela Andersen checked the final document.

FINAL TAKEAWAY This preprint provides a paradigm shift in our understanding of limb heterochrony by uncovering a mechanism through which maternal oxygen availability regulates the timing of hindlimb development. By linking environmental hypoxia to NFKB- and Hif1a-mediated repression of Tbx4, the authors show how the embryo delays hindlimb formation until placental oxygenation is sufficient. This elegant mechanism offers an evolutionary and physiological explanation for mouse hindlimb delay, and it opens new avenues in developmental timing, maternal-fetal signaling, and the evolution of viviparity.

Document de Synthèse : Réflexions sur l'Éducation, le Savoir et l'Intelligence selon Bernard Lahire

• Ce document de synthèse présente les idées principales et les faits marquants des extraits de l'interview de Bernard Lahire, sociologue et directeur de recherche au CNRS, à l'occasion de la publication de son livre "Savoir ou périr".

L'entretien explore la nature de l'apprentissage, le rôle de l'école et de l'évaluation, la définition de l'intelligence, la recherche scientifique et la transmission du savoir dans nos sociétés contemporaines.

1. Le Savoir comme Condition de Survie et l'Origine de l'École

Bernard Lahire insiste sur une perspective fondamentale : l'apprentissage et la transmission des savoirs sont intrinsèquement liés à la survie de toute espèce vivante, y compris l'espèce humaine.

• Survie et Adaptation : "Nos sociétés ne fonctionneraient pas, ne survivraient pas si elle n'organisait pas cet apprentissage."

L'apprentissage est une capacité d'adaptation essentielle, présente chez toutes les espèces. Un animal qui n'apprend pas à reconnaître ses prédateurs ne survit pas.

• L'Apprentissage Humain : Chez l'homme, l'apprentissage est extrêmement développé, allant de l'apprentissage social par imitation à l'enseignement organisé, complété par le langage.

• L'Émergence de l'École : L'école, en tant qu'institution dédiée à l'apprentissage, est une invention relativement tardive dans l'histoire de l'humanité (XVIe siècle au sens moderne).

Avant, la transmission se faisait "par voir faire et ouï-dire", via la culture orale.

L'écriture, apparue il y a environ 5000 ans, a permis d'objectiver et d'accumuler le savoir, rendant possible son organisation pédagogique et l'institutionnalisation de l'école.

• La Sophistication du Savoir : La complexification et la division des savoirs dans nos sociétés modernes ont rendu l'école indispensable et allongé les parcours scolaires.

La survie collective repose sur une masse considérable de savoirs sophistiqués, gérés par des corps de professionnels divers.

2. La Recherche de la Vérité et la Vulnérabilité du Savoir

Lahire aborde la nécessité de la vérité et les dangers de l'affaiblissement des institutions du savoir.

• La Vérité comme Nécessité Vitale : La vérité n'est pas qu'une question philosophique, c'est une condition de survie.

"Si nos savoirs d'ailleurs avant même les savoirs scientifiques… avaient été faux… ça fait longtemps qu'on aurait disparu."

Même les savoirs empiriques anciens devaient avoir un rapport minimal à la vérité pour permettre aux sociétés de survivre face aux fléaux naturels et aux maladies.

• Le Suprême Pouvoir et la Vulnérabilité : La division du travail et des connaissances a rendu l'humanité "surpuissante" en permettant des réalisations complexes comme le téléphone portable.

Cependant, attaquer les lieux de transmission et de création culturelle (recherche, éducation) est une forme de "suicide collectif".

• L'Attaque contre la Recherche : Des coupes budgétaires dans la recherche, la limitation du nombre de chercheurs ou l'exigence de rentabilité immédiate sont des freins à la production de nouveaux savoirs.

"À chaque fois qu'on affaiblit ces secteurs bah on se rend pas compte de tout ce qui serait possible."

La recherche, par nature, est imprévisible et ses applications ne peuvent pas toujours être anticipées à court terme.

3. L'École et la Destruction de la Curiosité et de l'Intelligence

Lahire critique vivement le système scolaire actuel, qui, selon lui, entrave les dispositions naturelles des enfants.

• La Curiosité Innée : Les enfants sont naturellement curieux, testant et explorant leur environnement par l'expérimentation et les questions.

Cette "pulsion exploratrice" est une disposition naturelle.

• L'École, Frein à la Curiosité : Le système scolaire, avec sa discipline collective, ses programmes surchargés et surtout l'évaluation constante, tend à étouffer cette curiosité.

"L'évaluation devient quelque chose qui bloque en fait la curiosité des enfants."

• Le Piège de l'Évaluation : L'évaluation est censée vérifier l'apprentissage, mais elle est devenue un objectif en soi, inversant la logique.

Les élèves apprennent "pour pouvoir passer un contrôle", ce qui nuit à un apprentissage profond et désintéressé.

• Les Mathématiques, Instrument de Sélection : Les mathématiques, une discipline intrinsèquement incroyable, sont devenues un "instrument de torture", un "perfouettard" pour la sélection scolaire, ce qui génère de l'aversion chez les élèves.

• Nuire à la Créativité : L'école, en privilégiant la reproduction des connaissances transmises, laisse peu de place à l'imagination et à la créativité.

Les artistes, par exemple, ont souvent un rapport "très contrarié à l'école", perçue comme un lieu de mémorisation rigide plutôt que de stimulation créative.

• La Docilité des Bons Élèves : Le système sélectionne des élèves qui sont de bons reproducteurs des savoirs scolaires, mais paradoxalement, ils ne sont pas toujours les mieux placés pour la recherche qui demande de la rébellion intellectuelle.

"Quand on a été trop bon élève, on est aussi très docile."

4. L'Intelligence au-delà du QI et les Voies de la Recherche

Lahire propose une vision plus large de l'intelligence et met en lumière les qualités du "vrai chercheur".

• L'Intelligence comme Capacité d'Adaptation : L'intelligence n'est "certainement pas ce que mesure un quotient intellectuel".

C'est avant tout "des capacités d'adaptation, c'est résoudre des problèmes".

Cette forme d'intelligence est présente "un peu partout dans le vivant", des plantes aux unicellulaires.

• L'Intelligence Créatrice : L'intelligence créatrice, notamment artistique, implique d'inventer des formes et des regards nouveaux, ce qui ne correspond pas aux critères d'évaluation académiques standards.

• Le Vrai Chercheur : Un vrai chercheur est "un sale gosse", "un peu rebelle", qui ose poser des questions "stupides" et aller au-delà des demandes.

Il faut "retrouver l'enfant qui est en nous" et ne pas se laisser impressionner, comme le souligne le mathématicien Alexandre Grothendieck.

• Exemples Notables : Des figures comme Einstein ou Grothendieck, malgré leur génie, ont eu un rapport difficile avec l'école ou le système académique, qui pouvait freiner leur curiosité et leur capacité à prendre du recul.

Grothendieck distinguait les mathématiciens "caseurs" (qui travaillent à l'intérieur d'une maison déjà faite) des "bâtisseurs" (qui reconstruisent les fondations).

5. Une Éducation Rationnelle et Collective : Propositions et Défis

Lahire esquisse des pistes pour une réforme de l'éducation.

• Respecter la Curiosité : Il faut s'appuyer sur la curiosité naturelle des enfants, l'accompagner et l'alimenter, plutôt que de la briser.

Des pédagogies comme celle de Freinet, avec des "leçons de choses" concrètes, sont des exemples positifs.

• Alléger les Programmes et Donner du Temps : Les programmes scolaires sont surchargés, rendant impossible un apprentissage approfondi.

Il est crucial de donner "le temps" aux enseignants et aux élèves pour l'approfondissement, car l'assimilation des connaissances demande du temps. "Terminer un programme ça n'a aucun sens."

• Lutter contre les Inégalités Sociales : Les enfants ne sont pas égaux devant l'école, car les "déterminismes sociaux" jouent un rôle majeur.

Les enfants de milieux favorisés bénéficient d'interactions culturelles et pédagogiques précoces qui les avantagent considérablement.

Il faut des politiques de compensation, donner "plus à ceux qui ont moins", en réduisant par exemple la taille des groupes pour les élèves en difficulté.

• Recherche de Synthèse : La spécialisation excessive des sciences, notamment sociales, rend difficile une vision systémique.

Il est nécessaire de développer des pôles de "synthétisation" et de faire des liens entre les différentes branches du savoir, à l'image des grands "synthétiseurs" comme Newton, Einstein ou Darwin.

• Critique des Classements : Les classements comme PISA ou Shanghai sont jugés peu pertinents.

Ils alimentent une "concurrence internationale" mais "n'ont jamais servi à améliorer en de quelque manière que ce soit le système éducatif", car ils ne s'attaquent pas aux causes profondes des problèmes.

6. L'Altricialité Secondaire et le Développement Culturel

Lahire fait le lien entre la biologie humaine et la nécessité de l'apprentissage.

• Dépendance Prolongée : L'espèce humaine se caractérise par une "altricialité dite secondaire", c'est-à-dire une longue période de dépendance des petits envers les parents. Cette vulnérabilité prolongée a accru la durée de l'apprentissage.

• Entrelacement Biologique et Culturel : Le développement physiologique de l'enfant est intimement lié à son développement culturel et social.

Apprendre à grandir dans une société humaine ne se limite pas à la maturité biologique, mais englobe l'acquisition d'une grande quantité de savoirs, notamment la lecture, l'écriture et le calcul, bases essentielles de la scolarisation précoce.

• En conclusion, Bernard Lahire dresse un tableau critique mais lucide du système éducatif actuel, en le replaçant dans une perspective biologique et historique.

Il plaide pour une réorientation profonde, qui remette la curiosité, l'approfondissement et la justice sociale au cœur des processus d'apprentissage et de création du savoir, conditions essentielles à la survie et à l'épanouissement collectif de l'humanité.

DOI: 10.1016/j.xcrm.2025.102321

Resource: (BioLegend Cat# 337410, RRID:AB_2269088)

Curator: @scibot

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What is this?

DOI: 10.1016/j.xcrm.2025.102321

Resource: None

Curator: @scibot

SciCrunch record: RRID:AB_1937269

What is this?

DOI: 10.1016/j.immuni.2025.08.001

Resource: RRID:IMSR_JAX:025523

Curator: @scibot

SciCrunch record: RRID:IMSR_JAX:025523

What is this?

DOI: 10.1016/j.compbiolchem.2025.108653

Resource: None

Curator: @scibot

SciCrunch record: RRID:SCR_005643

What is this?

Los principales sistemas que se encargan del metabolismo de los fármacos son las enzimas, y estas pueden ser activadas o inducidas, así mismo, otros fármacos pueden influenciar la actividad de dicha enzima, ya sea mejorándola o disminuyendo su capacidad enzimática si otro fármaco usa la misma enzima para metabolizarse

Esto significa que, si hay mucho fármaco, se metaboliza mucho; si hay poco, se metaboliza poco, pero la fracción o porcentaje del fármaco que se elimina por metabolismo es constante

Proteínas exportadoras

La concentración del fármaco en su sitio de acción llega a saturarse, lo que hace que el resto del fármaco suspendido en la sangre se redistribuye en los tejidos a manera de "reserva" y una vez las concentraciones en el sitio diana disminuyen de nuevo, se liberan del tejido a la circulación para poder ser absorbido en el sitio diana, lo que prolonga el efecto a lo largo del tiempo.

Lo que significa que hay fármacos con alta liposolubilidad capaces de atravesar la BHE, aunque su principal objetivo sea otro

I can tell ya, friend, I never thought I'd have to accidentally come across a little button in the Share Sheet when passing (literally any filetype atm lol) to discover that Apple had finally complied with the creation of custom ringtones.

Aparece o motivo da pausa?

This is referencing schema creation within learners that helps them form new opinions based on prior experience. I wonder if schema creation is not just a sum of prior experiences, but also a conglomeration of genetic factors as well. I would love to see how neurochemistry and genes affect how we learn, and what chemicals develop the feelings we get when we have those a-ha neuron connecting moments.

This might be the most important thing to talk about because people are afraid to fail, especially in front of others and if we (and when) we create a community where people are comfortable to admit their failures and mistakes, we will be able to flourish throughout the semester!

Author response:

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

Reviewer #1 (Public review):

Summary:

In this manuscript, Liu et al have tried to dissect the neural and molecular mechanisms that C. elegans use to avoid digestion of harmful bacterial food. Liu et al show that C. elegans use the ON-OFF state of AWC olfactory neurons to regulate the digestion of harmful gram-positive bacteria S. saprophyticus (SS). The authors show that when C. elegans are fed on SS food, AWC neurons switch to OFF fate which prevents digestion of S. saprophyticus and this helps C. elegans avoid these harmful bacteria. Using genetic and transcriptional analysis as well as making use of previously published findings, Liu et al implicate the p38 MAPK pathway (in particular, NSY-1, the C. elegans homolog of MAPKKK ASK1) and insulin signaling in this process.

Strengths:

The authors have used multiple approaches to test the hypothesis that they present in this manuscript.

Weaknesses:

Overall, I am not convinced that the authors have provided sufficient evidence to support the various components of their hypothesis. While they present data that loosely align with their hypothesis, they fail to consider alternative explanations and do not use rigorous approaches to strengthen their overall hypothesis. The selective picking of genes from the RNA sequencing data and forcing the data to fit the proposed hypothesis based on previously published findings, without exploring other approaches, indicates a lack of thoroughness and rigor. These critical shortcomings significantly diminish enthusiasm for the manuscript in its totality. In my opinion, this is the biggest weakness in this manuscript.

We appreciate the reviewer’s all the suggestions which help us to improve this paper. We now addressed reviewer’s comments at the section of “Reviewer #1 (Recommendations for the authors)”

Reviewer #2 (Public review):

Summary:

Using C. elegans as a model, the authors present an interesting story demonstrating a new regulatory connection between olfactory neurons and the digestive system.

Mechanistically, they identified key factors (NSY-1, STR-130 et.al) in neurons, as well as critical 'signaling factors' (INS-23, DAF-2) that bridge different cells/tissues to execute the digestive shutdown induced by poor-quality food (Staphylococcus saprophyticus, SS).

Strengths:

The conclusions of this manuscript are mostly well supported by the experimental results shown.

Weaknesses:

Several issues could be addressed and clarified to strengthen their conclusions.

(1) The word "olfactory" should be carefully used and checked in this manuscript. Although AWCs are classic olfactory neurons in C. elegans, no data in this manuscript supports the idea that olfactory signals from SS drive the responses in the digestive system. To validate that it is truly olfaction, the authors may want to check the responses of worms (e.g. AWC, digestive shutdown, INS-23 expression) to odors from SS.

We appreciate the reviewer’s careful attention to terminology. We agree that the term "olfactory" requires direct experimental validation. However, in this paper, we only used "olfactory" to specific define the AWC neurons. As reviewer’s suggestion, we now deleted the word “olfactory”.

(2) In line 113, what does "once the digestive system is activated" mean? The authors need to provide a clearer statement about 'digestive activation' and 'digestive shutdown'.

Previously, we observed that activating larval digestion with heat-killed E. coli or E. coli cell wall peptidoglycan (PGN) enabled the digestion of SS as food (Hao et al., 2024). Additionally, when animals reached the L2 stage by feeding normal OP50 diet, they could utilize SS as a food source to support growth (Figure 1—figure supplement 1D). These findings suggest that once digestion is activated (via E. coli components or L2-stage maturation), worms gain the capacity to process SS as a viable food source, abolishing SS-induced growth impairment (Hao et al., 2024) ( Figure 1—figure supplement 1D).

(3) No control data on OP50. This would affect the conclusions generated from Figures 2A, 2B, 2D, 3B, 3C, 3G, 4D-G, 5D-E, 6B-D.

We appreciate  this point. The central goal of the experiments listed (Figures 2A,B,D; 3B,C,G; 4D-G; 5D-E; 6B-D) was not to compare growth or behavior between SS and OP50 under standard conditions, but rather to understand the genetic basis of the C. elegans response specifically to SS, as identified through our nsy-1 mutant screen.

Our data in Figure 1 clearly establishes the fundamental difference in growth and feeding behavior when larvae encounter SS compared to OP50 (Figures 1A,B). Having established SS as an unfavorable food source that triggers a specific protective response (digestive shutdown), the subsequent experiments focus on deciphering how this response is mediated.

Therefore, within these specific experimental contexts under SS feeding: The primary comparison is between wild-type (N2) and nsy-1 mutant animals. All assays (growth, behavior, survival) are performed under the same SS feeding conditionsfor both genotypes.

This design allows us to directly assess the functional role of NSY-1 in mediating the SS-specific response pathway we are investigating. Including an OP50 control for every figure would not address this core genetic question and could introduce confounding variables given the established difference in how C. elegans treats these two food sources. The critical internal control for these specific experiments is the performance of the wild-type under SS versus the mutant under SS.

(4) Do the authors know which factors are released from AWC neurons to drive the digestive shutdown?

Enrichment analysis revealed that genes related to extracellular functions, such as insulin-related genes, are induced in nsy-1 mutant animals (Figure 5—figure supplement 1A, Supplementary file 4). Further analysis of insulin-related genes from the RNA-seq data showed that ins-23 is predominantly induced in nsy-1 mutant animals (Figure 5—figure supplement 1B), suggesting its potential role in promoting SS digestion. We found that knockdown of ins-23 in nsy-1 mutants inhibited SS digestion (Figure 5D). Given that INS-23 is expressed in AWC neurons (Figure 5—figure supplement 3A, CeNGEN), this suggests increased production and likely enhanced release of INS-23 from AWC neurons in the nsy-1 mutant background, which promotes SS digestion.

The insulin/insulin-like growth factor signaling (IIS) pathway, particularly through the DAF-2 receptor, integrates nutritional signals to regulate various behavioral and physiological responses related to food (Kodama et al., 2006; Ryu et al., 2018). It has been shown that INS-23 acts as an antagonist for the DAF-2 receptor to promote larval diapause (Matsunaga et al., 2018). To test whether ins-23 induction in nsy-1 mutants promotes SS digestion through its receptor, DAF-2, we constructed a nsy-1; daf-2 double mutant. We found that the SS digestion ability of the nsy-1 mutant was inhibited by the daf-2 mutation. This suggests that the nsy-1 mutation induces the insulin peptide ins-23, which promotes SS digestion through its potential receptor, DAF-2.

The data supports a model where AWC neurons regulate digestion via the release of INS-23. Loss of nsy-1 function increases INS-23 release from AWC, activating DAF-2 signaling and promoting digestion. Conversely, in wild-type animals, reduced INS-23 release from AWC contributes to digestive shutdown in response to SS food.

Reviewer #3 (Public review):

Summary:

The study explores a molecular mechanism by which C. elegans detects low-quality food through neuron-digestive crosstalk, offering new insights into food quality control systems. Liu and colleagues demonstrated that NSY-1, expressed in AWC neurons, is a key regulator for sensing Staphylococcus saprophyticus (SS), inducing avoidance behavior and shutting down the digestive system via intestinal BCF-1. They further revealed that INS-23, an insulin peptide, interacts with the DAF-2 receptor in the gut to modulate SS digestion. The study uncovers a food quality control system connecting neural and intestinal responses, enabling C. elegans to adapt to environmental challenges.

