Author response:

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

Public Reviews:

Reviewer #1 (Public review): 

Summary:

In this study, Lamberti et al. investigate how translation initiation and elongation are coordinated at the single-mRNA level in mammalian cells. The authors aim to uncover whether and how cells dynamically adjust initiation rates in response to elongation dynamics, with the overarching goal of understanding how translational homeostasis is maintained. To this end, the study combines single-molecule live-cell imaging using the SunTag system with a kinetic modeling framework grounded in the Totally Asymmetric Simple Exclusion Process (TASEP). By applying this approach to custom reporter constructs with different coding sequences, and under perturbations of the initiation/elongation factor eIF5A, the authors infer initiation and elongation rates from individual mRNAs and examine how these rates covary.

The central finding is that initiation and elongation rates are strongly correlated across a range of coding sequences, resulting in consistently low ribosome density ({less than or equal to}12% of the coding sequence occupied). This coupling is preserved under partial pharmacological inhibition of eIF5A, which slows elongation but is matched by a proportional decrease in initiation, thereby maintaining ribosome density. However, a complete genetic knockout of eIF5A disrupts this coordination, leading to reduced ribosome density, potentially due to changes in ribosome stalling resolution or degradation.

Strengths:

A key strength of this work is its methodological innovation. The authors develop and validate a TASEP-based Hidden Markov Model (HMM) to infer translation kinetics at single-mRNA resolution. This approach provides a substantial advance over previous population-level or averaged models and enables dynamic reconstruction of ribosome behavior from experimental traces. The model is carefully benchmarked against simulated data and appropriately applied. The experimental design is also strong. The authors construct matched SunTag reporters differing only in codon composition in a defined region of the coding sequence, allowing them to isolate the effects of elongation-related features while controlling for other regulatory elements. The use of both pharmacological and genetic perturbations of eIF5A adds robustness and depth to the biological conclusions. The results are compelling: across all constructs and conditions, ribosome density remains low, and initiation and elongation appear tightly coordinated, suggesting an intrinsic feedback mechanism in translational regulation. These findings challenge the classical view of translation initiation as the sole rate-limiting step and provide new insights into how cells may dynamically maintain translation efficiency and avoid ribosome collisions.

We thank the reviewer for their constructive assessment of our work, and for recognizing the methodological innovation and experimental rigor of our study.

Weaknesses:

A limitation of the study is its reliance on exogenous reporter mRNAs in HeLa cells, which may not fully capture the complexity of endogenous translation regulation. While the authors acknowledge this, it remains unclear how generalizable the observed coupling is to native mRNAs or in different cellular contexts.

We agree that the use of exogenous reporters is a limitation inherent to the SunTag system, for which there is currently no simple alternative for single-mRNA translation imaging. However, we believe our findings are likely generalizable for several reasons.

As discussed in our introduction and discussion, there is growing mechanistic evidence in the literature for coupling between elongation (ribosome collisions) and initiation via pathways such as the GIGYF2-4EHP axis (Amaya et al. 2018, Hickey et al. 2020, Juszkiewicz et al. 2020), which might operate on both exogenous and endogenous mRNAs.

As already acknowledged in our limitations section, our exogenous reporters may not fully recapitulate certain aspects of endogenous translation (e.g., ER-coupled collagen processing), yet the observed initiation-elongation coupling was robust across all tested constructs and conditions.

We have now expanded the Discussion (L393-395) to cite complementary evidence from Dufourt et al. (2021), who used a CRISPR-based approach in Drosophila embryos to measure translation of endogenous genes. We also added a reference to Choi et al. 2025, who uses a ER-specific SunTag reporter to visualize translation at the ER (L395-397).

Additionally, the model assumes homogeneous elongation rates and does not explicitly account for ribosome pausing or collisions, which could affect inference accuracy, particularly in constructs designed to induce stalling. While the model is validated under low-density assumptions, more work may be needed to understand how deviations from these assumptions affect parameter estimates in real data.

We agree with the reviewer that the assumption of homogeneous elongation rates is a simplification, and that our work represents a first step towards rigorous single-trace analysis of translation dynamics. We have explicitly tested the robustness of our model to violations of the low-density assumption through simulations (Figure 2 - figure supplement 2). These show that while parameter inference remains accurate at low ribosome densities, accuracy slightly deteriorates at higher densities, as expected. In fact, our experimental data do provide evidence for heterogeneous elongation: the waiting times between termination events deviate significantly from an exponential distribution (Figure 3 - figure supplement 2C), indicating the presence of ribosome stalling and/or bursting, consistent with the reviewer's concern. We acknowledge in the Limitations section (L402-406) that extending the model to explicitly capture transcript-dependent elongation rates and ribosome interactions remains challenging. The TASEP is difficult to solve analytically under these conditions, but we note that simulation-based inference approaches, such as particle filters to replace HMMs, could provide a path forward for future work to capture this complexity at the single-trace level.

Furthermore, although the study observes translation "bursting" behavior, this is not explicitly modeled. Given the growing recognition of translational bursting as a regulatory feature, incorporating or quantifying this behavior more rigorously could strengthen the work's impact.

While we do not explicitly model the bursting dynamics in the HMM framework, we have quantified bursting behavior directly from the data. Specifically, we measure the duration of translated (ON) and untranslated (OFF) periods across all reporters and conditions (Figure 1G for control conditions and Figure 4G-H for perturbed conditions), finding that active translation typically lasts 10-15 minutes interspersed with shorter silent periods of 5-10 minutes. This empirical characterization demonstrates that bursting is a consistent feature of translation across our experimental conditions. The average duration of silent periods is similar to what was inferred by Livingston et al. 2023 for a similar SunTag reporter; while the average duration of active periods is substantially shorter (~15 min instead of ~40 min), which is consistent with the shorter trace duration in our system compared to theirs (~15 min compared to ~80 min, on average). Incorporating an explicit two-state or multi-state bursting model into the TASEP-HMM framework would indeed be computationally intensive and represents an important direction for future work, as it would enable inference of switching rates alongside initiation and elongation parameters. We have added this point to the Discussion (L415-417).

Assessment of Goals and Conclusions:

The authors successfully achieve their stated aims: they quantify translation initiation and elongation at the single-mRNA level and show that these processes are dynamically coupled to maintain low ribosome density. The modeling framework is well suited to this task, and the conclusions are supported by multiple lines of evidence, including inferred kinetic parameters, independent ribosome counts, and consistent behavior under perturbation.

Impact and Utility:

This work makes a significant conceptual and technical contribution to the field of translation biology. The modeling framework developed here opens the door to more detailed and quantitative studies of ribosome dynamics on single mRNAs and could be adapted to other imaging systems or perturbations. The discovery of initiation-elongation coupling as a general feature of translation in mammalian cells will likely influence how researchers think about translational regulation under homeostatic and stress conditions.

The data, models, and tools developed in this study will be of broad utility to the community, particularly for researchers studying translation dynamics, ribosome behavior, or the effects of codon usage and mRNA structure on protein synthesis.

Context and Interpretation:

This study contributes to a growing body of evidence that translation is not merely controlled at initiation but involves feedback between elongation and initiation. It supports the emerging view that ribosome collisions, stalling, and quality control pathways play active roles in regulating initiation rates in cis. The findings are consistent with recent studies in yeast and metazoans showing translation initiation repression following stalling events. However, the mechanistic details of this feedback remain incompletely understood and merit further investigation, particularly in physiological or stress contexts. 

In summary, this is a thoughtfully executed and timely study that provides valuable insights into the dynamic regulation of translation and introduces a modeling framework with broad applicability. It will be of interest to a wide audience in molecular biology, systems biology, and quantitative imaging.

We appreciate the reviewer's thorough and positive assessment of our work, and that they recognize both the technical innovation of our modeling framework and its potential broad utility to the translation biology community. We agree that further mechanistic investigation of initiation-elongation feedback under various physiological contexts represents an important direction for future research.

Reviewer #2 (Public review):

Summary:

This manuscript uses single-molecule run-off experiments and TASEP/HMM models to estimate biophysical parameters, i.e., ribosomal initiation and elongation rates. Combining inferred initiation and elongation rates, the authors quantify ribosomal density. TASEP modeling was used to simulate the mechanistic dynamics of ribosomal translation, and the HMM is used to link ribosomal dynamics to microscope intensity measurements. The authors' main conclusions and findings are:

(1) Ribosomal elongation rates and initiation rates are strongly coordinated.

(2) Elongation rates were estimated between 1-4.5 aa/sec. Initiation rates were estimated between 0.5-2.5 events/min. These values agree with previously reported values.

(3) Ribosomal density was determined below 12% for all constructs and conditions.

(4) eIF5A-perturbations (KO and GC7 inhibition) resulted in non-significant changes in translational bursting and ribosome density.

(5) eIF5A perturbations resulted in increases in elongation and decreases in initiation rates.

Strengths:

This manuscript presents an interesting scientific hypothesis to study ribosome initiation and elongation concurrently. This topic is highly relevant for the field. The manuscript presents a novel quantitative methodology to estimate ribosomal initiation rates from Harringtonine run-off assays. This is relevant because run-off assays have been used to estimate, exclusively, elongation rates.

We thank the reviewer for their careful evaluation of our work and for recognizing the novelty of our quantitative methodology to extract both initiation and elongation rates from harringtonine run-off assays, extending beyond the traditional use of these experiments.

Weaknesses:

The conclusion of the strong coordination between initiation and elongation rates is interesting, but some results are unexpected, and further experimental validation is needed to ensure this coordination is valid. 

We agree that some of our findings need further experimental investigation in future studies. However, we believe that the coordination between initiation and elongation is supported by multiple results in our current work: (1) the strong correlation observed across all reporters and conditions (Figure 3E), and (2) the consistent maintenance of low ribosome density despite varying elongation rates. While additional experimental validation would be valuable, we note that directly manipulating initiation or elongation independently in mammalian cells remains technically challenging. Nevertheless, our findings are consistent with emerging mechanistic understanding of collision-sensing pathways (GIGYF2-4EHP) that could mediate such coupling, as discussed in our manuscript.

(1) eIF5a perturbations resulted in a non-significant effect on the fraction of translating mRNA, translation duration, and bursting periods. Given the central role of eIF5a, I would have expected a different outcome. I would recommend that the authors expand the discussion and review more literature to justify these findings.

We appreciate this comment. This finding is indeed discussed in detail in our manuscript (Discussion, paragraphs 6-7). As we note there, while eIF5A plays a critical role in elongation, the maintenance of bursting dynamics and ribosome density upon perturbation can be explained by compensatory feedback mechanisms. Specifically, the coordinated decrease in initiation rates that counterbalances slower elongation to maintain homeostatic ribosome density. We also discuss several factors that complicate interpretation: (1) potential RQC-mediated degradation masking stronger effects in proline-rich constructs, (2) differences between GC7 treatment and genetic knockout suggesting altered stalling resolution kinetics, and (3) the limitations of using exogenous reporters that lack ER-coupled processing, which may be critical for eIF5A function in endogenous collagen translation (as suggested by Rossi et al., 2014; Mandal et al., 2016; Barba-Aliaga et al., 2021). The mechanistic complexity and tissue-specific nature of eIF5A function in mammals, which differs substantially from the better-characterized yeast system, likely contributes to the nuanced phenotype we observe. We believe our Discussion adequately addresses these points.

(2) The AAG construct leading to slow elongation is very surprising. It is the opposite of the field consensus, where codon-optimized gene sequences are expected to elongate faster. More information about each construct should be provided. I would recommend more bioinformatic analysis on this, for example, calculating CAI for all constructs, or predicting the structures of the proteins.

We agree that the slow elongation of the AAG construct is counterintuitive and indeed surprising. Following the reviewer's suggestion, we have now calculated the Codon Adaptation Index (CAI) for all constructs (Renilla 0.89, Col1a1 0.78, Col1a1 mutated 0.74). It is therefore unlikely that codon bias explains the slow translation, particularly since we designed the mutated Col1a1 construct with alanine codons selected to respect human codon usage bias, thereby minimizing changes in codon optimality. As we discuss in the manuscript, we hypothesize that the proline-to-alanine substitutions disrupted co-translational folding of the collagen-derived sequence. Prolines are critical for collagen triple-helix formation (Shoulders and Raines, 2009), and their replacement with alanines likely generates misfolded intermediates that cause ribosome stalling (Barba-Aliaga et al., 2021; Komar et al., 2024). This interpretation is supported by the high frequency (>30%) of incomplete run-off traces for AAG, suggesting persistent stalling events. Our findings thus illustrate an important potential caveat: "optimizing" a sequence based solely on codon usage can be detrimental when it disrupts functionally important structural features or co-translational folding pathways.

This highlights that elongation rates depend not only on codon optimality but also on the interplay between nascent chain properties and ribosome progression.

(3) The authors should consider using their methodology to study the effects of modifying the 5'UTR, resulting in changes in initiation rate and bursting, such as previously shown in reference Livingston et al., 2023. This may be outside of the scope of this project, but the authors could add this as a future direction and discuss if this may corroborate their conclusions. 

We thank the reviewer for this excellent suggestion. We agree that applying our methodology to 5'-UTR variants would provide a complementary test of initiation-elongation coupling, and we have now added this as a future direction in the Discussion (L417-420).

(4) The mathematical model and parameter inference routines are central to the conclusions of this manuscript. In order to support reproducibility, the computational code should be made available and well-documented, with a requirements file indicating the dependencies and their versions. 

We have added the Github link in the manuscript (https://github.com/naef-lab/suntag-analysis) and have also deposited the data (.ome.tif) on Zenodo (https://zenodo.org/records/17669332).

Reviewer #3 (Public review):

Disclaimer:

My expertise is in live single-molecule imaging of RNA and transcription, as well as associated data analysis and modeling. While this aligns well with the technical aspects of the manuscript, my background in translation is more limited, and I am not best positioned to assess the novelty of the biological conclusions.

Summary:

This study combines live-cell imaging of nascent proteins on single mRNAs with time-series analysis to investigate the kinetics of mRNA translation.

The authors (i) used a calibration method for estimating absolute ribosome counts, and (ii) developed a new Bayesian approach to infer ribosome counts over time from run-off experiments, enabling estimation of elongation rates and ribosome density across conditions.

They report (i) translational bursting at the single-mRNA level, (ii) low ribosome density (~10% occupancy

{plus minus} a few percents), (iii) that ribosome density is minimally affected by perturbations of elongation (using a drug and/or different coding sequences in the reporter), suggesting a homeostatic mechanism potentially involving a feedback of elongation onto initiation, although (iv) this coupling breaks down upon knockout of elongation factor eIF5A.

Strengths:

(1) The manuscript is well written, and the conclusions are, in general, appropriately cautious (besides the few improvements I suggest below).

(2) The time-series inference method is interesting and promising for broader applications. 

(3) Simulations provide convincing support for the modeling (though some improvements are possible). 

(4) The reported homeostatic effect on ribosome density is surprising and carefully validated with multiple perturbations.

(5) Imaging quality and corrections (e.g., flat-fielding, laser power measurements) are robust.

(6) Mathematical modeling is clearly described and precise; a few clarifications could improve it further.

We thank the reviewer for recognizing the novelty of the approach and its rigour, and for providing suggestions to improve it further.

Weaknesses:

(1) The absolute quantification of ribosome numbers (via the measurement of $i_{MP}$ ) should be improved.This only affects the finding that ribosome density is low, not that it appears to be under homeostatic control. However, if $i_{MP}$ turns out to be substantially overestimated (hence ribosome density underestimated), then "ribosomes queuing up to the initiation site and physically blocking initiation" could become a relevant hypothesis. In my detailed recommendations to the authors, I list points that need clarification in their quantifications and suggest an independent validation experiment (measuring the intensity of an object with a known number of GFP molecules, e.g., MS2-GFP MS2-GFP-labeled RNAs, or individual GEMs).

We agree with the reviewer that the estimation of the number of ribosomes is central to our finding that translation happens at low density on our reporters. This result derives from our measurement of the intensity of one mature protein (i_MP), that we have achieved by using a SunTag reporter with a RH1 domain in the C terminus of the mature protein, allowing us to stabilise mature proteins via actin-tethering. In addition, as suggested by the reviewer, we already validated this result with an independent estimate of the mature protein intensity (Figure 5 - figure supplement 2B), which was obtained by adding the mature protein intensity directly as a free parameter of the HMM. The inferred value of mature protein intensity for each construct (10-15 a.u) was remarkably close to the experimental calibration result (14 ± 2 a.u.). Therefore, we have confidence that our absolute quantification of ribosome numbers is accurate.

(2) The proposed initiation-elongation coupling is plausible, but alternative explanations, such as changes in abortive elongation frequency, should be considered more carefully. The authors mention this possibility, but should test or rule it out quantitatively. 

We thank the reviewer for the comment, but we consider that ruling out alternative explanations through new perturbation experiments is beyond the scope of the present work.

(3) The observation of translational bursting is presented as novel, but similar findings were reported by Livingston et al. (2023) using a similar SunTag-MS2 system. This prior work should be acknowledged, and the added value of the current approach clarified.

We did cite Livingston et al. (2023) in several places, but we recognized that we could add a few citations in key places, to make clear that the observation of bursting is not novel but is in agreement with previous results. We now did so in the Results and Discussion sections.

(4) It is unclear what the single-mRNA nature of the inference method is bringing since it is only used here to report _average_ ribosome elongation rate and density (averaged across mRNAs and across time during the run-off experiments - although the method, in principle, has the power to resolve these two aspects).

While decoding individual traces, our model infers shared (population-level) rates. Inferring transcript-specific parameters would be more informative, but it is highly challenging due to the uncertainty on the initial ribosome distribution on single transcripts. Pooling multiple transcripts together allows us to use some assumptions on the initial distribution and infer average elongation and initiation-rate parameters, while revealing substantial mRNA-to-mRNA variability in the posterior decoding (e.g. Figure 3 - figure Supplement 2C). Indeed, the inference still informs on the single-trace run-off time distribution (Figure 3 A) and the waiting time between termination events (Figure 3 - figure supplement 2C), suggesting the presence of stalling and bursting. In addition, the transcript-to-transcript heterogeneity is likely accounted for by our model better than previous methods (linear fit of the average run-off intensity), as suggested by their comparison (Figure 3 - figure supplement 2 A). In the future the model could be refined by introducing transcript-specific parameters, possibly in a hierarchical way, alongside shared parameters.

(5) I did not find any statement about data availability. The data should be made available. Their absence limits the ability to fully assess and reproduce the findings.

We have added the Github link in the manuscript (https://github.com/naef-lab/suntag-analysis) and have also deposited the data (.ome.tif) on Zenodo (https://zenodo.org/records/17669332).

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors): 

Major Comments:

(1) Lack of Explicit Bursting Model

Although translation "bursts" are observed, the current framework does not explicitly model initiation as a stochastic ON/OFF process. This limits insight into regulatory mechanisms controlling burst frequency or duration. The authors should either incorporate a two-state/more-state (bursting) model of initiation or perform statistical analysis (e.g., dwell-time distributions) to quantify bursting dynamics. They should clarify how bursting influences the interpretation of initiation rate estimates.

We agree with the reviewer that an explicit bursting model (e.g., a two-state telegraph model) would be the ideal theoretical framework. However, integrating such a model into the TASEP-HMM inference framework is computationally intensive and complex. As a robust first step, we have opted to quantify bursting empirically based on the decoded single-mRNA traces. As shown in Figure 1G (control) and Figure 4G (perturbed conditions), we explicitly measured the duration of "ON" (translated) and "OFF" (untranslated) periods. This statistical analysis provides a quantitative description of the bursting dynamics without relying on the specific assumptions of a telegraph model. We have clarified this in the text (L123-125) and, as suggested, added a discussion (L415-417) on the potential extensions of the model to include explicit switching kinetics in the Outlook section.

(2) Assumption of Uniform Elongation Rates

The model assumes homogeneous elongation across coding sequences, which may not hold for stalling-prone inserts (e.g., PPG). This simplification could bias inference, particularly in cases of sequence-specific pausing. Adding simulations or sensitivity analysis to assess how non-uniform elongation affects the accuracy of inferred parameters. The authors should explicitly discuss how ribosome stalling, collisions, or heterogeneity might skew model outputs (see point 4).

A strong stalling sequence that affects all ribosomes equally should not deteriorate the inference of the initiation rate, provided that the low-density assumption holds. The scenario where stalling events lead to higher density, and thus increased ribosome-ribosome interactions, is comparable to the conditions explored in Figure 2E. In those simulations, we tested the inference on data generated with varying initiation and elongation rates, resulting in ribosome densities ranging from low to high. We demonstrated that the inference remains robust at low ribosome densities (<10%). At higher densities, the accuracy of the initiation rate estimate decreases, whereas the elongation rate estimate remains comparatively robust. Additionally, the model tends to overestimate ribosome density under high-density conditions, likely because it neglects ribosome interference at the initiation site (Figure 2 figure supplement 2C). We agree that a deeper investigation into the consequences of stochastic stalling and bursting would be beneficial, and we have explicitly acknowledged this in the Limitations section.

(3) Interpretation of eIF5A Knockout Phenotype

The observation that eIF5A KO reduces initiation more than elongation, leading to decreased ribosome density, is biologically intriguing. However, the explanation invoking altered RQC kinetics is speculative and not directly tested. The authors should consider validating the RQC hypothesis by monitoring reporter mRNA stability, ribosome collision markers, or translation termination intermediates.

We thank the reviewer for the comment, but we consider that ruling out alternative explanations through new experiments is beyond the scope of the present work.

(4) To strengthen the manuscript, the authors should incorporate insights from three studies.

- Livingston et al. (PMC10330622) found that translation occurs in bursts, influenced by mRNA features and initiation factors, supporting the coupling of initiation and elongation.

- Madern et al. (PMID: 39892379) demonstrated that ribosome cooperativity enhances translational efficiency, highlighting coordinated ribosome behavior.

- Dufourt et al. (PMID: 33927056) observed that high initiation rates correlate with high elongation rates, suggesting a conserved mechanism across cell cultures and organisms.

Integrating these studies could enrich the manuscript's interpretation and stimulate new avenues of thought.

We thank the reviewer for the valuable comment. We added citations of Livingston et al. in the context of translational bursting. We already cited Madern et al. in multiple places and, although its observations of ribosome cooperativity are very compelling, they cannot be linked with our observations of a feedback between initiation and elongation, and it would be very challenging to see a similar effect on our reporters. This is why we did not expressly discuss cooperativity. We also integrated Dufourt et al. in the Discussion about the possibility of designing genetically-encoded reporter. We also added a sentence about the possibility of using an ER-specific SunTag reporter, as done recently in Choi et al., Nature (2025) (https://doi.org/10.1038/s41586-025-09718-0).

Minor Comments:

(1) Use consistent naming for SunTag reporters (e.g., "PPG" vs "proline-rich") throughout.

Thank you for the comment. However, the term proline-rich always appears together with PPG, so we believe that the naming is clear and consistent.

(2) Consider a schematic overview of the experimental design and modeling pipeline for accessibility.

Thank you for the suggestion. We consider that experimental design and modeling is now sufficiently clearly described and does not justify an additional scheme. 

(3) Clarify how incomplete run-off traces are handled in the HMM inference.

Incomplete run-off traces are treated identically to complete traces in our HMM inference. This is possible because our model relies on the probability of transitions occurring per time step to infer rates. It does not require observing the final "empty" state to estimate the kinetic parameters ɑ and λ. The loss of signal (e.g., mRNA moving out of the focal volume or photobleaching) does not invalidate the kinetic information contained in the portion of the trace that was observed. We have clarified this in the Methods section.

Reviewer #2 (Recommendations for the authors):

(1) Reproducibility:

(1.1) The authors should use a GitHub repository with a timestamp for the release version.

The code is available on GitHub (https://github.com/naef-lab/suntag-analysis).

(1.2) Make raw images and data available in a figure repository like Figshare.

The raw images (.ome.tif) are now available on Zenodo (https://zenodo.org/records/17669332).

(2) Paper reorganization and expansion of the intensity and ribosome quantification:

(2.1) Given the relevance of the initiation and elongation rates for the conclusions of this study, and the fact that the authors inferred these rates from the spot intensities. I recommend that the authors move Figure 1 Supplement 2 to the main text and expand the description of the process to relate spot intensity and number of ribosomes. Please also expand the figure caption for this image.

We agree with the importance of this validation. We have expanded the description of the calibration experiment in the main text and in the figure caption.

(2.2) I suggest the authors explicitly mention the use of HMM in the abstract.

We have now explicitly mentioned the TASEP-based HMM in the abstract.

(2.3) In line 492, please add the frame rate used to acquire the images for the run-off assays.

We have added the specific frame rate (one frame every 20 seconds) to the relevant section.

(3) Figures and captions:

(3.1) Figure 1, Supplement 2. Please add a description of the colors used in plots B, C. 

We have expanded the caption and added the color description.

(3.2) In the Figure 2 caption. It is not clear what the authors mean by "traceseLife". Please ensure it is not a typo.

Thank you for spotting this. We have corrected the typo.

(3.3) Figure 1 A, in the cartoon N(alpha)->N-1, shouldn't the transition also depend on lambda?

The transition probability was explicitly derived in the “Bayesian modeling of run-off traces” section (Eqs. 17-18), and does not depend on λ, but only on the initiation rate under the low-density assumption.

(3.4) Figure 3, Supplement 2. "presence of bursting and stalling.." has a typo.

Corrected.

(3.5) Figure 5, panel C, the y-axis label should be "run-off time (min)."

Corrected.

(3.6) For most figures, add significance bars.

(3.7) In the figure captions, please add the total number of cells used for each condition.

We have systematically indicated the number of traces (n_t) and the number of independent experiments (n_e) in the captions in this format (n_t, n_e).

(4) Mathematical Methods:

We greatly thank the reviewer for their detailed attention to the mathematical notation. We have addressed all points below.

(4.1) In lines 555, Materials and Methods, subsection, Quantification of Intensity Traces, multiple equations are not numbered. For example, after Equation (4), no numbers are provided for the rest of the equations. Please keep consistency throughout the whole document.

We have ensured that all equations are now consistently numbered throughout the document.

(4.2) In line 588, the authors mention "$X$ is a standard normal random variable with mean $\mu$ and standard deviation $s_0$". Please ensure this is correct. A standard normal random variable has a 0 mean and std 1. 

Thank you for the suggestion, we have corrected the text (L678).

(4.3) Line 546, Equation 2. The authors use mu(x,y) to describe a 2d Gaussian function. But later in line 587, the authors reuse the same variable name in equation 5 to redefine the intensity as mu = b_0 + I.

We have renamed the 2D Gaussian function to \mu_{2D}(x,y) in the spot tracking section

(4.4) For the complete document, it could be beneficial to the reader if the authors expand the definition of the relationship between the signal "y" and the spot intensity "I". Please note how the paragraph in lines 582-587 does not properly introduce "y".

We have added an explicit definition of y and its relationship to the underlying spot intensity I in the text to improve readability and clarity.

(4.5) Please ensure consistency in variable names. For example, "I" is used in line 587 for the experimental spot intensity, then line 763 redefines I(t) as the total intensity obtained from the TASEP model; please use "I_sim(t)" for simulated intensities. Please note that reusing the variable "I" for different contexts makes it hard for the reader to follow the text. 

We agree that this was confusing. We have implemented the suggestion and now distinguish simulated intensities using the notation I_S .

(4.6) Line 555 "The prior on the total intensity I is an "uninformative" prior" I ~ half_normal(1000). Please ensure it is not "I_0 ~ half_normal(1000)."? 

We confirm that “I” is the correct variable representing the total intensity in this context; we do not use an “I₀” variable here.

(4.7) In lines 595, equation 6. Ensure that the equation is correct. Shouldn't it be: s_0^2 = ln ( 1 + (sigma_meas^2 / ⟨y⟩^2) )? Please ensure that this is correct and it is not affecting the calculated values given in lines 598.

Thank you for catching this typo. We have corrected the equation in the manuscript. We confirm that the calculations performed in the code used the correct formula, so the reported values remain unchanged.

(4.8) In line 597, "the mean intensity square ^2". Please ensure it is not "the square of the temporal mean intensity."

We have corrected the text to "the square of the temporal mean intensity."

(4.9) In lines 602-619, Bayesian modeling of run-off traces, please ensure to introduce the constant "\ell". Used to define the ribosomal footprint?

We have added the explicit definition of 𝓁 as the ribosome footprint size (length of transcript occupied by one ribosome) in the "Bayesian modeling of run-off traces" section.

(4.10) Line 687 has a minor typo "[...] ribosome distribution.. Then, [...]"

We have corrected the punctuation.

(4.11) In line 678, Equation 19 introduces the constant "L_S", Please ensure that it is defined in the text.

We have added the explicit definition of L_S (the length of the SunTag) to the text surrounding Equation 19.

(4.12) In line 695, Equation 22, please consider using a subscript to differentiate the variance due to ribosome configuration. For example, instead of "sigma (...)^2" use something like "sigma_c ^2 (...)". Ensure that this change is correctly applied to Equation 24 and all other affected equations.

Thank you, we have implemented the suggestions.

(4.13) In line 696, please double-check equations 26 and 27. Specifically, the denominator ^2. Given the previous text, it is hard to follow the meaning of this variable. 

We have revised the notation in Equations 26 and 27 to ensure the denominator is consistent with the definitions provided in the text.

(4.14) In lines 726, the authors mention "[...], but for the purposes of this dissertation [...]", it should be "[...], but for the purposes of this study [...]"

Thank you for spotting this. We have replaced "dissertation" with "study."

(4.15) Equations 5, 28, 37, and the unnumbered equation between Equations 16 and 17 are similar, but in some, "y" does not explicitly depend on time. Please ensure this is correct. 

We have verified these equations and believe they are correct.

(4.16) Please review the complete document and ensure that variables and constants used in the equations are defined in the text. Please ensure that the same variable names are not reused for different concepts. To improve readability and flow in the text, please review the complete Materials and Methods sections and evaluate if the modeling section can be written more clearly and concisely. For example, Equation 28 is repeated in the text.

We have performed a comprehensive review of the Materials and Methods section. To improve conciseness and flow, we have merged the subsection “Observation model and estimation of observation parameters” with the “Bayesian modeling of run-off traces” section. This allowed us to remove redundant definitions and repeated equations (such as the previous Equation 28). We have also checked that all variables and constants are defined upon first use and that variable names remain consistent throughout the manuscript.

Reviewer #3 (Recommendations for the authors):

(1) Data Presentation

(1.1) In main Figures 1D and 4E, the traces appear to show frequent on-off-on transitions ("bursting"), but in supplementary figures (1-S1A and 4-S1A), this behavior is seen in only ~8 of 54 traces. Are the main figure examples truly representative?

We acknowledge the reviewer's point. In Figure 1D, we selected some of the longest and most illustrative traces to highlight the bursting dynamics. We agree that the term "representative" might be misleading if interpreted as "average." We have updated the text to state "we show bursting traces" to more accurately reflect the selection.

(1.2) There are 8 videos, but I could not identify which is which.

Thank you for pointing this out. We have renamed the video files to clearly correspond to the figures and conditions they represent.

(2) Data Availability:

As noted above, the data should be shared. This is in accordance with eLife's policy: "Authors must make all original data used to support the claims of the paper, or that are required to reproduce them, available in the manuscript text, tables, figures or supplementary materials, or at a trusted digital repository (the latter is recommended). [...] eLife considers works to be published when they are posted as preprints, and expects preprints we review to meet the standards outlined here." Access to the time traces would have been helpful for reviewers.

We have now added the Github link for the code (https://github.com/naef-lab/suntag-analysis) and deposited the raw data (.ome.tif files) on Zenodo (10.5281/zenodo.17669332).

(3) Model Assumptions:

(3.1) The broad range of run-off times (Figure 3A) suggests stalling, which may be incompatible with the 'low-density' assumption used on the TASEP model, which essentially assumes that ribosomes do not bump into each other. This could impact the validity of the assumptions that ribosomes behave independently, elongate at constant speed (necessary for the continuum-limit approximation), and that the rate-limiting step is the initiation. How robust are the inferences to this assumption?

We agree that the deviation of waiting times from an exponential distribution (Figure 3 - figure supplement 2C) suggests the presence of stalling, which challenges the strict low-density assumption and constant elongation speed. We explicitly explored the robustness of our model to higher ribosome densities in simulations. As shown in Figure 2 - figure supplement 2, while the model accuracy for single parameters deteriorates at very high densities (overestimating density due to neglected interference), it remains robust for estimating global rates in the regime relevant to our data. We have expanded the discussion on the limitations of the low density and homogeneous elongation rate assumptions in the text (L404-408).

(3.2) Since all constructs share the same SunTag region, elongation rates should be identical there and diverge only in the variable region. This would affect $\gamma (t)$ and hence possibly affect the results. A brief discussion would be helpful.

This is a valid point. Currently, our model infers a single average elongation rate that effectively averages the behavior over the SunTag and the variable CDS regions. Modeling distinct rates for these regions would be a valuable extension but adds significant complexity. While our current "effective rate" approach might underestimate the magnitude of differences between reporters, it captures the global kinetic trend. We have added a brief discussion acknowledging this simplification (L408-412).

(3.3) A similar point applies to the Gillespie simulations: modeling the SunTag region with a shared elongation rate would be more accurate.

We agree. Simulating distinct rates for the SunTag and CDS would increase realism, though our current homogeneous simulations serve primarily to benchmark the inference framework itself. We have noted this as a potential future improvement (L413-414).

(3.4) Equation (13) assumes that switching between bursting and non-bursting states is much slower than the elongation time. First, this should be made explicit. Second, this is not quite true (~5 min elongation time on Figure 3-s2A vs ~5-15min switching times on Figure 1). It would be useful to show the intensity distribution at t=0 and compare it to the expected mixture distribution (i.e., a Poisson distribution + some extra 'N=0' cells). 

We thank the reviewer for this insightful comment. We have added a sentence to the text explicitly stating the assumption that switching dynamics are slower than the translation time. While the timescales are indeed closer than ideal (5 min vs. 5-15 min), this assumption allows for a tractable approximation of the initial conditions for the run-off inference. Comparing the intensity distribution at t=0 to a zero-inflated Poisson distribution is an excellent suggestion for validation, which we will consider for future iterations of the model.

(4) Microscopy Quantifications:

(4.1) Figure 1-S2A shows variable scFv-GFP expression across cells. Were cells selected for uniform expression in the analysis? Or is the SunTag assumed saturated? which would then need to be demonstrated. 

All cell lines used are monoclonal, and cells were selected via FACS for consistent average cytoplasmic GFP signal. We assume the SunTag is saturated based on the established characterization of the system by Tanenbaum et al. (2014), where the high affinity of the scFv-GFP ensures saturation at expression levels similar to ours.

(4.2) As translation proceeds, free scFv-GFP may become limiting due to the accumulation of mature SunTag-containing proteins. This would be difficult to detect (since mature proteins stay in the cytoplasm) and could affect intensity measurements (newly synthesized SunTag proteins getting dimmer over time).

This effect can occur with very long induction times. To mitigate this, we optimized the Doxycycline (Dox) incubation time for our harringtonine experiments to prevent excessive accumulation of mature protein. We also monitor the cytoplasmic background for granularity, which would indicate aggregation or accumulation.

(4.3) The statements "for some traces, the mRNA signal was lost before the run-off completion" (line 195) and "we observed relatively consistent fractions of translated transcripts and trace duration distributions across reporters" (line 340) should be supported by a supplementary figure.

The first statement is supported by Figure 2 - figure supplement 1, which shows representative run-off traces for all constructs, including incomplete ones.

The second statement regarding consistency is supported by the quantitative data in Figure 1E and G, which summarize the fraction of translated transcripts and trace durations across conditions.

(4.4) Measurements of single mature protein intensity $i_{MP}$:

(4.4.1) Since puromycin is used to disassemble elongating ribosomes, calibration may be biased by incomplete translation products (likely a substantial fraction, since the Dox induction is only 20min and RNAs need several minutes to be transcribed, exported, and then fully translated).

As mentioned in the “Live-cell imaging” paragraph, the imaging takes place 40 min after the end of Dox incubation. This provides ample time for mRNA export and full translation of the synthesized proteins. Consequently, the fraction of incomplete products generated by the final puromycin addition is negligible compared to the pool of fully synthesized mature proteins accumulated during the preceding hour.

(4.4.2) Line 519: "The intensity of each spot is averaged over the 100 frames". Do I understand correctly that you are looking at immobile proteins? What immobilizes these proteins? Are these small aggregates? It would be surprising that these aggregates have really only 1, 2, or 3 proteins, as suggested by Figure 1-S2A.

We are visualizing mature proteins that are specifically tethered to the actin cytoskeleton. This is achieved using a reporter where the RH1 domain is fused directly to the C-terminus of the Renilla protein (SunTag-Renilla-RH1). The RH1 domain recruits the endogenous Myosin Va motor, which anchors the protein to actin filaments, rendering it immobile. Since each Myosin Va motor interacts with one RH1 domain (and thus one mature protein), the resulting spots represent individual immobilized proteins rather than aggregates. We have now revised the text and Methods section to make this calibration strategy and the construct design clearer (L130-140).

(4.4.3) Estimating the average intensity $i_{MP}$ of single proteins all resides in the seeing discrete modes in the histogram of Figure 1-S2B, which is not very convincing. A complementary experiment, measuring *on the same microscope* the intensity of an object with a known number of GFP molecules (e.g., MS2-GFP labeled RNAs, or individual GEMs https://doi.org/10.1016/j.cell.2018.05.042 (only requiring a single transfection)) would be reassuring to convince the reader that we are not off by an order of magnitude.

While a complementary calibration experiment would be valuable, we believe our current estimate is robust because it is independently validated by our model. When we inferred i_MP as a free parameter in the HMM (Figure 5 - figure supplement 2B), the resulting value (10-15 a.u.) was remarkably consistent with our experimental calibration (14 ± 2 a.u.). We have clarified this independent validation in the text to strengthen the confidence in our quantification (L264-272).

(4.4.4) Further on the histogram in Figure 1-S2B:

- The gap between the first two modes is unexpectedly sharp. Can you double-check? It means that we have a completely empty bin between two of the most populated bins.

We have double-checked the data; the plot is correct, though the sharp gap is likely due to the small sample size (n=29).

- I am surprised not to see 3 modes or more, given that panel A shows three levels of intensity (the three colors of the arrows).

As noted below, brighter foci exist but fall outside the displayed range of the histogram.

- It is unclear what the statistical test is and what it is supposed to demonstrate.

The Student's t-test compares the means of the two identified populations to confirm they are statistically distinct intensity groups.

- I count n = 29, not 31. (The sample is small enough that the bars of the histogram show clear discrete heights, proportional to 1, 2, 3, 4, and 5 --adding up all the counts, I get 29). Is there a mistake somewhere? Or are some points falling outside of the displayed x-range?

You are correct. Two brighter data points fell outside the displayed range. The total number of foci in the histogram is 29. We have corrected the figure caption and the text accordingly.

(5) Miscellaneous Points: 

(5.1) Panel B in Figure 2-s1 appears to be missing.

The figure contains only one panel.

(5.2) In Equation (7), $l$ is not defined (presumably ribosome footprint length?). Instead, $J$ is defined right after eq (7), as if it were used in this equation.

Thank you for pointing this out, we have corrected it.

(5.3) Line 703, did you mean to write something else than "Equation 26" (since equation 26 is defined after)?

Yes, this was a typo. We have corrected the cross-reference.

Stratégies d’apaisement et d’autorégulation en milieu scolaire : Analyse et mise en œuvre

Résumé exécutif

Ce document synthétise les perspectives de Madame Claudia Verrette, docteure en sciences de l’activité physique et professeure à l’UQAM, sur le déploiement des mesures d'apaisement en milieu scolaire.

Initialement issues du domaine de la santé mentale et de l'ergothérapie pour des besoins spécifiques (autisme, troubles sensoriels), ces mesures sont désormais utilisées plus largement pour favoriser l'autorégulation de tous les élèves.

L'objectif central est de maintenir ou de restaurer la « disponibilité pour l’apprentissage » de l’élève.

L'analyse identifie quatre catégories majeures d'outils : l'aménagement de l'espace, les techniques physiques, les stratégies de diversion ou d'ancrage, et l'activité physique.

La réussite de ces interventions ne repose pas sur l'objet lui-même, mais sur un processus d'accompagnement réflexif mené par l'adulte.

Pour être efficaces, ces stratégies doivent s'inscrire dans un changement de paradigme au sein de l'équipe-école, passant d'une approche punitive à une gestion bienveillante et proactive des comportements.

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Définition et fondements des mesures d'apaisement

Les mesures d'apaisement constituent une famille d'outils et d'activités visant à aider l'élève à s'autocontrôler.

Bien que le terme « apaisement » suggère principalement le calme (référant aux calming tools en anglais), il est plus juste de parler de mesures d'autorégulation.

Objectifs clés

• Disponibilité : Permettre à l'élève de rester dans une zone propice à l'apprentissage.

• Modulation : Selon le besoin, activer l'élève (vigilance) ou le calmer.

• Alternative : Offrir une option aux mesures coercitives traditionnelles pour gérer les comportements.

Origines et évolution

Ces outils proviennent initialement de la psychiatrie et de l'ergothérapie, conçus pour des élèves présentant des troubles du spectre de l'autisme ou des troubles d'intégration sensorielle.

Par la médiation sensorielle (pression profonde, stimulation des récepteurs musculaires), ils envoient des signaux d'apaisement au cerveau.

Aujourd'hui, leur usage s'est généralisé, notamment au primaire, pour pallier l'hyperactivité ou l'inattention.

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Typologie des mesures d'autorégulation

Les interventions se divisent en quatre grandes catégories distinctes, chacune répondant à des besoins spécifiques de l'élève.

| Catégorie | Exemples d'outils et d'activités | Objectifs visés | | --- | --- | --- | | Aménagement de la salle | Coins calmes, coins « zen », chaises berçantes, coussins, musique douce, écouteurs. | Offrir un espace de retrait volontaire (non punitif) loin des stimulus de la classe. | | Mesures physiques | Respiration lente et profonde (yoga, méditation), automassage (balles, rouleaux), technique de Jacobson (contraction/relâchement). | Envoyer un signal physiologique de sécurité au cerveau par la voie sensorielle et musculaire. | | Diversion et Ancrage | Ancrage : Objets lourds (animaux lestés), musique, autocollants texturés, Fidget spinners. Diversion : Puzzles, démontage d'objets, tri de blocs. | Réorienter l'attention ou se « sortir » d'une situation difficile par l'imagerie positive ou la concentration sur un objet. | | Activité physique | Corridors actifs, pauses actives, séances de 20 min d'intensité élevée, décharge motrice. | Améliorer la concentration post-effort et utiliser le mouvement comme outil de gestion comportementale. |

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L'activité physique comme levier d'intervention multiniveau

L'activité physique occupe une place prépondérante dans les stratégies d'apaisement, structurée selon un modèle de réponse à l'intervention :

1. Niveau Universel : Éducation physique, récréations et corridors actifs accessibles à tous les élèves pour favoriser la santé et le calme général.

2. Niveau Ciblé : Périodes supplémentaires d'activité pour des sous-groupes d'élèves, parfois utilisées comme récompense pour un comportement attendu.

3. Niveau Individualisé (Le cas du « Ring ») :

◦ Concept : Salle de décharge motrice pour élèves avec troubles graves du comportement.  

◦ Fonctionnement : Séquences contrôlées (ex: 10 Jumping Jacks, poussées au mur, saut à la corde) entrecoupées de respirations profondes.    

◦ Accompagnement : Un adulte guide la réflexion de l'élève sur son état émotionnel (ex: passage de la colère à une zone de retour en classe).  

◦ Résultat : Ce dispositif est identifié par les élèves comme la mesure la plus efficace et appréciée.

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Conditions de réussite et mise en œuvre efficace

L'efficacité d'une mesure d'apaisement ne réside pas dans l'objet lui-même, qui peut sinon devenir une simple source de distraction.

Le processus d'autorégulation assistée

Pour que l'élève devienne autonome, l'adulte doit l'accompagner dans un processus cognitif en trois étapes :

• Reconnaissance : Aider l'élève à nommer son état (colère, agitation, envahissement par les pensées).

• Choix : Sélectionner l'outil approprié dans un répertoire personnel préalablement pratiqué (est-ce un besoin d'activation ou de calme ?).

• Retour réflexif : Évaluer après coup si l'outil a été efficace et s'il peut être réutilisé.

Facteurs de succès organisationnels

• Habituation : Permettre à tous les élèves d'explorer les outils au début pour dissiper l'effet de nouveauté (« lune de miel »).

• Cohérence de l'équipe-école : Les stratégies doivent être communes à tous les intervenants entourant l'élève pour assurer une prévisibilité et une efficacité accrue.

• Vision bienveillante : Abandonner le présupposé que l'élève « devrait être capable » de s'autoréguler seul, surtout au secondaire où les besoins persistent.

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Conclusion : Le changement de paradigme

Le passage aux mesures d'apaisement exige une réflexion profonde sur la discipline.

Un même objet (comme un banc) peut servir de punition ou d'outil d'autorégulation selon l'intention de l'adulte.

Le succès de ces mesures dépend de la volonté de l'équipe-école de s'engager vers des pratiques axées sur l'autodétermination et la bienveillance, plutôt que sur la coercition.

Sans cette concertation et cet accompagnement humain, les outils d'apaisement risquent d'être délaissés après quelques mois d'utilisation inefficace.

Synthèse de la Matinale Associations : Fiscalité, Mécénat et Fonds de Dotation

Résumé Exécutif

Ce document synthétise les interventions de la Direction Régionale des Finances Publiques (DRFIP) d’Île-de-France lors d'un webinaire consacré à l'actualité fiscale des organismes sans but lucratif (OSBL).

La gestion fiscale des associations et fonds de dotation est marquée par une recherche accrue de sécurité juridique, illustrée par une hausse constante des demandes de rescrit fiscal (près de 50 % des demandes totales concernent le secteur associatif).

Les points critiques à retenir sont le renforcement des contrôles sur l'émission des reçus fiscaux suite à la loi du 24 août 2021, l'application rigoureuse des critères de non-lucrativité (règle des « 4P » et gestion désintéressée), et la distinction impérative entre le mécénat et le parrainage commercial.

Enfin, le cadre des fonds de dotation, bien que plus souple, impose des obligations déclaratives et de dotation minimale (15 000 €) strictes.

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I. Le Cadre d'Action de la DRFIP et la Sécurité Juridique

La Direction Régionale des Finances Publiques d'Île-de-France, et plus particulièrement son pôle de contrôle fiscal et des affaires juridiques, assure une mission de sécurisation de la dépense fiscale.

1. La montée en puissance du rescrit fiscal

Le rescrit est une procédure volontaire permettant à un organisme d'obtenir une prise de position formelle de l'administration sur son régime fiscal.

• Statistiques : En 2025, la DRFIP prévoit de traiter environ 1 140 demandes de rescrits, dont 493 concernent spécifiquement les associations (soit environ 45 %).

• Objectif : Sécuriser l'émission des reçus fiscaux pour les donateurs afin d'éviter des remises en cause ultérieures lors de contrôles.

• Limites : Le rescrit ne protège l'organisme que si les informations fournies sont exhaustives et conformes à la réalité. Il n'empêche pas un contrôle fiscal ultérieur.

2. Le renforcement des contrôles (Loi du 24 août 2021)

La loi confortant le respect des principes de la République a transformé la nature des contrôles :

• Avant 2021 : Simple contrôle de concordance des montants.

• Depuis 2021 : Contrôle de validité sur le fond. L'administration vérifie si l'organisme est réellement fondé à émettre des reçus fiscaux au regard des critères d'intérêt général.

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II. Analyse de la Lucrativité : Critères et Méthodologie

Le régime par défaut d'une association est l'exonération des impôts commerciaux, basée sur une présomption simple de non-lucrativité.

L'administration peut toutefois apporter la preuve contraire en suivant une analyse par étapes.

1. La gestion désintéressée

C’est la condition préalable indispensable. Elle repose sur trois piliers :

• Absence de rémunération des dirigeants : Les dirigeants doivent être bénévoles.

Une tolérance existe pour une rémunération ne dépassant pas les 3/4 du SMIC, appréciée annuellement.

• Absence de distribution de ressources : Aucun bénéfice ne doit être reversé aux membres.

• Absence d'attribution de parts d'actif : Les membres ne peuvent pas s'approprier les biens de l'association, même lors de sa dissolution.

2. L'examen de la concurrence et la règle des « 4P »

Si une association intervient dans un secteur concurrentiel, l'administration évalue ses modalités de gestion par rapport aux entreprises commerciales selon le faisceau d'indices dit des « 4P » (par ordre d'importance décroissante) :

| Critère | Analyse | | --- | --- | | Produit | L'utilité sociale du service rendu (ex: méthodes adaptées pour les troubles dys). | | Public | Le service s'adresse-t-il à des personnes ne pouvant normalement pas y accéder (critères sociaux) ? | | Prix | Les tarifs sont-ils nettement inférieurs au marché ou modulés selon les revenus ? | | Publicité | L'association utilise-t-elle des méthodes commerciales de promotion ou une simple information ? |

3. La notion de communauté d'intérêt

Une association peut être jugée lucrative si elle constitue le prolongement d'une entreprise commerciale ou lui offre des débouchés.

• Jurisprudence "Audace" (2016) : Une association servant de « capteur de clientèle » pour une société d'assistance juridique dirigée par la même personne a été requalifiée en organisme lucratif.

• Relations privilégiées : Cette notion s'applique lorsque l'association permet à des entreprises membres de réduire leurs dépenses (ex: études de marché à moindre coût), leur offrant ainsi un avantage concurrentiel.

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III. Le Régime du Mécénat et du Parrainage

Le dispositif du mécénat a été libéralisé par la loi de décembre 2023 (entrée en vigueur en janvier 2024), mais reste soumis à des définitions strictes.

1. L'intérêt général fiscal

L'intérêt général au sens fiscal (articles 200 et 238 bis du CGI) diffère du sens commun. Il exige :

• Une gestion désintéressée.

• Une activité non lucrative.

• L'absence de bénéfice pour un « cercle restreint » de personnes.

2. Distinction Mécénat vs Parrainage (Sponsoring)

La distinction repose sur la valorisation des contreparties :

• Mécénat : Il doit exister une disproportion marquée entre le don et les contreparties reçues par le donateur (ex: simple mention du nom du donateur).

• Parrainage (Sponsoring) : Si les contreparties (publicité, logos sur maillots, cocktails premium, places réservées) ont une valeur proche du montant versé, il s'agit d'une prestation de service commerciale taxable.

3. Cas particulier du spectacle vivant

Le législateur autorise certains organismes lucratifs (ex: sociétés commerciales détenues par des entités publiques) à bénéficier du mécénat pour des activités de spectacle vivant, de cinéma ou d'expositions d'art contemporain, à condition que la gestion reste désintéressée.

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IV. Les Fonds de Dotation : Un Outil Spécifique

Créés par la loi de 2008, les fonds de dotation visent à favoriser le mécénat pour le financement de missions d'intérêt général.

1. Modes de fonctionnement

• Fonds opérateur : Réalise lui-même des activités d'intérêt général.

• Fonds redistributeur : Collecte des fonds pour les reverser à d'autres organismes d'intérêt général.

• Mixte : Combine les deux activités.

2. Obligations et fiscalité

• Dotation minimale : 15 000 €.

• Obligations déclaratives : Déclaration annuelle en préfecture précisant le montant de la collecte et des redistributions.

• Consomptibilité : Si les statuts prévoient que la dotation peut être consommée, le fonds perd certains avantages fiscaux sur ses revenus patrimoniaux (soumission à l'IS à taux réduit).

• Taxe sur les salaires : Les fonds de dotation y sont soumis sans l'abattement dont bénéficient les associations (2 144 €), sauf pour les salaires liés à l'organisation de six manifestations de bienfaisance annuelles.

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V. Jurisprudences et Exemples de Contrôle

L'administration s'appuie sur des cas concrets pour illustrer l'application des règles :

• École de voile de Carantec : Requalification lucrative car la zone de chalandise (touristes venant de toute la France) et les tarifs étaient comparables aux écoles de voile commerciales de la région.

• Arrêt "Piou-Piou" (2022) : Une association de ski pour enfants entretenait des relations privilégiées avec les moniteurs de l'ESF (membres de l'association), car elle leur fournissait un débouché économique direct.

• Défense de la mémoire (Affaire Maréchal Pétain) : Le mécénat est refusé si l'activité éligible (ex: un musée) est accessoire par rapport à l'objet principal de l'association qui, lui, ne rentre pas dans les critères de la loi.

VI. Secteur Lucratif Accessoire et Sectorisation

Une association non lucrative peut exercer des activités commerciales accessoires.

• Franchise d'impôts : Jusqu'à un seuil de 90 011 € (chiffre cité pour 2023/2024), ces revenus ne sont pas imposés si l'activité non lucrative reste prépondérante.

• Au-delà du seuil : L'association doit sectoriser ses activités. Elle paie des impôts commerciaux sur le secteur lucratif dès le premier euro.

• Critère de prépondérance : L'administration ne regarde pas seulement les recettes, mais aussi la mobilisation des ressources (temps de bénévolat, occupation des locaux, salaires) pour déterminer si l'activité non lucrative reste dominante.

Reviewer #3 (Public review):

Summary:

The manuscript by Chaya and Syed focuses on understanding the link between cell cycle and temporal patterning in central brain type II neural stem cells (NSCs). To investigate this, the authors perturb the progression of the cell cycle by delaying the entry into M phase and preventing cytokinesis. Their results convincingly show that temporal factor expression requires progression of the cell cycle in both Type 1 and Type 2 NSCs in the Drosophila central brain. Overall, this study establishes an important link between the two timing mechanisms of neurogenesis.

Strengths:

The authors provide solid experimental evidence for the coupling of cell cycle and temporal factor progression in Type 2 NSCs. The quantified phenotype shows an all-or-none effect of cell cycle block on the emergence of subsequent temporal factors in the NSCs, strongly suggesting that both nuclear division and cytokinesis are required for temporal progression. The authors also extend this phenotype to Type 1 NSCs in the central brain, providing a generalizable characterization of the relationship between cell cycle and temporal patterning.

Weaknesses:

One major weakness of the study is that the authors do not explore the mechanistic relationship between cell cycle and temporal factor expression. Although their results are quite convincing, they do not provide an explanation as to why Cdk1 depletion affects Syp and EcR expression but not the onset of svp. This result suggests that at least a part of the temporal cascade in NSCs is cell-cycle independent which isn't addressed or sufficiently discussed.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

Drosophila larval type II neuroblasts generate diverse types of neurons by sequentially expressing different temporal identity genes during development. Previous studies have shown that the transition from early temporal identity genes (such as Chinmo and Imp) to late temporal identity genes (such as Syp and Broad) depends on the activation of the expression of EcR by Seven-up (Svp) and progression through the G1/S transition of the cell cycle. In this study, Chaya and Syed examined whether the expression of Syp and EcR is regulated by cell cycle and cytokinesis by knocking down CDK1 or Pav, respectively, throughout development or at specific developmental stages. They find that knocking down CDK1 or Pav either in all type II neuroblasts throughout development or in single-type neuroblast clones after larval hatching consistently leads to failure to activate late temporal identity genes Syp and EcR. To determine whether the failure of the activation of Syp and EcR is due to impaired Svp expression, they also examined Svp expression using a Svp-lacZ reporter line. They find that Svp is expressed normally in CDK1 RNAi neuroblasts. Further, knocking down CDK1 or Pav after Svp activation still leads to loss of Syp and EcR expression. Finally, they also extended their analysis to type I neuroblasts. They find that knocking down CDK1 or Pav, either at 0 hours or at 42 hours after larval hatching, also results in loss of Syp and EcR expression in type I neuroblasts. Based on these findings, the authors conclude that cycle and cytokinesis are required for the transition from early to late temporal identity genes in both types of neuroblasts. These findings add mechanistic details to our understanding of the temporal patterning of Drosophila larval neuroblasts.

Strengths:

The data presented in the paper are solid and largely support their conclusion. Images are of high quality. The manuscript is well-written and clear.

We appreciate the reviewer’s detailed summary and recognition of the study’s strengths.

Weaknesses:

The quantifications of the expression of temporal identity genes and the interpretation of some of the data could be more rigorous.

(1) Expression of temporal identity genes may not be just positive or negative. Therefore, it would be more rigorous to quantify the expression of Imp, Syp, and EcR based on the staining intensity rather than simply counting the number of neuroblasts that are positive for these genes, which can be very subjective. Or the authors should define clearly what qualifies as "positive" (e.g., a staining intensity at least 2x background).

We thank the reviewer for this helpful suggestion. In the new version, we have now clarified how positive expression was defined and added more details of our quantification strategy to the Methods section (page 11, lines 380-388; lines 426-434 in tracked changes file). Fluorescence intensity for each neuroblast was normalized to the mean intensity of neighboring wild-type neuroblasts imaged in the same field. A neuroblast was considered positive for a given factor when its normalized nuclear intensity was at least 2× the local background. This scoring criterion was applied uniformly across all genotypes and time points. All quantifications were performed on the raw LSM files in Fiji prior to assembling the figure panels.

(2) The finding that inhibiting cytokinesis without affecting nuclear divisions by knocking down Pav leads to the loss of expression of Syp and EcR does not support their conclusion that nuclear division is also essential for the early-late gene expression switch in type II NSCs (at the bottom of the left column on page 5). No experiments were done to specifically block the nuclear division in this study specifically. This conclusion should be revised.

We blocked both cell cycle progression and cytokinesis, and both these manipulations affected temporal gene transitions, suggesting that both cell cycle and cytokinesis are essential. To our knowledge, no mechanism/tool exists that selectively blocks nuclear division while leaving cell cycle progression intact. We have added more clarification on page 4, line 123 onwards (lines 126 onwards in tracked changes file).

(3) Knocking down CDK1 in single random neuroblast clones does not make the CDK1 knockdown neuroblast develop in the same environment (except still in the same brain) as wild-type neuroblast lineages. It does not help address the concern whether "type 2 NSCS with cell cycle arrest failed to undergo normal temporal progression is indirectly due to a lack of feedback signaling from their progeny", as discussed (from the bottom of the right column on page 9 to the top of the left column on page 10). The CDK1 knockdown neuroblasts do not divide to produce progeny and thus do not receive a feedback signal from their progeny as wild-type neuroblasts do. Therefore, it cannot be ruled out that the loss of Syp and EcR expression in CDK1 knockdown neuroblasts is due to the lack of the feedback signal from their progeny. This part of the discussion needs to be clarification.

Thanks to the reviewer for raising this critical point. We agree and have added more clarification of our interpretations and limitations to our studies in the revised text on page 8, line 278-282 (lines 296-300 in tracked changes file)

(4) In Figure 2I, there is a clear EcR staining signal in the clone, which contradicts the quantification data in Figure 2J that EcR is absent in Pav RNAi neuroblasts. The authors should verify that the image and quantification data are consistent and correct.

When cytokinesis is blocked using pav-RNAi, the neuroblasts become extremely large and multinucleated. In some large pav RNAi clones, we observed a weak EcR signal near the cell membrane. However, more importantly, none of the nuclear compartments showed detectable EcR staining, where EcR is typically localized. We selected a representative nuclear image for the figure panel. To clarify this observation, we have now added an explanatory note to the discussion section on page 8, lines 283-291 (lines 301-309 in tracked changes file).

Reviewer #2 (Public review):

Summary:

Neural stem cells produce a wide variety of neurons during development. The regulatory mechanisms of neural diversity are based on the spatial and temporal patterning of neural stem cells. Although the molecular basis of spatial patterning is well-understood, the temporal patterning mechanism remains unclear. In this manuscript, the authors focused on the roles of cell cycle progression and cytokinesis in temporal patterning and found that both are involved in this process.

Strengths:

They conducted RNAi-mediated disruption on cell cycle progression and cytokinesis. As they expected, both disruptions affected temporal patterning in NSCs.

We appreciate the reviewer’s positive assessment of our experimental results.

Weaknesses:

Although the authors showed clear results, they needed to provide additional data to support their conclusion sufficiently.

For example, they need to identify type II NSCs using molecular markers (Ase/Dpn).The authors are encouraged to provide a more detailed explanation of each experiment. The current version of the manuscript is difficult for non-expert readers to understand.

Thanks for your feedback. We have now included a detailed description of how we identify type II NSCs in both wild-type and mutant clones. We have also added a representative Asense staining to clearly distinguish type 1 (Ase⁺) from type 2 (Ase^-) NSCs see Figure S1. We have also added a resources table explaining the genotypes associated with each figure, which was omitted due to an error in the previous version of the manuscript. 

Reviewer #3 (Public review):

Summary:

The manuscript by Chaya and Syed focuses on understanding the link between cell cycle and temporal patterning in central brain type II neural stem cells (NSCs). To investigate this, the authors perturb the progression of the cell cycle by delaying the entry into M phase and preventing cytokinesis. Their results convincingly show that temporal factor expression requires progression of the cell cycle in both Type 1 and Type 2 NSCs in the Drosophila central brain. Overall, this study establishes an important link between the two timing mechanisms of neurogenesis.