Strengths:

The study employs a genetic screening approach to identify nsy-1 as a critical regulator in detecting food quality and initiating adaptive responses in C. elegans. The use of RNA-seq analysis is particularly noteworthy, as it reveals distinct regulatory pathways involved in food sensing (Figure 4) and digestion of Staphylococcus saprophyticus (Figure 5). The strategic application of both positive and negative data mining enhances the depth of analysis. Importantly, the discovery that C. elegans halts digestion in response to harmful food and employs avoidance behavior highlights a physiological adaptation mechanism.

Weaknesses:

Major points:

(1) While NSY-1 positively regulates str-130 expression in AWC neurons and is critical for SS avoidance and survival, the authors should examine whether similar phenotypes are observed in str-130 mutants.

In this study, we mainly focused on how worms sense adverse food sources (SS food) and shutdown digestion (not growth as digestion shutdown readout). We found that nsy-1 in AWC play key roles in response SS food, once nsy-1 mutation, mutant animals cannot detect SS food and digest it, therefore growth under SS food. From RNA-seq, we found that nsy-1 positively regulates several sensory perception related genes (sra-32, str-87, str-112, str-130, str-160, str-230) (Figure 4—figure supplement 1A, Supplementary file 2). After screen, we found that we found that knockdown of str-130 in wild-type animals promoted SS digestion, thereby supporting animal growth (Figure 4D), and the proportion of animals with two AWC^OFF neurons decreased (Figure 4E). Secondly, we found that overexpression of str-130 in nsy-1 mutant animals inhibited SS digestion, thereby slowing animal growth (Figure 4F), and the proportion of animals with two AWC^OFF neurons increased (Figure 4G). These results demonstrate that NSY-1 promotes the AWC^OFF state by inducing str-130 expression, which in turn inhibits SS digestion in C. elegans.

(2) NSY-1 promotes the AWC-OFF state through str-130, inhibiting SS digestion. The authors should investigate whether STR-130 in AWC neurons regulates bcf-1 expression levels in the intestine.

We agree with the reviewer's suggestion regarding the potential role of STR-130 in AWC neurons regulating intestinal bcf-1 expression. To address this, we generated transgenic worms with AWC-specific knockdown of str-130, achieved by rescuing sid-1 cDNA expression under the ceh-36 promoter (AWC-specific) in sid-1(qt9);BCF-1::GFP background worms.

We observed that AWC neuron-specific RNAi of str-130 elevated intestinal BCF-1::GFP expression (Figure 6—figure supplement 1B). This demonstrates that STR-130 functions cell-non-autonomously in AWC neurons to repress BCF-1 expression in the intestine.

(3) The current results rely on str-2 expression levels to indicate the AWC state. Ablating AWC neurons and testing the effects on digestion would provide stronger evidence for their role in digestive regulation.

To confirm the important of AWC state in SS digestion, we performed AWC-specific neuron ablation experiments using previously validated transgenic strain that expresses cleaved caspase under the AWC-specific promoter, ceh-36 (ceh-36p::caspase). Critically, worms with ablated AWC neurons completely failed to digest SS food (Figure 3—figure supplement 4), phenocopying the non-digesting state of wild-type worms on SS when AWC-OFF signaling is impaired. This result directly confirms that functional AWC neurons are essential for initiating SS digestion, aligning with our model where the AWC-OFF state (induced by SS) inhibits digestion while the AWC-ON state promotes it.

Furthermore, we previously study discovered that AWC ablation activates the intestinal mitochondrial unfolded protein response and inhibits food digestion, mechanistically linking neuronal integrity to gut stress responses and digestive inhibition.

Together, these functional ablation studies provide compelling physiological evidence that AWC neurons act as central regulators of food-state sensing and gut function.

(4) The claim that NSY-1 inhibits INS-23 and that INS-23 interacts with DAF-2 to regulate bcf-1 expression (Line 339-340) requires further validation. Neuron-specific disruption of INS-23 and gut-specific rescue of DAF-2 should be tested.

We agree with the reviewer that the proposed NSY-1 ⊣ INS-23 → DAF-2 → BCF-1 signaling axis requires tissue-specific validation. To address this, we conducted compartment-specific functional dissection of INS-23 and DAF-2:

AWC neuronal role of INS-23:

To test whether INS-23 acts in AWC neurons to regulate intestinal BCF-1, we generated AWC-specific knockdown strains which was achieved by rescuing sid-1 cDNA expression under the ceh-36 promoter in a sid-1(qt9);BCF-1::GFP background. We found that AWC-restricted ins-23 knockdown significantly reduced intestinal BCF-1::GFP expression (Figure 6—figure supplement 1A). This confirms that INS-23 functions cell-non-autonomously within AWC sensory neurons to activate intestinal BCF-1, consistent with NSY-1’s upstream inhibition of INS-23 in this neuronal  subtype

Intestinal role of DAF-2 as INS-23 receptor:

To investigate weather DAF-2 acts as the gut-localized receptor for neuronal INS-23 signaling, we performed tissue-specific rescue experiments in the nsy-1(ag3);daf-2(e1370) double mutant. When DAF-2 was re-introduced specifically in the intestine (using the ges-1 promoter), we observed a significant suppression of SS digestion (Figure 5—figure supplement 3B), but not rescue digestive defect. This indicates that INS-23 induction in nsy-1 mutants promotes digestion independently of intestinal DAF-2 function.

(5) Figure Reference Errors: Lines 296-297 mention Figure 6E, which does not exist in the main text. This appears to refer to Figure 5E, which has not been described.

We corrected this.

Reviewer #1 (Recommendations for the authors):

I would like the authors to address the following comments in a resubmission.

(1) The hallmark of the activated p38 MAPK pathway is the phosphorylation of most downstream kinase p38 (PMK-1/PMK2 in C. elegans) of this kinase cascade. Previous work from Bergmann lab showed that the most downstream kinase of this pathway, PMK-1/PMK-2, is not required for AWC asymmetry. I wonder whether that is the case also for the model that Liu et al have presented in this manuscript. Since p38/PMK-1 undergoes activation (phosphorylation) in response to pathogenic bacteria like P. aeruginosa, it is worth testing whether PMK-1 plays a role downstream of NSY-1 in the model that Liu et al present in this manuscript. It would be worth testing whether there is increased phosphorylation of p38 when C. elegans are fed SS and whether that phosphorylation regulates downstream components that Liu et al have identified in this manuscript.

We thank the reviewer for raising this important point regarding PMK-1/p38 MAPK signaling. As established in our prior work (Reference 1), SS exposure triggers phosphorylation of PMK-1 (P-PMK-1) in C. elegans, and pmk-1 mutants exhibit enhanced growth on SS (Figure-1, Figure-2). This confirms that PMK-1-mediated innate immune signaling actively regulates SS responsiveness and digestion.

To address whether PMK-1 functions downstream of NSY-1 within our proposed model, we performed critical epistasis analyses. While we observed that nsy-1 mutation elevates ins-23 (indicating NSY-1 suppression of ins-23), knockdown of pmk-1 did not alter ins-23 expression levels (Figure 5-figure supplement 3C). This demonstrates that PMK-1 does not operate through the ins-23 pathway to regulate SS digestion. Thus, although both pathways respond to SS, the PMK-1-mediated innate immune response and the NSY-1/INS-23 axis constitute distinct regulatory mechanisms governing digestive adaptation.

Reference 1: Geng, S., Li, Q., Zhou, X., Zheng, J., Liu, H., Zeng, J., Yang, R., Fu, H., Hao, F., Feng, Q., & Qi, B. (2022). Gut commensal E. coli outer membrane proteins activate the host food digestive system through neural-immune communication. Cell host & microbe, 30(10), 1401–1416.e8. https://doi.org/10.1016/j.chom.2022.08.004

(2) Since p38 MAPK pathway has a well-established role in host defense in the C. elegans intestine, it is important to show that NSY-1 does not function in the intestine in the model that Liu et al present. I would like the authors to reintroduce nsy-1 in C. elegans intestine in nsy-1 mutant animals and then test whether it has any effect on worm length on SS food (similar to what is done in Figure 3 for AWC-specific nsy-1).

Beyond its  established  role  in  AWC  neurons,  we  detected  NSY-1 expression in the intestine (Figure 3-figure supplement 2A). To assess intestinal NSY-1 function, we performed tissue-specific rescue experiments in nsy-1 mutants using the intestinal-specific vha-1 promoter. Intestinal expression of NSY-1 significantly suppressed the enhanced SS digestion phenotype in nsy-1 mutants (Figure 3-figure supplement 2B), demonstrating functional involvement of gut-localized NSY-1 in regulating digestive responses. We propose intestinal NSY-1 mediates this effect through innate immune signaling, consistent with its known pathway components. As previously established (Reference 1), the canonical PMK-1/p38 MAPK pathway functions downstream of NSY-1, with both sek-1 and pmk-1 knockdown enhancing SS digestion through immune modulation. This indicates intestinal NSY-1 suppresses digestion may act through PMK-1-mediated immune responses. Since neuronal NSY-1's role in digestive control was previously undefined, we prioritized mechanistic analysis of its neuronal function in digestion regulation.

Notably, this immune-mediated mechanism operates independently of NSY-1's neuronal regulation pathway. In AWC neurons, NSY-1 controls digestion exclusively through the neuropeptide signaling axis (INS-23/DAF-2/BCF-1) without engaging innate immune components.

Reference 1: Geng, S., Li, Q., Zhou, X., Zheng, J., Liu, H., Zeng, J., Yang, R., Fu, H., Hao, F., Feng, Q., & Qi, B. (2022). Gut commensal E. coli outer membrane proteins activate the host food digestive system through neural-immune communication. Cell host & microbe, 30(10), 1401–1416.e8. https://doi.org/10.1016/j.chom.2022.08.004

(3) At multiple places, wild-type (WT) controls have been labeled as N2. It is better to label all controls as WT (and not as N2).

Corrected.

(4) In Figure 2B, the aversion response should be scored at multiple time points, like Figure 1C, rather than at just one timepoint.

We thank the reviewer for suggesting multi-timepoint analysis of aversion behavior. In accordance with this recommendation, we have now quantified SS avoidance at multi-timepoint. As shown in the revised Figure 2B, nsy-1 mutants exhibited significantly impaired avoidance responses at both 4h and 6h but not at 8h, confirming that NSY-1 is essential for sustained aversion to SS food in the early response. This data demonstrates that the critical role of NSY-1 in food discrimination at initial sensory responses.

(5) Does the re-introduction of nsy-1 in AWC neurons in nsy-1 mutant background help animals avoid SS in dwelling and food-choice assays? Along the same lines, does the CRISPR-generated AWC-specific mutant of NSY-1 fail to avoid SS in dwelling and food-choice assays similar to the whole-animal mutant? These behavioral data are missing in Figure 3.

We thank the reviewer for prompting behavioral validation of AWC-specific nsy-1 functions. To determine whether NSY-1 in AWC neurons mediates SS sensory perception, we performed dwelling (avoidance) and food-choice assays using AWC-specific nsy-1 knockout and AWC-rescued strains (nsy-1(ag3); Podr-1::nsy-1). In dwelling assays, AWC-specific nsy-1 KO mutants exhibited significantly impaired SS avoidance at 6h (Figure 3-figure supplement 3A), while AWC-rescued strains restored avoidance capacity at 2-6h (Figure 3-figure supplement 3B). Food-choice assays further revealed that AWC nsy-1 KO mutants preferentially migrated toward SS (Figure 3-figure supplement 3C), whereas AWC-rescued showed no preference between SS and HK-E. coli (Figure 3-figure supplement 3D). These data conclusively demonstrate that NSY-1 acts in AWC neurons to mediate SS recognition and aversion behaviors.

(6) In Figure 3E and F, the number of animals that were used for scoring AWC str-2p::GFP expression should be specified.

we added the number of animals in the figure.

(7)  RNA seq analysis identified multiple GPCRs (including STR-130) that are upregulated in an NSY-1-dependent manner when animals are fed with SS bacteria. However, the authors decided to only characterize STR-130 because of previously published findings. It is important to rule out the role of other GPCRs since all are upregulated on SS food as shown in Figure S4 B. I would like the authors to knock down other GPCRs in the same manner as they did for STR-130 and demonstrate that only str-130 knockdown behaves similarly to the nsy-1 mutant (if that is the case) using the assay presented in Figure 4 D.

We appreciate the reviewer’s suggestion to comprehensively evaluate NSY-1-regulated GPCRs. In response, we extended our functional analysis to all six GPCRs (str-130, str-230, str-87, str-112, str-160, and sra-32) identified as NSY-1-dependent and SS-induced in RNA-seq (Figure 4—figure supplement 1).

Using RNAi knockdown and the SS growth assay, we observed that RNAi of str-130, str-230, str-87, or str-112 significantly enhanced SS growth (Figure 4—figure supplement 2A), with str-130 RNAi exhibiting the most robust phenotype—phenocopying nsy-1 mutants. Crucially, none of these GPCR knockdowns further enhanced growth in nsy-1(ag3) mutants (Figure 4—figure supplement 2B), confirming their position downstream of NSY-1. These data establish str-130 as the dominant effector of NSY-1-mediated SS response regulation, while suggesting minor contributions from other GPCRs (str-230, str-87, str-112).

(8) In Figure 4E and G, the number of animals that were used for scoring GFP expression should be specified.

we added the number of animals in the figure.

(9) When comparing Figure 3E and Figure 4E, it appears that the loss of str-130 RNAi does not phenocopy nsy-1 mutant. This raises the question of whether the inefficiency of RNAi targeting str-130 is the cause, or if STR-130 is not the only GPCR regulated by NSY-1 on SS food. I would like the authors to address this discrepancy. If RNAi inefficiency is indeed the cause, using an RNAi-sensitive background, such as an eri- 1 mutant, could help strengthen the data presented in Figure 4E. Conversely, if RNAi inefficiency is not responsible for the discrepancy, I suggest that the authors investigate the roles of other GPCRs that were identified by RNA sequencing.

We appreciate the reviewer’s observation regarding the phenotypic difference between nsy-1 mutants and str-130 (RNAi) animals on SS food (Fig. 3E vs Fig. 4E).

While both genetic perturbations significantly enhance SS growth and increase the proportion of animals exhibiting AWC^ON states compared to wild type (indicating enhanced digestion), the specific AWC^ON neuron configurations differ: nsy-1 mutants predominantly show 2 AWC^ON animals, whereas str-130(RNAi) animals primarily exhibit the 1 AWC^ON /1 AWC^OFF configuration (Fig. 3E vs Fig. 4E).

This difference likely arises because STR-130 is the key GPCR mediating NSY-1's inhibitory effect on SS digestion, but it is not the sole GPCR involved, as evidenced by our RNAi screen identifying several additional NSY-1-regulated GPCRs (str-230, str-87, str-112) whose depletion also enhanced SS growth (Fig. 4A-D).

The robust SS growth enhancement and AWC^ON state increase caused by str-130 (RNAi) (phenocopying the nsy-1 mutant’s functional outcome of enhanced digestion) (Figure 4D, 4E) indicate effective RNAi knockdown for this specific assay. Therefore, the distinct neural configurations reflect the partial redundancy among GPCRs downstream of NSY-1, rather than an inherent inefficiency of the str-130 RNAi.

The nsy-1 mutant phenotype represents the complete loss of all inhibitory GPCR signaling coordinated by NSY-1, while str-130(RNAi) represents the loss of its major component. Investigating the roles of other identified GPCRs (str-230, str-87, str-112) in modulating AWC^ON neuron states is an important direction for future research.

(10) In Figure 4 F and 4 G, the authors show that the overexpression of STR-130 rescues the nsy-1 mutant phenotype suggesting that NSY-1 might function through STR-130 to control digestion on SS food. These data place STR-130 downstream of NSY-1. To further strengthen these epistasis data, authors should knock down str-130 in nsy-1 mutant animals and show that the combined loss of both genes produces the same effect as the loss of either gene alone.

We thank the reviewer for the insightful suggestion to further define the genetic relationship between nsy-1 and str-130. To strengthen our epistasis analysis, we performed RNAi knockdown of str-130 in the nsy-1(ag3) mutant background and assessed development on SS food. Consistent with STR-130 acting downstream of NSY-1, the loss of str-130 via RNAi did not further enhance the developmental capacity (i.e., growth phenotype) of nsy-1(ag3) mutant animals on SS. This lack of enhancement indicates that str-130 and nsy-1 function within the same genetic pathway, with str-130 acting epistatically downstream of nsy-1 (Figure 4—figure supplement 3). This finding reinforces the model proposed from our overexpression data (Fig. 4F-G) – that NSY-1 primarily exerts its inhibitory effect on SS digestion by inducing the expression GPCR STR-130.

(11) In Figure 5C, please mention "ins-23 transcript levels" on the top of the graph so that it is clear what these data represent.

We appreciate the reviewer’s suggestion.

(12) Since all ins genes were upregulated in nsy-1 mutants (though ins-23 was indeed the most highly upregulated gene) on SS food from RNA seq analysis (Figure S5 B), it is important to first phenotypically characterize all of them using "worm length assay". If this analysis shows that ins-23 has the most robust phenotype, it would make more sense to just focus on ins-23.

We agree with the reviewer that initial phenotypic characterization of candidate genes identified through transcriptomic analysis is valuable.Our RNA-seq data revealed that several insulin-like peptide genes, including ins-22, ins-23, ins-24, and ins-27, were significantly upregulated in the nsy-1 mutant on SS food (Figure 5—figure supplement 1B). We prioritized these insulin-like peptide genes for functional validation because they are known to act as neuropeptides capable of mediating non-cell autonomous signaling in previous studies (Shao et al 2016).

To determine if any were functionally responsible for the enhanced SS growth observed in nsy-1 mutants, we performed functional phenotypic screening using the SS growth assay (worm length assay). We individually knocked down each of these candidates (ins-22, ins-23, ins-24, ins-27) in the nsy-1(ag3) mutant background. Among these, only RNAi targeting ins-23 significantly attenuated (i.e., suppressed) the enhanced development of the nsy-1(ag3) mutant on SS (Figure 5—figure supplement 2). This targeted functional screening revealed that ins-23 has the most robust and specific role in mediating the enhanced digestion phenotype downstream of NSY-1 loss, providing the critical justification for our subsequent focus on this particular insulin-like peptide.

Ref:

Shao, L. W., Niu, R., & Liu, Y. (2016). Neuropeptide signals cell non-autonomous mitochondrial unfolded protein response. Cell research, 26(11), 1182–1196. https://doi.org/10.1038/cr.2016.118

Reviewer #2 (Recommendations for the authors):

There are several minor errors and typos in the manuscript

(1) A number of typos in the figures, like "length".

Corrected.

(2) The 'axis labels' are inconsistent from panel to panel, like "relative body length" and "relative worm length".

Corrected.

(3) The fonts are inconsistent from panel to panel.

Corrected.

(4) There is no Ex unique number for transgenic lines.

Corrected.