Strengths:

The authors provide solid experimental evidence for the coupling of cell cycle and temporal factor progression in Type 2 NSCs. The quantified phenotype shows an all-ornone effect of cell cycle block on the emergence of subsequent temporal factors in the NSCs, strongly suggesting that both nuclear division and cytokinesis are required for temporal progression. The authors also extend this phenotype to Type 1 NSCs in the central brain, providing a generalizable characterization of the relationship between cell cycle and temporal patterning.

We thank the reviewer for recognizing the robustness of our data linking the cell cycle to temporal progression.

Weaknesses:

One major weakness of the study is that the authors do not explore the mechanistic relationship between the cell cycle and temporal factor expression. Although their results are quite convincing, they do not provide an explanation as to why Cdk1 depletion affects Syp and EcR expression but not the onset of svp. This result suggests that at least a part of the temporal cascade in NSCs is cell-cycle independent, which isn't addressed or sufficiently discussed.

Thank you for bringing up this important point. We are equally interested in uncovering the mechanism by which the cell cycle regulates temporal gene transitions; however, such mechanistic exploration is beyond the scope of the present study. Interestingly, while the temporal switching factor Svp is expressed independently of the cell cycle, the subsequent temporal transitions are not. We have expanded our discussion on this intriguing finding (page 9, line 307-315; lines 345-355 in tracked changes file). Specifically, we propose that svp activation marks a cell-cycle–independent phase, whereas EcR/Syp induction likely depends on cell-cycle–coupled mechanisms, such as mitosis-dependent chromatin remodeling or daughter-cell feedback. Although further dissection of this mechanism lies beyond the current study, our findings establish a foundation for future work aimed at identifying how developmental timekeeping is molecularly coupled to cell-cycle progression.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors): 

(1) Figure 1 C and D, it would be better to put a question mark to indicate that these are hypotheses to be tested. 

We appreciate this suggestion and have added question marks in Figure 1C and 1D to clearly indicate that these panels represent hypotheses under investigation clearly.

(2) Figure 2A-I, Figure 4A-I, Figure 5A-I and K-S, in addition to enlarged views of single type II neuroblasts, it would be more convincing to include zoomed-out images of the entire larval brain or at least a portion of the brain to include neighboring wild-type type II neuroblasts as internal controls. Also, it would be ideal to show EcR staining from the same neuroblasts as IMP and Syp staining. 

We thank the reviewer for this valuable input. In our imaging setup, the number of available antibody channels was limited to four (anti-Ase, anti-GFP, anti-Syp, and antiImp). Adding EcR in the same sample was therefore not technically possible, we performed EcR staining separately. 

(3) The authors cited "Syed et al., 2024" (in the middle of the right column on page 5), but this reference is missing in the "References" section and should be added. 

The missing citation has been added to the reference section.  

(4) It would be better to include Ase staining in the relevant figure to indicate neuroblast identity as type I or type II. 

We agree and now include representative Ase staining for both type 1 and type 2 NSC clones in Figure S1, along with corresponding text updates that describe these markers.

Reviewer #2 (Recommendations for the authors): 

Major comments 

(1) The present conclusion relies on the results using Cdk1 RNAi and pav RNAi. It is still possible that Cdk1 and Pav are involved in the regulation of temporal patterning independent of the regulation of cell cycle or cytokinesis, respectively. To avoid this possibility, the authors need to inhibit cell cycle progression or cytokinesis in another alternative manner. 

We thank the reviewer for raising this important point. While we cannot completely exclude gene-specific, cell-cycle-independent roles for Cdk1 or Pav, we observe consistent phenotypes across several independent manipulations that slow or block the cell cycle. Also, earlier studies using orthogonal approaches that delay G1/S (Dacapo/Rbf) or impair mitochondrial OxPhos (which lengthens G1/S; van den Ameele & Brand, 2019) produce similar temporal delays. These concordant phenotypes strongly support the interpretation that altered cell-cycle progression—rather than specific roles of a single gene—is the primary cause of the defect. While we cannot exclude additional, gene-specific effects of Cdk1 or Pav, the concordant phenotypes across independent perturbations make the cell-cycle disruption model the most parsimonious interpretation. We have clarified this reasoning in the discussion section on pages 8-9, lines 293-305 (lines 311-343 in tracked changes file).

(2) To reach the present conclusion, the authors need to address the effects of acceleration of cell cycle progression or cytokinesis on temporal patterning. 

We thank the reviewer for this insightful suggestion. To our knowledge, there are currently no established genetic tools that can specifically accelerate cell-cycle progression in Drosophila neuroblasts. However, our results demonstrate that blocking the cell cycle impairs the transition from early to late temporal gene expression. These findings suggest that proper cell-cycle progression is essential for the transition from early to late temporal identity in neuroblasts.

Minor comments 

(3) P3L2 (right), ... we blocked the NSC cell cycle...

How did they do it? 

Which fly lines were used?

Why did they use the line? 

These details are now included in the Materials and Methods and the Resource Table (pages 11-13). We used Wor-Gal4, Ase-Gal80 to drive UAS-Cdk1RNAi and UASpavRNAi in type 2 NSCs 

(4) P5L1(left), ... we used the flip-out approach...

Why did they conduct it? 

Probably, the authors have reasons other than "to further ensure." 

We have clarified in the text on page 4, lines 137-139, that the flip-out approach was used to generate random single-cell clones, enabling quantitative analysis of type 2 NSCs within an otherwise wild-type brain. 

(5) P5L8(left), ... type 2 hits were confirmed by lack of the type 1 Asense...  The authors must examine Deadpan (Dpn) expression as well. Because there are a lot of Asense (Ase) negative cells in the brain (neurons, glial cell, and neuroepithelial cells). 

Type II NSCs can be identified as Dpn+/Ase- cells.

We agree that Dpn is a helpful marker. However, we reliably distinguished type II NSCs by their lack of Ase and larger cell size relative to surrounding neurons and glia, which are smaller in size and located deeper within the clone. These differences, together with established lineage patterns, allow unambiguous identification of type 2 NSCs across all genotypes. We have now added representative type I and type 2 NSC clones to the supplemental figure S1 (E-G’) with Asense stains to demonstrate how we differentiate type I from type II NSCs. 

(6) P5L32(left), To do this, we induced... 

This sentence should be made more concise.

Please rephrase it. 

The sentence has been rewritten for clarity and concision.

(7)  P5L42(left), ...lack of EcR/Syp expression (Figure 2).  However, EcR expression is still present (Figure 2I). 

In some large pavRNAi clones, a weak EcR signal can be observed near the cell membrane; however, none of the nuclear compartments—where EcR is typically localized—show detectable staining. We selected a representative nuclear image for the figure and addressed this observation on page 8, lines 283-291 (lines 301-309 in tracked changes file).

(8) P7L29(left), ......had persistent Imp expression...

Imp expression is faint compared to that in Figure 2G.

The differences between Figures 2G and 3G should be discussed. 

We thank the reviewer for this comment. We have added a note in the Methods section clarifying that brightness and contrast were adjusted per panel for optimal visualization; thus, apparent differences in signal intensity do not reflect biological variation. Fluorescence intensity for each neuroblast was normalized to the mean intensity of neighboring wild-type neuroblasts imaged in the same field. A neuroblast was considered Imp-positive when its normalized nuclear intensity was at least 2× the local background. This scoring criterion was applied uniformly across all genotypes and time points. All quantifications were performed on the raw LSM files in Fiji prior to assembling the figure panels.

(9) P8 (Figure 5)

The Imp expression is faint compared to that in Figure 5Q.

The difference between Figure 5G and 5Q should be discussed further. 

As mentioned above, we have clarified our image processing approach in the Methods section to explain any differences in signal appearance between these figures.

(10) P10 Materials and Methods

The authors did not mention the fly lines used. This is very important for the readers. 

We thank the reviewer for bringing this oversight to our attention. The Resource Table was inadvertently omitted from the initial submission. The complete list of fly lines and reagents used in this study is now provided in the updated Resource Table.

Reviewer #3 (Recommendations for the authors): 

Major points 

(1) The authors mention that the heat-shock induction at 42ALH is well after svp temporal window and therefore the cell cycle block independently affects Syp and EcR expression. However, Figure 3 shows svp-LacZ expression at 48ALH. If svp expression is indeed transient in Type 2 NSCs, then this must be validated using an immunostaining of the svp-LacZ line with svp antibody. This is crucial as the authors claim that cell cycle block doesn't affect does affect svp expression and is required independently. 

We thank the reviewer for bringing this important issue to our attention. As noted, Svp protein is expressed transiently and stochastically in type 2 NSCs (Syed et al., 2017), making direct antibody quantification challenging upon cell cycle block. Consistent with previous work (Syed et al., 2017), we used the svp-LacZ reporter line to visualize stabilized Svp expression, which reliably captures Svp expression in type 2 NSCs (Syed et al., 2017 https://doi.org/10.7554/eLife.26287, and Dhilon et al., 2024 https://doi.org/10.1242/dev.202504).

(2) The authors have successfully slowed down the cell cycle and showed that it affects temporal progression. However, a converse experiment where the cell cycle is sped up in NSCs would be an important test for the direct coupling of temporal factor expression and cell cycle, wherein the expectation would be the precocious expression of late temporal factors in faster cycle NSCs. 

We agree that such an experiment would be ideal. However, as noted above (Reviewer #2 comment 2), to our knowledge, no suitable tools currently exist to accelerate neuroblast cell-cycle progression without pleiotropic effects.

Minor point 

The authors must include Ray and Li (https://doi.org/10.7554/eLife.75879) in the references when describing that "...cell cycle has been shown to influence temporal patterning in some systems,...".  

We thank the reviewer for this helpful suggestion. The cited reference (Ray and Li, eLife, 2022) has now been included and appropriately referenced in the revised manuscript.

Réforme de l'éducation : Enjeux, modèles et perspectives systémiques

Résumé analytique

Le système éducatif européen, et particulièrement le modèle allemand, fait face à une remise en question fondamentale de ses structures centenaires.

Le débat oppose deux visions : une approche neuroscientifique et réformatrice, prônant l'abolition des notes et l'autonomie, et une approche sociologique et réaliste, soulignant les fonctions de sélection et de cohésion sociale de l'école.

Les points critiques incluent l'impact délétère de l'évaluation chiffrée sur le développement cérébral des jeunes enfants, la persistance des inégalités sociales à travers le tri précoce des élèves, et la nécessité de passer d'une motivation extrinsèque (notes) à une motivation intrinsèque.

Toutefois, les recherches convergent vers un constat central : au-delà de la structure du système, la qualité et l'investissement de l'enseignant demeurent le facteur le plus déterminant de la réussite scolaire.

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I. La problématique de l'évaluation : L'impact des notes

Le système de notation est au cœur des tensions entre partisans de la tradition et réformateurs.

L'analyse des sources révèle des conséquences divergentes selon le profil des élèves.

A. Perspectives neuroscientifiques

La professeure Michaela Brohm-Badri souligne que les notes modifient la chimie cérébrale des élèves :

• Pour les bons élèves : La réussite déclenche la libération de dopamine (motivation) et d'ocytocine.

Cependant, cela remplace la motivation intrinsèque (curiosité naturelle) par une motivation extrinsèque de récompense.

• Pour les élèves en difficulté : L'échec libère de l'adrénaline et du cortisol (hormones du stress).

L'amygdale bloque alors le cortex préfrontal, empêchant toute réflexion correcte et créant un cercle vicieux de contre-performance.

• Immaturité cérébrale : Le cortex préfrontal n'atteint sa maturité qu'entre 21 et 23 ans.

Noter et orienter les enfants dès 9 ou 10 ans revient à figer leur destin social avant la fin de leur développement biologique.

B. Biais cognitifs et subjectivité

L'évaluation est critiquée pour son manque d'objectivité, influencée par plusieurs phénomènes :

• La constante macabre : Tendance inconsciente des enseignants à reproduire une courbe de répartition (bons, moyens, faibles) quel que soit le niveau réel de la classe.

• L'effet d'ordre : Un devoir moyen semble meilleur s'il suit une copie très médiocre.

• Facteurs exogènes : L'apparence physique (lunettes, coiffure), l'origine sociale, le sexe ou l'humeur de l'enseignant interfèrent avec la note.

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II. Les fonctions sociales et politiques de l'école

Selon le professeur Roland Reichenbach, l'école ne peut être réduite à un simple lieu d'apprentissage ; elle remplit une dizaine de fonctions essentielles à la société.

• Instruction et intégration : Transmission des savoirs et apprentissage de la vie en communauté.

• Sélection : Bien que critiquée, la sélection prépare à la réalité du marché du travail et de l'économie.

• Gardiennage : Une fonction logistique fondamentale permettant le fonctionnement de la société.

• Éducation démocratique : L'école apprend à l'individu à s'autocorriger, à viser l'objectivité et à dépasser ses désirs individuels.

• Protection contre l'arbitraire privé : Si l'école publique renonçait à l'évaluation, cette mission incomberait au secteur privé, favorisant alors exclusivement les plus riches ou les plus puissants.

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III. Modèles pédagogiques et expérimentations

A. Comparaison des systèmes européens

Le document met en évidence des disparités majeures dans l'organisation scolaire en Europe :

| Pays | Caractéristiques du système | | --- | --- | | Allemagne | Système conservateur. Orientation précoce (10 ans) vers trois filières (professionnelle, technique, générale). | | France | État centralisé, programmes nationaux, style d'enseignement plutôt autoritaire et hiérarchisé. | | Finlande | Relation d'égalité prof-élève. Pas de notes avant la 3ème. Très haut niveau de performance. | | Royaume-Uni | Forte présence du privé. Innovation technologique précoce (programmation obligatoire dès le secondaire). |

B. L'exemple de l'Alemanon Schule (Wutöschingen)

Cette école allemande propose une alternative radicale au modèle frontal :

• Apprentissage autonome : Les élèves sont des "partenaires d'apprentissage". Les cours classiques ("inputs") sont réduits au profit d'ateliers libres.

• Responsabilisation : L'élève décide du moment où il passe ses tests de compétences.

• Mixité sociale et tutorat : L'entraide entre élèves de différentes filières est encouragée.

• Résultats : En 2022, les résultats au baccalauréat y étaient supérieurs à la moyenne régionale, avec une augmentation du nombre d'élèves brillants.

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IV. Le facteur humain : La centralité de l'enseignant

La méta-analyse "Visible Learning" de John Hattie, portant sur plus de 2 100 études, apporte des conclusions nuancées qui bousculent les idéologies :

1. L'enseignant est la variable clé : La réussite scolaire dépend avant tout de la clarté de l'enseignant, de sa gestion de classe et de son investissement individuel auprès des élèves.

2. Dépassement du clivage traditionnel/moderne : Si Hattie valide certains aspects de l'enseignement traditionnel (consignes directes), il soutient également des réformes comme le feedback individualisé et l'abolition des étiquettes (notes).

3. Valorisation de la profession : Dans les pays performants (Finlande, Suède), seuls les 10 % des meilleurs diplômés peuvent devenir enseignants, et la profession bénéficie d'une haute reconnaissance sociale.

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V. Synthèse des risques et perspectives

A. Le piège de la "pédagogie des privilégiés"

Une mise en garde est formulée concernant l'autonomie totale : certains élèves, issus de milieux éloignés de la culture scolaire, ont besoin d'un encadrement strict et d'un guidage direct.

L'apprentissage autonome peut, paradoxalement, accroître les inégalités s'il n'est pas accompagné d'un renforcement de l'affirmation de soi pour les élèves les plus fragiles.

B. L'objectif d'équité

L'égalité des chances ne signifie pas que tous les élèves doivent être identiques ou avancer au même rythme. Le défi moderne de l'école est de concilier :

• Le développement du goût du risque et de l'expérimentation.

• La nécessité d'un feedback pour grandir.

• Le maintien de la motivation intrinsèque face à un monde concurrentiel.

En conclusion, si le système de performance semble inévitable pour la structure sociale et économique, l'enjeu majeur reste de transformer l'autorité autoritaire en une autorité inspirante, capable de valoriser la différence sans la stigmatiser par l'échec.

Comprendre la Contre-volonté : Analyse de l'Opposition Instinctive chez l'Enfant

Résumé Exécutif

Ce document propose une analyse approfondie du concept de « contre-volonté », un phénomène souvent confondu avec l'opposition ou l'impolitesse dans le cadre de l'éducation et du développement de l'enfant.

Contrairement aux perceptions populaires qui valorisent l'obéissance immédiate, la recherche développementale démontre que la contre-volonté est une réaction instinctive, saine et nécessaire.

Elle assure la protection de l'individu contre les influences extérieures non sécurisées et constitue le socle de l'affirmation de soi et de l'esprit critique à l'âge adulte.

Le document souligne que les interventions basées sur la pression, les ultimatums ou la punition sont contre-productives, car elles alimentent la résistance au lieu de favoriser la coopération.

La clé d'une collaboration harmonieuse réside dans la réactivation intentionnelle du lien d'attachement.

En privilégiant la connexion émotionnelle, l'humour et la créativité, les adultes peuvent transformer une dynamique de confrontation en une adhésion naturelle, permettant à l'enfant de se développer sans sacrifier son intégrité personnelle.

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1. Définition et Origines de la Contre-volonté

La contre-volonté se distingue de la simple « opposition » par sa nature structurelle et instinctive dans le développement humain.

• Un être autodéterminé : L'humain est, par essence, un être doté d'une volonté propre. La contre-volonté émerge lorsque la volonté de l'adulte entre en conflit direct avec celle de l'enfant.

• Opposition vs Contre-volonté : Alors que le terme « opposition » est souvent utilisé de manière péjorative dans le jargon populaire pour décrire un manque de respect, la « contre-volonté » décrit plus précisément le processus biologique et psychologique de résistance à une consigne externe perçue comme intrusive.

• Le mythe de l'enfant « bien élevé » : Le modèle traditionnel valorise l'obéissance au doigt et à l'œil.

Or, une obéissance totale et immédiate s'apparente davantage au fonctionnement d'un robot ou d'une marionnette qu'à celui d'un être humain en développement.

2. La Valeur Développementale et Sécuritaire

Loin d'être un défaut de comportement, la contre-volonté remplit des fonctions vitales pour l'individu.

Protection et Survie

• Résistance instinctive : Les humains sont programmés pour résister aux directives de personnes avec lesquelles ils n'ont pas de lien d'attachement solide.

• Sécurité physique : Cette résistance est un mécanisme de protection essentiel (par exemple, refuser de suivre un inconnu dans la rue).

L'enfant fait alors preuve de contre-volonté pour préserver son intégrité.

Affirmation de Soi et Esprit Critique

• Préparation à l'âge adulte : L'affirmation de soi ne commence pas à 18 ou 22 ans.

Elle se cultive dès l'enfance. Un adulte capable de négocier son salaire ou de poser des limites dans son couple est un enfant qui a pu exercer sa contre-volonté.

• Développement du jugement : La capacité de remettre en question, d'argumenter et de ne pas tout accepter « pour argent comptant » est le fondement de l'esprit critique.

Sans contre-volonté, l'enfant devient un adolescent et un adulte vulnérable à l'influence d'autrui.

3. Les Causes de la Résistance au Quotidien

L'analyse identifie plusieurs facteurs exacerbant la contre-volonté dans les interactions quotidiennes :

| Facteur | Description | | --- | --- | | Immaturité cérébrale | Le cerveau de l'enfant traite souvent une seule information à la fois. S'il est absorbé par le jeu, il n'ignore pas l'adulte par mépris, mais par incapacité neurologique à basculer instantanément sa volonté. | | Pression extérieure | L'usage de l'autorité brute, des menaces, des punitions ou des ultimatums augmente la contre-volonté au lieu de susciter la collaboration. | | Déconnexion relationnelle | Donner une consigne à distance ou sans avoir préalablement établi un contact visuel ou émotionnel crée un fossé qui déclenche la résistance. |

4. Stratégies de Collaboration : De la Pression à la Connexion

Pour réduire la contre-volonté, l'adulte doit chercher à « augmenter la volonté » de l'enfant de collaborer par des leviers relationnels.

Le Concept de la « Bulle » et du « Velcro »

• La Bulle d'attachement : L'adulte doit inviter l'enfant à entrer dans sa « bulle » de sécurité. Lorsque l'enfant est connecté à l'adulte, il a naturellement tendance à suivre la direction de ce dernier.

• L'effet Velcro : Plutôt que d'être une « balle de ping-pong » (donner un ordre et repartir), l'adulte doit devenir « velcro » : s'approcher physiquement, s'intéresser à l'activité de l'enfant et établir un lien avant de formuler une demande.

Leviers d'Intervention Efficaces

• La Connexion avant la Consigne : Prendre quelques secondes pour saluer l'enfant, le flatter ou exprimer son plaisir de le retrouver.

• La Créativité et l'Humour : Utiliser le jeu pour contourner la résistance (ex: faire parler un jouet pour inviter au lavage des mains). La créativité est présentée comme une alternative supérieure à l'autorité pure.

• L'Empathie : Reconnaître que la volonté de l'enfant est légitime, même si elle diffère de la nôtre. L'objectif n'est pas de céder sur tout, mais d'imposer une structure dans le respect du stade développemental de l'enfant.

5. Perspectives Systémiques : Adolescence et Milieu Scolaire

La dynamique de la contre-volonté s'étend au-delà de la petite enfance et touche toutes les sphères sociales.

• Adolescence : C'est une période de contre-volonté intense.

Les interventions basées sur la déconnexion et les attentes irréalistes de soumission ne font qu'empirer la situation.

• Milieu Scolaire : Les enfants ayant les besoins relationnels les plus importants sont souvent ceux qui résistent le plus.

Le système tend malheureusement à les exclure ou à les punir (systèmes de couleurs, retrait de privilèges), ce qui rompt davantage le lien d'attachement et renforce leur comportement d'opposition.

• Vie Adulte : La contre-volonté persiste chez l'adulte.

Un employé réagira par la résistance face à un supérieur qui impose une directive sans considération pour son travail en cours ou sans politesse élémentaire.

Conclusion

La contre-volonté n'est pas un problème de comportement à éradiquer, mais un signal de besoin de connexion ou d'affirmation.

En changeant de perspective — en passant de la gestion de l'opposition à la culture de l'attachement — les éducateurs et parents favorisent le développement d'individus autonomes, critiques et capables de respecter leurs propres limites tout en collaborant avec la structure sociale.

Comprendre ce mécanisme permet de passer d'une éducation basée sur la force à une éducation basée sur la relation.

Reviewer #2 (Public review):

Summary:

This work addresses the question whether artificial deep neural network models of the brain could be improved by incorporating top-down feedback, inspired by the architecture of neocortex.

In line with known biological features of cortical top-down feedback, the authors model such feedback connections with both, a typical driving effect and a purely modulatory effect on the activation of units in the network.

To asses the functional impact of these top-down connections, they compare different architectures of feedforward and feedback connections in a model that mimics the ventral visual and auditory pathways in cortex on an audiovisual integration task.

Notably, one architecture is inspired by human anatomical data, where higher visual and auditory layers possess modulatory top-down connections to all lower-level layers of the same modality, and visual areas provide feedforward input to auditory layers, whereas auditory areas provide modulatory feedback to visual areas.

First, the authors find that this brain-like architecture imparts the models with a light visual bias similar to what is seen in human data, which is the opposite in a reversed architecture, where auditory areas provide feedforward drive to the visual areas.

Second, they find that, in their model, modulatory feedback should be complemented by a driving component to enable effective audiovisual integration, similar to what is observed in neural data.

Overall, the study shows some possible functional implications when adding feedback connections in a deep artificial neural network that mimic some functional aspects of visual perception in humans.

Strengths:

The study contains innovative ideas, such as incorporating an anatomically inspired architecture into a deep ANN, and comparing its impact on a relevant task to alternative architectures.

Moreover, the simplicity of the model allows it to draw conclusions on how features of the architecture and functional aspects of the top-down feedback affects performance of the network.

This could be a helpful resource for future studies of the impact of top-down connections in deep artificial neural network models of neocortex.

Weaknesses:

Some claims not yet supported.

The problem is that results are phrased quite generally in the abstract and discussion, while the actual results shown in the paper are very specific to certain implementations of top-down feedback and architectures. This could lead to misunderstanding and requires some revisions of the claims in the abstract and discussion (see below).

"Altogether our findings demonstrate that modulatory top-down feedback is a computationally relevant feature of biological brain..."

This claim is not supported, since no performance increase is demonstrated for modulatory feedback. So far, only the second half of the sentence is supported: "...and that incorporating it into ANNs affects their behavior and constrains the solutions it's likely to discover."

"This bias does not impair performance on the audiovisual tasks."

This is only true for the composite top-down feedback that combines driving and modulatory effects, whereas modulatory feedback alone can impair the performance (e.g., in the visual tasks VS1 and VS2). The fact that modulatory feedback alone is insufficient in ANNs to enable effective cross-modal integration and requires some driving component is actually very interesting, but it is not stressed enough in the abstract. This is hinted at in the following sentence, but should be made more explicitly:

"The results further suggest that different configurations of top-down feedback make otherwise identically connected models functionally distinct from each other, and from traditional feedforward and laterally recurrent models."

"Here we develop a deep neural network model that captures the core functional properties of top-down feedback in the neocortex" -> this is too strong, take out "the", because very likely there are other important properties that are not yet incorporated.

"Altogether, our results demonstrate that the distinction between feedforward and feedback inputs has clear computational implications, and that ANN models of the brain should therefore consider top-down feedback as an important biological feature."

This claim is still not substantiated by evidence provided in the paper. First, the wording is a bit imprecise, because mechanistically, it is not really the feedforward versus feedback (a purely feedforward model is not considered at all in the paper), but modulatory versus driving. Moreover, the second part of the sentence is problematic: The results imply that, computationally/functionally, driving connections are doing the job, while modulatory feedback does not really seem to improve performance (best case, it does not do any harm). It is true that it is a feature that is inspired by biology, but I don't see why the results imply that (modulatory) top-down feedback should be considered in ANN models of the brain. This would require to show that such models either improve performance, or do improve the ability to fit neural data, both which are beyond the scope of the paper.

The same argument holds for the following sentence, which is not supported by the results of the paper:

"More broadly, our work supports the conclusion that both the cellular neurophysiology and structure of feed-back inputs have critical functional implications that need to be considered by computational models of brain function."

Additional supplementary material required

Although the second version checked the influence of processing time, this was not done for the most important figure of the paper, Figure 4. A central claim in the abstract "This bias does not impair performance on the audiovisual tasks" relies on this figure, because only with composite feedback the performance is comparable between the the "drive-only" and "brain-like" models. Thus, the supplementary Figure 3 should also include the composite networks and drive only network to check the robustness of the claim with respect to process time. This robustness analysis should then also be mentioned in the text. For example, it should be mentioned whether results in these networks are robust or not with respect to process time, whether there are differences between network architectures or types of feedback in general etc.

Moreover, the current analysis for networks with modulatory feedback is a bit confusing. Why is the performance so low for the reverse model for a process time of 3 and 10? This is a very strong effect that warrants explanation. More details should be added in the caption as well. For example, are the models separately trained for the output after 3 and 10 processing steps for the comparison, or just evaluated at these times? Not training these networks separately might explain the low performance for some networks, so ideally networks are trained for each choice of processing steps.

Reviewer #3 (Public review):

Summary:

This study investigates the computational role of top-down feedback in artificial neural networks (ANNs), a feature that is prevalent in the brain but largely absent in standard ANN architectures. The authors construct hierarchical recurrent ANN models that incorporate key properties of top-down feedback in the neocortex. Using these models in an audiovisual integration task, they find that hierarchical structures introduce a mild visual bias, akin to that observed in human perception, not always compromising task performance.

Strengths:

The study investigates a relevant and current topic of considering top-down feedback in deep neural networks. In designing their brain-like model, they use neurophysiological data, such as externopyramidisation and hierarchical connectivity. Their brain-like model exhibits a visual bias that qualitatively matches human perception.

Weaknesses:

While the model is brain-inspired, it has limited bioplausibility. The model assumes a simplified and fixed hierarchy. The authors acknowledge this limitation in the discussion.

While the brain-like model showed an advantage in ignoring distracting auditory inputs, it struggled when visual information had to be ignored. This suggests that its rigid bias toward visual processing could make it less adaptive in tasks requiring flexible multimodal integration. It hence does not necessarily constitute an improvement over existing ANNs. The study does not evaluate whether the top-down feedback architecture scales well to more complex problems or larger datasets. A valuable future contribution would be to evaluate how the network's behaviour fits to human data.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

Here, the authors aim to investigate the potential improvements of ANNs when used to explain brain data using top-down feedback connections found in the neocortex. To do so, they use a retinotopic and tonotopic organization to model each subregion of the ventral visual (V1, V2, V4, and IT) and ventral auditory (A1, Belt, A4) regions using Convolutional Gated Recurrent Units. The top-down feedback connections are inspired by the apical tree of pyramidal neurons, modeled either with a multiplicative effect (change of gain of the activation function) or a composite effect (change of gain and threshold of the activation function).

To assess the functional impact of the top-down connections, the authors compare three architectures: a brain-like architecture derived directly from brain data analysis, a reversed architecture where all feedforward connections become feedback connections and vice versa, and a random connectivity architecture. More specifically, in the brain-like model the visual regions provide feedforward input to all auditory areas, whereas auditory areas provide feedback to visual regions.

First, the authors found that top-down feedback influences audiovisual processing and that the brain-like model exhibits a visual bias in multimodal visual and auditory tasks. Second, they discovered that in the brain-like model, the composite integration of top-down feedback, similar to that found in the neocortex, leads to an inductive bias toward visual stimuli, which is not observed in the feedforward-only model. Furthermore, the authors found that the brain-like model learns to utilize relevant stimuli more quickly while ignoring distractors. Finally, by analyzing the activations of all hidden layers (brain regions), they found that the feedforward and feedback connectivity of a region could determine its functional specializations during the given tasks.

Strengths:

The study introduces a novel methodology for designing connectivity between regions in deep learning models. The authors also employ several tasks based on audiovisual stimuli to support their conclusions. Additionally, the model utilizes backpropagation of error as a learning algorithm, making it applicable across a range of tasks, from various supervised learning scenarios to reinforcement learning agents. Conversely, the presented framework offers a valuable tool for studying top-down feedback connections in cortical models. Thus, it is a very nice study that also can give inspiration to other fields (machine learning) to start exploring new architectures.

We thank the reviewer for their accurate summary of our work and their kind assessment of its strengths.