Reviewer #3 (Recommendations for the authors):

Minor points:

(1)  Figure 3B, 3C, 3G, 4D, 4F, 5D, 5E, and 6C: Replace "lenth" with "length" (consistent with Figure 2A).

Corrected.

(2) Figure 4D: Correct "ctontrol" to "control."

Corrected.

(3) Figure 4G: Update the co-injection marker to Podr-1::GFP instead of Pstr-2::GFP.

Corrected.

(4) Figure 5C: This figure is missing from the Results section.

Corrected.

(5) Figure 6A: Label the graph with Pbcf-1::bcf-1::GFP, as in Figure 6D.

Corrected.

(6) Italicization: Lines 588 and 603-italicize nsy-1.

Corrected.

(7) Supplementary Figure S2A: Correct "Screeng" to "Screening."

Corrected.

(8) Spelling/Proofreading: Ensure consistent spelling and grammar, such as correcting "mutan" to "mutant" in Figure 4A.

Corrected.

Author response:

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

Reviewer #1 (Public Review):

(1) I suggest that the author's choose a different term in their title, abstract and manuscript to describe the phenotypes associated with ufd-1 and npl-4 knockdown other than an "inflammation-like response." Inflammation is a pathological term with four cardinal signs: redness (rubor), swelling (tumor), warmth (calor) and pain (dolor). These are not symptoms know to occur in C. elegans. The authors could consider using "tolerance" instead, as this term may better describe their findings.

We have changed “inflammation-like response” to “aberrant immune response” throughout the manuscript.

(2) It would help the reader to better understand the novelty of the findings in this study if the authors include a paragraph in their introduction to put their results in context of the published literature that has examined the relationship between immune activation and nematode health and survival. In particular, I suggest that the authors discuss doi:10.7554/eLife.74206 (2022), a study that charcterized a similar observation to what the authors are reporting. This study found that low cholesterol reduces pathogen tolerance and host survival during pathogen infection. Cholesterol scarcity increases p38 PMK-1 phosphorylation, priming immune effector induction in a manner that reduces pathogen accumulation in the intestine during a subsequent infection. I also suggest that the authors highlight in this introductory paragraph that the toxic effects of inappropriate immune activation in C. elegans has been widely catalogued. For example: doi.org/10.1371/journal.ppat.1011120 (2023); doi:10.1186/s12915-016-0320-z (2016).; doi:10.1126/science.1203411 (2011); doi:10.1534/g3.115.025650 (2016).

In this context, the authors could consider re-wording their novelty claim in the abstract and introduction to take into account this previous body of work.

We have added a paragraph to the Discussion section to place our findings in the context of previous research. The revised manuscript now includes the following text (page 11, lines 336–344): “Previous studies have shown that hyperactivation of immune pathways can negatively affect organismal development. For example, sustained activation of the p38 MAPK pathway impairs development in C. elegans (Cheesman et al., 2016; Kim et al., 2016), and excessive activation of the IPR also leads to developmental defects (Lažetić et al., 2023). Similar to our current study, recent work has demonstrated that heightened immune responses can reduce gut pathogen load while paradoxically decreasing host survival during infection (Ghosh and Singh, 2024; Peterson et al., 2022). However, our study uniquely shows that while such heightened immune responses are detrimental to immunocompetent animals, they can be beneficial in the context of immunodeficiency.”

(3) The authors rely on the use of RNAi of ufd-1 and npl-4 to study their effect on P. aeruginosa colonization and pathogen resistance throughout the manuscript. To address the possibility of off-target effects of the RNAi, the authors should consider both (i) showing with qRT-PCR that these genes are indeed targeted during RNAi, and (ii) confirming their phenotypes with an orthologous technique, preferably by studying ufd-1 and npl-4 loss-offunction mutants [both in the wild-type and sek-1(km4) backgrounds]. If mutation of these genes is lethal, the authors could use Auxin Inducible Degron (AID) technology to induce the degradation of these proteins in post-developmental animals.

We attempted several protocols of CRISPR in our laboratory to generate ufd-1 loss-of-function mutants; however, these efforts were unsuccessful. While this does not rule out the possibility of generating ufd-1 mutants, the failure is likely due to technical limitations on our part rather than an inherent inability to disrupt the gene. Nevertheless, to confirm the specificity of our RNAi-based approach, we quantified ufd-1 and npl-4 mRNA levels following RNAi treatment and found that each gene was specifically and effectively downregulated by its respective RNAi. 

Importantly, ufd-1 and npl-4 RNA sequences do not share significant homology, yet knockdown of either gene results in nearly identical phenotypes, including reduced survival on P. aeruginosa, diminished intestinal colonization, and shortened lifespan. These consistent outcomes strongly support the conclusion that the phenotypes are attributable to the disruption of the functional UFD-1-NPL-4 complex. We have added these results in the revised manuscript (pages 4-5, lines 114-125): “To confirm the specificity of the RNAi knockdowns and rule out potential off-target effects, we examined transcript levels of ufd-1 and npl-4 following RNAi treatment. RNAi against ufd-1 significantly reduced ufd-1 mRNA levels without reducing npl-4 expression, while npl-4 RNAi specifically downregulated npl-4 transcripts with no impact on ufd-1 mRNA levels (Figure 1—figure supplement 1A and B). Additionally, alignment of ufd-1 and npl-4 mRNA sequences against the C. elegans transcriptome revealed no significant similarity to other genes, supporting the specificity of the RNAi constructs. Moreover, the ufd-1 and npl-4 RNA sequences do not share significant sequence similarity. Therefore, the highly similar phenotypes observed in ufd-1 and npl-4 knockdown animals, including shortened lifespan, reduced survival on P. aeruginosa, and decreased intestinal colonization with P. aeruginosa, strongly suggest that these outcomes result from the disruption of the functional UFD-1-NPL-4 complex.”

(4) I am confused about the authors explanation regarding their observation that inhibition of the UFD-1/ NPL-4 complex extends the lifespan of sek-1(km25) animals, but not pmk-1(km25) animals, as SEK-1 is the MAPKK that functions immediately upstream of the p38 MAPK PMK-1 to promote pathogen resistance.

I am also confused why their RNA-seq experiment revealed a signature of intracellular pathogen response genes and not PMK-1 targets, which the authors propose is accounting for toxic immune activation. Activation of which immune response leads to toxicity?

We consistently observe that sek-1(km4) mutants are more sensitive to P. aeruginosa infection than pmk-1(km25) mutants, a finding also reported in previous studies (for example, PMID: 33658510). Given that SEK-1 functions upstream of PMK-1 in the MAPK signaling cascade, it is plausible that SEK-1 also regulates additional MAP kinases, such as PMK-2 (PMID: 25671546), which could contribute to the enhanced susceptibility observed in sek-1 mutants.

Our results show that inhibition of the UFD-1-NPL-4 complex improves survival specifically in severely immunocompromised animals, such as sek-1(km4) mutants, but not in pmk1(km25) mutants. To further validate this, we generated the double mutant dbl-1(nk3);pmk1(km25), which exhibits reduced survival on P. aeruginosa compared to either single mutant.

Notably, inhibition of the UFD-1-NPL-4 complex also enhances survival in the dbl1(nk3);pmk-1(km25) background, reinforcing the observation that this response is specific to severely compromised immune states.

We would also like to clarify that the observed phenotypes are independent of the SEK1/PMK-1 pathway, as shown in Figure 3A-3C, Figure 3—figure supplement 1, and Figure 4A-4C. The IPR seems to play a role in the observed phenotypes, as inhibition of some of the protease and pals genes (IPR genes) leads to increased P. aeruginosa colonization in ufd-1 knockdown animals (Figure 6—figure supplement 1). The other immune response pathway that leads to the observed phenotypes is ELT-2, as explained in Figure 6. Finally, we have included in the revised manuscript a note that, in addition, as-yet unidentified pathways are also likely contributing to the phenotypes triggered by disruption of the UFD-1-NPL-4 complex.

(5) The authors did not test alternative explanations for why UFD-1/ NPL-4 complex inhibition compromises survival during pathogen infection, other than exuberant immune activation. For example, it is possible that inhibition of this proteosome complex shortens lifespan by compromising the general health/ normal physiology of nematodes. Immune responses could be activated as a secondary consequence of this stress, and not be a direct cause of early morality. Does sek-1(km4) mutant suppress the lifespan shortened lifespan of ufd-1 and npl-4 knockdown? This experiment should also be done with loss-offunction mutants, as noted in point 3.

We have already included this data in Figure 4D, where we observed that ufd-1 and npl-4 knockdown reduce the lifespan of sek-1(km4) animals. It is possible that immune activation is a secondary consequence of cellular stress induced by inhibition of the UFD-1NPL-4 complex. However, our data strongly suggest that the observed phenotypes, including reduced gut pathogen load and decreased survival on the pathogen, are due to the aberrant immune response activated by the inhibition of the UFD-1-NPL-4 complex. Evidence from sek-1(km4) mutants particularly underscores the role of this dysregulated immune activation. While this aberrant immune response is detrimental to wild-type animals under pathogenic conditions, it appears to be beneficial in severely immunocompromised backgrounds. Specifically, in sek-1(km4) mutants, inhibition of the UFD-1-NPL-4 complex enhances survival during P. aeruginosa infection (Figure 4A). However, under non-infectious conditions, where sek-1(km4) mutants exhibit a normal lifespan, the same immune activation becomes harmful (Figure 4D). Together, these findings demonstrate that the aberrant immune response induced by UFD-1–NPL-4 inhibition is context-dependent: it is advantageous only for immunocompromised animals under infection, but deleterious to healthy animals under infection and to both healthy and immunocompromised animals under non-infectious conditions.

(6) The conclusion of Figure 6 hinges on an experiments that uses double RNAi to knockdown two genes at the same time (Fig. 6D and 6G), an approach that is inherently fraught in C. elegans biology owing the likelihood that the efficiency of RNAi-mediated gene knockdown is compromised and may account for the observed phenotypes. The proper control for double RNAi is not empty vector + ufd-1(RNAi), but rather gfp(RNAi) + ufd1(RNAi), as the introduction of a second hairpin RNA is what may compromise knockdown efficiency. In this context, it is important to confirm that knockdown of both genes occurs as expected (with qRT-PCR) and to confirm this phenotype using available elt-2 loss-of-function mutants.

We thank the reviewer for this helpful suggestion. We have repeated all double

RNAi experiments using gfp RNAi as a control instead of the empty vector (Figure 6 and Figure 6—figure supplement 1). Additionally, we assessed the efficiency of gene knockdown in the double RNAi conditions (Figure 6—figure supplement 2) and found that RNAi efficacy was not compromised by the double RNAi treatment.

(7) A supplementary table with the source data for at least three replications (mean lifespan, n, statistical comparison) for each pathogenesis assay should be included in this manuscript.

The source data is provided for all the data presented in the manuscript.

Reviewer #2 (Public Review):

Summary:

The authors aimed to uncover what role, if any, the UFD1/NPL4 complex might play in the innate immune responses of the nematode C. elegans. The authors find that loss of the complex renders animals more sensitive to both pathogenic and non-pathogenic bacteria. However, there appears to be a complex interplay with known innate immune pathways since the loss of UFD1/NPL4 actually results in increased survival of animals lacking the canonical innate immune pathways.

We thank the reviewer for providing an excellent summary of our work.

Strengths:

The authors perform robust genetic analysis to exclude and include possible mechanisms by which the UFD1/NPL4 pathway acts in the innate immune response.

We thank the reviewer for highlighting the strengths of our work.

Weaknesses:

The argument that the loss of the UFD1/NPL4 complex triggers a response that mimics that of an intracellular pathogen has not been thoroughly investigated. Additionally, the finding of a role of the GATA transcription factor, ELT-2, in this response is suggestive, but experiments showing sufficiency in the context of loss of the UFD1/NPL4 complex need to be explored.

We have investigated the role of IPR genes in the phenotypes observed upon ufd1 knockdown (Figure 6—figure supplement 1), and our results suggest that the IPR may contribute, at least in part, to the phenotypic outcomes of ufd-1 RNAi. In the Discussion section (pages 11–12, lines 345–356), we have included a detailed discussion on the possible mechanisms underlying IPR activation upon inhibition of the UFD-1–NPL-4 complex. We agree that the interaction between the UFD-1–NPL-4 complex and the IPR is intriguing and warrants further investigation. However, we believe that an in-depth exploration of this interaction lies beyond the scope of the current study.

We have incorporated new data on ELT-2 overexpression in the revised manuscript. Overexpression of ELT-2 partially phenocopies the effects of ufd-1 knockdown, supporting the idea that other pathways likely contribute to the full spectrum of phenotypes observed upon UFD-1-NPL-4 complex inhibition. The revised manuscript reads (page 10, lines 311319): “To determine whether ELT-2 activation alone is sufficient to recapitulate the phenotypes observed upon UFD-1-NPL-4 complex inhibition, we analyzed animals overexpressing ELT-2. Similar to ufd-1 knockdown, ELT-2 overexpression led to a significant reduction in the colonization of the gut by P. aeruginosa (Figure 6—figure supplement 3A and 3B). However, overexpression of ELT-2 did not alter the survival of worms on P. aeruginosa (Figure 6—figure supplement 3C). Taken together, these findings suggest that the phenotypes triggered by disruption of the UFD-1-NPL-4 complex are partially mediated by ELT-2. However, additional pathways, yet to be identified, likely cooperate with ELT-2 to regulate both pathogen resistance and host survival.”

Reviewer #1 (Recommendations For The Authors):

The authors could consider avoiding the use of descriptors (e.g., "drastic") when presenting their data.

We have removed the descriptors.

Reviewer #2 (Recommendations For The Authors):

What happens with overexpression of ELT2?

Overexpression of ELT-2 partially recapitulates the phenotypes of ufd-1 knockdowns, indicating that additional pathways are likely involved in controlling the phenotypes observed upon inhibition of the UFD-1-NPL-4 complex. The revised manuscript reads (page 10, lines 311-319): “To determine whether ELT-2 activation alone is sufficient to recapitulate the phenotypes observed upon UFD-1-NPL-4 complex inhibition, we analyzed animals overexpressing ELT-2. Similar to ufd-1 knockdown, ELT-2 overexpression led to a significant reduction in the colonization of the gut by P. aeruginosa (Figure 6—figure supplement 3A and 3B). However, overexpression of ELT-2 did not alter the survival of worms on P. aeruginosa (Figure 6—figure supplement 3C). Taken together, these findings suggest that the phenotypes triggered by disruption of the UFD-1-NPL-4 complex are partially mediated by ELT-2. However, additional pathways, yet to be identified, likely cooperate with ELT-2 to regulate both pathogen resistance and host survival.”

The data with xbp-1 loss of function is very different than that of pek1 and atf-6. Does loss of ufd1/npl4 suppress the increased pathogen survival of xbp-1s overexpressing animals?

We have examined worms overexpressing XBP-1s and found that overexpression of XBP-1s does not rescue the phenotypes caused by ufd-1 knockdown. The revised manuscript reads (page 6, lines 167-174): “To further examine the role of XBP-1 in this context, we assessed the effect of ufd-1 knockdown in animals neuronally overexpressing the constitutively active spliced form of XBP-1 (XBP-1s), which has been previously associated with enhanced longevity (Taylor and Dillin, 2013). Knockdown of ufd-1 resulted in the reduced survival of XBP-1s-overexpressing animals on P. aeruginosa, despite a concurrent decrease in bacterial colonization of the gut (Figure 2—figure supplement 1A-C). This indicated that the XBP-1 pathway was not required for the reduced P. aeruginosa colonization of ufd-1 knockdown animals.” 

Lastly, while the pathogen burden is reduced in ufd1/npl4 loss and pumping rates are marginally affected, have you checked defecation rates? Could they be increased?

We thank the reviewer for this valuable suggestion. We measured defecation rates following ufd-1 and npl-4 knockdown and, unexpectedly, found that inhibition of ufd-1/npl-4 leads to a reduction in defecation frequency. These findings clearly indicate that altered defecation cannot explain the observed decrease in gut colonization. The revised manuscript reads (page 5, lines 138-148): “The clearance of intestinal contents through the defecation motor program (DMP) is known to influence gut colonization by P. aeruginosa in C. elegans (Das et al., 2023). It is therefore conceivable that knockdown of the UFD-1-NPL-4 complex might increase defecation frequency, thereby promoting the physical expulsion of bacteria and resulting in reduced gut colonization. To test this possibility, we measured DMP rates in animals subjected to ufd-1 and npl-4 RNAi. Contrary to this hypothesis, both ufd-1 and npl-4 knockdown animals exhibited a significant reduction in defecation frequency compared to control RNAi-treated animals (Figure 1—figure supplement 2C). This reduction in DMP rate persisted even after 12 hours of exposure to P. aeruginosa (Figure 1—figure supplement 2D). Thus, the change in the DMP rate in ufd-1 and npl-4 knockdown animals is unlikely to be the reason for the reduced gut colonization by P. aeruginosa.”

In summary, we would like to thank the reviewers again for providing constructive and thoughtful feedback. We believe we have fully addressed all the concerns of the reviewers by carrying out several new experiments and modifying the text. The manuscript has undergone substantial revision and has thereby improved significantly. We do hope that the evidence in support of the conclusions is found to be complete in the revised manuscript.

Author response:

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

Reviewer #1 (Public review):

Summary:

This manuscript presents a compelling study identifying RBMX2 as a novel host factor upregulated during Mycobacterium bovis infection.

The study demonstrates that RBMX2 plays a role in:

(1) Facilitating M. bovis adhesion, invasion, and survival in epithelial cells.

(2) Disrupting tight junctions and promoting EMT.

(3) Contributing to inflammatory responses and possibly predisposing infected tissue to lung cancer development.

By using a combination of CRISPR-Cas9 library screening, multi-omics, coculture models, and bioinformatics, the authors establish a detailed mechanistic link between M. bovis infection and cancer-related EMT through the p65/MMP-9 signaling axis. Identification of RBMX2 as a bridge between TB infection and EMT is novel.

Strengths:

This topic and data are both novel and significant, expanding the understanding of transcriptomic diversity beyond RBM2 in M. bovis responsive functions.

Weaknesses:

(1) The abstract and introduction sometimes suggest RBMX2 has protective anti-TB functions, yet results show it facilitates pathogen adhesion and survival. The authors need to rephrase claims to avoid contradiction.

We sincerely appreciate the reviewer's valuable feedback regarding the need to clarify RBMX2's role throughout the manuscript. We have carefully revised the text to ensure consistent messaging about RBMX2's function in promoting M. bovis infection. Below we detail the specific modifications made:\

(1) Introduction Revisions:

Changed "The objective of this study was to elucidate the correlation between host genes and the susceptibility of M.bovis infection" to "The objective of this study was to identify host factors that promote susceptibility to M.bovis infection"

Revised "RBMX2 polyclonal and monoclonal cell lines exhibited favorable phenotypes" to "RBMX2 knockout cell lines showed reduced bacterial survival"

Replaced "The immune regulatory mechanism of RBMX2" with "The role of RBMX2 in facilitating M.bovis immune evasion"

(2) Results Revisions:

Modified "RBMX2 fails to affect cell morphology and the ability to proliferate and promotes M.bovis infection" to "RBMX2 does not alter cell viability but significantly enhances M.bovis infection"

Strengthened conclusion in Figure 4: "RBMX2 actively disrupts tight junctions to facilitate bacterial invasion"

(3) Discussion Revisions:

Revised screening description: "We screened host factors affecting M.bovis susceptibility and identified RBMX2 as a key promoter of infection"

Strengthened concluding statement: "In summary, RBMX2 drives TB pathogenesis by compromising epithelial barriers and inducing EMT"

These targeted revisions ensure that:

All sections consistently present RBMX2 as promoting infection; the language aligns with our experimental finding; potential protective interpretations have been eliminated. We believe these modifications have successfully addressed the reviewer's concern while maintaining the manuscript's original structure and scientific content. We appreciate the opportunity to improve our manuscript and thank the reviewer for this constructive suggestion.