Weaknesses:

Although the study explores some novel ideas on how to study the feedback connections of the neocortex, the data presented here are not complete in order to propose a concrete theory of the role of top-down feedback inputs in such models of the brain.

(1) The gap in the literature that the paper tries to fill in the ability of DL algorithms to predict behavior: "However, there are still significant gaps in most deep neural networks' ability to predict behavior, particularly when presented with ambiguous, challenging stimuli." and "[...] to accurately model the brain."

It is unclear to me how the presented work addresses this gap, as the only facts provided are derived from a simple categorization task that could also be solved by the feedforward-only model (see Figures 4 and 5). In my opinion, this statement is somewhat far-fetched, and there is insufficient data throughout the manuscript to support this claim.

We can see now that the way the introduction was initially written led to some confusion about our goal in this study. Our goal here was not to demonstrate that top-down feedback can enable superior matches to human behaviour. Rather, our goal was to determine if top-down feedback had any real implications for processing ambiguous stimuli. The sentence that the reviewer has highlighted was intended as an explanation for why top-down feedback, and its impact on ambiguous stimuli, might be something one would want to examine for deep neural networks. But, here, we simply wanted to (1) provide an overview of the code base we have created, (2) demonstrate that top-down feedback does impact the processing of ambiguous stimuli.

We agree with the reviewer that if our goal was to improve our ability to predict behaviour, then there was a big gap in the evidence we provided here. But, this was not our goal, and we believe that the data we provide here does convincingly show that top-down feedback has an impact on processing of ambiguous stimuli. We have updated the text in the introduction to make our goals more clear for the reader and avoid this misunderstanding of what we were trying to accomplish here. Specifically, the end of the introduction is changed to:

“To study the effect of top-down feedback on such tasks, we built a freely available code base for creating deep neural networks with an algorithmic approximation of top-down feedback. Specifically, top-down feedback was designed to modulate ongoing activity in recurrent, convolutional neural networks. We explored different architectural configurations of connectivity, including a configuration based on the human brain, where all visual areas send feedforward inputs to, and receive top-down feedback from, the auditory areas. The human brain-based model performed well on all audiovisual tasks, but displayed a unique and persistent visual bias compared to models with only driving connectivity and models with different hierarchies. This qualitatively matches the reported visual bias of humans engaged in audio-visual tasks. Our results confirm that distinct configurations of feedforward/feedback connectivity have an important functional impact on a model's behavior. Therefore, top-down feedback captures behaviors and perceptual preferences that do not manifest reliably in feedforward-only networks. Further experiments are needed to clarify whether top-down feedback helps an ANN fit better to neural data, but the results show that top-down feedback affects the processing of stimuli and is thus a relevant feature that should be considered for deep ANN models in computational neuroscience more broadly.”

(2) It is not clear what the advantages are between the brain-like model and a feedforward-only model in terms of performance in solving the task. Given Figures 4 and 5, it is evident that the feedforward-only model reaches almost the same performance as the brain-like model (when the latter uses the modulatory feedback with the composite function) on almost all tasks tested. The speed of learning is nearly the same: for some tested tasks the brain-like model learns faster, while for others it learns slower. Thus, it is hard to attribute a functional implication to the feedback connections given the presented figures and therefore the strong claims in the Discussion should be rephrased or toned down.

Again, we believe that there has been a misunderstanding regarding the goals of this study, as we are not trying to claim here that there are performance advantages conferred by top-down feedback in this case. Indeed, we share the reviewer’s assessment that the feedforward only model seems to be capable of solving this task well. To reiterate: our goal here was to demonstrate that top-down feedback alters the computations in the network and, thus, has distinct effects on behaviour that need to be considered by researchers who use deep networks to model the brain. But we make no claims of “superiority” of the brain-like model.

In-line with this, we’re not completely sure which claims in the discussion the reviewer is referring to. We note that we were quite careful in our claims. For example, in the first section of the discussion we say:

“Altogether, our results demonstrate that the distinction between feedforward and feedback inputs has clear computational implications, and that ANN models of the brain should therefore consider top-down feedback as an important biological feature.”

And later on:

“In summary, our study shows that modulatory top-down feedback and the architectural diversity enabled by it can have important functional implications for computational models of the brain. We believe that future work examining brain function with deep neural networks should therefore consider incorporating top-down modulatory feedback into model architectures when appropriate.”

If we have missed a claim in the discussion that implies superiority of the brain-like model in terms of task performance we would be happy to change it.

(3) The Methods section lacks sufficient detail. There is no explanation provided for the choice of hyperparameters nor for the structure of the networks (number of trainable parameters, number of nodes per layer, etc). Clarifying the rationale behind these decisions would enhance understanding. Moreover, since the authors draw conclusions based on the performance of the networks on specific tasks, it is unclear whether the comparisons are fair, particularly concerning the number of trainable parameters. Furthermore, it is not clear if the visual bias observed in the brain-like model is an emerging property of the network or has been created because of the asymmetries in the visual vs. auditory pathway (size of the layer, number of layers, etc).

We thank the reviewer for raising this issue, and want to provide some clarifications: First, the number of trainable parameters are roughly equal, since we were only switching the direction of connectivity (top-down versus bottom-up), not the number of connections. We confirmed the biggest difference in size is between models with composite and multiplicative feedback; models with composite feedback have roughly ~1K more parameters, and all models are within the 280K parameter range. We now state this in the methods.

Second, because superior performance was not the goal of this study, as stated above, we conducted limited hyperparameter tuning. Given the reviewer’s comment, we wondered whether this may have impacted our results. Therefore, we explored different hyperparameters for the model during the multimodal auditory tasks, which show the clearest example of the visual dominance in the brainlike model (Figure 3).

We explored different hidden state sizes, learning rates and processing times, and examined whether the core results were different. We found that extremely high learning rates (0.1) destabilize all models and that some models perform poorly under different processing times. But overall, the core results are evident across all hyperparameters where the models learn i.e the different behaviors of models with different connectivities and the visual dominance observed in the brainlike model. We now provide these results in a supplementary figure (Fig. S2, showing larger models trained with different learning rates, and Fig S3, which shows the effect of processing time on AS task performance).

Reviewer #2 (Public review):

Summary:

This work addresses the question of whether artificial deep neural network models of the brain could be improved by incorporating top-down feedback, inspired by the architecture of the neocortex.

In line with known biological features of cortical top-down feedback, the authors model such feedback connections with both, a typical driving effect and a purely modulatory effect on the activation of units in the network.

To assess the functional impact of these top-down connections, they compare different architectures of feedforward and feedback connections in a model that mimics the ventral visual and auditory pathways in the cortex on an audiovisual integration task.

Notably, one architecture is inspired by human anatomical data, where higher visual and auditory layers possess modulatory top-down connections to all lower-level layers of the same modality, and visual areas provide feedforward input to auditory layers, whereas auditory areas provide modulatory feedback to visual areas.

First, the authors find that this brain-like architecture imparts the models with a light visual bias similar to what is seen in human data, which is the opposite in a reversed architecture, where auditory areas provide a feedforward drive to the visual areas.

Second, they find that, in their model, modulatory feedback should be complemented by a driving component to enable effective audiovisual integration, similar to what is observed in neural data.

Last, they find that the brain-like architecture with modulatory feedback learns a bit faster in some audiovisual switching tasks compared to a feedforward-only model.

Overall, the study shows some possible functional implications when adding feedback connections in a deep artificial neural network that mimics some functional aspects of visual perception in humans.

Strengths:

The study contains innovative ideas, such as incorporating an anatomically inspired architecture into a deep ANN, and comparing its impact on a relevant task to alternative architectures.

Moreover, the simplicity of the model allows it to draw conclusions on how features of the architecture and functional aspects of the top-down feedback affect the performance of the network.

This could be a helpful resource for future studies of the impact of top-down connections in deep artificial neural network models of the neocortex.

We thank the reviewer for their summary and their recognition of the innovative components and helpful resources therein.

Weaknesses:

Overall, the study appears to be a bit premature, as several parts need to be worked out more to support the claims of the paper and to increase its impact.

First, the functional implication of modulatory feedback is not really clear. The "only feedforward" model (is a drive-only model meant?) attains the same performance as the composite model (with modulatory feedback) on virtually all tasks tested, it just takes a bit longer to learn for some tasks, but then is also faster at others. It even reproduces the visual bias on the audiovisual switching task. Therefore, the claims "Altogether, our results demonstrate that the distinction between feedforward and feedback inputs has clear computational implications, and that ANN models of the brain should therefore consider top-down feedback as an important biological feature." and "More broadly, our work supports the conclusion that both the cellular neurophysiology and structure of feed-back inputs have critical functional implications that need to be considered by computational models of brain function" are not sufficiently supported by the results of the study. Moreover, the latter points would require showing that this model describes neural data better, e.g., by comparing representations in the model with and without top-down feedback to recorded neural activity.

To emphasize again our specific claims, we believe that our data shows that top-down feedback has functional implications for deep neural network behaviour, not increased performance or neural alignment. Indeed, our results demonstrate that top-down feedback alters the behaviour of the networks, as shown by the differences in responses to various combinations of ambiguous stimuli. We agree with the reviewer that if our goal was to claim either superior performance on these tasks, or better fit to neural data, we would need to actually provide data supporting that claim.

Given the comments from the reviewer, we have tried to provide more clarity in the introduction and discussion regarding our claims. In particular, we now highlight that we are not trying to demonstrate that the models with top-down feedback exhibit superior performance or better fit to neural data.

As one final note, yes, the reviewer understood correctly that the “only feedforward” model is a model with only driving inputs. We have renamed the feedforward-only models to drive only models and added additional emphasis in the text to ensure that the distinction is clear for all readers.

Second, the analyses are not supported by supplementary material, hence it is difficult to evaluate parts of the claims. For example, it would be helpful to investigate the impact of the process time after which the output is taken for evaluation of the model. This is especially important because in recurrent and feedback models the convergence should be checked, and if the network does not converge, then it should be discussed why at which point in time the network is evaluated.

This is an excellent point, and we thank the reviewer for raising it. We allowed the network to process the stimuli for seven time-steps, which was enough for information from any one region to be transmitted to any other. We found in some initial investigations that if we shortened the processing time some seeds would fail to solve the task. But, based on the reviewer’s comment, we have now also run additional tests with longer processing times for the auditory tasks where we see the clearest visual bias (Figure 3). We find that different process times do not change the behavioral biases observed in our models, but may introduce difficulties ignoring visual stimuli for some models. Thus, while process time is an important hyperparameter for optimal performance of the model, the central claim of the paper remains. We include this new data in a supplementary figure S3.

Third, the descriptions of the models in the methods are hard to understand, i.e., parameters are not described and equations are explained by referring to multiple other studies. Since the implications of the results heavily rely on the model, a more detailed description of the model seems necessary.

We agree with the reviewer that the methods could have been more thorough. Therefore, we have greatly expanded the methods section. We hope the model details are now more clear.

Lastly, the discussion and testable predictions are not very well worked out and need more details. For example, the point "This represents another testable prediction flowing from our study, which could be studied in humans by examining the optical flow (Pines et al., 2023) between auditory and visual regions during an audiovisual task" needs to be made more precise to be useful as a prediction. What did the model predict in terms of "optic flow", how can modulatory from simple driving effect be distinguished, etc.

We see that the original wording of this prediction was ambiguous, thank you for pointing this out. In the study highlighted (Pines et al., 2023) the authors use an analysis technique for measuring information flow between brain regions, which is related to analysis of optical flow in images, but applied to fMRI scans. This is confusing given the current study, though. Therefore, we have changed this sentence to make clear that we are speaking of information flow here. 

Reviewer #3 (Public review):

Summary:

This study investigates the computational role of top-down feedback in artificial neural networks (ANNs), a feature that is prevalent in the brain but largely absent in standard ANN architectures. The authors construct hierarchical recurrent ANN models that incorporate key properties of top-down feedback in the neocortex. Using these models in an audiovisual integration task, they find that hierarchical structures introduce a mild visual bias, akin to that observed in human perception, not always compromising task performance.

Strengths:

The study investigates a relevant and current topic of considering top-down feedback in deep neural networks. In designing their brain-like model, they use neurophysiological data, such as externopyramidisation and hierarchical connectivity. Their brain-like model exhibits a visual bias that qualitatively matches human perception.

We thank the reviewer for their summary and evaluation of our paper’s strengths.

Weaknesses:

While the model is brain-inspired, it has limited bioplausibility. The model assumes a simplified and fixed hierarchy. In the brain with additional neuromodulation, the hierarchy could be more flexible and more task-dependent.

We agree, there are still many facets of top-down feedback that we have not captured here, and the modulation of hierarchy is an interesting example. We have added some consideration of this point to the limitations section of the discussion.

While the brain-like model showed an advantage in ignoring distracting auditory inputs, it struggled when visual information had to be ignored. This suggests that its rigid bias toward visual processing could make it less adaptive in tasks requiring flexible multimodal integration. It hence does not necessarily constitute an improvement over existing ANNs. It is unclear, whether this aspect of the model also matches human data. In general, there is no direct comparison to human data. The study does not evaluate whether the top-down feedback architecture scales well to more complex problems or larger datasets. The model is not well enough specified in the methods and some definitions are missing.

We agree with the reviewer that we have not demonstrated anything like superior performance (since the brain-like network is quite rigid, as noted) nor have we shown better match to human data with the brain-like network. This was not our intended claim. Rather, we demonstrated here simply that top-down feedback impacts behavior of the networks in response to ambiguous stimuli. We have now added statements to the introduction and discussion to make our specific claims (which are supported by our data, we believe) clear.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

I believe that the work is very nice but not so mature at this stage. Below, you can find some comments that eventually could improve your manuscript.

(1) Intro, last sentence: "Therefore, top-down feedback is a relevant feature that should be considered for deep ANN models in computational neuroscience more broadly." I don't understand what the authors refer to with this sentence. There are numerous models (deep ANNs) that have been used to model the neural activity and are much simpler than the one proposed here which contains very complex models and connectivity. Although I do agree that the top-down connections are very important there is no data to support their importance for modeling the brain.

Respectfully, we disagree with the reviewer that we don’t provide data to demonstrate the importance of top-down feedback for modelling. Indeed, we provided a great deal of data to show that top-down feedback in the networks has real functional implications for behaviour, e.g., it can induce a human-like visual bias. Thus, top-down feedback is a factor that one should care about when modelling the brain. But, we agree with the reviewer that more demonstration of the utility of using top-down feedback for achieving better fits to neural data would be an important next step. 

(2) I suggest adding some extra supplementary simulations where, for example, the number of data for visual and auditory pathways is equal in size (i.e., the same number of examples), the number of layers is identical (3 per pathway), and also the number of parameters. Doing this would help strengthen the claims presented in the paper.

In fact, all of the hyperparameters the reviewer mentions here were identical for the different networks, so the experiments the reviewer is requesting here were already part of the paper. We now clarify this in the text.

(3) Results: I suggest adding Tables with quantifications of the presented results. For example, best performance, epochs to converge, etc. As it is now, it is very hard to follow the evidence shown in Figures.

This is a good suggestion, we have now added this table to the start of the supplemental figures.

(4) Figure 2e, 3e: Although VS3, and AS3 have been used only for testing, the plot shows alignments with respect to training epochs. The authors should clarify in the Methods if they tested the network with all intermediate weights during VS1/VS2 or AS1/AS2 training.

Testing scenarios in this context meant that the model was never shown the scenario/task during training, but the models were indeed evaluated on the VS3 and AS3 after each training epoch. We have added clarifications to the figure legends.

(5) Methods: It would be beneficial to discuss how specific hyperparameters were selected based on prior research, empirical testing, or theoretical considerations. Also, it is not clear how the alignment (visual or audio) is calculated. Do the authors use the examples that have been classified correctly for both stimuli or do they exclude those from the analysis (maybe I have missed it).

As noted above, because superior performance was not the goal of this study, we conducted limited hyperparameter tuning. But we have extended the results with additional hyperparameter tuning in a supplementary figure, and describe the hyperparameter choices more thoroughly in the methods. As well, all data includes all model responses, regardless of whether they were correct or not. We now clarify this in the methods.

(6) Code: The code repository lacks straightforward examples demonstrating how to utilize the modeling approach. Given that it is referred to as a "framework", one would expect it to facilitate easy integration into various models and tasks. Including detailed instructions or clear examples would significantly improve usability and help users effectively apply the proposed methodology.

We agree with the reviewer, this would be beneficial. We have revised the README of the codebase to explain the model and its usage more clearly and included an interactive jupyter notebook with example training on MNIST.

Some minor comments are given below. Generally speaking, the Figures need to be more carefully checked for consistent labels, colors, etc.

(1) Page 4, 1st paragraph - grammar correction: "a larger infragranular layer" or "larger infragranular layers"

Thank you for catching this, we have fixed the text.

(2) Page 4, 2nd para - rephrase: "In three additional control ANNs" → "In the third additional control ANN"

In fact, we did mean three additional control ANNs, each one representing a different randomized connectivity profile. We now clarify this in the text and provide the connectivity of the two other random graphs in the supplemental figures.

(3) Page 4, VAE acronym needs to be defined before its first use

The variational autoencoder is introduced by its full name in the text now.

(4) Page 4: Fig. 2c reference should be Fig. 2b, Fig. 2d should be Fig. 2c, Fig. 2b should be Fig. 2d, VS4; Fig. 2b, bottom should be VS4; Fig. 2f, Fig. 2f to Fig. 2g. Double check the Figure references in the text. Here is very confusing for the reader.

We have now fixed this, thank you for catching it.

(5) Page 5, 1st para: "Altogether, our results demonstrated both" → "Altogether, our results demonstrated that both"

This has been updated.

(6) Figure 2: In the e and g panels the x label is missing.

This was actually because the x-axis were the same across the panels, but we see how this was unclear, so we have updated the figure.

(7) Figure 3: There is no panel g (the title is missing); In panels b, c, e, and g the y label is missing, and in panels e and g the x label is missing. Also, the Feedforward model is shown in panel g but it is introduced later in the text. Please remove it from Figure 3. Also in legend: "AV Reverse graph" → "Reverse graph". Also, "Accuracy" and "Alignment" should be presented as percentages (as in Figure 2).

This has been corrected.

(8) Figure 4; x labels are missing.

As with point (6), this was actually because the x-axis were the same across the panels, but we see how this was unclear, so we have updated the figure.

(9) Page 7; I can’t find the cited Figure S1.

Apologies, we have added the supplemental figure (now as S4). It shows the results of models with multiplicative feedback on the task in Fig 5 (as opposed to models with composite feedback shown in the main figure).

Reviewer #2 (Recommendations for the authors):

(1) Discussion Section 3.1 is only a literature review, and does not really add any value.

Respectfully, we think it is important to relate our work to other computational work on the role of top-down feedback, and to make clear what our specific contribution is. But, we have updated the text to try to place additional emphasis on our study’s contribution, so that this section is more than just a literature review.

“Our study adds to this previous work by incorporating modulatory top-down feedback into deep, convolutional, recurrent networks that can be matched to real brain anatomy. Importantly, using this framework we could demonstrate that the specific architecture of top-down feedback in a neural network has important computational implications, endowing networks with different inductive biases.”

(2) Including ipython notebooks and some examples would be great to make it easier to use the code.

We now provide a demo of how to use the code base in a jupyter notebook.

(3) The description of the model is hard to comprehend. Please name and describe all parameters. Also, a figure would be great to understand the different model equations.

We have added definitions of all model terms and parameters.

(4) The terminology is not really clear to me. For example "The results further suggest that different configurations of top-down feedback make otherwise identically connected models functionally distinct from each other and from traditional feedforward only recurrent models." The feedforward and only recurrent seem to contradict each other. Would maybe driving and modulatory be a better term here? I also saw in the code that you differentiate between three types of inputs, modulatory, threshold offset and basal (like feedforward). How about you only classify connections based on these three type? I was also confused about the feedforward only model, because I was unsure whether it is still feedback connections but with "basal" quality, or whether feedback connections between modalities and higher-to-lower level layers were omitted altogether.

We take the reviewer’s point here. To clarify this, we have updated the text to refer to “driving only” rather than “feedforward only”, to make it obvious that what we change in these models is simply whether the connection has any modulatory impact on the activity. 

(5) "incorporating it into ANNs can affect their behavior and help determine the solutions that the network can discover." -> Do you mean constrain? Overall, I did not really get this point.

Yes, we mean that it constrains the solutions that the network is likely to discover.

(6) "ignore the auditory inputs when they visual inputs were unambiguous" -> the not they

This has been fixed. Thank you for catching it.

(7) xlabel in Figure 4 is missing.

This has been fixed, thank you for catching it.

Reviewer #3 (Recommendations for the authors):

Major:

(1) How alignment is computed is not defined. In addition to a proper definition in the methods section, it would be nice to briefly define it when it first appears in the results section.

We’ve added an explicit definition of how alignment is calculated in the methods and emphasized the calculation when its first explained in the results

(2) A connectivity matrix for the feedforward-only model is missing and could be added.

We have added this to Figure 1.

(3) The connectivity matrix for each random model should also be shown.

We’ve shown each of the random model configurations in the new supplemental figure S1.

(4) Initial parameters are not defined, such as W, b etc. A table with all model parameters would be great.

We have added a table to the methods listing all of the parameters.

(5) Would be nice to show the t-sne plots (not just the NH score) for each model and each task in the appendix.

We can provide these figures on request. They massively increase the file size of the paper pdf, as there’s 49 of them for each task and each model, 980 in total. An example t-SNE plot is provided in figure 6.

Minor:

(1) Page 4:

"we refer to this as Visual-dominant Stimulus case 1, or VS1; Fig. 1a, top)." This should be Fig. 2a.

(2) "In stimulus condition VS1, all of the models were able to learn to use the auditory clues to disambiguate the images (Fig. 2c)."

This should be Fig. 2b.

(3) "In comparison, in VS2, we found that the brainlike model learned to ignore distracting audio inputs quickly and consistently compared to the random models, and a bit more rapidly than the auditory information (Fig 2d)."

This should be Fig. 2c.

(4) "VS3; Fig. 2b, top"

This should be Fig. 2d

(5) "while all other models had to learn to do so further along in training (Fig. 2e)."

It is not stated explicitly, but this suggests that the image-aligned target was considered correct, and that weight updates were happening.

(6) "VS4; Fig. 2b, bottom"

This should be Fig. 2f

(7) "adept at learning (Fig. 2f)."

This should be Fig. 2g

(8) Figure 3:b,c,e y-labels are missing

3f: both x and y labels are missing

(9) Figure labeling in the text is not consistent (Fig. 1A versus Fig. 2a)

(10) Doubled "the" in ""This shows that the inductive bias towards vision in the brainlike model depended on the presence of the multiplicative component of the the feedback"

(11) Page 9 Figure 6: The caption says b shows the latent spaces for the VS2 task, whereas the main text refers to 6b as showing the latent space for the AS2 task. Please correct which task it is.

(12) Methods 4.1 page 13

"which is derived from the feedback input (h_{l−1})"

This should be h_{l+1}

(13) r_l, u_l, u and c are not defined to which aspects of the model they refer to

Even though this is based on a previous model, the methods section should completely describe the model.

Equations 1,2,3: the notation [x;y] is unclear and should be defined.

Equation 5: u should probably be u_l.

(14) Page 14 typo: externopyrmidisation.

(15) It is confusing to use different names for the same thing: the all-feedforward model, the all feedforward network, the feedforward network, and the feedforward-only model are probably all the same? Consistent naming would help here.

Thank you for the detailed comments! We’ve fixed the minor errors and renamed the feedforward models to drive-only models.

NRSVUE says "God Almighty" and the footnote says "Traditional rendering of Heb El Shaddai"

Reviewer #1 (Public review):

Summary:

Jeay-Bizot and colleagues investigate the neural correlates of the preparation of, and commitment to, a self-initiated motor action. In their introduction, they differentiate between theoretical proposals relating to the timing of such neural correlates relative to the time of a recorded motor action (e.g., a keypress). These are categorised into 'early' and 'late' timing accounts. The authors advocate for 'late' accounts based on several arguments that align well with contemporary models of decision-making in other domains (for example, evidence accumulation models applied to perceptual decisions). They also clearly describe prevalent methodological issues related to the measurement of event-related potentials (ERPs) and time-frequency power to gauge the timing of the commitment to making a motor action. These methodological insights are communicated clearly and denote potentially important limitations on the inferences that can be drawn from a large body of existing work.

To attempt to account for such methodological concerns, the authors devise an innovative experiment that includes an experimental condition whereby participants make a motor action (a right-hand keypress) to make an image disappear. They also include a condition whereby the stimulus presentation program automatically proceeds at a set time that is matched to the response timing in a previous trial. In this latter condition, no motor action is required by the participant. The authors then attempt to determine the times at which they can differentiate between these two conditions (motor action vs no motor action) based on EEG and MEG data, using event-related potential analyses, time-frequency analyses, and multivariate classifiers. They also apply analysis techniques based on comparing M/EEG amplitudes at different time windows (as used in previous work) to compare these results to those of their key analyses.

When using multivariate classifiers to discriminate between conditions, they observed very high classification performance at around -100ms from the time of the motor response or computer-initiated image transition, but lower classification performance and a lack of statistically significant effects across analyses for earlier time points. Based on this, they make the key claim that measured M/EEG responses at the earlier time points (i.e., earlier than around -100ms from the motor action) do not reliably correlate with the execution of a motor action (as opposed to no such action being prepared or made). This is argued to favour 'late' accounts of motor action commitment, aligning with the well-made theoretical arguments in favour of these accounts in the introduction. Although the exact time window related to 'late' accounts is not concretely specified, an effect that occurs around -100ms from response onset is assumed here to fall within that window.

Importantly, this claim relies on accepting the null hypothesis of zero effect for the time points preceding around -100ms based on a somewhat small sample of n=15 and some additional analyses of individual participant datasets. Although the authors argue that their classifiers are sensitive to detecting relevant effects, and the study appears well-powered to detect the (likely to be large magnitude) M/EEG signal differences occurring around the time of the response or computer-initiated image transition, there is no guarantee that the study is adequately sensitive to detect earlier differences in M/EEG signals. These earlier effects are likely to be more subtle and exhibit lower signal-to-noise ratios, but would still be relevant to the 'early' vs 'late' debate framed in the manuscript. This, along with some observed patterns in the data, may substantially reduce the confidence one may have in the key claim about the onset timing of M/EEG signal differences.

Notably, there is some indication of above-chance (above 0.5 AUC) classification performance at time points earlier than -100ms from the response, as visible in Figure 3A for the task-based EEG analyses (EEG OC dataset, blue line). While this was not statistically significantly above chance for their n=15 sample, these results do not appear to be clear evidence in favour of a zero-effect null-hypothesis. In Figures 2A-B, there are also visible differences in the ERPs across conditions, from around the time that motor action-related components have been previously observed (around -500ms from the response). The plotted standard errors in the data are large enough to indicate that the study may not have been adequately powered to differentiate between the conditions.

Although the authors acknowledge this limitation in the discussion section of their manuscript, their counter-argument is that the classifiers could reliably differentiate between conditions at time points very close to the motor response, and in the time-based analyses where substantive confounds are likely to be present, as demonstrated in a set of analyses. Based on this data, the authors imply that the study is sufficiently powered to detect effects across the range of time points used in the analyses. While it's commendable that these extra analyses were run, they do not provide convincing evidence that the study is necessarily sensitive to detecting more subtle effects that may occur at earlier time points. In other words, the ability of classifiers (or other analysis methods) to detect what are likely to be very prominent, large effects around the time of the motor response does not guarantee that such analyses will detect smaller magnitude effects at other time points.

In summary, the authors develop some very important lines of argument for why existing work may have misestimated the timing of neural signals that precede motor actions. This in itself is an important contribution to the field. However, their attempt to better estimate the timing of such signals is limited by a reliance on accepting the null hypothesis based on non-statistically significant results, and arguably a limited degree of sensitivity to detect subtle but meaningful effects.

Strengths:

This manuscript provides compelling reasons why existing studies may have misestimated the timing of the neural correlates of motor action preparation and execution. They provide additional analyses as evidence of the relevant confounds and provide simulations to back up their claims. This will be important to consider for many in the field. They also endeavoured to collect large numbers of trials per participant to also examine effects in individuals, which is commendable and arguably better aligned with contemporary theory (which pertains to how individuals make decisions to act, rather than groups of people).

The innovative control condition in their experiment may also be very useful for providing complementary evidence that can better characterise the neural correlates of motor action preparation and commitment. The method for matching image durations across active and passive conditions is particularly well thought-out and provides a nice control for a range of potential confounding factors.

Weaknesses:

There is a mismatch between the stated theoretical phenomenon of interest (commitment to making a motor action) and what is actually tested in the study (differences in neural responses when an action is prepared and made compared to when no action is required). The assumed link between these concepts could be made more explicit for readers, particularly because it is argued in the manuscript that neural correlates of motor action preparation are not necessarily correlates of motor action commitment.

As mentioned in the summary, the main issue is the strong reliance on accepting the null hypothesis of no differences between motor action and computer initiation conditions based on a lack of statistically significant results from the modest (n=15) sample. Although a larger sample will increase measurement precision at the group level, there are some EEG data processing changes that could increase the signal-to-noise ratio of the analysed data and produce more precise estimates of effects, which may improve the ability to detect more subtle effects, or at least provide more confidence in the claims of null effects.

First, it is stated in the EEG acquisition and preprocessing section that the 64-channel Biosemi EEG data were recorded with a common average reference applied. Unless some non-standard acquisition software was used (of which we are not aware exists), Biosemi systems do not actually apply this reference at recording (it is for display purposes only, but often mistaken to be the actual reference applied). As stated in the Biosemi online documentation, a reference should be subsequently applied offline; otherwise, there is a substantial decrease in the signal-to-noise ratio of the EEG data, and a large portion of ambient alternating current noise is retained in the recordings. This can be easily fixed by applying a referencing scheme (e.g., the common average reference) offline as one of the first steps of data processing. If this was, in fact, done offline, it should be clearly communicated in the manuscript.

In addition, the data is downsampled using a non-integer divisor of the original sampling rate (a 2,048 Hz dataset is downsampled to 500 Hz rather than 512 Hz). Downsampling using a non-integer divisor is not recommended and can lead to substantial artefacts in raw data as a result, as personally observed by this Reviewer in Biosemi data. Finally, although a 30 Hz low-pass filter is applied for visualisation purposes of ERPs, no such filter is applied prior to analyses, and no method is used to account for alternating current noise that is likely to be in the data. As noted above, much of the alternating current noise will be retained when an offline reference is not applied, and this is likely to further degrade the quality of the data and reduce one's ability to identify subtle patterns in EEG signals. Changes in data processing to address these issues would likely lead to more precise estimates of EEG signals (and by extension differences across conditions).

With regard to possible effects extending hundreds of milliseconds before the response, it would be helpful for the authors to more precisely clarify the time windows associated with 'early' and 'late' theories in this case. The EEG data that would be required to support 'early' theories is also not made sufficiently clear. For example, even quite early neural correlates of motor actions in this task (e.g., around -500ms from the response, or earlier) could still be taken as evidence for the 'late' theories if these correlates simply reflect the accumulation of evidence toward making a decision and associated motor action, as implied by the Leaky Stochastic Accumulator model described by the authors. In other words, even observations of neural correlates of motor action preparation that occur much earlier than the response would not constitute clear evidence against the 'late' account if this neural activity represents an antecedent to a decision and action (rather than commitment to the action), as the authors point out in the introduction.

In addition, there is some discrepancy regarding the data that is used by the classifiers to differentiate between the conditions in the EEG data and the claims about the timing of neural responses that differentiate between conditions. Unless we reviewers are mistaken, the Sliding Window section of the methods states that the AUC scores in Figure 3 are based on windows of EEG data that extend from the plotted time point until 0.5 seconds into the past. In other words, an AUC value at -100ms from the response is based on classifiers applied to data ranging from -600 to -100 milliseconds relative to the response. In this case, the range of data used by the classifiers extends much earlier than the time points indicated by Figure 3, and it is difficult to know whether the data at these earlier time points may have contributed (even in subtle ways) to the success of the classifiers. This may undermine the claim that neural responses only become differentiable from around -100ms from response onset. The spans of these windows used for classification could be made more explicit in Figure 3, and classification windows that are narrower could be included in a subset of analyses to ensure that classifiers only using data in a narrow window around the response show the high degree of classification performance in the dataset. If we are mistaken, then perhaps these details could be clarified in the method and results sections.

Reviewer #1 (Public review):

Summary:

This study reports the effects of psilocin on iPSC-derived human cortical neurons.

Strengths:

The characterization was comprehensive, involving immunohistochemistry of various markers, 5-HT2A receptors, BDNF, and TrkB, transcriptomics analyses, morphological determination, electrophysiology, and finally synaptic protein measurements. The results are in close agreement with prior work (PMID 29898390) on rat cultured cortical neurons. Nevertheless, there is value in confirming those earlier findings and furthermore to demonstrate the effects in human neurons, which are important for translation. The genetic, proteomics, and cell structure analyses used in this paper are its major strength. The study supports the value of using iPSC-derived human cortical neurons for drug development involving psychedelics-related compounds.

Weaknesses:

(1) Line 140: 5-HT2A receptor expression was found via immunocytochemistry to reside in the somatodendritic and axonal compartments. However, prior work from ex vivo tissue using electron microscopy has found predominantly 5-HT2A receptor expression in the somatodendritic compartment (PMID: 12535944). Was this antibody validated to be 5-HT2A receptor-specific? Can the authors reason why the discrepancy may arise, and if the axonal expression is specific to the cultured neurons?

(2) Line 143: It would be helpful to specify the dose of psilocin tested, and describe how this dose was chosen.

(3) Figure 1: The interpretation is that the differential internalization in the axonal and somatodendritic compartments is time-dependent. However, given that only one dose is tested, it is also possible that this reflects dose dependence, with the longer time exposure leading to higher dose exposure, so these variables are related. That is, if a higher dose is given, internalization may also be observed after 10 minutes in the dendritic compartment.

(4) Figure 3 & 4: What is the 'control' here? A more appropriate control for the 24 hours after psilocin application would be 24 hours after vehicle application. Here the authors are looking at before and after, but the factor of time elapsed and perturbation via application is not controlled for.

(5) The sample size was not clearly described. In the figure legend, N = the number of neurites is provided, but it is unclear how many cells have been analyzed, and then how many of those cells belong to the same culture. These are important sample size information that should be provided. Relatedly, statistical analyses should consider that the neurites from the same cells are not independent. If the neurites indeed come from the same cells, then the sample size is much smaller and a statistical analysis considering the nested nature of the data should be used.

Comments on revisions:

The authors performed substantial experiments to check validity of the HTR2A antibody for the revision. Briefly, they found that western blot shows a single band, abolished by a blocking peptide, in neural progenitors and iPSC-derived neurons, suggesting positive results. However, they also detected immunofluorescence signals in HEK293 and HeLa cells, which do not express 5-HT2A receptors as scRNAseq analysis of these cells show complete absence of the transcript. Therefore the antibody has epitope-selective binding but also has some non-specific binding, precluding its use. The authors rightfully removed the data related to the antibody in the revised manuscript. The account is repeated here to highlight to anyone who may find the information helpful. Overall, the additional results added rigor to the study.

Author response:

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

Reviewer #1:

Comment 1: 5-HT2A Antibody Specificity

Was this antibody validated to be 5-HT2A receptor-specific? Can the authors reason why the discrepancy may arise, and if the axonal expression is specific to the cultured neurons?

We performed extensive validation of the anti-5-HT2A receptor antibody (Alomone #ASR-033), which is summarized in the accompanying Author response images:

Positive findings (Author response image 1c-e, Author response image 2a): (1) Western blot showed a single band at the expected molecular weight (~50 kDa) in neural progenitors and iPSCderived neurons. (2) The blocking peptide (#BLP-SR033) abolished Western blot bands and markedly reduced immunofluorescence signals in neurons, confirming epitope-specific binding.

Negative findings (Author response image 1a-b, Author response image 2a-b, Author response image 3): (1) We detected positive immunofluorescence signals in HEK293 and HeLa cells (Author response image 1a-b), which do not express 5-HT2AR. (2) Western blot also showed bands in HEK293 and HeLa cells (Author response image 2a-b). (3) Single-cell RNA-seq analysis of HEK293T cells confirmed complete absence of HTR2A expression (Author response image 3a). (4) qPCR showed no detectable HTR2A transcripts in iPSCs or HeLa cells (Ct > 36), while neural progenitors and neurons showed clear expression (Author response image 3b). (5) siRNA knockdown experiments failed to produce a corresponding decrease in immunofluorescence or Western blot signals, despite reduced HTR2A transcript levels (data not shown).

BLAST analysis: Protein BLAST analysis of the 13-amino acid immunogenic peptide sequence identified the human 5-HT2A receptor as the top hit (9/13 amino acids overlap). However, shorter sequence similarities were also found with other proteins, including APPBP1 (6/9 amino acids), Immunoglobulin Heavy Chain (6/7 amino acids), and Interleukin31 receptor (6/8 amino acids). While these partial homologies do not provide a definitive mechanistic explanation for the observed off-target binding, they illustrate that the epitope sequence is not entirely unique to the 5-HT2A receptor.

Conclusion: While our validation confirmed epitope-specific binding (blocking peptide effective in neurons), the antibody clearly detects something in cells that demonstrably lack HTR2A gene expression. This indicates off-target binding to other proteins sharing the epitope sequence. We have therefore removed all antibody-based 5-HT2A receptor experiments from the revised manuscript. This includes the receptor internalization data from Figure 1. The remaining findings (BDNF upregulation, gene expression changes, morphological effects, electrophysiology) are supported by independent methods including pharmacological blockade with ketanserin.

Comment 2: Psilocin Dose Selection

It would be helpful to specify the dose of psilocin tested, and describe how this dose was chosen.

We used 10 µM psilocin based on: (1) The seminal study by Ly et al. (2018), which demonstrated neuroplasticity effects at this concentration in rat cortical neurons. (2) Our own dose-response experiments (Figure S2B) showing maximal BDNF increase at 10 µM compared to lower concentrations (10 nM, 100 nM, 1 µM). We have clarified this in the revised Methods section.

Comment 3: Dose vs. Time Dependence

Given that only one dose is tested, it is also possible that this reflects dose dependence, with the longer time exposure leading to higher dose exposure.

We agree that dose dependence cannot be excluded with our current experimental design. This point is now moot as we have removed the 5-HT2A receptor internalization experiments from the manuscript. Future studies in our group will address dose-dependent effects on other readouts.

Comment 4: Control Conditions

What is the 'control' here? A more appropriate control would be 24 hours after vehicle application.

The control condition is indeed a vehicle (DMSO) control collected at the same time point as the experimental condition (i.e., 24 hrs post-treatment). We have clarified this in the revised figure legends and Methods section to avoid confusion.

Comment 5: Sample Size Description

The sample size was not clearly described. Statistical analyses should consider that neurites from the same cells are not independent.

We have expanded the sample size descriptions in the figure legends. Analyses were performed using 5-10 microscope images per condition, with 15 ROIs per image, across at least two independent differentiations from two genetic backgrounds. Regarding independence: each neurite segment exists within a distinct microenvironment and can be considered an independent measurement unit, consistent with established practices in the field (Paul et al., 2021, CNS Neurosci Ther). We acknowledge this increases statistical power and have noted this in the Methods.

Reviewer #2:

Comment 1: 5-HT2A Antibody Validation

Without validation (using for example knockdown techniques to decrease expression of 5HT2A), the experiments using this antibody should be excluded from the manuscript.

We agree with this assessment. As detailed in our response to Reviewer 1 (Comment 1) and documented in the Response to Reviewer Figure, our extensive validation attempts—including siRNA knockdown—could not conclusively demonstrate antibody specificity. We have removed all antibody-based 5-HT2A receptor experiments from the revised manuscript.

Comment 2: Serotonin in Cell Media

Did the authors evaluate whether 5-HT is present in the cell media?

The cell culture media used in our experiments does not contain serotonin. We have explicitly stated this in the revised Methods section.

Comment 3: Statistical Analysis of Figure S1F

Some of the datasets are not statistically analyzed, such as Figure S1F.

Figure S1F related to the 5-HT2A receptor experiments and has been removed from the revised manuscript along with the associated data.

Comment 4: Translational Validity of Prolonged Exposure

The authors continuously exposed cells to psilocin for hours or days. Since this is not the model of what occurs in vivo, the findings lack translational validity.

We acknowledge this limitation. Most experiments (BDNF, gene expression, branching) were conducted 24–48 hrs after a brief 10-minute exposure, which better reflects the in vivo situation. Prolonged exposures (96 hrs) were used specifically for synaptogenesis experiments based on literature showing that repeated LSD administration enhances spine density (Inserra et al., 2022; De Gregorio et al., 2022). Our in vitro system lacks metabolizing enzymes and glial cells, which may introduce temporal biases. We have added a discussion of these limitations in the revised manuscript.

Comment 5: Ketanserin Effect on BDNF

In Figure 2E, ketanserin by itself seems to reduce BDNF density. How do the authors conclude that ketanserin blocks psi-induced effects?

We identified that one cell line (Ctrl 1) with inherently higher BDNF density was inadvertently excluded from the ketanserin-only condition. After removing Ctrl 1 from all conditions and reanalyzing, the difference between Ctrl and Ket alone is no longer significant. The significant difference between Psi+Ket and Ket alone demonstrate that psilocin exerts effects that ketanserin can block, consistent with 5-HT2A receptor mediation. The revised figure and statistical analysis are included in the updated manuscript.

Comment 6: mCherry Localization mCherry (Fig 4A) seems to be retained in the nucleus.

The CamKII promoter drives expression of cytoplasmic mCherry, which fills the entire neuron including soma, dendrites, and axons. The apparent nuclear signal reflects mCherry accumulation in the soma, which surrounds the nucleus. The images clearly show mCherry extending into neurites, which was essential for our Sholl analysis of neuronal complexity.

Comment 7: Reference 36

Reference 36 is a review article that does not mention psilocin.

Our statement refers broadly to serotonergic psychedelics increasing neurotrophic factors. Reference 36 (Colaço et al., 2020) examines ayahuasca, which contains the serotonergic psychedelic DMT. We have revised the text to clarify this point.

Summary of Major Revisions

(1) Removed all 5-HT2A receptor antibody-based experiments from Figure 1 and supplementary figures due to inconclusive specificity validation. An Author response image documenting our validation attempts is provided.

(2) Clarified control conditions (vehicle controls at matched time points) in figure legends.

(3) Expanded sample size descriptions in Methods and figure legends.

(4) Re-analyzed ketanserin experiments with consistent cell line inclusion.

(5) Added discussion of translational limitations.

(6) Added new Figure S5 summarizing proposed signaling pathways.

(7) Expanded discussion on the relevance of iPSC-derived neurons for drug development.

Author response image 1.

Immunostaining for 5-HT2A receptor across cell types and peptide-blocking control. (a) HEK293 cells display a positive immunofluorescent signal despite not endogenously expressing 5-HT2AR, indicating nonspecific antibody reactivity. (b) HeLa cells also exhibit a positive signal despite lacking endogenous 5-HT2AR expression, further demonstrating nonspecific antibody binding in non-expressing cell types. (c) Neural progenitor cells show clear positive 5-HT2AR staining. (d) iPSC-derived neurons exhibit robust and well-defined 5-HT2AR staining. (e) Application of the Alomone 5-HT2AR blocking peptide (#BLP-SR033) markedly reduces neuronal signal intensity, supporting epitope-specific binding.

Author response image 2.

Western blot analysis of 5-HT2A receptor abundance and peptide-blocking control. (a-b) In line with the immunofluorescence a single band is detected in iPSCs, HEK cells, neural progenitors, iPSC-derived neurons and (b) HeLa cells. (a) Preincubation of the primary antibody with the corresponding blocking peptide abolishes this band across all samples, consistent with specific binding of the antibody to its intended epitope.

Author response image 3.

Lack of detectable 5-HT2AR expression in HEK and HeLa cells. (a) Analysis of a human-only HEK293T single-cell RNA-seq dataset (10x Genomics; https://www.10xgenomics.com/datasets/293-t-cells-1-standard-1-1-0, accessed 2025-11-25) shows no meaningful HTR2A expression, whereas other genes such as GAPDH, TP53, MYC, and ACTB are robustly detected. Consistently, evaluation of a “Barnyard” dataset - an equal mixture of human HEK293T and mouse NIH3T3 cells (10x Genomics; https://www.10xgenomics.com/datasets/20-k-1-1mixture-of-human-hek-293-t-and-mouse-nih-3-t-3-cells-3-ht-v-3-1-3-1-high-6-1-0, accessed 2025-1125) reveals only ~4 of ~10,000 droplets with minimal HTR2A signal, confirming the absence of meaningful expression.(b) (b) qPCR analysis further demonstrates no detectable HTR2A transcripts in iPSCs or HeLa cells (Ct > 36), while neural progenitors and iPSC-derived cortical neurons show expression when normalized to housekeeping genes GAPDH and TBP.

Reviewer #1 (Public review):

Summary:

This manuscript presents findings on the adaptation mechanisms of Saccharomyces cerevisiae under extreme stress conditions. The authors try to generalize this to adaptation to stress tolerance. A major finding is that S. cerevisiae evolves a quiescence-like state with high trehalose to adapt to freeze-thaw tolerance independent of their genetic background. The manuscript is comprehensive, and each of the conclusions is well supported by careful experiments.

Strengths:

This is excellent interdisciplinary work.

I have commented on the response of the authors, in-line, below. This is to maintain the conversation thread with the authors.

Comment 1:

Earlier papers have shown that loss of ribosomal proteins, that slow growth, leads to better stress tolerance in S. cerevisiae. Given this, isn't it expected that any adaptation that slows down growth would, overall, increase stress tolerance? Even for other systems, it has been shown that slowing down growth (by spore formation in yeast or bacteria/or dauer formation in C. elegans) is an effective strategy to combat stress and hence is a likely route to adaptation. The authors stress this as one of the primary findings. I would like the authors to explain their position, detailing how their findings are unexpected in the context of the literature.

Response:

We agree that the link between slower growth and higher stress tolerance has been well stud-ied. What is distinctive here is that repeated, near-lethal freeze-thaw selected not only for a tolerant/quiescent-like state but also for a shorter lag on re-entry. In this regime of freeze-thaw-regrowth, cells that are tolerant but slow to restart would be outcompeted by naive fast growers. Our quiescence-based selection simulations reproduce exactly this constraint. We have added this explanation to the Results to make clear that the novelty is the co-evolution of a tolerant, trehalose-rich state together with rapid regrowth under an alternating regime.

Comment to Response: I get the point. I believe that the outcome is highly dependent on how selection pressure is administered. So, generalizing this over all stresses (as done in the abstract) may not be accurate.

Comment 2:

Convergent evolution of traits: I find the results unsurprising. When selecting for a trait, if there is a major mode to adapt to that stress, most of the strains would adapt to that mode, independent of the route. According to me, finding out this major route was the objective of many of the previous reports on adaptive evolution. The surprising part in the previous papers (on adaptive evolution of bacteria or yeast) was the resampling of genes that acquired mutations in multiple replicates of an evolution experiments, providing a handle to understand the major genetic route or the molecular mechanism that guides the adaptation (for example in this case it would be - what guides the over-accumulation of trehalose). I fail to understand why the authors find the results surprising, and I would be happy to understand that from the authors. I may have missed something important.

Response:

Our surprise was precisely that we did not see the classical pattern of "phenotypic convergence + repeated mutations in the same locus/module." All independently evolved lines converged on a trehalose-rich, mechanically reinforced, quiescence-like phenotype, but population sequencing across lines did not reveal a single repeatedly hit gene or small shared pathway, even when we increased selection stringency (1-3 freeze-thaw cycles per round). We have now stated in the manuscript that this decoupling (strong phenotypic convergence, non-overlapping genetic routes) is the central inference: selection is acting on a physiologically defined state that multiple genotypes can reach.

Comment to Response: You indeed saw a case of phenotypic convergence. Converging towards trehalose-rich, mechanically reinforced, quiescent like - are phenotypes that have converged. This is what prevented lysis. The same locus need not be mutated over and over again, if the trehalose pathway is controlled by many processes (it is, and many are still unknown as I point in the next comment), many different mutations on different loci can result in the same regulation! I do not see the decoupling between phenotypic convergence and decoupling of genetic mutations as surprising or novel; molecular and cellular biology is replete with such examples where deletion(mutation) of hundreds of different genes can have the same phenotypic outcome (yeast deletion library screening, indirect effects etc). If this was a specific question unsolved in evolutionary biology, then the matter is different.

A minor point: Here I would also like to point out that the three phenotypes you measure may be linked to each other, so their independent evolution may just be a cause-effect relationship. For example Trehalose accumulation may drive the other two. This has not been deconvoluted in this manuscript.

Comment 3:

Adaptive evolution would work on phenotype, as all of selective evolution is supposed to. So, given that one of the phenotypes well-known in literature to allow free-tolerance is trehalose accumulation, I think it is not surprising that this trait is selected. For me, this is not a case of "non-genetic" adaptation as the authors point out: it is likely because perturbation of many genes can individually result in the same outcome - up-regulation of trehalose accumulation. Thereby, although the adaptation is genetic, it is not homogeneous across the evolving lines - the end result is. Do the authors check that the trait is actually a non-genetic adaptation, i.e., if they regrow the cells for a few generations without the stress, the cells fall back to being similarly only partially fit to freeze-thaw cycles? Additionally, the inability to identify a network that is conserved in the sequencing does not mean that there is no regulatory pathway. A large number of cryptic pathways may exist to alter cellular metabolic states.<br /> This is a point in continuation of point #2, and I would like to understand what I have missed.

Response:

We agree, and we have removed the wording "non-genetic adaptation." The evolved populations retain high survival even after regrowth for {greater than or equal to}25 generations without freeze-thaw, so the adaptation is clearly genetically maintained. What our data show is that there is no single genetic route to the shared phenotype; different mutations can all drive cells into the same trehalose-rich, quiescence-like, mechanochemically reinforced state. We now describe this as "genetic diversification with phenotypic convergence."

Comment to Response: While the last term does explain what is going on, isn't it an outcome that is routine in cell biology (as pointed out in my previous comment to your response)? I apologize for not understanding the punchline that is provided in the last few sentences of the abstract.

Comment 4:

To propose the convergent nature, it would be important to check for independently evolved lines and most probably more than 2 lines. It is not clear from their results section if they have multiple lines that have evolved independently.

Response:

We indeed evolved four independent lines and maintained two independent controls. We have added this information at the start of the Results so that the level of replication is immediately clear.

Comment to Response: Previous large scale studies have done hundreds of sequencing to oversample the pathway and figure out reproducible loci. With pooled sequencing (as mentioned below) and only 4 sample evolution, I am not sure that you would have the power in your study to conclude in the loci are sampled or not! If there were 10 gene LOFs that control Trehalose levels (which you can find from the published deletion screening experiment), then four of the experiments are likely to go through one of these routes; what is the likely event that you would identify the same route in two pools? It is unlikely, and therefore, sequencing of 4 pools cannot tell you if the mutation path is repeatedly sampled or not.

Comment 5:

For the genomic studies, it is not clear if the authors sequenced a pool or a single colony from the evolved strains. This is an important point, since an average sequence will miss out on many mutations and only focus on the mutations inherited from a common ancestral cell. It is also not clear from the section.

Response:

We sequenced population samples from the evolved lines. Our specific question was whether independently evolved lines would show the same high-frequency genetic solution, as is often seen in parallel evolution. Pool sequencing may under-sample rare/private variants, but it is appropriate for detecting such shared, high-frequency routes - and we do not find any. We have clarified this rationale in the Methods/Results.

Comment to Response: Please provide the average sequencing depth of each sequencing run. It is essential to understand the power of this study in identifying mutations. What coverage was used in Xgenome size?

Author response:

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

We thank the editor and the reviewers for the detailed and constructive comments. In revising the manuscript we have: (i) clarified what is new relative to prior stress tolerance work, (ii) made explicit that we observe phenotypic convergence without a shared genetic route, (iii) stated upfront that we evolved four independent lines plus two controls, and (iv) corrected figure legends, statistics, and the missing citations. Below we respond point-by-point.

Public Reviews:

Reviewer #1 (Public review):

Summary:

This manuscript presents findings on the adaptation mechanisms of Saccharomyces cerevisiae under extreme stress conditions. The authors try to generalize this to adaptation to stress tolerance. A major finding is that S. cerevisiae evolves a quiescence-like state with high trehalose to adapt to freeze-thaw tolerance independent of their genetic background. The manuscript is comprehensive, and each of the conclusions is well supported by careful experiments.

Strengths:

This is excellent interdisciplinary work.

Weaknesses:

I have questions regarding the overall novelty of the proposal, which I would like the authors to explain.

(1) Earlier papers have shown that loss of ribosomal proteins, that slow growth, leads to better stress tolerance in S. cerevisiae. Given this, isn’t it expected that any adaptation that slows down growth would, overall, increase stress tolerance? Even for other systems, it has been shown that slowing down growth (by spore formation in yeast or bacteria/or dauer formation in C. elegans) is an effective strategy to combat stress and hence is a likely route to adaptation. The authors stress this as one of the primary findings. I would like the authors to explain their position, detailing how their findings are unexpected in the context of the literature.

We agree that the link between slower growth and higher stress tolerance has been well studied. What is distinctive here is that repeated, near-lethal freeze–thaw selected not only for a tolerant/quiescent-like state but also for a shorter lag on re-entry. In this regime of freeze–thaw–regrowth, cells that are tolerant but slow to restart would be outcompeted by naive fast growers. Our quiescence-based selection simulations reproduce exactly this constraint. We have added this explanation to the Results to make clear that the novelty is the co-evolution of a tolerant, trehaloserich state together with rapid regrowth under an alternating regime.

(2) Convergent evolution of traits: I find the results unsurprising. When selecting for a trait, if there is a major mode to adapt to that stress, most of the strains would adapt to that mode, independent of the route. According to me, finding out this major route was the objective of many of the previous reports on adaptive evolution. The surprising part in the previous papers (on adaptive evolution of bacteria or yeast) was the resampling of genes that acquired mutations in multiple replicates of an evolution experiments, providing a handle to understand the major genetic route or the molecular mechanism that guides the adaptation (for example in this case it would be - what guides the overaccumulation of trehalose). I fail to understand why the authors find the results surprising, and I would be happy to understand that from the authors. I may have missed something important.

Our surprise was precisely that we did not see the classical pattern of “phenotypic convergence + repeated mutations in the same locus/module.” All independently evolved lines converged on a trehalose-rich, mechanically reinforced, quiescence-like phenotype, but population sequencing across lines did not reveal a single repeatedly hit gene or small shared pathway, even when we increased selection stringency (1–3 freeze–thaw cycles per round). We have now stated in the manuscript that this decoupling (strong phenotypic convergence, non-overlapping genetic routes) is the central inference: selection is acting on a physiologically defined state that multiple genotypes can reach.

(3) Adaptive evolution would work on phenotype, as all of selective evolution is supposed to. So, given that one of the phenotypes well-known in literature to allow free-tolerance is trehalose accumulation, I think it is not surprising that this trait is selected. For me, this is not a case of ”non-genetic” adaptation as the authors point out: it is likely because perturbation of many genes can individually result in the same outcome - up-regulation of trehalose accumulation. Thereby, although the adaptation is genetic, it is not homogeneous across the evolving lines - the end result is. Do the authors check that the trait is actually a non-genetic adaptation, i.e., if they regrow the cells for a few generations without the stress, the cells fall back to being similarly only partially fit to freeze-thaw cycles? Additionally, the inability to identify a network that is conserved in the sequencing does not mean that there is no regulatory pathway. A large number of cryptic pathways may exist to alter cellular metabolic states.

This is a point in continuation of point #2, and I would like to understand what I have missed.

We agree, and we have removed the wording “non-genetic adaptation.” The evolved populations retain high survival even after regrowth for ≥25 generations without freeze–thaw, so the adaptation is clearly genetically maintained. What our data show is that there is no single genetic route to the shared phenotype; different mutations can all drive cells into the same trehalose-rich, quiescencelike, mechanochemically reinforced state. We now describe this as “genetic diversification with phenotypic convergence.”

(4) To propose the convergent nature, it would be important to check for independently evolved lines and most probably more than 2 lines. It is not clear from their results section if they have multiple lines that have evolved independently.

We indeed evolved four independent lines and maintained two independent controls. We have added this information at the start of the Results so that the level of replication is immediately clear.

(5) For the genomic studies, it is not clear if the authors sequenced a pool or a single colony from the evolved strains. This is an important point, since an average sequence will miss out on many mutations and only focus on the mutations inherited from a common ancestral cell. It is also not clear from the section.

We sequenced population samples from the evolved lines. Our specific question was whether independently evolved lines would show the same high-frequency genetic solution, as is often seen in parallel evolution. Pool sequencing may under-sample rare/private variants, but it is appropriate for detecting such shared, high-frequency routes — and we do not find any. We have clarified this rationale in the Methods/Results.

Reviewer #2 (Public review):

Summary:

The authors used experimental evolution, repeatedly subjecting Saccharomyces cerevisiae populations to rapid liquid-nitrogen freeze-thaw cycles while tracking survival, cellular biophysics, metabolite levels, and whole-genome sequence changes. Within 25 cycles, viability rose from ~2 % to ~70 % in all independent lines, demonstrating rapid and highly convergent adaptation despite distinct starting genotypes. Evolved cells accumulated about threefold more intracellular trehalose, adopted a quiescence-like phenotype (smaller, denser, non-budding cells), showed cytoplasmic stiffening and reduced membrane damage, and re-entered growth with shorter lag traits that together protected them from ice-induced injury. Whole-genome sequencing indicated that multiple genetic routes can yield the same mechano-chemical survival strategy. A population model in which trehalose controls quiescence entry, growth rate, lag, and freeze-thaw survival reproduced the empirical dynamics, implicating physiological state transitions rather than specific mutations as the primary adaptive driver. The study therefore concludes that extreme-stress tolerance can evolve quickly through a convergent, trehalose-rich quiescence-like state that reinforces membrane integrity and cytoplasmic structure.

Strengths:

The strengths of the paper are the experimental design, data presentation and interpretation, and that it is well-written.

(1) While the phenotyping is thorough, a few more growth curves would be quite revealing to determine the extent of cross-stress protection. For example, comparing growth rates under YPD vs. YPEG (EtOH/glycerol), and measuring growth at 37ºC or in the presence of 0.8 M KCl.

We thank the referee for the interesting suggestions. However, growth rates alone may be difficult to interpret since WT strains also show different growth rates under these conditions. Therefore, comparing the relative fitness or survival of the evolved strains versus the WT under these stresses would be more informative. In the present study we limited growth/survival measurements to what was needed to parameterize the adaptation model in YPD under the freeze–thaw regime. We have now added a statement in the Discussion that, given the shared trehalose/mechanical mechanism, such cross-stress assays are an expected and straightforward follow-up.

(2) Is GEMS integrated prior to evolution? Are the evolved cells transformable?

Yes. GEMs were integrated prior to evolution, because the non-integrated evolved population showed low transformation efficiency, likely due to altered cell-wall properties.

(3) From the table, it looks like strains either have mutations in Ras1/2 or Vac8. Given the known requirements of Ras/PKA signaling for the G1/S checkpoint (to make sure there are enough nutrients for S phase), this seems like a pathway worth mentioning and referencing. Regarding Vac8, its emerging roles in NVJ and autophagy suggest another nutrient checkpoint, perhaps through TORC1. The common theme is rewired metabolism, which is probably influencing the carbon shuttling to trehalose synthesis.

We appreciate the reviewer’s suggestion to consider pathways like Ras/PKA (linked to Ras1/2) and autophagy/TORC1 (linked to Vac8) as potential upstream modulators. While these pathways are involved in nutrient sensing and metabolic regulation, we choose not to emphasize them specifically. This is because (i) some evolved lines lack Ras1/2 or Vac8 variants, and (ii) none of the variants lies directly in trehalose synthesis/degradation pathways. Furthermore, direct links to trehalose accumulation are not well established for these specific variants in this context, and pathways like Ras are global regulators with broad effects. Together with the strongly convergent phenotype, this supports our main inference that multiple genetic/metabolic routes can feed into the same trehalose-rich, mechanochemically reinforced, quiescence-like state. We have added a note in the discussion regarding metabolic rewiring and trehalose.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Generally, the results sections should have more details. The figures should be corrected, and the legends should be checked for correctness. The manuscript seems to have been assembled in haste?

We have expanded the relevant Results subsections with one-sentence motivations (why each measurement was performed) and we have corrected the figure legends for ordering and consistency.

Figure 3: It will be good to have the correct p-values on the figure itself. P-values are typically less than 1, unless there is some special method (here the values presented are , etc). Please explain how the P-values were obtained in the figure legend itself.

Figure 3 now shows the actual p-values. The legend specifies the details and the sample sizes used.