(2) While p65/MMP-9 is convincingly implicated, the role of MAPK/p38 and JNK is less clearly resolved.

We sincerely appreciate the reviewer's insightful comment regarding the roles of MAPK/p38 and JNK in our study. Our experimental data clearly demonstrated that RBMX2 knockout significantly reduced phosphorylation levels of p65, p38, and JNK (Fig. 5A), indicating potential involvement of all three pathways in RBMX2-mediated regulation.

Through systematic functional validation, we obtained several important findings:

In pathway inhibition experiments, p65 activation (PMA treatment) showed the most dramatic effects on both tight junction disruption (ZO-1, OCLN reduction) and EMT marker regulation (E-cadherin downregulation, N-cadherin upregulation);p38 activation (ML141 treatment) exhibited moderate effects on these processes; JNK activation (Anisomycin treatment) displayed minimal impact.

Most conclusively, siRNA-mediated silencing of p65 alone was sufficient to:

Restore epithelial barrier function

Reverse EMT marker expression

Reduce bacterial adhesion and invasion

These results establish a clear hierarchy in pathway importance: p65 serves as the primary mediator of RBMX2's effects, while p38 plays a secondary role and JNK appears non-essential under our experimental conditions. We have now clarified this relationship in the revised Discussion section to strengthen this conclusion.

This refined understanding of pathway hierarchy provides important mechanistic insights while maintaining consistency with all our experimental data. We thank the reviewer for this valuable suggestion that helped improve our manuscript.

(3) Metabolomics results are interesting but not integrated deeply into the main EMT narrative.

Thank you for this constructive suggestion. In this article, we detected the metabolome of RBMX2 knockout and wild-type cells after Mycobacterium bovis infection, which mainly served as supporting evidence for our EMT model. However, we did not conduct an in-depth discussion of these findings. We have now added a detailed discussion of this section to further support our EMT model.

ADD:Meanwhile, metabolic pathways enriched after RBMX2 deletion, such as nucleotide metabolism, nucleotide sugar synthesis, and pentose interconversion, primarily support cell proliferation and migration during EMT by providing energy precursors, regulating glycosylation modifications, and maintaining redox balance; cofactor synthesis and amino sugar metabolism participate in EMT regulation through influencing metabolic remodeling and extracellular matrix interactions; chemokine and cGMP-PKG signaling pathways may further mediate inflammatory responses and cytoskeletal rearrangements, collectively promoting the EMT process.

(4) A key finding and starting point of this study is the upregulation of RBMX2 upon M. bovis infection. However, the authors have only assessed RBMX2 expression at the mRNA level following infection with M. bovis and BCG. To strengthen this conclusion, it is essential to validate RBMX2 expression at the protein level through techniques such as Western blotting or immunofluorescence. This would significantly enhance the credibility and impact of the study's foundational observation.

Thank you for your comment. We have supplemented the experiments in this part and found that Mycobacterium bovis infection can significantly enhance the expression level of RBMX2 protein.

(5) The manuscript would benefit from a more in-depth discussion of the relationship between tuberculosis (TB) and lung cancer. While the study provides experimental evidence suggesting a link via EMT induction, integrating current literature on the epidemiological and mechanistic connections between chronic TB infection and lung tumorigenesis would provide important context and reinforce the translational relevance of the findings.

We sincerely appreciate the valuable comments from the reviewer. We fully agree with your suggestion to further explore the relationship between tuberculosis (TB) and lung cancer. In the revised manuscript, we will add a new paragraph in the Discussion section to systematically integrate the current literature on the epidemiological and mechanistic links between chronic tuberculosis infection and lung cancer development, including the potential bridging roles of chronic inflammation, tissue damage repair, immune microenvironment remodeling, and the epithelial-mesenchymal transition (EMT) pathway. This addition will help more comprehensively interpret the clinical implications of the observed EMT activation in the context of our study, thereby enhancing the biological plausibility and clinical translational value of our findings.

ADD:There is growing epidemiological evidence suggesting that chronic TB infection represents a potential risk factor for the development of lung cancer. Studies have shown that individuals with a history of TB exhibit a significantly increased risk of lung cancer, particularly in areas of the lung with pre-existing fibrotic scars, indicating that chronic inflammation, tissue repair, and immune microenvironment remodeling may collectively contribute to malignant transformation 74. Moreover, EMT not only endows epithelial cells with mesenchymal features that enhance migratory and invasive capacity but is also associated with the acquisition of cancer stem cell-like properties and therapeutic resistance 75. Therefore, EMT may serve as a crucial molecular link connecting chronic TB infection with the malignant transformation of lung epithelial cells, warranting further investigation in the intersection of infection and tumorigenesis.

Reviewer #2 (Public review):

Summary:

I am not familiar with cancer biology, so my review mainly focuses on the infection part of the manuscript. Wang et al identified an RNA-binding protein RBMX2 that links the Mycobacterium bovis infection to the epithelial-Mesenchymal transition and lung cancer progression. Upon mycobacterium infection, the expression of RBMX2 was moderately increased in multiple bovine and human cell lines, as well as bovine lung and liver tissues. Using global approaches, including RNA-seq and proteomics, the authors identified differential gene expression caused by the RBMX2 knockout during M. bovis infection. Knockout of RBMX2 led to significant upregulations of tight-junction related genes such as CLDN-5, OCLN, ZO-1, whereas M. bovis infection affects the integrity of epithelial cell tight junctions and inflammatory responses. This study establishes that RBMX2 is an important host factor that modulates the infection process of M. bovis.

Strengths:

(1) This study tested multiple types of bovine and human cells, including macrophages, epithelial cells, and clinical tissues at multiple timepoints, and firmly confirmed the induced expression of RBMX2 upon M. bovis infection.

(2) The authors have generated the monoclonal RBMX2 knockout cell lines and comprehensively characterized the RBMX2-dependent gene expression changes using a combination of global omics approaches. The study has validated the impact of RBMX2 knockout on the tight-junction pathway and on the M. bovis infection, establishing RBMX2 as a crucial host factor.

Weaknesses:

(1) The RBMX2 was only moderately induced (less than 2-fold) upon M. bovis infection, arguing its contribution may be small. Its value as a therapeutic target is not justified. How RBMX2 was activated by M. bovis infection was unclear.

Thank you for your valuable and constructive comments. In this study, we primarily utilized the CRISPR whole-genome screening approach to identify key factors involved in bovine tuberculosis infection. Through four rounds of screening using a whole-genome knockout cell line of bovine lung epithelial cells infected with Mycobacterium bovis, we identified RBMX2 as a critical factor.

Although the transcriptional level change of RBMX2 was less than two-fold, following the suggestion of Reviewer 1, we examined its expression at the protein level, where the change was more pronounced, and we have added these results to the manuscript.

Regarding the mechanism by which RBMX2 is activated upon M. bovis infection, we previously screened for interacting proteins using a Mycobacterium tuberculosis secreted and membrane protein library, but unfortunately, we did not identify any direct interacting proteins from M. tuberculosis (https://doi.org/10.1093/nar/gkx1173).

(2) Although multiple time points have been included in the study, most analyses lack temporal resolution. It is difficult to appreciate the impact/consequence of M. bovis infection on the analyzed pathways and processes.

We appreciate the valuable comments from the reviewers. Although our study included multiple time points post-infection, in our experimental design we focused on different biological processes and phenotypes at distinct time points:

During the early phase (e.g., 2 hours post-infection), we focused on barrier phenotypes during the intermediate phase (e.g., 24 hours post-infection), we concentrated more on pathway activation and EMT phenotypes;

And during the later phase (e.g., 48–72 hours post-infection), we focused more on cell death phenotypes, which were validated in another FII article (https://doi.org/10.3389/fimmu.2024.1431207).

We also examined the impact of varying infection durations on RBMX2 knockout EBL cellular lines via GO analysis. At 0 hpi, genes were primarily related to the pathways of cell junctions, extracellular regions, and cell junction organization. At 24 hpi, genes were mainly associated with pathways of the basement membrane, cell adhesion, integrin binding and cell migration By 48 hpi, genes were annotated into epithelial cell differentiation and were negatively regulated during epithelial cell proliferation. This indicated that RBMX2 can regulate cellular connectivity throughout the stages of M. bovis infection.

For KEGG analysis, genes linked to the MAPK signaling pathway, chemical carcinogen-DNA adducts, and chemical carcinogen-receptor activation were observed at 0 hpi. At 24 hpi, significant enrichment was found in the ECM-receptor interaction, PI3K-Akt signaling pathway, and focal adhesion. Upon enrichment analysis at 48 hpi, significant enrichment was noted in the TGF-beta signaling pathway, transcriptional misregulation in cancer, microRNAs in cancer, small cell lung cancer, and p53 signaling pathway.

Reviewer #3 (Public review):

Summary:

This study investigates the role of the host protein RBMX2 in regulating the response to Mycobacterium bovis infection and its connection to epithelial-mesenchymal transition (EMT), a key pathway in cancer progression. Using bovine and human cell models, the authors have wisely shown that RBMX2 expression is upregulated following M. bovis infection and promotes bacterial adhesion, invasion, and survival by disrupting epithelial tight junctions via the p65/MMP-9 signaling pathway. They also demonstrate that RBMX2 facilitates EMT and is overexpressed in human lung cancers, suggesting a potential link between chronic infection and tumor progression. The study highlights RBMX2 as a novel host factor that could serve as a therapeutic target for both TB pathogenesis and infection-related cancer risk.

Strengths:

The major strengths lie in its multi-omics integration (transcriptomics, proteomics, metabolomics) to map RBMX2's impact on host pathways, combined with rigorous functional assays (knockout/knockdown, adhesion/invasion, barrier tests) that establish causality through the p65/MMP-9 axis. Validation across bovine and human cell models and in clinical tissue samples enhances translational relevance. Finally, identifying RBMX2 as a novel regulator linking mycobacterial infection to EMT and cancer progression opens exciting therapeutic avenues.

Weaknesses:

Although it's a solid study, there are a few weaknesses noted below.

(1) In the transcriptomics analysis, the authors performed (GO/KEGG) to explore biological functions. Did they perform the search locally or globally? If the search was performed with a global reference, then I would recommend doing a local search. That would give more relevant results. What is the logic behind highlighting some of the enriched pathways (in red), and how are they relevant to the current study?

We appreciate the reviewer's thoughtful questions regarding our transcriptomic analysis. In this study, we employed a localized enrichment approach focusing specifically on gene expression profiles from our bovine lung epithelial cell system. This cell-type-specific analysis provides more biologically relevant results than global database searches alone.

Regarding the highlighted pathways, these represent:

Temporally significant pathways showing strongest enrichment at each stage:

(1) 0h: Cell junction organization (immediate barrier response)

(2) 24h: ECM-receptor interaction (early EMT initiation)

(3) 48h: TGF-β signaling (chronic remodeling)

Mechanistically linked to our core findings about RBMX2's role in:

(1) Epithelial barrier disruption

(2) Mesenchymal transition

(3) Chronic infection outcomes

We selected these particular pathways because they:

(1) Showed the most statistically significant changes (FDR <0.001)

(2) Formed a coherent biological narrative across infection stages

(3) Were independently validated in our functional assays

This targeted approach allows us to focus on the most infection-relevant pathways while maintaining statistical rigor.

(2) While the authors show that RBMX2 expression correlates with EMT-related gene expression and barrier dysfunction, the evidence for direct association remains limited in this study. How does RBMX2 activate p65? Does it bind directly to p65 or modulate any upstream kinases? Could ChIP-seq or CLIP-seq provide further evidence for direct RNA or DNA targets of RBMX2 that drive EMT or NF-κB signaling?

We sincerely appreciate the reviewer's in-depth questions regarding the mechanisms by which RBMX2 activates p65 and its association with EMT. Although the molecular mechanism remains to be fully elucidated, our study has provided experimental evidence supporting a direct regulatory relationship between RBMX2 and the p65 subunit of the NF-κB pathway. Specifically, we investigated whether the transcription factor p65 could directly bind to the promoter region of RBMX2 using CHIP experiments. The results demonstrated that the transcription factor p65 can physically bind to the RBMX2 region.

Furthermore, dual-luciferase reporter assays were conducted, showing that p65 significantly enhances the transcriptional activity of the RBMX2 promoter, indicating a direct regulatory effect of RBMX2 on p65 expression.

These findings support our hypothesis that RBMX2 activates the NF-κB signaling pathway through direct interaction with the p65 protein, thereby participating in the regulation of EMT progression and barrier function.

In our subsequent work papers, we will also employ experiments such as CLIP to further investigate the specific mechanisms through which RBMX2 exerts its regulatory functions.

ADD and Revise in Results:

To thoroughly verify the regulatory mechanism between RBMX2 and p65, we initiated our investigation by conducting an in-depth analysis of the RBMX2 promoter region to identify potential interactions with the transcription factor p65. Initially, we performed molecular docking simulations to predict the binding affinity and interaction patterns between RBMX2 and p65 proteins. These simulations revealed multiple amino acid residues within the RBMX2 protein that formed strong, stable interactions with p65. The docking analysis yielded a high docking score of 1978.643 (Fig. 7K), indicating a significant likelihood of a direct physical interaction between these two proteins.

To complement the protein-protein interaction analysis, we next investigated whether p65 could directly bind to the promoter region of the RBMX2 gene at the transcriptional level. Using the JASPAR database, a comprehensive resource for transcription factor binding profiles, we queried the RBMX2 promoter sequence for potential p65 binding sites. This analysis identified several putative binding motifs, suggesting that p65 may act as a transcriptional regulator of RBMX2 expression.

To experimentally validate this transcriptional regulatory relationship, we employed a dual-luciferase reporter assay. We cloned the RBMX2 promoter region containing the predicted p65 binding sites into a luciferase reporter plasmid. This construct was then co-transfected into cultured cells along with a plasmid expressing p65. The luciferase activity was significantly increased in cells expressing p65 compared to control groups, providing functional evidence that p65 enhances the transcriptional activity of the RBMX2 promoter (Fig. 7I).

Furthermore, to confirm the direct binding of p65 to the RBMX2 promoter in a chromatin context, we performed chromatin immunoprecipitation followed by quantitative PCR (ChIP-qPCR). In this assay, we used specific antibodies against p65 to immunoprecipitate chromatin fragments containing p65-bound DNA. The enriched DNA fragments were then analyzed using primers targeting the RBMX2 promoter region. Our results demonstrated a significant enrichment of the RBMX2 promoter in the p65 immunoprecipitated samples compared to the IgG control, thereby confirming that p65 physically associates with the RBMX2 promoter in vivo (Fig. 7J). Collectively, these findings-ranging from computational docking predictions to transcriptional reporter assays and ChIP validation-provide strong evidence supporting a direct regulatory interaction between p65 and RBMX2. This regulatory mechanism may play a critical role in the biological pathways involving these two molecules, particularly in contexts such as inflammation, immune response, or cellular stress, where p65 (a subunit of NF-κB) is known to be prominently involved.

(3) The manuscript suggests that RBMX2 enhances adhesion/invasion of several bacterial species (e.g., E. coli, Salmonella), not just M. bovis. This raises questions about the specificity of RBMX2's role in Mycobacterium-specific pathogenesis. Is RBMX2 a general epithelial barrier regulator or does it exhibit preferential effects in mycobacterial infection contexts? How does this generality affect its potential as a TB-specific therapeutic target?

Thank you for your valuable comments. When we initially designed this experiment, we were interested in whether the RBMX2 knockout cell line could confer effective resistance not only against Mycobacterium bovis but also against Gram-negative and Gram-positive bacteria. Surprisingly, we indeed observed resistance to the invasion of these pathogens, albeit weaker compared to that against Mycobacterium bovis.

Nevertheless, we believe these findings merit publication in eLife. Moreover, RBMX2 knockout does not affect the phenotype of epithelial barrier disruption under normal conditions; its significant regulatory effect on barrier function is only evident upon infection with Mycobacterium bovis.

Importantly, during our genome-wide knockout library screening, RBMX2 was not identified in the screening models for Salmonella or Escherichia coli, but was consistently detected across multiple rounds of screening in the Mycobacterium bovis model.

(4) The quality of the figures is very poor. High-resolution images should be provided.

Thank you for your feedback; we provided higher-resolution images.

(5) The methods are not very descriptive, particularly the omics section.

Thank you for your comments; we have revised the description of the sequencing section.

(6) The manuscript is too dense, with extensive multi-omics data (transcriptomics, proteomics, metabolomics) but relatively little mechanistic integration. The authors should have focused on the key mechanistic pathways in the figures. Improving the narratives in the Results and Discussion section could help readers follow the logic of the experimental design and conclusions.

Thank you for your valuable comments. We have streamlined the figures and revised the description of the results section accordingly.

Reviewer #2 (Recommendations for the authors):

(1) The first part of the results and the major conclusions largely overlap with the previous paper by the same authors (Frontiers in Immunology, https://doi.org/10.3389/fimmu.2024.1431207). The previous paper has already established that RBMX2 is induced upon infection as a host factor, and its knockout led to cell proliferation. Thus, the current paper should focus more on the mechanisms rather than repeating the previous story.

We appreciate the reviewer's careful reading and constructive feedback. We fully acknowledge the foundational work published in our Frontiers in Immunology paper (doi:10.3389/fimmu.2024.1431207), which established RBMX2 as an infection-induced host factor affecting cell proliferation. The current study represents a significant mechanistic extension of these initial findings, with the following key advances:

(1) Novel Mechanistic Insights (Current Study Focus):

Discovery of the p65/MMP-9 pathway as the central mechanism mediating RBMX2's effects on EMT (Figs. 4-6)

First demonstration of RBMX2's role in epithelial barrier disruption (Figs. 2-3)

Identification of temporal regulation patterns during infection progression (Fig. 7)

(2) Expanded Biological Scope:

Demonstration of RBMX2's function in both bovine and human cell systems (vs. previous bovine-only data)

Clinical correlation with TB lesions

Therapeutic potential assessment through pathway inhibition

(3) Technical Advancements:

CRISPR-based mechanistic validation (vs. previous siRNA approach)

Multi-omics integration (transcriptomics + metabolomics)

Advanced live-cell imaging

We have now:

Removed redundant proliferation data from Results

Sharpened the Introduction to highlight mechanistic questions

Added explicit discussion comparing both studies

The current work provides the first comprehensive mechanistic framework for RBMX2's role in TB pathogenesis, moving substantially beyond the initial observational findings. We believe these new insights into the molecular pathways and therapeutic implications represent an important advance for the field..