Figure 5: It is not clear what the error bars show in 5B, E (different evolved population/ clones/ cells?). All the figure legends are mixed up, please correct them. It is difficult to follow the paper.

Figure 5 legends now state clearly what the error bars represent (biological replicates) and which panels are from single-cell measurements. We have checked the panel lettering and legend order for consistency with the flow of the main text.

Reviewer #3 (Recommendations for the authors):

Overall, the paper is outstanding, well-written, and insightful.

A point to address is that there are missing citations on lines 60, 91.

We have added the missing citations at both locations. We apologize for the omission, which was due to a compilation error. This error has been fixed, and the bibliography has been corrected (now containing 74 references).

A demás de lo que se menciona sobre la ciencia de la informacion, agregaria que es una necesidad del ser humano para su desarrollo integral.

Agregaría a esta afirmación que la información deber ser también legitimizada, llegar hasta su fuente y complementarla con su contexto para así acercarnos desde un perspectiva óptima.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Authors state, "we identified ETF dehydrogenase (ETFDH) as one of the most dispensable metabolic genes in neoplasia." Surely there are thousands of genes that are dispensable for neoplasia. Perhaps the authors can revise this sentence and similar sentiments in the text.

We agree with the reviewer and have corrected the text accordingly. Specifically, we rephrased the sentence: “Surprisingly, we observed that in contrast to muscle, ETFDH is one of the most non-essential metabolic genes in cancer cells.” to “Surprisingly, we observed that in contrast to muscle, ETFDH is a non-essential gene in acute lymphoblastic leukemia NALM-6 cells”

Authors state, "These findings show that ETFDH loss elevates glutamine utilization in the CAC to support mitochondrial metabolism." While elevated glutamine to CAC flux is consistent with the statement that increased glutamine, the authors have not measured the effect of restoring glutamine utilization to baseline on mitochondrial metabolism. Thus, the causality implied by the authors can only be inferred based on the data presented. Indeed, the increased glutamine consumption may be linked to the increase in ROS, as glutamate efflux via system xCT is a major determinant of glutamine catabolism in vitro.

Indeed. We changed the statement "These findings show that ETFDH loss elevates glutamine utilization in the CAC to support mitochondrial metabolism." to "Collectively, these data demonstrate that ETF insufficiency in cancer cells remodels mitochondrial metabolism and increases the glutamine consumption and anaplerosis."

Authors state that the mechanism described is an example of "retrograde signaling". However, the mechanism seems to be related to a reduction in BCAA catabolism, suggesting that the observed effects may be a consequence of altered metabolic flux rather than a direct signaling pathway. The data presented do not delineate whether the observed effects stem from disrupted mitochondrial communication or from shifts in nutrient availability and metabolic regulation.

Notwithstanding that the term “retrograde” was used to refer to signaling from mitochondria to mTORC1, rather than from mTORC1 to mitochondria [1], we have removed the term “retrograde signaling” throughout the manuscript.

The authors should discuss which amino acids that are ETFDH substrates might affect mTORC1 activity or consider whether other ETFDH substrates might also affect mTORC1 in their discussion. Along these lines, the authors might consider discussing why amino acids that are not ETFDH substrates are increased upon ETFDH loss.

Based on the literature, we expect that branched chain amino acids that are ETFDH substrates (e.g., leucine) are likely to play a major role in activating mTORC1 upon ETFDH abrogation. As expected, the aforementioned amino acids are among those that are the most highly upregulated in ETFDH deficient cells (Fig 3A). We have, however, never formally tested the role of branched chain amino acid in activating mTORC1 in the context of ETFDH disruption. The increase in amino acids that are not metabolized via ETFDH, is likely to stem from global metabolic rewiring of ETFDH-deficient cells and observed alterations in amino acid uptake (e.g., glutamine; Fig 2F). We discuss this in the revised version of the paper as follows:

“Several metabolites can be sensed via signaling partners upstream of mTORC1, including leucine, arginine, methionine/SAM, and threonine [2]. Branched-chain amino acids (leucine, isoleucine, and valine), which are among the highest upregulated metabolites in ETFDH deficient cells (Fig 3A) serve as ETFDH substrates, and have been described to display strong activation capabilities towards mTORC1 in the literature [3,4]. Glutamine can also activate mTORC1 through Arf family of GTPases [5]. Indeed, glutamine can supplement the non-essential amino acid (NEAA) pool through transamination [6] and amino acid uptake [7]. Accordingly, the maintenance of NEAA that are non-ETFDH substrates may be supported by the global metabolic rewiring fueled by enhanced glutamine metabolism in ETFDH-deficient cells. Deciphering the mechanisms leading to accumulation of specific amino acids and their role in ETFDH-dependent mTORC1 modulation is warranted.”

Reviewer #2 (Public review):

The authors would strengthen the paper considerably by adding back catalytically inactive ETFDH to show that the activity of this enzyme is responsible for the increased growth phenotypes and changes in labeling that they observe.

Based on the Reviewers’ suggestions we performed these experiments. Herein, we took advantage of Y304A/G306E ETFDH mutant that impairs electron transfer from ETF and cannot substitute for the wild type (WT) gene function in ETFDH-deficient myoblasts [8]. We expressed WT and Y304A/G306E ETFDH mutant in ETFDH KO HCT116 colorectal cancer cells and confirmed that they are expressed to a comparable level (Supplementary Figure 6C). Re-expression of WT decreased proliferation, while suppressing mTORC1 signaling and increasing 4E-BP1 levels relative to control (vector infected) ETFDH KO EV HCT116 cells (Supplementary Figure 6D). In contrast, proliferation rates, mTORC1 signaling and 4E-BP1 levels remained largely unchanged upon Y304A/G306E ETFDH mutant expression in ETFDH KO HCT116 cells (Supplementary Figure 6D). Similarly, re-expression of WT ETFDH disrupted the bioenergetic phenotype associated with ETFDH loss, in contrast to re-expression of Y304A/G306E ETFDH mutant, which exhibited similar bioenergetic profiles as ETFDH KO control (Supplementary Figure 6E-F). Collectively these findings argue that the ETFDH activity is required for its tumor suppressive effects.

If nucleotide pool and labeling data are available, or can be obtained readily, this would significantly strengthen the tracing data already obtained.

We followed Reviewer’s suggestion and measured nucleotide levels. This revealed that loss of ETFDH results in increase in steady-state nucleotide pools (Supplementary Figure 2K), consistent with increased aspartate labelling and accelerated tumor growth.

References

(1) Morita, M. et al. mTORC1 controls mitochondrial activity and biogenesis through 4EBP-dependent translational regulation. Cell Metab 18, 698-711 (2013). https://doi.org/10.1016/j.cmet.2013.10.001

(2) Valenstein, M. L. et al. Structural basis for the dynamic regulation of mTORC1 by amino acids. Nature 646, 493-500 (2025). https://doi.org/10.1038/s41586-025-09428-7

(3) Appuhamy, J. A., Knoebel, N. A., Nayananjalie, W. A., Escobar, J., & Hanigan, M. D. Isoleucine and leucine independently regulate mTOR signaling and protein synthesis in MAC-T cells and bovine mammary tissue slices. J Nutr 142, 484-491 (2012). https://doi.org/10.3945/jn.111.152595

(4) Herningtyas, E. H. et al. Branched-chain amino acids and arginine suppress MaFbx/atrogin-1 mRNA expression via mTOR pathway in C2C12 cell line. Biochim Biophys Acta 1780, 1115-1120 (2008). https://doi.org/10.1016/j.bbagen.2008.06.004

(5) Jewell, J. L. et al. Metabolism. Differential regulation of mTORC1 by leucine and glutamine. Science 347, 194-198 (2015). https://doi.org/10.1126/science.1259472

(6) Tan, H. W. S., Sim, A. Y. L. & Long, Y. C. Glutamine metabolism regulates autophagy-dependent mTORC1 reactivation during amino acid starvation. Nat Commun 8, 338 (2017). https://doi.org/10.1038/s41467-017-00369-y

(7) Chen, R. et al. The general amino acid control pathway regulates mTOR and autophagy during serum/glutamine starvation. J Cell Biol 206, 173-182 (2014).https://doi.org/10.1083/jcb.201403009

(8) Herrero Martin, J. C. et al. An ETFDH-driven metabolon supports OXPHOS efficiency in skeletal muscle by regulating coenzyme Q homeostasis. Nat Metab 6, 209-225 (2024). https://doi.org/10.1038/s42255-023-00956-y

Reviewer #2 (Public review):

The substantially revised paper has increased in clarity and is much more accessibe and straightforward than the first version. The analyses are now clearer and support the conclusions better. There are however some remaining methodological weakness, which in my mind still renders the evidence to not be entirely convincing.

(1) The temporal autocorrelation concern is not fully convincingly addressed. The temporal autocorrelation curves supplied in the supplements are really helpful, but linearly regressing out the temporal distance from the neural distance clearly does not work, as one can see from the right panel of supplementary Figure 1. If the method had worked correctly the line should have been flat. The analysis however shows that decision trials with a lag > 2 are basically independent - so a simple way to address this is to restrict the RSA analysis to trials with a decision lag of > 2. This analysis would strengthen the paper a lot.

(2) In the final analysis, the authors use all the trials to make the claim that the hippocampus represents the characters in a shared social space. However, as within-character distances are still included in the analysis, this result could still be driven by the effects of within-character representations that are not shared across characters. A simple way of addressing this concern would be to only include between-character distances in this analysis, making it truly complementary to the previous within-character analysis. It would also be very interesting to compare the the within- and between-character analyses in the hippocampus directly.

(3) Overall, the correction for multiple comparisons in the fMRI and the resulting corrected p-values are not sufficiently explained and documented in the paper. What was exactly permuted in the tests? Was correction applied in a voxel-wise or cluster-wise fashion? If cluster-wise, the cluster-wise p-values need to be reported.

Author response:

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

Public reviews:

Reviewer #1 (Public review):

Summary:

Schafer et al. tested whether the hippocampus tracks social interactions as sequences of neural states within an abstract social space defined by dimensions of affiliation and power, using a task in which participants engaged in narrative-based social interactions. The findings of this study revealed that individual social relationships are represented by unique sequences of hippocampal activity patterns. These neural trajectories corresponded to the history of trial-to-trial affiliation and power dynamics between participants and each character, suggesting an extended role of the hippocampus in encoding sequences of events beyond spatial relationships.

The current version has limited information on details in decoding and clustering analyses which can be improved in the future revision.

Strengths:

(1) Robust Analysis: The research combined representational similarity analysis with manifold analyses, enhancing the robustness of the findings and the interpretation of the hippocampus's role in social cognition.

(2) Replicability: The study included two independent samples, which strengthens the generalizability and reliability of the results.

Weaknesses:

I appreciate the authors for utilizing contemporary machine-learning techniques to analyze neuroimaging data and examine the intricacies of human cognition. However, the manuscript would benefit from a more detailed explanation of the rationale behind the selection of each method and a thorough description of the validation procedures. Such clarifications are essential to understand the true impact of the research. Moreover, refining these areas will broaden the manuscript's accessibility to a diverse audience.

We thank the reviewer for these comments and have addressed them in various ways.

First, we removed the spline-based decoding and spectral clustering analyses. As we detail in our response to the recommendations, these approaches were complex and raised legitimate interpretational concerns, making it unclear how they supported our core claims. The revised manuscript now focuses on a set of representational similarity analyses to show representations consistent with social dimension similarity (affiliation vs. power decision trials) and social location similarity (trajectory/map-like coding based on participant choices).

Second, we expanded the Methods and Results to more clearly explain the analyses, the questions they address, and associated controls and robustness tests. The dimension similarity analysis tests whether hippocampal patterns differentiate affiliation and power decisions in a way consistent with an abstract dimension representation. The location similarity RSAs test whether within-character neural pattern distances scale with Euclidean distance in social space (relationship-specific trajectories), and whether pattern distances across all characters scale with location distances when distances are globally standardized, consistent with a shared map-like coordinate system.

Third, we emphasize new controls. For the dimension similarity RSA, we test for potential confounds such as word count, text sentiment, and reaction time differences between affiliation and power trials. For the location similarity RSA, we control for temporal distance between trials and show (in the Supplement) that the reported effects cannot be explained by temporal autocorrelation in the fMRI data or by the relationship between temporal distance and behavioral location distance.

We believe that these changes address the reviewer’s request for clearer rationale and validation.

Reviewer #2 (Public review):

Summary:

Using an innovative task design and analysis approach, the authors set out to show that the activity patterns in the hippocampus related to the development of social relationships with multiple partners in a virtual game. While I found the paper highly interesting (and would be thrilled if the claims made in the paper turned out to be true), I found many of the analyses presented either unconvincing or slightly unconnected to the claims that they were supposed to support. I very much hope the authors can alleviate these concerns in a revision of the paper.

Strengths & Weaknesses:

(1) The innovative task design and analyses, and the two independent samples of participants are clear strengths of the paper.

We thank the reviewer for this comment.

(2) The RSA analysis is not what I expected after I read the abstract and tile of the result section "The hippocampus represents abstract dimensions of affiliation and power". To me, the title suggests that the hippocampus has voxel patterns, which could be read out by a downstream area to infer the affiliation and power value, independent of the exact identity of the character in the current trial. The presented RSA analysis however presents something entirely different - namely that the affiliation trials and power trials elicit different activity patterns in the area indicated in Figure 3. What is the meaning of this analysis? It is not clear to me what is being "decoded" here and alternative explanations have not been considered. How do affiliation and power trials differ in terms of the length of sentences, complexity of the statements, and reaction time? Can the subsequent decision be decoded from these areas? I hope in the revision the authors can test these ideas - and also explain how the current RSA analysis relates to a representation of the "dimensions of affiliation and power".

We agree that this analysis needed to be better justified and explained. We have revised the text to clarify that by “represents the interaction decision trials along abstract social dimensions” we mean that hippocampal multivoxel patterns differentiate affiliation and power decisions in a way consistent with the conceptual framework of underlying latent dimensions. The analysis tests one simple prediction of this view – that on average these trial types are separable in the neural patterns. We have added details to the Methods, showing how the affiliation and power trials do not differ in word count or in sentiment, but do differ in their semantics, as assessed by a Large Language Model, as we expect from our task assumptions. Thanks to the reviewer’s comment, we also tested for and found a reaction time difference between affiliation and power trials, that we now control for.

(3) Overall, I found that the paper was missing some more fundamental and simpler RSA analyses that would provide a necessary backdrop for the more complicated analyses that followed. Can you decode character identity from the regions in question? If you trained a simple decoder for power and affiliation values (using the LLE, but without consideration of the sequential position as used in the spline analysis), could you predict left-out trials? Are affiliation and power represented in a way that is consistent across participants - i.e. could you train a model that predicts affiliation and power from N-1 subjects and then predict the Nth subject? Even if the answer to these questions is "no", I believe that they are important to report for the reader to get a full understanding of the nature of the neural representations in these areas. If the claim is that the hippocampus represents an "abstract" relationship space, then I think it is important to show that these representations hold across relationships. Otherwise, the claim needs to be adjusted to say that it is a representation of a relationship-specific trajectory, but not an abstract social space.

We appreciate this comment and agree on the value of clear, conceptually simple analyses. To address this concern, we have simplified our main analysis significantly by removing the spline-based analysis and substituting it with a multiple regression representational similarity analysis approach. We test whether within-character neural pattern distances scale with distance in social space (relationship-specific trajectories), and whether pattern distances across all characters scale with location distances when distances are globally standardized. We find evidence for both, consistent with a shared map-like coordinate system.

We agree that decoding character identity and an across-participant decoding approach could be informative. However, our current task is not well designed for such analyses and as such would complicate the paper. Although we agree that these questions are interesting, they would test questions that are outside the scope of this paper. 

(4) To determine that the location of a specific character can be decoded from the hippocampal activity patterns, the authors use a sequential analysis in a lowdimensional space (using local linear embedding). In essence, each trial is decoded by finding the pair of two temporally sequential trials that is closest to this pattern, and then interpolating the power/affiliation values linearly between these two points. The obvious problem with this analysis is that fMRI pattern will have temporal autocorrelation and the power and affiliation values have temporal autocorrelation. Successful decoding could just reflect this smoothness in both time series. The authors present a series of control analyses, but I found most of them to not be incisive or convincing and I believe that they (and their explanation of their rationale) need to be improved. For example, the circular shifting of the patterns preserves some of the autocorrelation of the time series - but not entirely. In the shifted patterns, the first and last items are considered to be neighboring and used in the evaluation, which alone could explain the poor performance. The simplest way that I can see is to also connect the first and last item in a circular fashion, even when evaluating the veridical ordering. The only really convincing control condition I found was the generation of new sequences for every character by shuffling the sequence of choices and re-creating new artificial trajectories with the same start and endpoint. This analysis performs much better than chance (circular shuffling), suggesting to me that a lot of the observed decoding accuracy is indeed simply caused by the temporal smoothness of both time series.

We thank the reviewer for emphasizing this important concern; we agree that we did not sufficiently address this in the initial submission. This concern is one main reason we removed the spline-based analysis and now use regression-based representational similarity analyses in its place. In the revision, we report autocorrelation-related analyses in the supplement, and via controls and additional analysis show that temporal distance (or its square) cannot explain the location-like effects. This substantially improves our ability to interpret the findings.

(5) Overall, I found the analysis of the brain-behavior correlation presented in Figure 5 unconvincing. First, the correlation is mostly driven by one individual with a large network size and a 6.5 cluster. I suspect that the exclusion of this individual would lead to the correlation losing significance. Secondly, the neural measure used for this analysis (determining the number of optimal clusters that maximize the overlap between neural clustering and behavioral clustering) is new, non-validated, and disconnected from all the analyses that had been reported previously. The authors need to forgive me for saying so, but at this point of the paper, would it not be much more obvious to use the decoding accuracy for power and affiliation from the main model used in the paper thus far? Does this correlate? Another obvious candidate would be the decoding accuracy for character identity or the size of the region that encodes affiliation and power. Given the plethora of candidate neural measures, I would appreciate if the authors reported the other neural measures that were tried (and that did not correlate). One way to address this would have been to select the method on the initial sample and then test it on the validation sample - unfortunately, the measure was not pre-registered before the validation sample was collected. It seems that the correlation was only found and reported on the validation sample?

We agree that this analysis was too complicated and under constrained, and thus not convincing. We think that removing this cluster-based analysis is the most conservative response to the reviewer’s concerns and have removed it from the revised paper.

Recommendations to the authors:

Reviewer #1 (Recommendations for the authors):

The manuscript's description of the shuffling analysis performed during decoding is currently ambiguous, particularly concerning the control variables. This ambiguity is present only in the Figure 4 legends and requires a more detailed explanation within the methods section. It is essential to clarify whether the permutation process was conducted within each character's data set or across multiple characters' data sets. If permutations were confined to within-character data, the conclusion would be that the hippocampus encodes context-specific information rather than providing a twodimensional common space.

We thank the reviewer for this comment. We have now removed the spline analysis due to these and other problems and have replaced it with representational similarity analyses that are both more rigorous and easier to interpret. We think these analyses allow us to make the claim that the characters are represented in a common space. 

In the methods, we explain the analyses (page 23-24, lines 475-500):

“We also expected the hippocampus to represent the different characters’ changing social locations, which are implicit in the participant’s choices. We used multiple regression searchlight RSA to test whether hippocampal pattern dissimilarity increases with social location distance, based on participant-specific trial-wise beta images where boxcar regressors spanned each trial’s reaction time.”

“We ran two complementary regression analyses to address two related questions. First, we asked whether the hippocampus represents how a specific relationship changes over time. For this analysis, for each participant and each searchlight, we computed character-specific (i.e., only for same character trial pairs) correlation distances between trial-wise beta patterns and Euclidean distances between the social location behavioral coordinates. Distances were zscored within character trial pairs to isolate character-specific changes. The second analysis asked whether the there is a common map-like representation, where all trials, regardless of relationship, are represented in a shared coordinate system. Here, we included all trial pairs and z-scored the distances globally. For both regression analyses, we included control distances to control for possible confounds. To account for generic time-related changes, we controlled for absolute scan-time difference, as this correlated with location distance across participants (see Temporal autocorrelation of hippocampal beta patterns in the supplement). Although the square of this temporal distance did not explain any additional variance in behavioral distances, we ran a robustness analysis including both temporal distance and its square and saw qualitatively the same clusters with similar effect sizes. As such, we report the main analysis only. We included binary dimension difference (0 = trial pairs of different dimension, 1 = trials pairs of the same dimension), to ensure effects could not be explained by dimension-related effects. In the group-level model, we controlled for sample and the average reaction time between affiliation and power decisions.”

In the results, we describe the results and our interpretation (pages 11-12, lines 185208):

“We have shown that the left hippocampus represents the affiliation and power trials differently, consistent with an abstract dimensional representation. Does it also represent the changing social coordinates of each character? To test this, we multiple-regression RSA searchlight to test whether left hippocampus patterns represent the characters’ changing social locations across interactions (see Figure 3). We restricted the distances to those from trial pairs from the same character and standardized the distances within character (see Figure 3BD). We controlled for temporal distance to ensure the effect was not explainable by the time between trials, and for whether the trials shared the same underlying dimension (affiliation or power; see Location similarity searchlight analyses for more details). At the group level, we controlled for sample and the average reaction time difference between affiliation and power trials. Using the same testing logic as the dimensionality similarity analysis, we first tested our hypothesis in the bilateral hippocampus and found widespread effects in both the left (peak voxel MNI x/y/z = -35/-22/-15, cluster extent = 1470 voxels) and right (peak voxel MNI x/y/z = 37/-19/-14, cluster extent = 1953 voxels) hemispheres. The whole-brain searchlight analysis revealed additional clusters in the left putamen (-27/-3/14, cluster extent = 131 voxels) and left posterior cingulate cortex (-10/-28/41, cluster extent = 304 voxels).”

“We then asked a second, complementary question: does the hippocampus represent all interactions, across characters, within a shared map? To test for this map-like structure, we repeated the analysis but now included all trial pairs, z-scoring distances globally rather than within character (Figure 3E-F). The remainder of the procedure followed the same logic as the preceding analysis. The hippocampus analysis revealed an extensive right hippocampal cluster (27/27/-14, cluster extent = 1667 voxels). The whole-brain analysis did not show any significant clusters.”

We also describe the results in the discussion (page 12, lines 220-226): 

“Then, we show that the hippocampus tracks the changing social locations (affiliation and power coordinates), above and beyond the effects of dimension or time; the hippocampus seemed to reflect both the changing within-character locations, tracking their locations over time, and locations across characters, as if in a shared map. Thus, these results suggest that the hippocampus does not just encode static character-related representations but rather tracks relationship changes in terms of underlying affiliation and power.”

The manuscript's description of the decoding analysis is unclear regarding the variability of the decoded positions. The authors appear to decode the position of a character along a spline, which raises the question of whether this position correlates with time, since characters are more likely to be located further from the center in later trials. There is a concern that the decoded position may not solely reflect the hippocampal encoding of spatial location, but could also be influenced by an inherent temporal association. Given that a character's position at time t is likely to be similar to its positions at t−1 and t+1, it is crucial that the authors clearly articulate their approach to separating spatial representation from temporal autocorrelation. While this issue may have been addressed in the construction of the test set, the manuscript does not seem to adequately explain how such biases were mitigated in the training set.

We agree that temporal confounding needs to be better accounted for, as our claims depend on space-like signals being separable from time-like ones. We address this in several ways in the revised manuscript.

First, we emphasize that this is a narrative-based task, where temporal structure is relevant. As such, our analyses aim to demonstrate that effects go beyond simple temporal confounds, like trial order or time elapsed.

Despite the temporal structure to the task, the decisions for the same character are spaced in time, and interleaved with other characters’ decisions, reducing the chance that a simple temporal confound could explain trajectory-related effects. We now describe the task better in the revised methods (page 16, lines 314-318):

“All six characters’ decision trials are interleaved with one another and with narrative slides. On average, after a decision trial for a given character, participants view ~11 narrative slides and complete ~3 decisions for other characters before returning to that same character, such that each character’s choices are separated by an average of ~20 seconds (range 12 seconds to 10 min).”

To address temporal autocorrelation in the fMRI time series, we used SPM’s FAST algorithm. Briefly, FAST models temporal autocorrelation as a weighted combination of candidate correlation functions, using the best estimate to remove autocorrelated signal.

We also now report the temporal autocorrelation profile of the hippocampal beta series in the supplement, including (pages 29-31, lines 593-656):

“The Social Navigation Task is a narrative-based task, where the relationships with characters evolve over time; trial pairs that are close in time may have more similar fMRI patterns for reasons unrelated to social mapping (e.g., slow drift). It is important to account for the role of time in our analyses, to ensure effects go beyond simple temporal confounds, like the time between decision trials. To aid in this, we quantified how fMRI signals change over time using a pattern autocorrelation function across decision trial lags. We defined the left and right hippocampus and the left and right intracalcarine cortex using the HarvardOxford atlas and thresholded them at 50% probability. We chose intracalcarine corex as an early visual control region that largely corresponds to primary visual cortex (V1), as it is likely to be driven by the visually presented narrative. We used the same trial-wise beta images as in the location similarity RSA (boxcar regressors spanning each decision trial’s reaction time). For each participant and region-of-interest (ROI), we extracted the decision trial-by-voxel beta matrix and quantified three kinds of temporal dependence: beta autocorrelation, multivoxel pattern correlation and multivoxel pattern correlation after regressing out temporal distance.”

“To estimate the temporal autocorrelation of the trial-wise beta values, we treated each voxel’s beta values as a time series across trials and measured how much a voxel’s response on one trial correlated (Pearson) with its response on previous trials. We averaged these voxel wise autocorrelations within each ROI. At one trial apart (lag 1), both the hippocampus and V1 showed small positive autocorrelations, indicating modest trial-to-trial carryover in response amplitude (see Supplemental figure 1) that by three trials apart was approximately 0.”

“Because our representational similarity analyses depend on trial-by-trial pattern similarity, we also estimated how multivoxel patterns were autocorrelated over time. For each lag, we computed the Pearson correlation between each trial’s voxelwise pattern and the pattern from the trial that many trials earlier, then averaged those correlations to obtain a single autocorrelation value for that lag. At one trial apart, both regions showed positive autocorrelation, with V1 having greater autocorrelation than the hippocampus; pattern correlations between trials 3 or 4 trials apart reduced across participants, settling into low but positive values. Then, for each participant and ROI, we regressed out the effect of absolute trial onset differences from all pairwise pattern correlations, to mirror the effects of controlling for these temporal distances in regressions. After removing this temporal distance component, the short lag pattern autocorrelation dropped substantially in both regions. The similarity in autocorrelation profiles between the two regions suggests that significant similarity effects in the hippocampus are unlikely to be driven by generic temporal autocorrelation.”

“Relationship between behavioral location distance and temporal distance “

“We also quantified how temporal distances between trials relates to their behavioral location distances, participant by participant. Our dimension similarity analysis controls for temporal distance between trials by design (see Social dimension similarity searchlight analysis), but our location similarity analysis does not. To decide on covariates to include in the analysis, we tested whether temporal distances can explain behavioral location distances. For each participant, we computed the correlations between trial pairs’ Euclidean distances in social locations and their linear temporal distances (“linear”) and the temporal distances squared (“quadratic”), to test for nonlinear effects. We then summarized the correlations using one-sample t-tests. The linear relationship was statistically significant (t₄₉ = 12.24, p < 0.001), whereas the quadratic relationship was not (t₄₉ = -0.55, p = 0.586). Similarly, in participant specific regressions with both linear and quadratic temporal distances, the linear effect was significant (t₄₉ = 5.69, p < 0.001) whereas the quadratic effect was not (t₄₉ = 0.20, p = 0.84). Based on this, we included linear temporal distances as a covariate in our location similarity analyses (see Location similarity searchlight analyses), and verified that adding a quadratic temporal distance covariate does not alter the results. Thus, the reported location-related pattern similarity effects go beyond what can be explained by temporal distance alone.”

How the free parameter of spectral clustering was determined, if there is any?

The interpretation of the number of hippocampal activity clusters is ambiguous. It is suggested that this number could fluctuate due to unique activity patterns or the fit to behaviorally defined trajectories. A lower number of clusters might indicate either a noisier or less distinct representation, raising the question of the necessity and interpretability of such a complex analysis. This concern is compounded by the potential sensitivity of the clustering to the variance in Euclidean distances of each trial's position relative to the center. If a character's position is consistently near the center, this could artificially reduce the perceived number of clusters. Furthermore, the manuscript should address whether there is any correlation between the number of clusters and behavioral performance. Specifically, what are the implications if participants are able to perform the task adequately with a smaller number of distinct hippocampal representation states?

The rationale for conducting both cluster analysis and position decoding as separate analyses remains unclear. While cluster analysis can corroborate the findings of position decoding, it is not apparent why the authors chose to include trials across characters for cluster analysis but not for decoding analysis. An explanation of the reasoning behind this methodological divergence would help in understanding the distinct contributions of each analysis to the study's findings.

The paper by Cohen et al. (1997), which provides the questionnaire for measuring the social network index, is not cited in the references. Upon reviewing the questionnaire that the author may have used, it appears that the term "social network size" does not refer to the actual size but to a score or index derived from the questionnaire responses. It may be more appropriate to replace the term "size" with a different term to more accurately reflect this distinction.

Thank you for seeking these clarifications. Given the complexity of this analysis, we have decided to drop it to focus instead on our dimension and location representational similarity analysis results.

Reviewer #2 (Recommendations for the authors):

How did the participants' decisions on previous trials influence the future trials that the subjects saw? If the different participants were faced with different decision trials, then how did you compare their decision? If two participants made the same decisions, would they have seen exactly the same sequence of trials (see point X on how the trial sequence was randomized).

All participants experience the same narrative, with the same decisions (i.e., the same available options); their choices (i.e., the options they select) are what implicitly shape each character’s affiliation and power locations, and thus each character’s trajectory. In other words, the narrative is fixed; what changes is the social coordinates assigned to each trial’s outcome depending on the participant’s choice of how to interact from the two narrative options. This means that we can meaningfully compare participants' neural patterns, given that every participant received the same text and images throughout.

We have now added details on the narrative structure, replacing more ambiguous statements with a clearer description (page 16, lines 309-318):

“The sequence of trials, including both narrative and decision trials, were fixed across participants; all that differs are the choices that the participants make. Narrative trials varied in duration, depending on the content (range 2-10 seconds), but were identical across participants. Decision trials always lasted 12 seconds, with two options presented until the participant made a choice, after which a blank screen was presented for the remainder of the duration. All six characters’ decision trials are interleaved with one another, and with the narrative slides. On average, after a decision trial for a given character, participants view ~11 narrative slides and complete ~3 decisions for other characters before returning to another decision with the same character, such that each character’s choices are separated by an average of ~20 seconds (ranging from 12 seconds to 10 min).”