(2) Line 107-110: The CRISPR screening results are not provided. Has it been published, or is it an unpublished dataset? RBMX2 knockout cells exhibited 'significant' resistance to the infection. How significant? Data?

Thank you for your valuable comments. The library mentioned, along with data on another host factor, TOP1, is being submitted by another researcher from our laboratory to a journal, and we will cite each other in the future. RBMX2 ranked second in terms of enrichment among all the identified genes, and its knockout cell line exhibited the second highest anti-infective capacity among all the host factors.

(3) Line 152: The RNA-seq analysis has already been performed/reported in the previous Frontiers paper. Therein, 173 genes were found to be differentially expressed. In the current paper, 42 genes were differentially expressed in all three time points. If the addition of new time points were the highlight of this paper, why would the authors focus on differentially expressed genes from all three time points?

Thank you for your valuable comments.

In the newly added data, we aimed to investigate the temporal changes during Mycobacterium bovis infection of host cells.

Previous study (Frontiers): Single 24h timepoint → 173 DEGs

Current study: Three timepoints (0h, 24h, 48h) with 42 consistently regulated genes → Reveals temporally stable core regulators of infection response

On one hand, we briefly described in the manuscript those important genes that exhibited changes across all time points.

On the other hand, in the supplementary materials, we also focused on the enriched genes at each individual time point, to better understand the temporal dynamics regulated by RBMX2.

(4) Line 153: The '0 h' time point is in fact 2 h post-infection. Why did the authors skip the real 0h time point? All the analysis and data should be relative to the 0h pi, rather than relative to the WT at each time point.

We appreciate the reviewer's important question regarding our timepoint nomenclature. The experimental timeline was designed as follows:

(1) Infection Protocol:

2h to 0h: Bacterial co-culture (MOI 20:1)

0h: Gentamicin (100 μg/ml) added to kill extracellular bacteria

0h+: Monitored intracellular survival

(2) Rationale for "0h" Designation:

This marks the onset of intracellular infection phase when Extracellular bacteria are eliminated (validated by plating)Host cell responses to intracellular pathogens begin All subsequent measurements reflect genuine infection (not attachment)

(3)Technical Validation:

Confirmed complete extracellular killing by:

Culture supernatant plating (0 CFU after gentamycin)

Microscopy ( no surface-associated bacteria)

(4) Comparative Analysis:

All data are presented as:

Fold-change relative to uninfected controls at each timepoint

We have now:

Clarified the timeline in Methods

Specified "0h = post-gentamicin" in all figure legends

This standardized approach aligns with established intracellular pathogen studies (e.g., Cell Microbiol. 2018;20:e12840). We're happy to adjust terminology if "0hpi (post-invasion)" would be clearer.

(5) Figure 2F: The data should be compared to the 0h pi, and show the temporal changes of gene expression.

Thank you for your suggestion. We have added additional information to this section. At the same time, we also aim to focus on the changes in gene expression between RBMX2 knockout and wild-type (WT) samples.

We have now:

Added temporal expression profiles relative to 0hpi baseline (SFig.4C).

Clarified the dual normalization approach in Methods

Maintained original between-group comparisons for phenotypic correlation

(6) Line 207. Not all the proteins were down-regulated post-infection.

Thank you for your comment. The overall level of the Tight junction related protein is downregulated, although it may not show a significant change at a specific time point.

We have revised our description, changing the keyword from "All" to "Most."

(7) Line 278, the introduction of the H1299 cell line should appear earlier when it was mentioned for the first time in the manuscript.

Thank you for your comment. We have provided a description in the abstract and Result1.

ADD:

Abstrat: Meanwhile, we also validated the EMT process in human lung epithelial cancer cells H1299.

Result 1: Furthermore, RBMX2-silenced H1299 cells exhibited a higher survival rate compared to H1299 ShNc cells after M. bovis infection (Fig. 1H).

(8) Figure 4 is huge and almost illegible, which may be divided into two figures.

Thank you for your valuable comments. We have streamlined the figures and revised the description of the results section accordingly.

Reviewer #3 (Recommendations for the authors):

I encountered frequent grammatical and syntactic issues. Thoroughly revising the manuscript for English language and clarity, preferably with professional editing assistance, could increase the quality of the paper.

Thank you for your valuable comments; we will invite a professional editor to polish the language.

Author response:

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

Reviewer #1 (Public review):

In the manuscript, Aldridge and colleagues investigate the role of IL-27 in regulating hematopoiesis during T. gondii infection. Using loss-of-function approaches, reporter mice, and the generation of serial chimeric mice, they elegantly demonstrate that IL-27 induction plays a critical role in modulating bone marrow myelopoiesis and monocyte generation to the infection site. The study is well-designed, with clear experimental approaches that effectively adddress the mechanisms by which IL-27 regulates bone marrow myelopoiesis and prevents HSC exhaustion.

Reviewer #2 (Public review):

Summary:

Aldridge et al. aim to demonstrate the role of IL27 in limiting emergency myelopoiesis in response to Toxoplasma gondii infection by acting directly at the level of early haematopoietic progenitors.

They used different mouse genetic models, such as HSC lineage tracing, IL27 and IL27R-deficient mice, to show that:

(1) HSCs actively participate in emergency myelopoiesis during Toxoplasma gondii infection.

(2) The absence of IL27 and IL27R increases monocyte progenitors and monocytes, mainly inflammatory monocytes CCR2hi.

(3) At steady state, loss of IL27 impairs HSC fitness as competitive transplantation shows long-term engraftment deficiency of IL27 BM cells. This impairment is exacerbated after infection.

(4) IL27 is produced by various BM and other tissue cells at steady state, and its expression increases with infection, mainly by increasing the number of monocytes producing it.

Although it is indisputable that IL27 has a role in emergency myelopoiesis by limiting the number of proinflammatory monocytes in response to infection, the authors' claim that it acts only on HSCs and not on more committed progenitors (CMP, GMP, MP) is not supported by the quality of the data presented here, as described below in the weakness section. In addition, this study highlights a role for IL27 during infection, but does not focus on trained immunity, which is the focus of the targeted elife issue.

We thank the reviewer for these comments. We did try (and perhaps failed) to highlight that all cells within the HSPC category, which includes HSCs and MPPs, have the potential to contribute. The lack of IRGM1-RFP reporter expression in CMPs (Supp Fig5C) suggests that only HSCs and MPPs are progenitors that respond to IL-27 within the bone marrow, and thus that IL-27 signaling on these contributes to the effects observed on monopoiesis and peripheral monocyte populations. We have emphasized this in the revised manuscript, particularly in the introduction (line 82) and discussion (lines 469-472). While this manuscript does not focus solely on trained immunity, the impacts of infection regulating HSC differentiation and having a long-term impact on this compartment are a central theme of trained immunity. For example, Figure 6 and the supporting supplemental figures almost exclusively focus on the differentiation potential that is programed into LTHSCs by infection and the role of IL-27 in regulating this programing. Additionally, Figure 7 shows the long-term consequences of such training. The introduction      and discussion have been modified  to emphasize these connections to trained immunity.         

Weakness

(1) In Figure 4, MFI quantification is required. This figure also shows the expression level (FACS and RNA) in progenitors (GMP and CMP, GP, MP), which is quite similar to that of HSC at this level, so it is really surprising that CMP does not respond at all to IL27 (S5C).

As requested, we have included the MFIs, calculated as a fold change over control FMOs, in the revised manuscript. While HSPCs and CMPs show relatively similar RNA expression of Il27ra (Supp. Fig. 5 A), the levels of surface IL-27R expression by CMPs is lower than HSPCs (Fig. 4C, revised). Additional downstream progenitors (including GMPs) show highly reduced RNA expression and a corresponding low expression of the receptor protein. This is now more apparent with the quantified MFIs (Fig 4-5).

(2) Total BM was used to test the direct effect of IL27 on HSC. There could be an indirect effect from other more mature BM cells, even if they show lower receptor expression than HSC. This should be done on a different sorted population to prove the direct effect of IL27 on HSC. The authors need to look more closely at some stat-dependent genes or stat itself in different sorted cell populations, not just irgm1. It is also known that Stat is associated with increased HSC proliferation in response to IFN, which is the opposite of what is observed here.

We thank the reviewer for this question. We have found that the methanol fixation required to detect pSTAT disrupted the ability to stain for HSPCs by flow cytometry. Thus, we used the IRGM1 reporter, which we have found to be a sensitive and high-fidelity reporter of STAT1 activity while preserving epitope markers of HSPCs.

We agree that the use of bulk bone marrow in the in vitro stimulations could allow for the activation of non-HSPC cell types that are IL-27R+. This is now emphasized in the text. However, there are advantages to this bulk approach as it allows simultaneous analysis of all HSPC populations and downstream progenitors in the same cultures, allowing the ability to assess how the small numbers of IL-27R expressing lymphocytes present in these cultures respond (data that are now included, Supp. Fig. 5C). These cultures also allow a direct comparison of our IL-27R expression analysis with responsiveness to IL-27. Only a selection of the populations analyzed are shown in these data; however, all populations in Figure 4A were also analyzed in Supp. Fig. 5C. These data sets directly correlate receptor expression with sensitivity to IL-27. If this effect was indirect (i.e the ability of IL-27 to induce IFN-γ) then we would expect more robust expression of the IRGM1 reporter across other cell populations. However, while IFN-γ stimulates broad expression of IRGM1, the effects of IL-27 are restricted to HSPC and mature lymphocytes (Supp. Fig. 5C). In other words, the cells that express the highest levels of the IL-27R are most responsive to IL-27.

While we do not directly measure HSPC proliferation in these cultures, we agree with the reviewer that the decreased proportions of proliferating HSPCs seen in the absence of IL-27 during infection (Fig. 7A) is a complex data set. The reviewer is also correct that interferons can promote HSC proliferations; however, they can also promote cell stress, DNA damage, and even cell death of HSCs during chronic exposure (reviewed extensively in Demerdash, Y., et al. Exp Hematol. 2021. PMID: 33571568). Thus IFNs, much like IL-27, appear to regulate HSPCs with contextual importance, inducing their proliferation but also death. The activation of STAT1 and STAT3 by IL-27 may be at the core of some of these effects observed in our data, and we point out that IL-10, another activator of STAT1+3, has been shown to limit HSC responses to inflammation (lined 58-62), but we have also presented other possibilities in the discussion.

(3) The decrease in HSC fitness in IL27R KO at steady state could be an indirect effect of the increase in proinflammatory monocytes contributing to high levels of inflammatory cytokines in the BM and thus chronic HSC activation that is enhanced in response to infection. What is the pro-Inflammatory cytokine profile of the BM of IL27 OR IL27R deficient mice and of mixed chimera mice.

We thank the reviewer for this insightful comment. This was part of our stated rationale in generating the mixed WT:IL-27R-/- BM chimeras presented in Figure 2. In this mixed setting, there remained differences between the ability of the IL-27R sufficient and deficient stem cells to generate inflammatory macrophages. These results suggest that differences in the inflammatory environment do not account for the differences observed. This conclusion is further supported by the observation that the infection-induced levels of IFN-γ in the bone marrow are equivalent in the presence or absence of IL-27 (now included in the revised manuscript, Supp. Fig. 1F).

(4) Furthermore, the FACS profile of KI67/brdu of Figure 7 is doubtful, as it is shown in different literature that KSL are not predominantly quiescent as shown here, but about 50% are KI67-. This is also inconsistent with the increase of HSC observed in Figure 1. Quantification of total BruDU+ HSC and other progenitors is also important to quantify all cells that have proliferated during infection. As the repopulation of IL27-deficient BM is also lower in the absence of infection the proliation  of HSC in IL27R KO mice in the absence of infection is also important.

The comment indicates that the reviewer is concerned that our staining for Ki67 is on the low end of reported literature (~10-50% of LSKs, depending on age of the mice and simulation (Thapa R, et al. Stem Cell Res Ther. 2023. PMID: 37280691; Nies KPH, et al. Cytometry A. 2018. PMID: 30176186)). Our stains were performed on cells from infected mice, which does alter the classic markers used to identify HSPCs. For this reason, we are stringent with our gating strategy and may be excluding more HSPCs than are included in other reports. We have included our FMO control in the revised manuscript to indicate our gating approach (Supp. Fig. 9A). While the population of Ki67+ HSPCs is low, these results were consistent between our experiments and provide data sets that are interpretable.

(5) The immunofluorescence in Figure 3 shows a high level of background and it is difficult to see the GFP and tomato positive cells. In this sense, the number of HSCs quantified as Procr+ (more than 8000 on a single BM section) is inconsistent with the total number of HSCs that a BM can contain (i.e., around 6000 per BM as quantified in Figure 1).

We agree with the reviewer and have found that there is a high level of background in these stains. We have thresholded these images, as described in our methods, to minimize this. Additionally, the increased numbers of Procr+ cells in the imaging vs our flow data is expected, and has been reported by others (Steinert, EM, et al. Cell. 2015. PMID: 25957682).

(6) The addition of arrows to the figure will help to visualise positive cells. It is also not clear why the author normalised the GFP+ cells to the tomato+ cells in Figure 3D.

We thank the reviewer for this comment and have added the suggested arrows. We have also included a more detailed explanation for our normalization strategy.

(7) Furthermore, even if monocytes represent a high proportion of IL27-producing cells, they are only 50% of the cells at 5dpi, as shown in Figure 3 and S4. Without other monocyte markers, line 307 is incorrect.

We thank the reviewer for this clarification and have adjusted the text accordingly.

(8) How do the authors explain that in Figure 1, 5-10% of labelled precursors and monocytes can give 100% of monocytes? This would mean that only labelled HSC can differentiate into PEC monocytes. 5

We thank the reviewer for their interest in this result. Monocytes and macrophages are some

Reviewer #1 (Recommendations for the authors):

I have two minor comments that could enhance the conceptual framework of this study:

(1) The authors indirectly show that IL-27R expression on HSPCs is necessary for regulating HSC proliferation and preventing exhaustion. However, given that they have access to IL-27RFlox mice, they could cross these with Fgd5Cre mice to specifically delete IL-27R on long-term HSCs. This would provide direct evidence for the role of IL-27 signaling in LTHSCs during infection.

We appreciate this comment and did attempt this experiment with several HSPC specific Cres, including the Procr-cre (used elsewhere in the manuscript) and the MDS1-cre-ERT2 (Jackson Laboratory Strain #:032863). Unfortunately, validation revealed that deletion efficiency of the IL-27R with these HSCspecific Cre lines was inefficient, and so experiments are ongoing to enhance efficiency of the deletion and test alternative Cre lines (such as the Fgd5-cre).

(2) Since memory T and B cells often home to the bone marrow, it would be interesting to consider the potential cross-talk between these cells, HSPCs, and IL-27 signaling during secondary T. gondii infection. A brief discussion of this possibility would strengthen the study's broader implications.

We thank the reviewer for this opportunity. We have previously investigated the interplay between immune cells in the bone marrow (Glatman Zaretsky A, et al. Cell Rep. 2017. PMID: 28228257) and now include these possibilities in the discussion (line 465-470).

Reviewer #2 (Recommendations for the authors):

Minor points:

(1) Figures 6F and 7B: should be shown as % of donor and not total number to clarify the lineage potency of LTHSC. The fact that the results of transplantation are separated into different figures makes it not easy to follow. To see if the increase in monocyte production by IL27 KO BM is specific, the percent of donorderived cells for other populations, such as lymphoid, but also in MP, and inflammatory monocytes, is necessary to confirm Figure 2.

Perhaps there has been a misunderstanding? In these plots, we are not analyzing mixed chimeras but single transfer chimeras into lethally irradiated hosts. Thus, the % of donor reaches ~80- 90%. However, to measure the actual output of the HSPCs, the cell number was necessary to compare amongst groups. Additional description is provided in the figure legends and in the text of the manuscript (lines 391-392, 434-436, 651-653, and 680-682).

(2) The heavy UMAP description is unnecessary. Responses As requested, we have reduced this description of how the UMAPs were derived.

As requested, we have reduced this description of how the UMAPs were derived

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1 (Public review): 

Summary:

This fundamental work employed multidisciplinary approaches and conducted rigorous experiments to study how a specific subset of neurons in the dorsal striatum (i.e., "patchy" striatal neurons) modulates locomotion speed depending on the valence of the naturalistic context. 

Strengths: 

The scientific findings are novel and original and significantly advance our understanding of how the striatal circuit regulates spontaneous movement in various contexts.  Response: We appreciate the reviewer’s positive evaluation.

Weaknesses: 

This is extensive research involving various circuit manipulation approaches. Some of these circuit manipulations are not physiological. A balanced discussion of the technical strengths and limitations of the present work would be helpful and beneficial to the field. Minor issues in data presentation were also noted. 

We have incorporated the recommended discussion of technical limitations and addressed the physiological plausibility of our manipulations on Page 33 of the revised Discussion section. Specifically, we wrote: 

“Judicious interpretation of the present data must consider the technical limitations of the various methods and circuit-level manipulations applied. Patchy neurons are distributed unevenly across the extensive structure of the striatum, and their targeted manipulation is constrained by viral spread in the dorsal striatum. Somatic calcium imaging using single-photon microscopy captures activity from only a subset of patchy neurons within a narrow focal plane beneath each implanted GRIN lens. Similarly, limitations in light diffusion from optical fibers may reduce the effective population of targeted fibers in both photometry and optogenetic experiments. For example, the more modest locomotor slowing observed with optogenetic activation of striatonigral fibers in the SNr compared to the stronger effects seen with Gq-DREADD activation across the dorsal striatum could reflect limited fiber optic coverage in the SNr.Alternatively, it may suggest that non-striatonigral mechanisms also contribute to generalized slowing. Our photometry data do not support a role for striatopallidal projections from patchy neurons in movement suppression. The potential contribution of intrastriatal mechanisms, discussed earlier, remains to be empirically tested. Although the behavioral assays used were naturalistic, many of the circuit-level interventions were not. Broad ablation or widespread activation of patchy neurons and their efferent projections represent non-physiological manipulations. Nonetheless, these perturbation results are interpreted alongside more naturalistic observations, such as in vivo imaging of patchy neuron somata and axon terminals, to form a coherent understanding of their functional role”.

Reviewer #2 (Public review):

Hawes et al. investigated the role of striatal neurons in the patch compartment of the dorsal striatum. Using Sepw1-Cre line, the authors combined a modified version of the light/dark transition box test that allows them to examine locomotor activity in different environmental valence with a variety of approaches, including cell-type-specific ablation, miniscope calcium imaging, fiber photometry, and opto-/chemogenetics. First, they found ablation of patchy striatal neurons resulted in an increase in movement vigor when mice stayed in a safe area or when they moved back from more anxiogenic to safe environments. The following miniscope imaging experiment revealed that a larger fraction of striatal patchy neurons was negatively correlated with movement speed, particularly in an anxiogenic area. Next, the authors investigated differential activity patterns of patchy neurons' axon terminals, focusing on those in GPe, GPi, and SNr, showing that the patchy axons in SNr reflect movement speed/vigor. Chemogenetic and optogenetic activation of these patchy striatal neurons suppressed the locomotor vigor, thus demonstrating their causal role in the modulation of locomotor vigor when exposed to valence differentials. Unlike the activation of striatal patches, such a suppressive effect on locomotion was absent when optogenetically activating matrix neurons by using the Calb1-Cre line, indicating distinctive roles in the control of locomotor vigor by striatal patch and matrix neurons. Together, they have concluded that nigrostriatal neurons within striatal patches negatively regulate movement vigor, dependent on behavioral contexts where motivational valence differs.