Figure 2B: I assume that "count" is "count of participants"? It would be good to indicate this on the axis/caption.

Thank you for noting this. We have now removed this figure to improve the clarity of our figures. 

We have shown that the hippocampus represents the interaction decision trials along abstract social dimensions, but does it track each relationship's unique sequence of abstract social coordinates?". Please clarify what you mean by "represents the interaction decision trials”.

By “represents the interaction decision trials along abstract social dimensions”, we mean that when the participant makes a choice during the social interactions the hippocampal patterns represent the current social dimension of the choice (affiliation vs power). In other words, the hippocampal BOLD patterns differentiate affiliation and power decisions, consistent with our hypothesis of abstract social dimension representation in the hippocampus. We have clarified this (page 11, lines 185-187):

“We have shown that the left hippocampus represents the affiliation and power trials differently, consistent with an abstract dimensional representation.”

Page 8: "Hippocampal sequences are ordered like trajectories": It is not entirely clear to me what is meant by the split midpoint. Is this the midpoint of the piece-wise linear interpolation between two points, or simply the mean of all piecewise splines from one character? If the latter, is the null model the same as simply predicting the mean affiliation and power value for this character? If yes, please clarify and simplify this for the reader.

Page 8: "Hippocampal sequences track relationship-specific paths". First, I was misled by the "relationship-specific". I first understood this to mean that you wanted to test whether two relationships (i.e. the identity of the partner) had different representations in Hippocampus, even if the power/affiliation trajectories are the same. I suggest changing the title of this section.

The analysis in this section also breaks any temporal autocorrelation of measured patterns - so I am not sure if this is a strong analysis that should be interpreted at all. This analysis seems to not address the claim and conclusion that is drawn from it. I assume that the random trajectories have different choices and different affiliation/power values than the true trajectories. So the fact that the true trajectories can be better decoded simply shows that either choices or affiliation and power (or both) are represented in the neural code - but not necessarily anything beyond this.

Page 9: "Neural trajectories reflect social locations, not just choices". The motivation of this analysis is not clear to me. As I understand this analysis, both social location and choices are changed from the real trajectories. How can it then show that it reflects social locations, not just the choices?

Figure 4 caption: "on the -based approximation" Is there a missing "point"-[based] here?

We agree with the reviewer that this analysis is hard to interpret and does not adequately address concerns regarding temporal autocorrelation, and as such we have removed it from the manuscript. We describe the new results that include controlling for temporal distance between trials (pages 11-12, lines 185-208):

“We have shown that the left hippocampus represents the affiliation and power trials differently, consistent with an abstract dimensional representation. Does it also represent the changing social coordinates of each character? To test this, we multiple-regression RSA searchlight to test whether left hippocampus patterns represent the characters’ changing social locations across interactions (see Figure 3). We restricted the distances to those from trial pairs from the same character and standardized the distances within character (see Figure 3BD). We controlled for temporal distance to ensure the effect was not explainable by the time between trials, and for whether the trials shared the same underlying dimension (affiliation or power; see Location similarity searchlight analyses for more details). At the group level, we controlled for sample and the average reaction time difference between affiliation and power trials. Using the same testing logic as the dimensionality similarity analysis, we first tested our hypothesis in the bilateral hippocampus and found widespread effects in both the left (peak voxel MNI x/y/z = -35/-22/-15, cluster extent = 1470 voxels) and right (peak voxel MNI x/y/z = 37/-19/-14, cluster extent = 1953 voxels) hemispheres. The whole-brain searchlight analysis revealed additional clusters in the left putamen (-27/-3/14, cluster extent = 131 voxels) and left posterior cingulate cortex (-10/-28/41, cluster extent = 304 voxels).”

“We then asked a second, complementary question: does the hippocampus represent all interactions, across characters, within a shared map? To test for this map-like structure, we repeated the analysis but now included all trial pairs, z-scoring distances globally rather than within character (Figure 3E-F). The remainder of the procedure followed the same logic as the preceding analysis. The hippocampus analysis revealed an extensive right hippocampal cluster (27/27/-14, cluster extent = 1667 voxels). The whole-brain analysis did not show any significant clusters.”

We emphasize that the results are robust to the inclusion of temporal distance squared, in the methods (pages 23-24, lines 493-496):

“Although the square of this temporal distance did not explain any additional variance in behavioral distances, we ran a robustness analysis including both temporal distance and its square and saw qualitatively the same clusters with similar effect sizes.”

Page 8: last paragraph: The text sounds like you have already shown that you can decode character identity from the patterns - but I do not believe you have it this point. I would consider this would be an interesting addition to the paper, though.

This section has been removed, and we have been careful to not imply this in the current version of the manuscript. While we agree a character identity decoding would enrich our argument, we do not believe our task is well-suited to capture a character identity effect. Each character only has 12 decision trials, and these trials are partially clustered in time - this is one problem of temporal autocorrelation that we thank the reviewers for pushing us to consider in more detail. Dimension and location patterns, on the other hand, are more natural to analyze in our task, especially in representational similarity analyses that test whether the relevant differences scale with neural distances.

Page 14ff: Why is "Analysis section" not part of "Materials and Methods"? I believe adding the analysis after a careful description of the methods would improve the clarity of this section.

We agree with the reviewer and have now consolidated these two sections.

Two or three examples of Affiliation and Power decision trials should be provided, so the reader can form a more thorough understanding of how these dimensions were operationalized. For the RSA analysis, it is important to consider other differences between these two types of trials.

We agree that adding examples will clarify the operationalization of these dimensions. We now include example affiliation and power trials in a table (page 17-18).

We thank the reviewer for noting the need to rule out alternative hypotheses; we have added several such tests. Affiliation and power trials were not different in word count (page 17, lines 329-332):

“To ensure that any observed neural or behavioral differences were not confounded by trivial features of the text, we tested for differences between the affiliation and power trials (where the two options are concatenated). There were no differences in word count (affiliation average = 26.6, power average = 25.6; t-test p = 0.56).”

They were also not different in their sentiment, as assessed by a Large Language Model (LLM) analysis (page 17, lines 332-335): 

“The text’s sentiment also did not differ between these trial types (t-test p = 0.72), as quantified by comparing sentiment compound scores (from most negative, −1, to most positive, +1), using a Large Language Model (LLM) specialized for sentiment analysis [26]. “

The affiliation and power trials were different in terms of semantic content, consistent with our assumptions (page 17, lines 337-347):

“Our framework assumes that affiliation and power trials differ in their semantic content–that is, in the conceptual meaning of the text, beyond word count or sentiment. To test this assumption, we used an LLM-based semantic embedding analysis. Each decision trial was embedded into a semantic vector. We then measured the cosine similarity between pairs of trials and calculated the difference between average within-dimension similarity (affiliation-affiliation and power-power comparisons) and average between-dimension similarity (affiliationpower comparisons) and assessed its statistical significance with permutation testing (1,000 shuffles of trial labels). As expected, decision trials of the same dimension were more similar to each other than trials of different dimension, across multiple LLMs (OpenAI’s text-embedding-3-small [27]: similarity difference = 0.041, p < 0.001; all-MiniLM-L12-v2 [28]: similarity difference = 0.032, p < 0.001).”

The affiliation and power trials were different in average reaction time. To control for this difference in the dimension RSA analysis, we added each participant’s absolute value reaction time difference between the trial types as a covariate. The results were nearly identical to what they were before. We updated the text to reflect this new control (page 23, lines 471-474):

“However, there was a significant difference in the average reaction time between affiliation and power decisions across participants (t₄₉ = 6.92, p < 0.001; affiliation mean = 4.92 seconds (s), power mean = 4.51 s), so we controlled for this in the group-level analysis.”

The exact implementation and timing of the behavioral tasks should be described better. How many narrative trials were intermixed with the decision trials? Which characters were they assigned to? How was the sequence of trials determined? Was it fixed across participants, or randomized?

We agree that additional details are helpful. In the Methods, we now describe this with more detail (page 16, lines 301-318):

“There are two types of trials: “narrative” trials where background information is provided or characters talk or take actions (a total of 154 trials), and “decision” trials where the participant makes decisions in one-on-one interactions with a character that can change the relationship with that character (a total of 63 trials). On each decision, participants used a button response box to select between the two options. The options (1 or 2, assigned to the index and middle fingers) choice directions (+/-1 arbitrary unit on the current dimension) were counterbalanced.”

“The sequence of trials, including both narrative and decision trials, were fixed across participants; all that differs are the choices that the participants make. Narrative trials varied in duration, depending on the content (range 2-10 seconds), but were identical across participants. Decision trials always lasted 12 seconds, with two options presented until the participant made a choice, after which a blank screen was presented for the remainder of the duration. All six characters’ decision trials are interleaved with one another, and with the narrative slides. On average, after a decision trial for a given character, participants view ~11 narrative slides and complete ~3 decisions for other characters before returning to another decision with the same character, such that each character’s choices are separated by an average of ~20 seconds (ranging from 12 seconds to 10 min).”

What is the exact timing of trials during fMRI acquisition - i.e. how long were the trials, what was the ITI, were there long phases of rest to determine the resting baseline? These are all important factors that will determine the covariance between regressors and should be reported carefully. Ideally, I would like to see the trial-by-trial temporal auto-correlation structure across beta-weights to be reported.

We thank the reviewer for asking for this clarification. We have added the following text to clarify the trial timing (page 16, lines 314-318):

“All six characters’ decision trials are interleaved with one another and with narrative slides. On average, after a decision trial for a given character, participants view ~11 narrative slides and complete ~3 decisions for other characters before returning to that same character, such that each character’s choices are separated by an average of ~20 seconds (range 12 seconds to 10 min).”

We now describe the temporal autocorrelation patterns in the supplement, including how we decided on how to control for temporal distance in representational similarity analyses (pages 29-31, lines 593-656):

“The Social Navigation Task is a narrative-based task, where the relationships with characters evolve over time; trial pairs that are close in time may have more similar fMRI patterns for reasons unrelated to social mapping (e.g., slow drift). It is important to account for the role of time in our analyses, to ensure effects go beyond simple temporal confounds, like the time between decision trials. To aid in this, we quantified how fMRI signals change over time using a pattern autocorrelation function across decision trial lags. We defined the left and right hippocampus and the left and right intracalcarine cortex using the HarvardOxford atlas and thresholded them at 50% probability. We chose intracalcarine corex as an early visual control region that largely corresponds to primary visual cortex (V1), as it is likely to be driven by the visually presented narrative. We used the same trial-wise beta images as in the location similarity RSA (boxcar regressors spanning each decision trial’s reaction time). For each participant and region-of-interest (ROI), we extracted the decision trial-by-voxel beta matrix and quantified three kinds of temporal dependence: beta autocorrelation, multivoxel pattern correlation and multivoxel pattern correlation after regressing out temporal distance.”

“To estimate the temporal autocorrelation of the trial-wise beta values, we treated each voxel’s beta values as a time series across trials and measured how much a voxel’s response on one trial correlated (Pearson) with its response on previous trials. We averaged these voxel wise autocorrelations within each ROI. At one trial apart (lag 1), both the hippocampus and V1 showed small positive autocorrelations, indicating modest trial-to-trial carryover in response amplitude (see Supplemental figure 1) that by three trials apart was approximately 0.”

“Because our representational similarity analyses depend on trial-by-trial pattern similarity, we also estimated how multivoxel patterns were autocorrelated over time. For each lag, we computed the Pearson correlation between each trial’s voxelwise pattern and the pattern from the trial that many trials earlier, then averaged those correlations to obtain a single autocorrelation value for that lag. At one trial apart, both regions showed positive autocorrelation, with V1 having greater autocorrelation than the hippocampus; pattern correlations between trials 3 or 4 trials apart reduced across participants, settling into low but positive values. Then, for each participant and ROI, we regressed out the effect of absolute trial onset differences from all pairwise pattern correlations, to mirror the effects of controlling for these temporal distances in regressions. After removing this temporal distance component, the short lag pattern autocorrelation dropped substantially in both regions. The similarity in autocorrelation profiles between the two regions suggests that significant similarity effects in the hippocampus are unlikely to be driven by generic temporal autocorrelation.”

“Relationship between behavioral location distance and temporal distance “

“We also quantified how temporal distances between trials relates to their behavioral location distances, participant by participant. Our dimension similarity analysis controls for temporal distance between trials by design (see Social dimension similarity searchlight analysis), but our location similarity analysis does not. To decide on covariates to include in the analysis, we tested whether temporal distances can explain behavioral location distances. For each participant, we computed the correlations between trial pairs’ Euclidean distances in social locations and their linear temporal distances (“linear”) and the temporal distances squared (“quadratic”), to test for nonlinear effects. We then summarized the correlations using one-sample t-tests. The linear relationship was statistically significant (t₄₉ = 12.24, p < 0.001), whereas the quadratic relationship was not (t₄₉ = -0.55, p = 0.586). Similarly, in participant specific regressions with both linear and quadratic temporal distances, the linear effect was significant (t₄₉ = 5.69, p < 0.001) whereas the quadratic effect was not (t₄₉ = 0.20, p = 0.84). Based on this, we included linear temporal distances as a covariate in our location similarity analyses (see Location similarity searchlight analyses), and verified that adding a quadratic temporal distance covariate does not alter the results. Thus, the reported location-related pattern similarity effects go beyond what can be explained by temporal distance alone.”

Briefing : Feuille de Route de l'Éducation Nationale pour les Droits et le Bien-être des Enfants

Synthèse

Ce document synthétise les axes stratégiques et les constats chiffrés présentés par Édouard Geffray, ministre de l'Éducation nationale, lors de son audition devant la délégation aux droits des enfants.

L'école y est définie par deux fonctions cardinales : instruire et protéger. Les priorités ministérielles s'articulent autour de trois piliers majeurs : la santé mentale des élèves, la lutte contre le harcèlement scolaire et la sécurisation des parcours pour les enfants les plus vulnérables (situation de handicap ou sous protection).

Le ministre souligne une situation alarmante de la santé mentale des jeunes, exacerbée par les usages numériques, et propose des mesures systémiques : déploiement du programme "Phare", interdiction du portable au lycée, et création d'un cadre de "scolarité protégée".

Malgré une baisse démographique drastique (un million d'élèves en moins d'ici 2029), le ministère affirme vouloir maintenir une trajectoire de recrutement pour les personnels médico-sociaux afin de répondre à l'explosion des besoins de détection et d'orientation.

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I. Santé Mentale et Lutte contre le Harcèlement Scolaire : Un Enjeu de Sécurité Absolue

Le ministre place la santé mentale parmi ses trois priorités absolues, s'appuyant sur des indicateurs de détresse psychologique en forte hausse.

État des lieux et chiffres clés

• Risques de dépression : 14 % des collégiens et 15 % des lycéens présentent un risque important.

• Idées suicidaires : 24 % des lycéens déclarent avoir eu des pensées suicidaires au cours des 12 derniers mois.

• Harcèlement : Environ 5 % des élèves (soit un élève par classe en moyenne) sont victimes de harcèlement chaque année.

• Urgences : Augmentation de 80 % des passages aux urgences pour intentions ou tentatives de suicide depuis la crise du COVID-19.

Stratégies de réponse

• Désanonymisation des questionnaires : Le questionnaire annuel de harcèlement (rempli du CE2 à la Terminale) permet désormais aux élèves de décliner leur identité en fin de document pour être recontactés par l'équipe enseignante.

• Formation des personnels : L'objectif est de former deux personnels "sentinelles" par établissement pour repérer et orienter les élèves. Actuellement, la moyenne est de 1,6 personnel formé.

• Dispositif "Coupe-file" : Un mécanisme est en cours de finalisation avec le ministère de la Santé pour garantir aux infirmiers et médecins scolaires une prise de rendez-vous rapide vers les Centres Médico-Psychologiques (CMP) ou la médecine de ville, évitant des délais d'attente de 3 à 6 mois.

• Arsenal répressif : La loi du 2 mars 2022 fait du harcèlement un délit. 10 000 affaires ont été enregistrées par les parquets depuis 2022. Le décret du 16 août 2023 permet désormais de changer d'école l'élève auteur de harcèlement ou de violences intentionnelles.

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II. Protection de l'Enfance et "Scolarité Protégée"

L'école s'affirme comme le premier émetteur d'informations préoccupantes (IP) et d'articles 40 en France.

• Signalements : Le nombre d'informations préoccupantes émises par l'école est passé de 50 000 à 80 000 en deux ans. Un guide national de standardisation des alertes est en cours de publication.

• Circulaire "Scolarité Protégée" : Publiée prochainement, elle vise à garantir la continuité pédagogique des enfants confiés à l'Aide Sociale à l'Enfance (ASE), dont 70 % sortent actuellement du système sans diplôme. Elle prévoit :

◦ Un suivi individuel par les services départementaux (DASEN).  

◦ Des appuis scolaires spécifiques pour éviter les ruptures liées aux changements de foyers ou de familles d'accueil.  

◦ Un soutien renforcé à l'orientation et à l'estime de soi.

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III. École Inclusive et Évolution de l'Accompagnement

Le ministre distingue les élèves "non accompagnés" (disposant d'une solution pédagogique mais attendant une aide humaine) des élèves "sans solution" (exclus du système faute de structure adaptée).

• De la compensation à l'accessibilité : Le ministère souhaite sortir d'un modèle basé uniquement sur l'aide humaine systématique (AESH) pour privilégier l'accessibilité pédagogique et matérielle. L'objectif est d'éviter "l'externalisation" du handicap à l'intérieur de la classe.

• Pôles d'Appui à la Scolarité (PAS) : Déployés pour favoriser l'intervention du médico-social directement dans les murs de l'école et fluidifier les parcours entre le milieu ordinaire et les structures spécialisées.

• Besoins : 42 000 élèves seraient encore en attente d'accompagnement après les vacances de la Toussaint, malgré la création de 1 200 postes d'AESH supplémentaires pour 2026.

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IV. Numérique et Éducation à la Vie Affective (EVARS)

La régulation des écrans

Le ministre défend une interdiction stricte du portable au lycée (prévue pour 2026), justifiée par des enjeux cognitifs et de santé publique :

• Corrélation scientifique : La dégradation psychique des élèves est proportionnelle à la consommation d'écrans (le risque de troubles anxio-dépressifs passe de 30 % à 60 % pour les gros utilisateurs).

• Conscience avant contenu : Le ministre souhaite rétablir une primauté de l'éducation aux risques numériques avant l'exposition massive aux contenus violents ou faux.

Éducation à la vie affective, relationnelle et sexuelle (EVARS)

• Obligation : Les trois séances annuelles sont présentées comme "non négociables", tant dans le public que dans le privé sous contrat.

• Constats : 15 % des filles et 12 % des garçons au collège déclarent avoir subi une forme de violence sexuelle.

• Déploiement : Au 31 décembre, 66 % des écoles et 48 % des collèges publics avaient réalisé au moins une séance.

• Formation des enseignants : Le ministère reconnaît la nécessité de protéger les personnels qui, étant parfois eux-mêmes d'anciennes victimes, pourraient subir des traumatismes en dispensant ces enseignements.

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V. Pilotage Institutionnel et Défis Démographiques

La gestion des moyens humains

Le système éducatif fait face à une chute démographique sans précédent :

• Données : Perte d'un million d'élèves entre 2019 et 2029 dans le premier degré. Une génération de 200 000 élèves "disparaît" tous les quatre ans.

• Ajustements : Le ministre justifie les suppressions de postes d'enseignants (4 000 prévus) par cette baisse, tout en souhaitant augmenter progressivement les effectifs médico-sociaux (300 à 500 postes par an) pour compenser l'explosion des besoins en santé mentale.

L'éducation prioritaire (REP/REP+)

Le ministre admet que la carte actuelle, figée depuis 2015, est obsolète. Cependant, il refuse une révision avant 2027 pour deux raisons :

1. Technique : Le processus de concertation avec les collectivités et les syndicats nécessite 15 à 18 mois.

2. Démocratique : Il considère que ce débat doit appartenir à la prochaine échéance présidentielle et refuse de "figer" une carte qui s'imposerait au futur gouvernement.

Création d'un défenseur des droits des enfants

Un adjoint à la médiatrice de l'Éducation nationale sera spécifiquement chargé de la protection de l'enfance. Sa mission sera de traiter les litiges entre scolaire et périscolaire pour assurer une sécurité "de la porte à la porte" et de produire un rapport annuel dédié à ces enjeux.

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VI. Tableau Synthétique : Chiffres de la Santé Mentale et du Bien-être

| Indicateur | Donnée Statistique | | --- | --- | | Élèves victimes de harcèlement | 5 % (stable du CE2 à la Terminale) | | Lycéens avec idées suicidaires | 24 % | | Passage aux urgences (suicide) | \+ 80 % depuis le Covid | | Information préoccupantes (École) | 80 000 / an (en hausse de 30 000) | | Sortie de l'ASE sans diplôme | 70 % | | Couverture EVARS (Écoles) | 66 % (au 31/12) | | Élèves en attente d'AESH | 42 000 (Toussaint 2025) |

Reviewer #1 (Public review):

Summary:

The manuscript by Mengxing et al., reports an assessment of three first-order thalamic nuclei (auditory, visual, somatosensory) in a 3 x 2 factorial design to test for specificity of responses in first-order thalamic nuclei to linguistic processing particularly in the left hemisphere. The conditions are reading, speech production, and speech comprehension and their respective control conditions. The authors report the following results:

(1) BOLD-response analyses: left MGB linguistic vs non-linguistic significant; left LGN linguistic vs non-linguistic significant. There is no hemisphere x stimulus interaction.

(2) MVPA: left MGB linguistic vs. non-linguistic significant; bilateral VLN linguistic vs. non-linguistic significant; significant lateralisation in MGB (left MGB responses better classified linguistic vs. non-linguistic in contrast to right).

(3) Functional connectivity: there is, in general, connectivity between the thalamic ROIs and the respective primary cortices independent of linguistics.

Strengths:

The study has a clear and comprehensive design and addresses a timely topic. First-order thalamic nuclei and their interaction with the respective cerebral cortex area are likely key to understanding how perception works in a world where one has to compute highly dynamic stimuli often in an instant. Speech is a prime example of an ecologically important, extremely dynamic, and complex stimulus. The field of the contribution of cerebral cortex-thalamic loops is wide open, and the study presents a solid approach to address their role in different speech modalities (i.e., reading, comprehension, production).

Weaknesses:

I see two major overall weaknesses in the manuscript in its current form:

(1) Statistics:

Unfortunately, I have doubts about the solidity of the statistics. In the analyses of the BOLD responses, the authors do not find significant hemisphere x stimulus interactions. In my view, such results would pre-empt doing a post-hoc t-test. Nevertheless, the authors motivate their post-hoc t-test by 'trends' in the interaction and prior hypotheses. I see two difficulties with that. First, the origin of the prior hypotheses is somewhat unclear (see also the comment below on hypotheses), and the post-hoc t-test is not corrected for multiple comparisons. I find that it is a pity that the authors did not derive more specific hypotheses grounded in the literature to guide the statistical testing, as I think these would have been available, and the response properties of the MGB and LGN also make sense in light of them. In addition, I was wondering whether the MVPA results would also need to be corrected for the three tests, i.e., the three ROIs.

Hypotheses:

In my view, it is relatively unclear where the hypotheses precisely come from. For example, the paragraph on the hypotheses in the introduction (p. 6-7) is devoid of references. I also have the impression that the hypotheses are partly not taking into account previous reports on first-order thalamic nuclei involvement in linguistic vs. non-linguistic processing. For example, the authors test for lateralisation of linguistic vs. non-linguistic responses in all nuclei. However, from previous literature, one could derive the hypothesis that the lateralisation in MGB for speech might be there - previous work shows, for example, that speech recognition abilities consistently correlate with left MGB only (von Kriegstein et al., 2008 Curr Biol; Mihai et al., 2019 eLife). In addition, the involvement of the MGB in speech in noise processing is present in the left MGB (Mihai et al., 2021, J Neuroscience). Developmental dyslexia, which is supposed to be based on imprecise phonological processing (Ramus et al., 2004 TiCS), has alterations in left MGB (Diaz et al., 2012 PNAS; Galaburda et al., 1994 PNAS) and left MGB connections to planum temporale (Tschentscher et al., 2019 J Neurosci) as well as altered lateralisation (Müller-Axt et al., 2025 Brain). Conversely, in the LGN, I'm not aware of any studies showing lateralisation for speech. See, for example, Diaz et al., 2018, Neuroimage, where there are correlations of LGN task-dependent modulation with visual speech recognition behaviour in both LGNs. Thus, based on this literature, one could have predicted the result pattern displayed, for example, in Figure 3A at least for MGB and LGN.

In summary, the motivation for the different hypotheses needs to be carved out more and couched into previous literature that is directly relevant to the topic. The above paragraph is, of course, my view on the topic, but currently, the paper lacks different literature as references to fully understand where the hypotheses are derived from.

Reviewer #2 (Public review):

Summary:

This study investigates the involvement of first-order thalamic nuclei in language-related tasks using task-based fMRI in a 3 × 2 design contrasting linguistic and non-linguistic versions of reading, speech comprehension, and speech production. By focusing on the LGN, MGN, and VLN and combining activation, connectivity, lateralization, and multivariate pattern analyses, the authors aim to characterize modality-specific and language-related thalamic contributions.

Strength:

A major strength of the work is its hypothesis-driven and multimodal analytical approach, and the modality-specific engagement of first-order thalamic nuclei is robust and consistent with known thalamocortical organization. This is a very sound study overall.

Weaknesses:

However, several conceptual issues complicate the interpretation of the results as evidence for linguistic modulation per se. A central concern relates to the operationalization of the linguistic versus non-linguistic contrast. In the present design, linguistic and non-linguistic stimuli differ along multiple dimensions beyond linguistic content. For example, written words and scrambled images differ in spatial frequency structure, edge composition, contrast regularities, and familiarity, while intelligible speech and acoustically scrambled sounds differ substantially in temporal and spectral statistics. This is particularly relevant given that first-order thalamic nuclei such as the LGN are known to be highly sensitive to low-level sensory properties. As a result, observed differences in thalamic responses may reflect sensitivity to stimulus properties rather than linguistic processing per se, and this limits the specificity of claims regarding linguistic modulation.

Relatedly, although the manuscript frequently refers to effects "depending on the linguistic nature of the stimuli," the statistical evidence for linguistic versus non-linguistic modulation is uneven across analyses. Whole-brain contrasts collapse across stimulus type and primarily test modality effects. Similarly, the primary ROI analyses of activation amplitude are collapsed across linguistic and non-linguistic conditions and convincingly demonstrate modality-specific engagement of thalamic nuclei, but do not in themselves provide evidence for linguistic modulation. Linguistic effects emerge only in later, more targeted analyses focusing on hemispheric lateralization and multivariate pattern classification, and these effects are nucleus-, modality-, and analysis-specific rather than general. Taken together, these results suggest that linguistic modulation constitutes a secondary and selective finding, whereas modality-specific task engagement represents the primary and most robust outcome of the study.

An additional interpretational issue concerns task engagement and attention. The tasks differ substantially in cognitive demands (e.g., passive reading and listening versus overt speech production), and linguistic and non-linguistic blocks may differ systematically in salience or engagement. This is particularly important given prior evidence, cited by the authors, that LGN and MGN activity can be modulated by task demands and attention. In the absence of behavioral measures indexing task engagement or compliance, it is difficult to determine whether differences between linguistic and non-linguistic conditions reflect linguistic processing per se or are mediated by attentional factors.

Finally, while the manuscript emphasizes the novelty of evaluating thalamic involvement in language, thalamic contributions to language have been documented previously in both lesion and functional imaging studies. The contribution of the present work, therefore, lies less in establishing thalamic involvement in language per se, and more in its focus on specific first-order nuclei, its multimodal design, and its combination of univariate, connectivity, and multivariate analyses. Moderating claims of novelty would help place the findings more clearly within the existing literature.

Reviewer #2 (Public review):

Summary:

The manuscript reports a cryo-EM structure of TMAO demethylase from Paracoccus sp. This is an important enzyme in the metabolism of trimethylamine oxide (TMAO) and trimethylamine (TMA) in human gut microbiota, so new information about this enzyme would certainly be of interest.

Strengths:

The cryo-EM structure for this enzyme is new and provides new insights into the function of the different protein domains, and a channel for formaldehyde between the two domains.

Weaknesses:

(1) The proposed catalytic mechanism in this manuscript does not make sense. Previous mechanistic studies on the Methylocella silvestris TMAO demethylase (FEBS Journal 2016, 283, 3979-3993, reference 7) reported that, as well as a Zn2+ cofactor, there was a dependence upon non-heme Fe2+, and proposed a catalytic mechanism involving deoxygenation to form TMA and an iron(IV)-oxo species, followed by oxidative demethylation to form DMA and formaldehyde.

In this work, the authors do not mention the previously proposed mechanism, but instead say that elemental analysis "excluded iron". This is alarming, since the previous work has a key role for non-heme iron in the mechanism. The elemental analysis here gives a Zn content of about 0.5 mol/mol protein (and no Fe), whereas the Methylocella TMAO demethylase was reported to contain 0.97 mol Zn/mol protein, and 0.35-0.38 mol Fe/mol protein. It does, therefore, appear that their enzyme is depleted in Zn, and the absence of Fe impacts the mechanism, as explained below.

The proposed catalytic mechanism in this manuscript, I am sorry to say, does not make sense to me, for several reasons:

(i) Demethylation to form formaldehyde is not a hydrolytic process; it is an oxidative process (normally accomplished by either cytochrome P450 or non-heme iron-dependent oxygenase). The authors propose that a zinc (II) hydroxide attacks the methyl group, which is unprecedented, and even if it were possible, would generate methanol, not formaldehyde.

(ii) The amine oxide is then proposed to deoxygenate, with hydroxide appearing on the Zn - unfortunately, amine oxide deoxygenation is a reductive process, for which a reducing agent is needed, and Zn2+ is not a redox-active metal ion;

(iii) The authors say "forming a tetrahedral intermediate, as described for metalloproteinase", but zinc metalloproteases attack an amide carbonyl to form an oxyanion intermediate, whereas in this mechanism, there is no carbonyl to attack, so this statement is just wrong.

So on several counts, the proposed mechanism cannot be correct. Some redox cofactor is needed in order to carry out amine oxide deoxygenation, and Zn2+ cannot fulfil that role. Fe2+ could do, which is why the previously proposed mechanism involving an iron(IV)-oxo intermediate is feasible. But the authors claim that their enzyme has no Fe. If so, then there must be some other redox cofactor present. Therefore, the authors need to re-analyse their enzyme carefully and look either for Fe or for some other redox-active metal ion, and then provide convincing experimental evidence for a feasible catalytic mechanism. As it stands, the proposed catalytic mechanism is unacceptable.