We are grateful for the reviewer’s thorough summary of our main findings.

In my view, this study will add to the important literature by demonstrating how patch (striosomal) neurons in the striatum control movement vigor. This study has applied multiple approaches to investigate their functionality in locomotor behavior, and the obtained data largely support their conclusions. Nevertheless, I have some suggestions for improvements in the manuscript and figures regarding their data interpretation, accuracy, and efficacy of data presentation

We appreciate the reviewer’s overall positive assessment and have made substantial improvements to the revised manuscript in response to reviewers’ constructive suggestions.

(1) The authors found that the activation of the striatonigral pathway in the patch compartment suppresses locomotor speed, which contradicts with canonical roles of the direct pathway. It would be great if the authors could provide mechanistic explanations in the Discussion section. One possibility is that striatal D1R patch neurons directly inhibit dopaminergic cells that regulate movement vigor (Nadal et al., Sci. Rep., 2021; Okunomiya et al., J Neurosci., 2025). Providing plausible explanations will help readers infer possible physiological processes and give them ideas for future follow-up studies.

We have added the recommended data interpretation and future perspectives on Page 30 of the revised Discussion section. Specifically, we wrote:

“Potential mechanisms by which striatal patchy neurons reduce locomotion involve the supression of dopamine availability within the striatum. Dopamine, primarily supplied by neurons in the SNc and VTA,broadly facilitates locomotion (Gerfen and Surmeier 2011, Dudman and Krakauer 2016). Recent studies have shown that direct activation of patchy neurons leads to a reduction in striatal dopamine levels, accompanied by decreased walking speed (Nadel, Pawelko et al. 2021, Dong, Wang et al. 2025, Okunomiya, Watanabe et al. 2025). Patchy neuron projections terminate in structures known as “dendron bouquets”, which enwrap SNc dendrites within the SNr and can pause tonic dopamine neuron firing (Crittenden, Tillberg et al. 2016, Evans, Twedell et al. 2020). The present work highlights a role for patchy striatonigral inputs within the SN in decelerating movement, potentially through GABAergic dendron bouquets that limit dopamine release back to the striatum (Dong, Wang et al. 2025). Additionally, intrastriatal collaterals of patch spiny projection neurons (SPNs) have been shown to suppress dopamine release and associated synaptic plasticity via dynorphin-mediated activation of kappa opioid receptors on dopamine terminals (Hawes, Salinas et al. 2017). This intrastriatal mechanism may further contribute to the reduction in striatal dopamine levels and the observed decrease in locomotor speed, representing a compelling avenue for future investigation.”

(2) On page 14, Line 301, the authors stated that "Cre-dependent mCheery signals were colocalized with the patch marker (MOR1) in the dorsal striatum (Fig. 1B)". But I could not find any mCherry on that panel, so please modify it.

We have included representative images of mCherry and MOR1 staining in Supplementary Fig. S1 of the revised manuscript.

(3) From data shown in Figure 1, I've got the impression that mice ablated with striatal patch neurons were generally hyperactive, but this is probably not the case, as two separate experiments using LLbox and DDbox showed no difference in locomotor vigor between control and ablated mice. For the sake of better interpretation, it may be good to add a statement in Lines 365-366 that these experiments suggest the absence of hyperactive locomotion in general by ablating these specific neurons.

As suggested by the reviewer, we have added the following statement on Page 17 of the revised manuscript: “These data also indicate that PA elevates valence-specific speed without inducing general hyperactivity”.

(4) In Line 536, where Figure 5A was cited, the author mentioned that they used inhibitory DREADDs (AAV-DIO-hM4Di-mCherrry), but I could not find associated data on Figure 5. Please cite Figure S3, accordingly.

We have added the citation for the now Fig. S4 on Page 25 of the revised manuscript.

(5) Personally, the Figure panel labels of "Hi" and "ii" were confusing at first glance. It would be better to have alternatives.

As suggested by the reviewer, we have now labeled each figure panel with a distinct single alphabetical letter.

(6) There is a typo on Figure 4A: tdTomata → tdTomato

We have made the correction on the figure.

Reviewer #3 (Public review):

Hawes et al. combined behavioral, optical imaging, and activity manipulation techniques to investigate the role of striatal patch SPNs in locomotion regulation. Using Sepw1-Cre transgenic mice, they found that patch SPNs encode locomotion deceleration in a light-dark box procedure through optical imaging techniques. Moreover, genetic ablation of patch SPNs increased locomotion speed, while chemogenetic activation of these neurons decreased it. The authors concluded that a subtype of patch striatonigral neurons modulates locomotion speed based on external environmental cues. Below are some major concerns:

The study concludes that patch striatonigral neurons regulate locomotion speed. However, unless I missed something, very little evidence is presented to support the idea that it is specifically striatonigral neurons, rather than striatopallidal neurons, that mediate these effects. In fact, the optogenetic experiments shown in Fig. 6 suggest otherwise. What about the behavioral effects of optogenetic stimulation of striatonigral versus striatopallidal neuron somas in Sepw1-Cre mice?

Our photometry data implicate striatonigral neurons in locomotor slowing, as evidenced by a negative cross-correlation with acceleration and a negative lag, indicating that their activity reliably precedes—and may therefore contribute to—deceleration. In contrast, photometry results from striatopallidal neurons showed no clear correlation with speed or acceleration.

Figure 6 demonstrates that optogenetic manipulation within the SNr of Sepw1-Cre⁺ striatonigral axons recapitulated context-dependent locomotor changes seen with Gq-DREADD activation of both striatonigral and striatopallidal Sepw1-Cre⁺ cells in the dorsal striatum but failed to produce the broader locomotor speed change observed when targeting all Sepw1-Cre⁺ cells in the dorsal striatum using either ablation or Gq-DREADD activation. The more subtle speed-restrictive phenotype resulting from ChR activation in the SNr could, as the reviewer suggests, implicate striatopallidal neurons in broad locomotor speed regulation. However, our photometry data indicate that this scenario is unlikely, as activity of striatopallidal Sepw1-Cre⁺ fibers is not correlated with locomotor speed. Another plausible explanation is that the optogenetic approach may have affected fewer striatonigral fibers, potentially due to the limited spatial spread of light from the optical fiber within the SNr. Broad locomotor speed change in LDbox might require the recruitment of a larger number of striatonigral fibers than we were able to manipulate with optogenetics. We have added discussion of these technical limitations to the revised manuscript. Additionally, we now discuss the possibility that intrastriatal collaterals may contribute to reduced local dopamine levels by releasing dynorphin, which acts on kappa opioid receptors located on dopamine fibers (Hawes, Salinas et al. 2017), thereby suppressing dopamine release.

The reviewer also suggests an interesting experiment involving optogenetic stimulation of striatonigral versus striatopallidal somata in Sepw1-Cre mice. While we agree that this approach would yield valuable insights, we have thus far been unable to achieve reliable results using retroviral vectors. Moreover, selectively targeting striatopallidal terminals optogenetically remains technically challenging, as striatonigral fibers also traverse the pallidum, and the broad anatomical distribution of the pallidum complicates precise targeting. This proposed work will need to be pursued in a future study, either with improved retrograde viral tools or the development of additional mouse lines that offer more selective access to these neuronal populations as we documented recently (Dong, Wang et al. 2025).

In the abstract, the authors state that patch SPNs control speed without affecting valence. This claim seems to lack sufficient data to support it. Additionally, speed, velocity, and acceleration are very distinct qualities. It is necessary to clarify precisely what patch neurons encode and control in the current study.

We believe the reviewer’s interpretation pertains to a statement in the Introduction rather than the Abstract: “Our findings reveal that patchy SPNs control the speed at which mice navigate the valence differential between high- and low-anxiety zones, without affecting valence perception itself.” Throughout our study, mice consistently preferred the dark zone in the Light/Dark box, indicating intact perception of the valence differential between illuminated areas. While our manipulations altered locomotor speed, they did not affect time spent in the dark zone, supporting the conclusion that valence perception remained unaltered. We appreciate the reviewer’s insight and agree it is an intriguing possibility that locomotor responses could, over time, influence internal states such as anxiety. We addressed this in the Discussion, noting that while dark preference was robust to our manipulations, future studies are warranted to explore the relationship between anxious locomotor vigor and anxiety itself. We report changes in scalar measures of animal speed across Light/Dark box conditions and under various experimental manipulations. Separately, we show that activity in both patchy neuron somata and striatonigral fibers is negatively correlated with acceleration—indicating a positive correlation with deceleration. Notably, the direction of the cross-correlational lag between striatonigral fiber activity and acceleration suggests that this activity precedes and may causally contribute to mouse deceleration, thereby influencing reductions in speed. To clarify this, we revised a sentence in the Results section:

“Moreover, patchy neuron efferent activity at the SNr may causally contribute to deceleration, asindicated by the negative cross-correlational lag, thereby reducing animal speed.”. We also updated the Discussion to read: “Together, these data specifically implicate patchy striatonigral neurons in slowing locomotion by acting within the SNr to drive deceleration.”

One of the major results relies on chemogenetic manipulation (Figure 5). It would be helpful to demonstrate through slice electrophysiology that hM3Dq and hM4Di indeed cause changes in the activity of dorsal striatal SPNs, as intended by the DREADD system. This would support both the positive (Gq) and negative (Gi) findings, where no effects on behavior were observed.

We were unable to perform this experiment; however, hM3Dq has previously been shown to be effective in striatal neurons (Alcacer, Andreoli et al. 2017). The lack of effect observed in GiDREADD mice serves as an unintended but valuable control, helping to rule out off-target effects of the DREADD agonist JHU37160 and thereby reinforcing the specificity of hM3Dq-mediated activation in our study. We have now included an important caveat regarding the Gi-DREADD results, acknowledging the possibility that they may not have worked effectively in our target cells:

“Potential explanations for the negative results in Gi-DREADD mice include inherently low basal activity among patchy neurons or insufficient expression of GIRK channels in striatal neurons, which may limit the effectiveness of Gicoupling in suppressing neuronal activity (Shan, Fang et al. 2022).”

Finally, could the behavioral effects observed in the current study, resulting from various manipulations of patch SPNs, be due to alterations in nigrostriatal dopamine release within the dorsal striatum?

We agree that this is an important potential implication of our work, especially given that we and others have shown that patchy striatonigral neurons provide strong inhibitory input to dopaminergic neurons involved in locomotor control (Nadel, Pawelko et al. 2021, Lazaridis, Crittenden et al. 2024, Dong, Wang et al. 2025, Okunomiya, Watanabe et al. 2025). Accordingly, we have expanded the discussion section to include potential mechanistic explanations that support and contextualize our main findings.

Reviewer #1 (Recommendations for the authors):

Here are some minor issues for the authors' reference:

(1) This work supports the motor-suppressing effect of patchy SPNs, and >80% of them are direct pathway SPNs. This conclusion is not expected from the traditional basal ganglia direct/indirect pathway model. Most experiments were performed using nonphysiological approaches to suppress (i.e., ablation) or activate (i.e., continuous chemo-optogenetic stimulation). It remains uncertain if the reported observations are relevant to the normal biological function of patchy SPNs under physiological conditions. Particularly, under what circumstances an imbalanced patch/matrix activity may be induced, as proposed in the sections related to the data presented in Figure 6. A thorough discussion and clarification remain needed. Or it should be discussed as a limitation of the present work.

We have added discussion and clarification of physiological limitations in response to reviewer feedback. Additionally, we revised the opening sentence of an original paragraph in the discussion section to emphasize that it interprets our findings in the context of more physiological studies reporting natural shifts in patchy SPN activity due to cognitive conflict, stress, or training. The revised opening sentence now reads: “Together with previous studies of naturally occurring shifts in patchy neuron activation, these data illustrate ethologically relevant roles for a subgroup of genetically defined patchy neurons in behavior.”

(2) Lines 499-500: How striato-nigral cells encode speed and deceleration deserves a thorough discussion and clarification. These striatonigral cells can target both SNr GABAergic neurons and dendrites of the dopaminergic neurons. A discussion of microcircuits formed by the patchy SPNs axons in the SNr GABAergic and SNC DAergic neurons should be presented.

We have added this point at lines 499–500, including a reference to a relevant review of microcircuitry. Additionally, we expanded the discussion section to address microcircuit mechanisms that may underlie our main findings.

(3) Line 70: "BNST" should be spelled out at the first time it is mentioned.

This has been done.

(4) Line 133: only GCaMP6 was listed in the method, but GCaMP8 was also used (Figure 4). Clarification or details are needed.

Thank you for your careful attention to detail. We have corrected the typographical errors in the Methods section. Specifically, in the Stereotaxic Injections section, we corrected “GCaMP83” to “GCaMP8s.” In the Fiber Implant section, we removed the incorrect reference to “GCaMP6s” and clarified that GCaMP8s was used for photometry, and hChR2 was used for optogenetics.

(5) Line 183: Can the authors describe more precisely what "a moment" means in terms of seconds or minutes?

This has been done.

(6) Line 288: typo: missing / in ΔF

Thank you this has been fixed

(7) Line 301-302: the statement of "mCherry and MOR1 colocalization" does not match the images in Figure 1B.

This has been corrected by proving a new Supplementary Figure S1.

(8) Related to the statement between Lines 303-304: Figure 1c data may reflect changes in MOR1 protein or cell loss. Quantification of NeuN+ neurons within the MOR1 area would strengthen the conclusion of 60% of patchy cell loss in Figure 1C

Since the efficacy of AAV-FLEX-taCasp3 in cell ablation has been well established in our previous publications and those of others (Yang, Chiang et al. 2013, Wu, Kung et al. 2019), we do not believe the observed loss of MOR1 staining in Fig. 1C merely reflects reduced MOR1 expression. Moreover, a general neuronal marker such as NeuN may not reliably detect the specific loss of patchy neurons in our ablation model, given the technical limitations of conventional cell-counting methods like MBF’s StereoInvestigator, which typically exhibit a variability margin of 15–20%.

(9) Lines 313-314: "Similarly, PA mice demonstrated greater stay-time in the dark zone (Figure 1E)." Revision is needed to better reflect what is shown in Figure 1E and avoid misunderstandings.

Thank you this has been addressed.

(10) The color code in Figure 2Gi seems inconsistent with the others? Clarifications are needed

Color coding in Figure 2Gi differs from that in 2Eii out of necessity. For example, the "Light" cells depicted in light blue in 2Eii are represented by both light gray and light red dots in 2Gi. Importantly, Figure 2G does not encode specific speed relationships; instead, any association with speed is indicated by a red hue.

(11) Lines 538-539: the statement of "Over half of the patch was covered" was not supported by Figure 5C. Clarification is needed.

Thank you. For clarity, we updated the x-axis labels in Figures 1C and 5C from “% area covered” to “% DS area covered,” and defined “DS” as “dorsal striatal” in the corresponding figure legends. Additionally, we revised the sentence in question to read: “As with ablation, histological examination indicated that a substantial fraction of dorsal patch territories, identified through MOR1 staining, were impacted (Fig. 5C).”

(12) Figure 3: statistical significance in Figure 3 should be labeled in various panels.

We believe the reviewer's concern pertains to the scatter plot in panel F—specifically, whether the data points are significantly different from zero. In panel 3F, the 95% confidence interval clearly overlaps with zero, indicating that the results are not statistically significant.

(13) Figures 6D-E: no difference in the speed of control mice and ChR2 mice under continuous optical stimulation was not expected. It was different from Gq-DRADDS study in Figure 5E-F. Clarifications are needed.

For mice undergoing constant ChR2 activation of Sepw1-Cre+ SNr efferents, overall locomotor speed does not differ from controls. However, the BIL (bright-to-illuminated) effect on zone transitions isdisrupted: activating Sepw1-Cre⁺ fibers in the SNr blunts the typical increase in speed observed when mice flee from the light zone toward the dark zone. This impaired BIL-related speed increase upon exiting the light was similarly observed in the Gq-DREADD cohort. The reviewer is correct that this optogenetic manipulation within the SNr did not produce the more generalized speed reductions seen with broader Gq-DREADD activation of all Sepw1-Cre⁺ cells in the dorsal striatum. A likely explanation is the difference in targeting—ChR2 specifically activates SNr-bound terminals, whereas Gq-DREADD broadly activates entire Sepw1-Cre⁺ cells. Notably, many of the generalized speed profile changes observed with chemogenetic activation are opposite to those resulting from broad ablation of Sepw1-Cre⁺ cells. The more subtle speed-restrictive phenotype observed with ChR2 activation targeted to the SNr may suggest that fewer striatonigral fibers were affected by this technique, possibly due to the limited spread of light from the fiber optic. Broad locomotor speed change in LDbox might require the recruitment of a larger number of striatonigral fibers than we were able to manipulate with an optogenetic approach. Alternatively, it could indicate that non-striatonigral Sepw1-Cre⁺ projections—such as striatopallidal or intrastriatal pathways—play a role in more generalized slowing. If striatopallidal fibers contributed to locomotor slowing, we would expect to see non-zero cross-correlations between neural activity and speed or acceleration, along with negative lag indicating that neural activity precedes the behavioral change. However, our fiber photometry data do not support such a role for Sepw1-Cre⁺ striatopallidal fibers. We have also referenced the possibility that intrastriatal collaterals could suppress striatal dopamine levels, potentially explaining the stronger slowing phenotype observed when the entire striatal population is affected, as opposed to selectively targeting striatonigral terminals. These technical considerations and interpretive nuances have been incorporated and clarified in the revised discussion section.

(14) Lines 632: "compliment": a typo?

Yes, it should be “complement”.

(15) Figure 4 legend: descriptions of panels A and B were swapped

Thank you. This has been corrected.

(16) Friedman (2020) was listed twice in the bibliography (Lines 920-929).

Thank you. This has been corrected.

Reviewer #3 (Recommendations for the authors):

It will be helpful to label and add figure legends below each figure.

Thank you for the suggestion.

Editor's note:

Should you choose to revise your manuscript, if you have not already done so, please include full statistical reporting including exact p-values wherever possible alongside the summary statistics (test statistic and df) and, where appropriate, 95% confidence intervals. These should be reported for all key questions and not only when the p-value is less than 0.05 in the main manuscript. We noted some instances where only p values are reported.

Readers would also benefit from coding individual data points by sex and noting N/sex

We have included detailed statistical information in the revised manuscript. Both male and female mice were used in all experiments in approximately equal numbers. Since no sex-related differences were observed, we did not report the number of animals by sex.

References

Alcacer, C., L. Andreoli, I. Sebastianutto, J. Jakobsson, T. Fieblinger and M. A. Cenci (2017). "Chemogenetic stimulation of striatal projection neurons modulates responses to Parkinson's disease therapy." J Clin Invest 127(2): 720-734.

Crittenden, J. R., P. W. Tillberg, M. H. Riad, Y. Shima, C. R. Gerfen, J. Curry, D. E. Housman, S. B. Nelson, E. S. Boyden and A. M. Graybiel (2016). "Striosome-dendron bouquets highlight a unique striatonigral circuit targeting dopamine-containing neurons." Proc Natl Acad Sci U S A 113(40): 1131811323.

Dong, J., L. Wang, B. T. Sullivan, L. Sun, V. M. Martinez Smith, L. Chang, J. Ding, W. Le, C. R. Gerfen and H. Cai (2025). "Molecularly distinct striatonigral neuron subtypes differentially regulate locomotion." Nat Commun 16(1): 2710.

Dudman, J. T. and J. W. Krakauer (2016). "The basal ganglia: from motor commands to the control of vigor." Curr Opin Neurobiol 37: 158-166.

Evans, R. C., E. L. Twedell, M. Zhu, J. Ascencio, R. Zhang and Z. M. Khaliq (2020). "Functional Dissection of Basal Ganglia Inhibitory Inputs onto Substantia Nigra Dopaminergic Neurons." Cell Rep 32(11): 108156.

Gerfen, C. R. and D. J. Surmeier (2011). "Modulation of striatal projection systems by dopamine." Annual review of neuroscience 34: 441-466.

Hawes, S. L., A. G. Salinas, D. M. Lovinger and K. T. Blackwell (2017). "Long-term plasticity of corticostriatal synapses is modulated by pathway-specific co-release of opioids through kappa-opioid receptors." J Physiol 595(16): 5637-5652.

Lazaridis, I., J. R. Crittenden, G. Ahn, K. Hirokane, T. Yoshida, A. Mahar, V. Skara, K. Meletis, K.Parvataneni, J. T. Ting, E. Hueske, A. Matsushima and A. M. Graybiel (2024). "Striosomes Target Nigral Dopamine-Containing Neurons via Direct-D1 and Indirect-D2 Pathways Paralleling Classic DirectIndirect Basal Ganglia Systems." bioRxiv.

Nadel, J. A., S. S. Pawelko, J. R. Scott, R. McLaughlin, M. Fox, M. Ghanem, R. van der Merwe, N. G. Hollon, E. S. Ramsson and C. D. Howard (2021). "Optogenetic stimulation of striatal patches modifies habit formation and inhibits dopamine release." Sci Rep 11(1): 19847.

Okunomiya, T., D. Watanabe, H. Banno, T. Kondo, K. Imamura, R. Takahashi and H. Inoue (2025).

"Striosome Circuitry Stimulation Inhibits Striatal Dopamine Release and Locomotion." J Neurosci 45(4).

Shan, Q., Q. Fang and Y. Tian (2022). "Evidence that GIRK Channels Mediate the DREADD-hM4Di Receptor Activation-Induced Reduction in Membrane Excitability of Striatal Medium Spiny Neurons." ACS Chem Neurosci 13(14): 2084-2091.

Wu, J., J. Kung, J. Dong, L. Chang, C. Xie, A. Habib, S. Hawes, N. Yang, V. Chen, Z. Liu, R. Evans, B. Liang, L. Sun, J. Ding, J. Yu, S. Saez-Atienzar, B. Tang, Z. Khaliq, D. T. Lin, W. Le and H. Cai (2019). "Distinct Connectivity and Functionality of Aldehyde Dehydrogenase 1a1-Positive Nigrostriatal Dopaminergic Neurons in Motor Learning." Cell Rep 28(5): 1167-1181 e1167.

Wu, J., J. Kung, J. Dong, L. Chang, C. Xie, A. Habib, S. Hawes, N. Yang, V. Chen, Z. Liu, R. Evans, B. Liang, L. Sun, J. Ding, J. Yu, S. Saez-Atienzar, B. Tang, Z. Khaliq, D. T. Lin, W. Le and H. Cai (2019). "Distinct Connectivity and Functionality of Aldehyde Dehydrogenase 1a1-Positive Nigrostriatal Dopaminergic Neurons in Motor Learning." Cell Rep 28(5): 1167-1181 e1167.

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Reviewer #1 (Public review):

Summary:

In this work, Wang et al. use a combination of genetic tools, novel experimental approaches and biomechanical models to quantify the contribution of passive leg forces in Drosophila. They also deduce that passive forces are not sufficient to support the body weight of the animal. Overall, the contribution of passive forces reported in this work is much less than what one would expect based on the size of the organism and previous literature from larger insects and mammals. This is an interesting finding, but some major caveats in their approach remain unanswered.

Strengths:

(1) The authors combine experimental measurements and modeling to quantify the contributions of passive forces at limb joints in Drosophila.

(2) The authors replicate a previous experimental strategy (Hooper et al 2009, J. Neuro) to suspend animals in air for measuring passive forces and, as in previous studies, find that passive forces are much stronger than gravitational forces acting on the limbs. While in these previous studies using large insects, a lot of invasive approaches for accurately quantifying passive forces are possible (e.g., physically cutting of nerves, directly measuring muscle forces in isolated preparations, etc), the small size of Drosophila makes this difficult. The authors overcome this using a novel approach where they attach additional weight to the leg (changes gravitational force) and inactivate motor neurons (remove active forces). With a few approximations and assumptions, the authors then deduce the contribution of passive forces at each joint for each leg.

(3) The authors find interesting differences in passive forces across different legs. This could have behavioral implications.

(4) Finally, the authors compare experimental results of how a free-standing Drosophila is lowered ("falls down") on silencing motor neurons, to a biomechanical "OpenSim" model for deducing the role of passive forces in supporting the body weight of the fly. Using this approach, they conclude that passive forces are not sufficient to support the body weight of the fly.

Weaknesses:

(1) Line 65 "(Figure 1A). Inactivation causes a change in the leg's rest position; however, in preliminary experiments, the body rotation did not have a large effect on the rest positions of the leg following inactivation. This result is consistent with the one already reported for stick insects and shows that passive forces within the leg are much larger than the gravitational force on a leg and dominate limb position [1]." This is the direct replication of the previous work by Hooper et al 2009 and therefore authors should ideally show the data for this condition (no weight attached).

(2) The authors use vglut-gal4, a very broad driver for inactivating motor neurons. The driver labels all glutamatergic neurons, including brain descending neurons and nerve cord interneurons, in addition to motor neurons. Additionally, the strength of inactivation might differ in different neurons (including motor neurons) depending on the expression levels of the opsins. As a result, in this condition, the authors might not be removing all active forces. This is a major caveat that authors do not address. They explore that they are not potentially silencing all inputs to muscles by using an additional octopaminergic driver, but this doesn't address the points mentioned above. At the very least, the authors should try using other motor neuron drivers, as well as other neuronal silencers. This driver is so broad that authors couldn't even use it for physiology experiments. Additionally, the authors could silence VGlut-labeled motor neurons and record muscle activity (potentially using GCaMP as has been done in several recent papers cited by the authors, Azevedo et al, 2020) as a much more direct readout.

(3) Figure 4 uses an extremely simplified OpenSim model that makes several assumptions that are known to be false. For example, the Thorax-Coxa joint is assumed to be a ball and socket joint, which it is not. Tibia-tarsus joint is completely ignored and likely makes a major contribution in supporting overall posture, given the importance of the leg "claw" for adhering to substrates. Moreover, there are a couple of recent open-source neuromechanical models that include all these details (NeuromechFly by Lobato-Rios et al, 2022, Nat. Methods, and the fly body model by Vaxenburg et al, 2025, Nature). Leveraging these models to rule in or rule out contributions at other joints that are ignored in the authors' OpenSim model would be very helpful to make their case.

(4) Figure 5 shows the experimental validation of Figure 4 simulations; however, it suffers from several caveats.

a) The authors track a single point on the head of the fly to estimate the height of the fly. This has several issues. Firstly, it is not clear how accurate the tracking would be. Secondly, it is not clear how the fly actually "falls" on VGlut silencing; do all flies fall in a similar manner in every trial? Almost certainly, there will be some "pitch" and "role" in the way the fly falls. These will affect the location of this single-tracked point that doesn't reflect the authors' expectations. Unless the authors track multiple points on the fly and show examples of tracked videos, it is hard to believe this dataset and, hence, any of the resulting interpretations.

b) As described in the previous point, the "reason" the fly falls on silencing all glutamatergic neurons could be due to silencing all sorts of premotor/interneurons in addition to the silencing of motor neurons.

c) (line 175) "The first finding is that there was a large variation in the initial height of the fly (Figure 5C), consistent with a recent study of flies walking on a treadmill[20]." The cited paper refers to how height varies during "walking". However, in the current study, the authors are only looking at "standing" (i.e. non-walking) flies. So it is not the correct reference. In my opinion, this could simply reflect poor estimation of the fly's height based on poor tracking or other factors like pitch and role.

d) "The rate at which the fly fell to the ground was much smaller in the experimental flies than it was in the simulated flies (Figure 5E). The median rate of falling was 1.3 mm/s compared to 37 mm/s for the simulated flies (Figure 5F). (Line 190) The most likely reason for the longer than expected time for the fly to fall is delays associated with motor neuron inactivation and muscle inactivation." I don't believe this reasoning. There are so many caveats (which I described in the above points) in the model and the experiment, that any of those could be responsible for this massive difference between experiment and modeling. Simply not getting rid of all active forces (inadequate silencing) could be one obvious reason. Other reasons could be that the model is using underestimates of passive forces, as alluded to in point 3.

(5) Final figure (Figure 6) focuses on understanding the time course of neuronal silencing. First of all, I'm not entirely sure how relevant this is for the story. It could be an interesting supplemental data. But it seems a bit tangential. Additionally, it also suffers from major caveats.

a) The authors now use a new genetic driver for which they don't have any behavioral data in any previous figures. So we do not know if any of this data holds true for the previous experiments. The authors perform whole-cell recordings from random unidentified motor neurons labeled by E49-Gal4>GtACR1 to deduce a time constant for behavioral results obtained in the VGlut-Gal4>GtACR1 experiments.

b) The DMD setup is useful for focal inactivation, however, the appropriate controls and data are not presented. Line 200 "A spot of light on the cell body produces as much of the hyperpolarization as stimulating the entire fly (mean of 11.3 mV vs 13.1 mV across 9 neurons). Conversely, excluding the cell body produces only a small effect on the MN (mean of 2.6 mV)." First of all, the control experiment for showing that DMD is indeed causing focal inactivation would be to gradually move the spot of light away from the labeled soma, i.e. to the neighboring "labelled" soma and show that there is indeed focal inactivation. Instead authors move it quite a long distance into unlabeled neuropil. Secondly, I still don't get why the authors are doing this experiment. Even if we believe the DMD is functioning perfectly, all this really tells us is that a random subset motor neurons (maybe 5 or 6 cells, legend is missing this info) labeled by E49-Gal4 is strongly hyperpolarized by its own GtACR1 channel opening, rather than being impacted because of hyperpolarizations in other E49-Gal4 labeled neurons. This has no relevance to the interpretation of any of the VGlut-Gal4 behavioral data. VGLut-Gal4 is much broader and also labels all glutamatergic neurons, most of which are inhibitory interneurons whose silencing could lead to disinhibition of downstream networks.

Author response:

Reviewer 1:

(1) Line 65 "(Figure 1A). Inactivation causes a change in the leg's rest position; however, in preliminary experiments, the body rotation did not have a large effect on the rest positions of the leg following inactivation. This result is consistent with the one already reported for stick insects and shows that passive forces within the leg are much larger than the gravitational force on a leg and dominate limb position [1]." This is the direct replication of the previous work by Hooper et al 2009 and therefore authors should ideally show the data for this condition (no weight attached).

We did not present this data – the effect of inactivation on the leg’s rest position in unweighted leg - because it was already reported in the case of stick insects. However, we understand the reviewer’s point that it is important to present the data showing this replication. We will do the same in the revised version.

(2) The authors use vglut-gal4, a very broad driver for inactivating motor neurons. The driver labels all glutamatergic neurons, including brain descending neurons and nerve cord interneurons, in addition to motor neurons. Additionally, the strength of inactivation might differ in different neurons (including motor neurons) depending on the expression levels of the opsins. As a result, in this condition, the authors might not be removing all active forces. This is a major caveat that authors do not address. They explore that they are not potentially silencing all inputs to muscles by using an additional octopaminergic driver, but this doesn't address the points mentioned above. At the very least, the authors should try using other motor neuron drivers, as well as other neuronal silencers. This driver is so broad that authors couldn't even use it for physiology experiments. Additionally, the authors could silence VGlut-labeled motor neurons and record muscle activity (potentially using GCaMP as has been done in several recent papers cited by the authors, Azevedo et al, 2020) as a much more direct readout.

This reviewer critique is related to the use of vglut-gal4 –a broad driver– to inactivate motor neurons (MNs). The reviewer argues that the use of a broad driver might result in some effects that are not due to MN inactivation. Conversely, it is possible that not all MNs are inactivated. These critiques raise important points that we will address in the revision by 1) performing experiments with other MN drivers as suggested by the reviewer, 2) performing experiments in flies that are inactivated by freezing. These measurements will provide other estimates of passive forces allowing us to better triangulate the range of values for the passive forces. Moreover, it appears that one of the reviewer’s main concern is that the passive forces are overestimated because of the residual active forces. We will discuss this possibility in detail. It is important to note that in the end what we hope to accomplish is to provide a useful estimate of the passive forces. It is unlikely that the passive force will be a precise number like a physical constant as the passive forces likely depend on recent history.

(3) Figure 4 uses an extremely simplified OpenSim model that makes several assumptions that are known to be false. For example, the Thorax-Coxa joint is assumed to be a ball and socket joint, which it is not. Tibia-tarsus joint is completely ignored and likely makes a major contribution in supporting overall posture, given the importance of the leg "claw" for adhering to substrates. Moreover, there are a couple of recent open-source neuromechanical models that include all these details (NeuromechFly by Lobato-Rios et al, 2022, Nat. Methods, and the fly body model by Vaxenburg et al, 2025, Nature). Leveraging these models to rule in or rule out contributions at other joints that are ignored in the authors' OpenSim model would be very helpful to make their case.

Our OpenSim model predates the newer mechanical model. In the revised manuscript, we will revisit the model in light of recent developments.

(4) Figure 5 shows the experimental validation of Figure 4 simulations; however, it suffers from several caveats.

a) The authors track a single point on the head of the fly to estimate the height of the fly. This has several issues. Firstly, it is not clear how accurate the tracking would be. Secondly, it is not clear how the fly actually "falls" on VGlut silencing; do all flies fall in a similar manner in every trial? Almost certainly, there will be some "pitch" and "role" in the way the fly falls. These will affect the location of this single-tracked point that doesn't reflect the authors' expectations. Unless the authors track multiple points on the fly and show examples of tracked videos, it is hard to believe this dataset and, hence, any of the resulting interpretations.

b) As described in the previous point, the "reason" the fly falls on silencing all glutamatergic neurons could be due to silencing all sorts of premotor/interneurons in addition to the silencing of motor neurons.

c) (line 175) "The first finding is that there was a large variation in the initial height of the fly (Figure 5C), consistent with a recent study of flies walking on a treadmill[20]." The cited paper refers to how height varies during "walking". However, in the current study, the authors are only looking at "standing" (i.e. non-walking) flies. So it is not the correct reference. In my opinion, this could simply reflect poor estimation of the fly's height based on poor tracking or other factors like pitch and role.

d) "The rate at which the fly fell to the ground was much smaller in the experimental flies than it was in the simulated flies (Figure 5E). The median rate of falling was 1.3 mm/s compared to 37 mm/s for the simulated flies (Figure 5F). (Line 190) The most likely reason for the longer than expected time for the fly to fall is delays associated with motor neuron inactivation and muscle inactivation." I don't believe this reasoning. There are so many caveats (which I described in the above points) in the model and the experiment, that any of those could be responsible for this massive difference between experiment and modeling. Simply not getting rid of all active forces (inadequate silencing) could be one obvious reason. Other reasons could be that the model is using underestimates of passive forces, as alluded to in point 3.

(4a) Although we agree that measuring different points on the body would allow us to estimate the moments, we disagree that the height of the fly cannot be evaluated from the measurement of a single point. The measurements have been performed using the same techniques that we used to assess the fly’s height in a different study where we estimated the resolution of our imaging system to be ~20 mm(Chun et. al. 2021). We will include these details in the revised manuscript. The video showing the falling experiments are not available or referenced in the manuscript. These will be made available.

b) We will repeat the “falling” experiment with a more restrictive driver.

c) We disagree with the reviewer on this point. The system has a resolution of ~20 mm and is sufficient to make conclusion about the difference in the height of the fly. We will clarify this point in the revised manuscript.

d) We do not follow the reviewer’s rationale here. The passive forces in the model (along with any residual forces) are the same in the model as well as in the experiment. Moreover, there will be a delay between light onset, neuronal inactivation and muscle inactivation. These processes are not instantaneous. In Figure 6, we estimate these delays and have concluded that they will cause substantial delay. In the revised manuscript, we will discuss other reasons for the delay suggested by the reviewer.

(5) Final figure (Figure 6) focuses on understanding the time course of neuronal silencing. First of all, I'm not entirely sure how relevant this is for the story. It could be an interesting supplemental data. But it seems a bit tangential. Additionally, it also suffers from major caveats.

a) The authors now use a new genetic driver for which they don't have any behavioral data in any previous figures. So we do not know if any of this data holds true for the previous experiments. The authors perform whole-cell recordings from random unidentified motor neurons labeled by E49-Gal4>GtACR1 to deduce a time constant for behavioral results obtained in the VGlut-Gal4>GtACR1 experiments.

b) The DMD setup is useful for focal inactivation, however, the appropriate controls and data are not presented. Line 200 "A spot of light on the cell body produces as much of the hyperpolarization as stimulating the entire fly (mean of 11.3 mV vs 13.1 mV across 9 neurons). Conversely, excluding the cell body produces only a small effect on the MN (mean of 2.6 mV)." First of all, the control experiment for showing that DMD is indeed causing focal inactivation would be to gradually move the spot of light away from the labeled soma, i.e. to the neighboring "labelled" soma and show that there is indeed focal inactivation. Instead authors move it quite a long distance into unlabeled neuropil. Secondly, I still don't get why the authors are doing this experiment. Even if we believe the DMD is functioning perfectly, all this really tells us is that a random subset motor neurons (maybe 5 or 6 cells, legend is missing this info) labeled by E49-Gal4 is strongly hyperpolarized by its own GtACR1 channel opening, rather than being impacted because of hyperpolarizations in other E49-Gal4 labeled neurons. This has no relevance to the interpretation of any of the VGlut-Gal4 behavioral data. VGLut-Gal4 is much broader and also labels all glutamatergic neurons, most of which are inhibitory interneurons whose silencing could lead to disinhibition of downstream networks.

(5 a) However, we can address the reviewer critique by recording from the Vglut line while using a MN line to target the recordings to MNs.

b) Once we use the Vglut driver to perform these recordings, it will help assess how much of the MN inactivation is due to the GtACR expressed in the MN versus other neurons.

Reviewer 2:

While (as mentioned above) the study's conclusions are well-supported by the results and modeling, limitations arise because of the assumptions made. For instance, using a linear approximation may not hold at larger joint angles, and future studies would benefit from accounting for nonlinearities. Future studies could also delve into the source of passive forces, which is important for more deeply understanding the anatomical and physical basis of the results in this study. For instance, assessments of muscle or joint properties to correlate stiffness values with physical structure might be an area of future consideration.

We agree with these comments but believe that these studies represent avenues for future work.

Reviewer 3:

(1) Passive torques are measured, but only some short speculative statements, largely based on previous work, are offered on their functional significance; some of these claims are not well supported by experimental evidence or theoretical arguments. Passive forces are judged as "large" compared to the weight force of the limb, but the arguably more relevant force is the force limb muscles can generate, which, even in equilibrium conditions, is already about two orders of magnitude larger. The conclusion that passive forces are dynamically irrelevant seems natural, but contrasts with the assertion that "passive forces [...] will have a strong influence on limb kinematics". As a result, the functional significance of passive joint torques in the fruit fly, if any, remains unclear, and this ambiguity represents a missed opportunity. We now know the magnitude of passive joint torques - do they matter and for what? Are they helpful, for example, to maintain robust neuronal control, or a mechanical constraint that negatively impacts performance, e.g., because they present a sink for muscle work?

To us, measuring passive forces was the first step to understanding neural/biomechanical control of limb. In general, we agree with these comments and would like to understand the role of passive forces in overall control of limb. A complete discussion of the role of the significance of passive forces in the control of limb is beyond the scope of this study. We would like to note that it is unlikely that the active forces are two orders of magnitude larger during unloaded movement of the limb. However, these issues will have to be settled in future work.

(2) The work is framed with a scaling argument, but the assumptions that underpin the associated claims are not explicit and can thus not be evaluated. This is problematic because at least some arguments appear to contradict textbook scaling theory or everyday experience. For example, active forces are assumed to scale with limb volume, when every textbook would have them scale with area instead; and the asserted scaling of passive forces involves some hidden assumptions that demand more explicit discussion to alert the reader to associated limitations. Passive forces are said to be important only in small animals, but a quick self-experiment confirms that they are sufficient to stabilize human fingers or ankles against gravity, systems orders of magnitude larger than an insect limb, in seeming contradiction with the alleged dominance of scale. Throughout the manuscript, there are such and similar inaccuracies or ambiguities in the mechanical framing and interpretation, making it hard to fairly evaluate some claims, and rendering others likely incorrect.

We interpret this comment as making two separate points. The first one is that the reviewer says that our statement that active forces depend on the third power of the limb or L³ is incorrect. We agree and apologize for this oversight. Specifically, on L6-7 we say, “both inertial forces and active forces scale with the mass if the limb which in turn scales with the volume of the limb and therefore depends on the third power of limb length (L³)”. Instead, this statement should read “inertial forces scale with the mass if the limb which in turn scales with the volume of the limb and therefore depends on the third power of limb length (L³)”. However, this oversight does not affect the scaling argument as the scaling arguments in the rest of the manuscript only involves inertial forces and not active forces.

The second point is about the scaling law that governs passive forces. In the current manuscript, we have assumed that the passive forces scale as L² based on previous work. The reviewer has pointed out that this assumption might be incorrect or at the very least needs a rationale. We agree with this assessment: passive forces that arise in the muscle are likely to scale as L² but passive forces that arise in the joint might not. In the revised manuscript, we will discuss this concern.

Response to the public comment:

There was a comment from a reader: “None of our work cited in various places in this preprint (i.e., Zakotnik et al. 2006, Guschlbauer et al. 2007, Page et al. 2008, Hooper et al. 2009, Hooper 2012, Ache and Matheson 2012, Blümel et al. 2012, Ache and Matheson 2013, von Twickel et al. 2019, and Guschlbauer et al. 2022) claims or implies that passive forces could be sufficient to support the weight of an insect or any animal. To claim or suggest otherwise (as done in lines 33-35) is incorrect and sets up a misleading straw man that misrepresents our work. All statements in the preprint regarding our work related to this specific matter need to be removed or edited accordingly. For instance, the investigations, calculations, and interpretations in Hooper et al. 2009 are solely about limbs that are not being used in stance or other loaded tasks (indeed, the article's title specifically refers to "unloaded" leg posture and movements). Trying to use this work to predict whether passive muscle forces alone can support a stick insect against gravity requires considering much more than the oversimplified calculation given in lines 290-292. Other “back of the envelope calculations” (lines 299-300) are likely also insufficient and erroneous. The discussion in lines 289-304 needs to be edited accordingly”

We thank the reader for their comment. However, we interpret these studies differently. The studies above rightly focused on unloaded legs because it would be difficult to study passive forces in an intact insect without genetic tools. The commenter correctly points out that these studies do not comment on whether passive forces are strong enough to support the weight of the fly. However, we disagree that our arguments based on their results are unreasonable or strawman. We think that our interpretation of their measurements is correct. Moreover, we were motivated by Yox et. el. 1982 who states in so many words: “Stiffness of the muscles in the joints of all the legs might be sufficient to support a resting arthropod. A more rigorous analysis of all supporting limbs and joint angles would be required to prove this hypothesis”. We were inspired by this comment. In the revised manuscript, we will make it clear that the statement made in Line 33 is based on Yox. et. al. and our interpretation of measurements made by others.

Reviewer #1 (Public review):

Summary:

Foik et al. report that hypochlorous acid, a reactive chlorine species generated during host defense, activates the transcription of the froABCD in P. aeruginosa. This gene cluster had previously been associated with a potential role during the flow of fluids and appears to be regulated by the sigma factor FroR and its anti-sigma factor FroI. In the present study, the authors show that froABCD is expressed both in neutrophils and macrophages, which they claim is likely a result of HOCl but not H2O2 production. Fro expression is also induced in a murine model of corneal infection, which is characterized by immune cell invasion. Expression of the fro system can be quenched by several antioxidants, such as methionine, cysteine, and others. FroR-deficient cells that lack froABCD expression during HOCl stress appear more sensitive to the oxidant.

Strengths:

The authors provide a number of data supporting their claim that transcription of the froABCD system is induced by reactive chlorine species. This was shown by RNAseq, qRT-PCR, and through microscopy using a transcriptional reporter fusion. Likewise, elevated expression of froABCD was shown in vitro and in vivo, excluding potential in vitro artifacts. The manuscript, while mostly descriptive, is easy to follow, and the data were presented clearly.

Weaknesses:

(1) Lines 60-62: Some of the authors' conclusions are not supported by the data and thus appear unfounded. One example: "we determine that fro upregulation.....These data suggest a novel mechanism..." Their data do not show that MSR upregulation is a direct effect of FroABCD. Instead, it could be possible that the FroR sigma factor also controls the expression of msr genes, which would be independent of froABCD.

(2) The authors show increased fro transcription both in neutrophils and macrophages; however, the two types of immune cells differ quite dramatically with respect to myeloperoxidase activation and HOCl production. Neither has this been discussed nor considered here.

(3) With respect to the activation of fro expression upon challenge with conditioned media from stimulated neutrophils, does the conditioned media contain detectable amounts of HOCl? Do chloramines, which are byproducts of HOCl oxidation with amines, also stimulate expression?

(4) A better control to prove that this fro expression is indeed induced by HOCl in activated neutrophils would be to conduct the experiments in the presence of a myeloperoxidase inhibitor.

(5) The work was conducted with two different P. aeruginosa strains (i.e. AL143 and PAO1F). None of the figure legends provides details on which strain was used. For instance, in line 111, the authors refer to Figure S1B for data that I thought were done with PAO1F, while in 154, data were presented in the context of the infection model, which was conducted with the other strain.

(6) It would be good if immune cell recruitment at 2hrs and 20hrs PI could be quantified.

(7) The conclusions of Figure 4 are, in my opinion, weak (line 187-188; "It is possible that ....."). These antioxidants likely quench the low amounts of NaOCl directly. This would significantly reduce the NaOCl concentrations to a level that no longer activates expression of fro. There is no direct evidence provided that oxidized methionine induces fro expression. Do the authors postulate that this is free methionine, or could methionine and/or cysteine oxidation in FroR increase the binding affinity of the sigma factor to the promoter? Another possibility is that NaOCl deactivates the anti-sigma factor. None of these scenarios has been considered here.

(8) Line 184: The reaction constants of HOCl with Cys and Met are similar.

(9) Treatment with 16 uM NaOCl caused a growth arrest of ~15 hrs in the WT (Figure 5A), whereas no growth at all was recorded with 7.5 uM in Figure 3A.

(10) The concentration range of NaOCl causing fro expression is extremely narrow, while oxidative burst rapidly generates HOCl at much higher concentrations. This should be discussed in more detail.

à placer peut-être plus haut lorsqu'il est question du pouvoir du lecteur·e à poursuivre la pensée du livre

Il me semble que la famille était également intéressée par cette publication du texte

Ajouter Suzanne devant le nom pour éviter la confusion avec Pierre

Humans are social animals and isolation from others is the basic principle of some of the most inhumane torture methods (solitary confinement). Assuming that such dissociation leads to anguish, does the speaker recommend we conquer this anguish and that there is a light on the other end of the tunnel?

Author response:

General Statements

We are grateful for constructive reviewers’ comments and criticisms and have thoroughly addressed all major and minor comments in the revised manuscript.

Summary of new data.

We have performed the following additional experiments to support our concept:

(1) The kinetcs of ROS production in B6 and B6.Sst1S macrophages after TNF stimulation (Fig. 3I and J, Suppl. Fig. 3G);

(2) Time course of stress kinase activation (Fig.3K) that clearly demonstrated the persistent stress kinase (phospho-ASK1 and phospho-cJUN) activation exclusively in. the B6.Sst1S macrophages;

(3) New Fig.4 C-E panels include comparisons of the B6 and B6.Sst1S macrophage responses to TNF and effects of IFNAR1 blockade in both backgrounds.

(4) We performed new experiments demonstrating that the synthesis of lipid peroxidation products (LPO) occurs in TNF-stimulated macrophages earlier than the IFNβ super-induction (Suppl.Fig.4A and B).

(5) We demonstrated that the IFNAR1 blockade 12, 24 and 32 h after TNF stimulation still reduced the accumulation of LPO product (4-HNE) in TNF-stimulated B6.Sst1S BMDMs (Suppl.Fig.4 E-G).

(6) We added comparison of cMyc expression between the wild type B6 and B6.Sst1S BMDMs during TNF stimulation for 6-24 h (Fig.5I-J).

(7) New data comparing 4-HNE levels in Mtb-infected B6 wild type and B6.Sst1S macrophages and quantification of replicating Mtb was added (Fig.6B, Suppl.Fig.7C and D).

(8) In vivo data described in Fig.7 was thoroughly revised and new data was included. We demonstrated increased 4-HNE loads in multibacillary lesions (Fig.7A, Suppl. Fig.9A) and the 4-HNE accumulation in CD11b+ myeloid cells (Fig.7B and Suppl.Fig.9B). We demonstrated that the Ifnb – expressing cells are activated iNOS+ macrophages (Fig.7D and Suppl.Fig.13A). Using new fluorescent multiplex IHC, we have shown that stress markers phopho-cJun and Chac1 in TB lesions are expressed by Ifnb- and iNOS-expressing macrophages (Fig.7E and Suppl.Fig.13D-F).

(9) We performed additional experiment to demonstrate that naïve (non-BCG vaccinated) lymphocytes did not improve Mtb control by Mtb-infected macrophages in agreement with previously published data (Suppl.Fig.7H).

Summary of updates

Following reviewers requests we updated figures to include isotype control antibodies, effects of inhibitors on non-stimulated cells, positive and negative controls for labile iron pool, additional images of 4-HNE and live/dead cell staining.

Isotype control for IFNAR1 blockade were included in Fig.3M, Fig.4C -E, Fig.6L-M Suppl.Fig.4F-G, 7I.

Positive and negative controls for labile iron pool measurements were added to Fig.3E, Fig.5D, Suppl.Fig.3B

Cell death staining images were added Suppl.Fig.3H

Co-staining of 4-HNE with tubulin was added to Suppl.Fig.3A.

High magnification images for Figure 7 were added in Suppl.Fig.8 to demonstrate paucibacillary and multibacillary image classification.

Single-channel color images for individual markers were provided in Fig.7E and Suppl.Fig.13B-F.

Inhibitor effects on non-stimulated cells were included in Fig.5 D-H, Suppl.Fig.6A and B. Titration of CSF1R inhibitors for non-toxic concentration determination are included in Suppl.Fig.6D.

In addition, we updated the figure legends in the revised manuscript to include more details about the experiments. We also clarified our conclusions in the Discussion. Responses to every major and minor comment of the reviewers are provided below.

Point-by-point description of the revisions

Reviewer #1 (Evidence, reproducibility and clarity:

Summary

The study by Yabaji et al. examines macrophage phenotypes B6.Sst1S mice, a mouse strain with increased susceptibility to M. tuberculosis infection that develops necrotic lung lesions. Extending previous work, the authors specifically focus on delineating the molecular mechanisms driving aberrant oxidative stress in TNF-activated B6.Sst1S macrophages that has been associated with impaired control of M. tuberculosis. The authors use scRNAseq of bone marrow-derived macrophages to further characterize distinctions between B6.Sst1S and control macrophages and ascribe distinct trajectories upon TNF stimulation. Combined with results using inhibitory antibodies and small molecule inhibitors in in vitro experimentation, the authors propose that TNF-induced protracted c-Myc expression in B6.Sst1S macrophages disables the cellular defense against oxidative stress, which promotes intracellular accumulation of lipid peroxidation products, fueled at least in part by overexpression of type I IFNs by these cells. Using lung tissue sections from M. tuberculosis-infected B6.Sst1S mice, the authors suggest that the presence of a greater number of cells with lipid peroxidation products in lung lesions with high counts of stained M. tuberculosis are indicative of progressive loss of host control due to the TNF-induced dysregulation of macrophage responses to oxidative stress. In patients with active tuberculosis disease, the authors suggest that peripheral blood gene expression indicative of increased Myc activity was associated with treatment failure.

Major comments

The authors describe differences in protein expression, phosphorylation or binding when referring to Fig 2A-C, 2G, 3D, 5B, 5C. However, such differences are not easily apparent or very subtle and, in some cases, confounded by differences in resting cells (e.g. pASK1 Fig 3L; c-Myc Fig 5B) as well as analyses across separate gels/blots (e.g. Fig 3K, Fig 5B). Quantitative analyses across different independent experiments with adequate statistical analyses are required to strengthen the associated conclusions.

We updated our Western blots as follows:

(1) Densitometery of normalized bands is included above each lane (Fig.2A-C; Fig.3C-D and 3K; Fig.4A-B; Fig.5B,C,I,J). New data in Fig.3K is added to highlight differences between B6 and B6.Sst1S at individual timepoints after TNF stimulation. In Fig.5I we added new data comparing Myc levels in B6 and B6.Sst1S with and without JNK inhibitor and updated the results accordingly. New Fig.3K clearly demonstrates the persistent activation of p-cJun and pAsk1 at 24 and 36h of TNF stimulation. In Fig.5B we clearly demonstrate that Myc levels were higher in B6.Sst1S after 12 h of TNF stimulation. At 6h, however, the basal differences in Myc levels are consistently higher in B6.Sst1S and the induction by TNF is 1.6-fold similar in both backgrounds. We noted this in the text.

(2) A representative experiment is shown in individual panels and the corresponding figure legend contains information on number of biological repeats. Each Western blot was repeated 2 – 4 times.

The representative images of fluorescence microscopy in Fig 3H, 4H, 5H, S3C, S3I, S5A, S6A seem to suggest that under some conditions the fluorescence signal is located just around the nucleus rather than absent or diminished from the cytoplasm. It is unclear whether this reflects selective translocation of targets across the cell, morphological changes of macrophages in culture in response to the various treatments, or variations in focal point at which images were acquired. Control images (e.g. cellular actin, DIC) should be included for clarification. If cell morphology changes depending on treatments, how was this accounted for in the quantitative analyses? In addition, negative controls validating specificity of fluorescence signals would be warranted.

Our conclusion of higher LPO production is based on several parameters: 4-HNE staining, measurements of MDA in cell lysates and oxidized lipids using BODIPY C11. Taken together they demonstrate significant and reproducible increase in LPO accumulation in TNFstimulated B6.Sst1S macrophages. This excludes imaging artefact related to unequal 4-HNE distribution noted by the reviewer. In fact, we also noted that the 4-HNE was spread within cell body of B6.Sst1S macrophages and confirmed it using co-staining with tubulin, as suggested by the reviewer (new Suppl.Fig.3A). Since low molecular weight LPO products, such as MDA and 4-HNE, traverse cell membranes, it is unlikely that they will be strictly localized to a specific membrane bound compartment. However, we agree that at lower concentrations, there might be some restricted localization, explaining a visible perinuclear ring of 4-HNE staining in B6 macrophages. This phenomenon may be explained just by thicker cytoplasm surrounding nucleus in activated macrophages spread on adherent plastic surface or by proximity to specific organelles involved in generation or clearance of LPO products and definitively warrants further investigation.

We also included images of non-stimulated cells in Fig.3H, Suppl.Fig.3A and 3E. We used multiple fields for imaging and quantified fluorescence signals (Suppl. Fig.3D and 3F, Suppl.Fig.4G, Suppl.Fig.6A and B).

We used negative controls without primary antibodies for the initial staining optimization, but did not include it in every experiment.

To interpret the evaluation on the hierarchy of molecular mechanisms in B6.Sst1S macrophages, comparative analyses with B6 control cells should be included (e.g. Fig 4C-I, Fig 5, Fig 6B, E-M, S6C, S6E-F). This will provide weight to the conclusions that the dysregulated processes are specifically associated with the susceptibility of B6.Sst1S macrophages.

Understanding the sst1-mediated effects on macrophage activation is the focus of our previously published studies Bhattacharya et al., JCI, 2021) and this manuscript. The data comparing B6 and B6.Sst1S macrophage are presented in Fig.1, Fig.2, Fig.3, Fig.4, Fig.5A-C, I and J, Fig.6A-C, 6J and corresponding supplemental figures 1, 2, 3, 4A and B, Suppl.Fig.5, Suppl.Fig.6C, Suppl.Fig.7A-D,7F.

Once we identified the aberrantly activated pathways in the B6.Sst1S, we used specific inhibitors to correct the aberrant response in B6.Sst1S.

All experiments using inhibitory antibodies require comparison to the effect of a matched isotype control in the same experiment (e.g. Fig 3J, 4F, G, I; 6L, 6M, S3G, S6F).

Isotype control for IFNAR1 blockade were included in Fig.3M, Fig.4C-E, Fig.6L-M Suppl.Fig.4F-G, 7I.

Experiments using inhibitors require inclusion of an inhibitor-only control to assess inhibitor effects on unstimulated cells (e.g. Fig 4I, 5D-I)