(2) Given the metal content reported here, it is important to be able to compare the specific activity of the enzyme reported here with earlier preparations. The authors do quote a Vmax of 16.52 µM/min/mg; however, these are incorrect units for Vmax, they should be µmol/min/mg. There is a further inconsistency between the text saying µM/min/mg and the Figure saying µM/min/µg.

(3) The consumption of formaldehyde to form methylene-THF is potentially interesting, but the authors say "HCHO levels decreased in the presence of THF", which could potentially be due to enzyme inhibition by THF. Is there evidence that this is a time-dependent and protein-dependent reaction? Also in Figure 1C, HCHO reduction (%) is not very helpful, because we don't know what concentration of formaldehyde is formed under these conditions; it would be better to quote in units of concentration, rather than %.

(4) Has this particular TMAO demethylase been reported before? It's not clear which Paracoccus strain the enzyme is from; the Experimental Section just says "Paracoccus sp.", which is not very precise. There has been published work on the Paracoccus PS1 enzyme; is that the strain used? Details about the strain are needed, and the accession for the protein sequence.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

Thach et al. report on the structure and function of trimethylamine N-oxide demethylase (TDM). They identify a novel complex assembly composed of multiple TDM monomers and obtain high-resolution structural information for the catalytic site, including an analysis of its metal composition, which leads them to propose a mechanism for the catalytic reaction.

In addition, the authors describe a novel substrate channel within the TDM complex that connects the N-terminal Zn²-dependent TMAO demethylation domain with the C-terminal tetrahydrofolate (THF)-binding domain. This continuous intramolecular tunnel appears highly optimized for shuttling formaldehyde (HCHO), based on its negative electrostatic properties and restricted width. The authors propose that this channel facilitates the safe transfer of HCHO, enabling its efficient conversion to methylenetetrahydrofolate (MTHF) at the C-terminal domain as a microbial detoxification strategy.

Strengths:

The authors provide convincing high-resolution cryo-EM structural evidence (up to 2 Å) revealing an intriguing complex composed of two full monomers and two half-domains. They further present evidence for the metal ion bound at the active site and articulate a plausible hypothesis for the catalytic cycle. Substantial effort is devoted to optimizing and characterizing enzyme activity, including detailed kinetic analyses across a range of pH values, temperatures, and substrate concentrations. Furthermore, the authors validate their structural insights through functional analysis of active-site point mutants.

In addition, the authors identify a continuous channel for formaldehyde (HCHO) passage within the structure and support this interpretation through molecular dynamics simulations. These analyses suggest an exciting mechanism of specific, dynamic, and gated channeling of HCHO. This finding is particularly appealing, as it implies the existence of a unique, completely enclosed conduit that may be of broad interest, including potential applications in bioengineering.

Weaknesses:

Although the idea of an enclosed channel for HCHO is compelling, the experimental evidence supporting enzymatic assistance in the reaction of HCHO with THF is less convincing. The linear regression analysis shown in Figure 1C demonstrates a THF concentration-dependent decrease in HCHO, but the concentrations used for THF greatly exceed its reported KD (enzyme concentration used in this assay is not reported). It has previously been shown that HCHO and THF can couple spontaneously in a non-enzymatic manner, raising the possibility that the observed effect does not require enzymatic channeling. An additional control that can rule out this possibility would help to strengthen the evidence. For example, mutating the THF binding site to prevent THF binding to the protein complex could clarify whether the observed decrease in HCHO depends on enzyme-mediated proximity effects. A mutation which would specifically disable channeling could be even more convincing (maybe at the narrowest bottleneck).

We agree with the reviewer that HCHO and THF can react spontaneously in a non-enzymatic manner, and our experiments were not intended to demonstrate enzymatic channeling. The linear regression analysis in Figure 1C was designed solely to confirm that HCHO reacts with THF under our assay conditions. Accordingly, THF was titrated over a broad concentration range starting from zero, and the observed THF concentration–dependent decrease in HCHO reflects this chemical reactivity.

We do not interpret these data as evidence that the enzyme catalyzes or is required for the HCHO–THF coupling reaction. Instead, the structural observation of an enclosed channel is presented as a separate finding. We have clarified this point in the revised text to avoid overinterpretation of the biochemical data (page 2, line 16).

Another concern is that the observed decrease in HCHO could alternatively arise from a reduced production of HCHO due to a negative allosteric effect of THF binding on the active site. From this perspective, the interpretation would be more convincing if a clear coupled effect could be demonstrated, specifically, that removal of the product (HCHO) from the reaction equilibrium leads to an increase in the catalytic efficiency of the demethylation reaction.

We agree that, in principle, a decrease in detectable HCHO could also arise from an indirect effect of THF binding on enzyme activity. However, in our study the experiment was not designed to assess catalytic coupling or allosteric regulation. The assay in question monitors HCHO levels under defined conditions and does not distinguish between changes in HCHO production and downstream consumption.

Additionally, we do not interpret the observed decrease in HCHO as evidence that THF binding enhances catalytic efficiency, or that removal of HCHO shifts the reaction equilibrium. Instead, the data are presented to establish that HCHO can react with THF under the assay conditions. Any potential allosteric effects of THF on the demethylation reaction, or kinetic coupling between HCHO removal and catalysis, are beyond the scope of the current study, and are not claimed.

While the enzyme kinetics appear to have been performed thoroughly, the description of the kinetic assays in the Methods section is very brief. Important details such as reaction buffer composition, cofactor identity and concentration (Zn²⁺), enzyme concentration, defined temperature, and precise pH are not clearly stated. Moreover, a detailed methodological description could not be found in the cited reference (6), if I am not mistaken.

Thank you for the suggestion. We have added reference [24] to the methodological description on page 8. The Methods section has been revised accordingly on page 8 under “TDM Activity Assay,” without altering the Zn²⁺ concentration.

The composition of the complex is intriguing but raises some questions. Based on SDS-PAGE analysis, the purified protein appears to be predominantly full-length TDM, and size-exclusion chromatography suggests an apparent molecular weight below 100 kDa. However, the cryo-EM structure reveals a substantially larger complex composed of two full-length monomers and two half-domains.

We appreciate the reviewer’s careful analysis of the apparent discrepancy between the biochemical characterization and the cryo-EM structure. This issue is addressed in Figure S1, which may have been overlooked.

As shown in Figure S1, the stability of TDM is highly dependent on protein and salt conditions. At 150 mM NaCl, SEC reveals a dominant peak eluting between 10.5 and 12 mL, corresponding to an estimated molecular weight of ~170–305 kDa (blue dot, Author response image 1). This fraction was explicitly selected for cryo-EM analysis and yields the larger complex observed in the reconstruction. At lower salt concentrations (50 mM) or higher (>150 mM NaCl), the protein either aggregates or elutes near the void volume (~8 mL).

SDS–PAGE analysis detects full-length TDM together with smaller fragments (~40–50 kDa and ~22–25 kDa). The apparent predominance of full-length protein on SDS–PAGE likely reflects its greater staining intensity per molecule and/or a higher population, rather than the absence of truncated species.

Author response image 1.

Given the lack of clear evidence for proteolytic fragments on the SDS-PAGE gel, it is unclear how the observed stoichiometry arises. This raises the possibility of higher-order assemblies or alternative oligomeric states. Did the authors attempt to pick or analyze larger particles during cryo-EM processing? Additional biophysical characterization of particle size distribution - for example, using interferometric scattering microscopy (iSCAT)-could help clarify the oligomeric state of the complex in solution.

Cryo-EM data were collected exclusively from the size-exclusion chromatography fraction eluting between 10.5 and 12 mL. This fraction was selected to isolate the dominant assembly in solution. Extensive 2D and 3D particle classification did not reveal distinct classes corresponding to smaller species or higher-order oligomeric assemblies. Instead, the vast majority of particles converged to a single, well-defined structure consistent with the 2 full-length + 2 half-domain stoichiometry.

A minor subpopulation (~2%) exhibited increased flexibility in the N-terminal region of the two full-length subunits, but these particles did not form a separate oligomeric class, indicating conformational heterogeneity rather than alternative assembly states (Author response image 2). Together, these data support the 2+2½ architecture as the predominant and stable complex under the conditions used for cryo-EM. Additional techniques, such as iSCAT, would provide complementary information, but are not required to support the conclusions drawn from the SEC and cryo-EM analyses presented here.

Author response image 2.

The authors mention strict symmetry in the complex, yet C2 symmetry was enforced during refinement. While this is reasonable as an initial approach, it would strengthen the structural interpretation to relax the symmetry to C1 using the C2-refined map as a reference. This could reveal subtle asymmetries or domain-specific differences without sacrificing the overall quality of the reconstruction.

We thank the reviewer for this thoughtful suggestion. In standard cryo-EM data processing, symmetry is typically not imposed initially to minimize potential model bias; accordingly, we first performed C1 refinement before applying C2 symmetry. The resulting C1 reconstructions revealed no detectable asymmetry or domain-specific differences relative to the C2 map. In addition, relaxing the symmetry consistently reduced overall resolution, indicating lower alignment accuracy and further supporting the presence of a predominantly symmetric assembly.

In this context, the proposed catalytic role of Zn²⁺ raises additional questions. Why is a 2:1 enzyme-to-metal stoichiometry observed, and how does this reconcile with previous reports? This point warrants discussion. Does this imply asymmetric catalysis within the complex? Would the stoichiometry change under Zn²⁺-saturating conditions, as no Zn²⁺ appears to be added to the buffers? It would be helpful to clarify whether Zn²⁺ occupancy is equivalent in both active sites when symmetry is not imposed, or whether partial occupancy is observed.

The observed ~2:1 enzyme-to-Zn²⁺ stoichiometry likely reflects the composition of the 2 full-length + 2 half-domain (2+2½) complex. In this assembly, only the core domains that are fully present in the complex contribute to metal binding. The truncated or half-domains lack the Zn²⁺ binding domain. As a result, only two metal-binding sites are occupied per assembled complex, consistent with the measured stoichiometry.

We note that Zn²⁺ was not deliberately added to the buffers, so occupancy may not reflect full saturation. Based on our cryo-EM and biochemical data, both metal-binding sites in the full-length subunits appear to be occupied to an equivalent extent, and no clear evidence of asymmetric catalysis is observed under these current experimental conditions. Full Zn²⁺ saturation could potentially increase occupancy, but was not explored in these experiments.

The divalent ion Zn²⁺ is suggested to activate water for the catalytic reaction. I am not sure if there is a need for a water molecule to explain this catalytic mechanism. Can you please elaborate on this more? As one aspect, it might be helpful to explain in more detail how Zn-OH and D220 are recovered in the last step before a new water molecule comes in.

Thank you for your suggestion. We revised our text in page 2 as bellow.

Based on our structural and biochemical data, we propose a structurally informed working model for TMAO turnover by TDM (Scheme 1). In this model, Zn²⁺ plays a non-redox role by polarizing the O–H bond of the bound hydroxyl, thereby lowering its pK_a. The D220 carboxylate functions as a general base, abstracting the proton to generate a hydroxide nucleophile. This hydroxide then attacks the electrophilic N-methyl carbon of TMAO, forming a tetrahedral carbinolamine (hemiaminal) intermediate. Subsequent heterolytic cleavage of the C–N bond leads to the release of HCHO. D220 then switches roles to act as a general acid, donating a proton to the departing nitrogen, which facilitates product release and regenerates the active site. This sequence allows a new water molecule to rebind Zn²⁺, enabling subsequent catalytic turnovers. This proposed pathway is consistent with prior mechanistic studies, in which water addition to the azomethine carbon of a cationic Schiff base generates a carbinolamine intermediate, followed by a rate-limiting breakdown to yield an amino alcohol and a carbonyl compound, in the published case, an aldehyde (Pihlaja et al., J. Chem. Soc. Perkin Trans. 2, 1983, 8, 1223–1226).

Overall, the authors were successful in advancing our structural and functional understanding of the TDM complex. They suggest an interesting oligomeric complex composition which should be investigated with additional biophysical techniques.

Additionally, they provide an intriguing hypothesis for a new type of substrate channeling. Additional kinetic experiments focusing on HCHO and THF turnover by enzymatic proximity effects would strengthen this potentially fundamental finding. If this channeling mechanism can be supported by stronger experimental evidence, it would substantially advance our understanding and knowledge of biologic conduits and enable future efforts in the design of artificial cascade catalysis systems with high conversion rate and efficiency, as well as detoxification pathways.

Reviewer #2 (Public review):

Summary:

The manuscript reports a cryo-EM structure of TMAO demethylase from Paracoccus sp. This is an important enzyme in the metabolism of trimethylamine oxide (TMAO) and trimethylamine (TMA) in human gut microbiota, so new information about this enzyme would certainly be of interest.

Strengths:

The cryo-EM structure for this enzyme is new and provides new insights into the function of the different protein domains, and a channel for formaldehyde between the two domains.

Weaknesses:

(1) The proposed catalytic mechanism in this manuscript does not make sense. Previous mechanistic studies on the Methylocella silvestris TMAO demethylase (FEBS Journal 2016, 283, 3979-3993, reference 7) reported that, as well as a Zn2+ cofactor, there was a dependence upon non-heme Fe²⁺, and proposed a catalytic mechanism involving deoxygenation to form TMA and an iron(IV)-oxo species, followed by oxidative demethylation to form DMA and formaldehyde.

In this work, the authors do not mention the previously proposed mechanism, but instead say that elemental analysis "excluded iron". This is alarming, since the previous work has a key role for non-heme iron in the mechanism. The elemental analysis here gives a Zn content of about 0.5 mol/mol protein (and no Fe), whereas the Methylocella TMAO demethylase was reported to contain 0.97 mol Zn/mol protein, and 0.35-0.38 mol Fe/mol protein. It does, therefore, appear that their enzyme is depleted in Zn, and the absence of Fe impacts the mechanism, as explained below.

The proposed catalytic mechanism in this manuscript, I am sorry to say, does not make sense to me, for several reasons:

(i) Demethylation to form formaldehyde is not a hydrolytic process; it is an oxidative process (normally accomplished by either cytochrome P450 or non-heme iron-dependent oxygenase). The authors propose that a zinc (II) hydroxide attacks the methyl group, which is unprecedented, and even if it were possible, would generate methanol, not formaldehyde.

(ii) The amine oxide is then proposed to deoxygenate, with hydroxide appearing on the Zn - unfortunately, amine oxide deoxygenation is a reductive process, for which a reducing agent is needed, and Zn2+ is not a redox-active metal ion;

(iii) The authors say "forming a tetrahedral intermediate, as described for metalloproteinase", but zinc metalloproteases attack an amide carbonyl to form an oxyanion intermediate, whereas in this mechanism, there is no carbonyl to attack, so this statement is just wrong.

So on several counts, the proposed mechanism cannot be correct. Some redox cofactor is needed in order to carry out amine oxide deoxygenation, and Zn²⁺cannot fulfil that role. Fe²⁺ could do, which is why the previously proposed mechanism involving an iron(IV)-oxo intermediate is feasible. But the authors claim that their enzyme has no Fe. If so, then there must be some other redox cofactor present. Therefore, the authors need to re-analyse their enzyme carefully and look either for Fe or for some other redox-active metal ion, and then provide convincing experimental evidence for a feasible catalytic mechanism. As it stands, the proposed catalytic mechanism is unacceptable.

We thank the reviewer for the detailed and thoughtful mechanistic critique. We fully agree that Zn²⁺ is not redox-active, and cannot directly mediate oxidative demethylation or amine oxide deoxygenation. We acknowledge that the oxidative step required for the conversion of TMAO to HCHO is not explicitly resolved in the present study. Accordingly, we have revised the manuscript to remove any implication of Zn²⁺-mediated redox chemistry, and have eliminated the previously imprecise analogy to zinc metalloproteases.

We recognize and now discuss prior biochemical work on TMAO demethylase from Methylocella silvestris (MsTDM), which proposed an iron-dependent oxidative mechanism (Zhu et al., FEBS 2016, 3979–3993). That study reported approximately one Zn²⁺ and one non-heme Fe²⁺ per active enzyme, implicated iron in catalysis through homology modeling and mutagenesis, and used crossover experiments suggesting a trimethylamine-like intermediate and oxygen transfer from TMAO, consistent with an Fe-dependent redox process. However, that system lacked experimental structural information, and did not define discrete metal-binding sites.

In contrast,

(1) Our high-resolution cryo-EM structures and metal analyses of TDM consistently reveal only a single, well-defined Zn²⁺-binding site, with no structural evidence for an additional iron-binding site as in the previous report (Zhu et al., FEBS 2016, 3979–3993).

(2) To investigate the potential involvement of iron, we expressed TDM in LB medium supplemented with Fe(NH₄)₂SO₄ and determined its cryo-EM structure. This structure is identical to the original one, and no EM density corresponding to a second iron ion was observed. Moreover, the previously proposed Fe²⁺-binding residues are spatially distant (Figure S6).

(3) ICP-MS analysis shows undetectable Iron, and only Zinc ion (Figure S5).

(4) Our enzyme kinetics analysis with the TDM without Iron is comparable to that of from MsTDM (Figure 1A). The differences in Km and Vmax we propose is due to the difference in the overall sequence of the enzymes. Please also see comment at the end on a new published paper on MsTDM.

While we cannot comment on the MsTDM results, our ‘experimental’ results do not support the presence of an iron-binding site. Our data indicate that this chemistry is unlikely to be mediated by a canonical non-heme iron center as proposed for MsTDM. We therefore revised our model as a structural framework that rationalizes substrate binding, metal coordination, and product stabilization, while clearly delineating the limits of mechanistic inference supported by the current data.

The scheme 1 and proposal mechanism section were revised in page 4. Figure S6 was added.

(2) Given the metal content reported here, it is important to be able to compare the specific activity of the enzyme reported here with earlier preparations. The authors do quote a Vmax of 16.52 µM/min/mg; however, these are incorrect units for Vmax, they should be µmol/min/mg. There is a further inconsistency between the text saying µM/min/mg and the Figure saying µM/min/µg.

Thank you for the correction. We converted the V_max unit to nmol/min/mg. and revised the text in page 2. We also compared with the value of the previous report in the TDM enzyme by revising the text on page 2. See also the note on a newly published manuscript and its comparison.

(3) The consumption of formaldehyde to form methylene-THF is potentially interesting, but the authors say "HCHO levels decreased in the presence of THF", which could potentially be due to enzyme inhibition by THF. Is there evidence that this is a time-dependent and protein-dependent reaction? Also in Figure 1C, HCHO reduction (%) is not very helpful, because we don't know what concentration of formaldehyde is formed under these conditions; it would be better to quote in units of concentration, rather than %.

We appreciate this important point. We have revised Figure 1C to present HCHO levels in absolute concentration units. While the current data demonstrate reduced detectable HCHO in the presence of THF, we agree that distinguishing between HCHO consumption and potential THF-mediated enzyme inhibition would require dedicated time-course and protein-dependence experiments. We have therefore revised the description to avoid overinterpretation and limit our conclusions to the observed changes in HCHO concentration in page 2, line 18-19.

(4) Has this particular TMAO demethylase been reported before? It's not clear which Paracoccus strain the enzyme is from; the Experimental Section just says "Paracoccus sp.", which is not very precise. There has been published work on the Paracoccus PS1 enzyme; is that the strain used? Details about the strain are needed, and the accession for the protein sequence.

Thank you for this comment. We now indicate that the enzyme is derived from Paracoccus sp. DMF and provide the accession number for the protein sequence (WP_263566861) in the Experimental Section (page 8, line 4).

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) The ITC experiment requires a ligand-into-buffer titration as an additional control. Also, maybe I misunderstood the molar ratio or the concentrations you used, but if you indeed added a total of 4.75 μL of 20 μM THF into 250 μL of 5 μM TDM, it is not clear to me how this leads to a final molar ratio of 3.

We thank the reviewer for this suggestion. A ligand-into-buffer control ITC experiment was performed and is now included in Figure S8C, which shows no realizable signal.

Regarding the molar ratio, it is our mistake. The experiment used 2.45 μL injections of 80 μM THF into 250 μL of 5 μM TDM. This corresponds to a final ligand concentration of ~12.8 μM, giving a ligand-to-protein molar ratio of ~2.6. We revised our text in page 9, ITC section.

(2) Characterization/quality check of all mutant enzymes should be performed by NanoDSF, CD spectroscopy or similar techniques to confirm that proteins are properly folded and fit for kinetic testing.

We appreciate the reviewer’s suggestion. All mutant proteins, including D220A, D367A, and F327A, were purified with yields similar to the wild-type enzyme. Additionally, cryo-EM maps of the mutants show well-defined density and overall structural integrity consistent with the wild-type. These findings indicate that the introduced mutations do not significantly affect protein folding, supporting their use for kinetic analysis. While NanoDSF might reveal differences in thermal stability due to mutations, it does not provide structural information. Our conclusions are not based on minor differences in thermostability. Our cryo-EM structures of the mutants offer much more reliable structural data than CD spectroscopy.

(3) Best practice would suggest overlapping pH ranges between different buffer systems in the pH-dependence experiments to rule out buffer-specific effects independent of pH.

We thank the reviewer for this helpful suggestion. We agree that overlapping pH ranges between different buffer systems can be valuable for excluding buffer-specific effects. In this study, the pH-dependence experiments were intended to provide a qualitative assessment of pH sensitivity rather than a detailed analysis of buffer-independent pKa values. While we cannot fully exclude minor buffer-specific contributions, the overall trends observed were reproducible and sufficient to support the conclusions drawn. We have added a clarifying statement to the revised manuscript to reflect this consideration, page 2, line 12.

(4) Structural comparison revealed high similarity to a THF-binding protein, with superposition onto a T protein.": It would be nice to show this as an additional figure, as resolution and occupancy for THF are low.

We thank the reviewer for this suggestion. To address this point, we have revised Figure S6 by adding an additional panel (C, now is Figure S7C) showing the structural superposition of TDM with the THF-binding T protein. This comparison is included to better illustrate the structural similarity, despite the limited resolution and partial occupancy of THF density in our map.

(5) Editing could have been done more thoroughly. Some spelling mistakes, e.g. "RESEULTS", "redius", "complec"; kinetic rate constants should be written in italic (not uniform between text and figures); Prism version is missing; Vmax of 16.52 µM/min/mg - doublecheck units; Figure S1B: The "arrow on the right" might have gone missing.

We corrected the spelling in page 2 ~ line 10, page 5 ~ line 34, page 6 ~ line40. Prism version was added. The arrow was added into figure S1B. The Vmax unit is corrected to nmol/min/mg.

Reviewer #2 (Recommendations for the authors):

(1) The authors must re-examine the metal content of their purified enzyme, looking in particular for Fe or another redox-active metal ion, which could be involved in a reasonable catalytic mechanism.

We thank the reviewer for this suggestion and have carefully re-examined the metal content of TDM. Elemental analyses by EDX and ICP-MS consistently detected Zn²⁺ in purified TDM (Zn:protein ≈ 1:2), whereas Fe was below the detection limit across multiple independent preparations (Fig. S5A,B). To assess whether iron could be incorporated or play a functional role, we expressed TDM in E. coli grown in LB medium supplemented with Fe(NH₄SO₄)₂ and performed activity assays in the presence of exogenous Fe²⁺. Neither condition resulted in enhanced enzymatic activity.

Consistent with these biochemical data, all cryo-EM structures reveal a single, well-defined metal-binding site coordinated by three conserved cysteine residues and occupied by Zn²⁺, with no evidence for an additional iron species or other redox-active metal site.

(2) The specific activity of the enzyme should be quoted in the same units as other literature papers, so that the enzyme activity can be compared. It could be, for example, that the content of Fe (or other redox-active metal) is low, and that could then give rise to a low specific activity.

Thank you for the suggestion, we quoted the enzyme units as similar with previous report. and revised the text in in page 2.

Since the submission of our paper a new report on MsTDM has been published (Cappa et al., Protein Science 33(11), e70364). It further supports our findings. First, the reported kinetic parameters using ITC (Vmax = 0.309 μmol/s, approximately 240 nmol/min/mg; Km = 0.866 mM) are comparable to our observed (156 nmol/min/mg and 1.33 mM, respectively) in the absence of exogenous iron. Second, the optimal pH for enzymatic activity similar to that observed in our paraTDM. Third, the reported two-state unfolding behavior is consistent with our cryo-EM structural observations, in which the more dynamic subunits appear to destabilize prior to unfolding of the core domains. Based on these findings, we now propose that Zn²⁺ appears to function primarily as an organizational cofactor at the core catalytic domain (revised Scheme 1).

Reviewer #1 (Public review):

This study by Radziun and colleagues investigates the effects of using a hand-augmentation device on mental body representations. The authors use a proprioceptive localisation task to measure metric representations of finger length before and after participants wear the device, and then before and after they learn to use the device, which extends the lengths of the fingers by 10 cm. The authors find changes between different time points, which they interpret as evidence for three distinct forms of plasticity: one related to simply wearing the device, one related to learning to use it, and an aftereffect after taking the device off. A control experiment with a similar device, which does not lengthen the fingers, showed the first and third of these forms of plasticity, but not the second.

This study takes an interesting approach to a timely and theoretically significant issue. The study appears to be appropriately designed and conducted. There are, however, some points which require clarification.

(1) The nature of the localization task is unclear. On its face, the task appears to involve localization of each landmark within the 2-dimensional surface of the touchscreen. However, the regression analysis presupposes that localization is made in a 1-dimensional space. Figure S2 shows that three lines are presented on the screen above the index, middle, and ring fingers, which I imagine the participant is meant to use as a guide. But it is at least conceivable that the perceived location or orientation of the finger might not correspond exactly to these lines. While the method can deal gracefully with proximal-distal translations of the fingers (i.e., with the intercept parameter of the regression), it isn't clear how the participant is supposed to respond if their proprioceptive perception of finger location is translated left-right or rotated relative to the lines on the screen. I also worry that presenting a long, thin line to represent each finger on the screen may not be a neutral method and may prime participants to represent the finger as long and thin.

(2) The task used here fits within a wider family of tasks in the literature using localization judgments of multiple landmarks to map body representations. I feel that some discussion of this broader set of tasks and their use to measure body representation and plasticity is notably absent from the paper. It is also striking to me that some of the present authors have themselves recently criticized the use of landmark localization methods as a measure of represented body size and shape (Peviani et al, 2024, Current Biology). It is therefore surprising to see them use this task here as a measure of represented finger length without commenting on this issue.

(3) 18 participants strikes me as a relatively small sample size for this type of study. It weakens the manuscript that the authors do not provide any justification, or even comment on, the sample size. This is especially true as participants are excluded from the entire sample, and from specific analyses, on rather post-hoc grounds.

(4) I have some concerns about the interpretation of contraction in stage 2. The authors claim that wearing the finger extended produces "a contraction",i.e., an "under-representation" (page 12). But in both experiments, regression slopes in stage 2 were not significantly different from 1 (i.e., 0.98 [SE: 0.07] in Exp 1a and 1.04 [SE: 0.09] in Experiment 1b). So how can that be interpreted as "under-representation"?

(5) I also have concerns about the interpretation of the stretch that is claimed to occur following training. In Exp 1a, regression slopes in stage 3 are on average 1.15. That is LESS than in the pretest at stage 1 (mean: 1.16). The idea of stretch only comes about because of the lower slopes in stage 2, which the authors have interpreted as reflecting contraction. So what the authors call stretch and a 2nd form of plasticity could just be the contraction from stage 2 wearing off or dissipating, since perceived finger length in stage 3 just appears to return to the baseline level seen in stage 1. While the authors describe their results in terms of three distinct forms of plasticity, these are not in fact statistically independent. The dip in regression slopes in stage 2 is interpreted as evidence for two distinct plasticity effects, which I do not find convincing.

(6) The distinction between plasticity at stage 3 (which appears specific to augmentation) and plasticity at stage 4 (which does not appear specific, as it also occurs in Experiment 1b) feels strained. This feels like a very subtle distinction, and the theoretical significance of it is not convincingly developed.

(7) The reporting of statistics is not always consistent. For example, 95%CIs are presented for regression slopes in stages 1, 3, and 4, but not for stage 2. Statistics are performed on regression slopes, except for one t-test on page 7 comparing lengths in cm. Estimates of effect size would be nice additions to statistical tests.

(8) Minor point: On page 4, the authors write, "These included sorting colored blocks, stacking a Jenga tower, and sorting pegs into holes; the latter task required fine-grained manipulation and was used as our outcome measure of motor learning." This suggests that peg sorting was the outcome measure, but in Figure 1D, Jenga is presented as the outcome measure.

Reviewer #2 (Public review):

Summary:

This study aimed to explore dynamic changes in the somatosensory representation of both the body and artificial body parts. The study investigated how proprioceptive localisation along the finger changes when participants wear, actively use, and then remove a hand augmentation device - a rigid finger-extension. By mapping perceived target locations along the biological finger and the extension across multiple stages, the authors aim to characterise how the somatosensory system updates our spatial body representation during and after interaction with body augmentation technology.

Strengths:

The manuscript addresses an interesting question of how augmentation devices alter proprioceptive localisation abilities. Conceptually, the work moves beyond classic tool-use paradigms by focusing on a device that is used with the hand to extend the fingers' abilities (versus a tool that is simply used by the hand), and by attempting to map perceived spatial structure across both biological and artificial segments within the same framework.

A major strength is the multi-stage design, which samples localisation abilities at baseline, the beginning of device wear, post-training, and immediately post-removal. This provides a richer characterisation of short-term adaptation compared to a simple pre/post comparison. The dense sampling across stages and target locations generates a rich behavioural dataset that will be valuable to readers interested in somatosensory body representation. The within-subject, counterbalanced control session further strengthens interpretability, providing a useful comparison for interpreting stage-dependent effects, and to probe how functional training shapes changes in the perceptual representations. Finally, the augmentation device itself appears carefully engineered, with thoughtful design decisions regarding wearability, including comfort and customised fit. The manuscript is also communicated clearly, with transparent reporting of analyses and succinct figures that make the pattern changes across stages straightforward to evaluate.

Weaknesses:

There is conceptual ambiguity in how the regression outcomes are interpreted in relation to perceived length and spatial integration. The manuscript treats regression slope as a proxy for "length perception" and discards the intercept as "spatial bias," but in this localisation task translation (intercept) and scaling (slope) are coupled: changes in anchoring at the proximal baseline (intercept) or distal endpoint can generate slope differences without uniform rescaling across the mapped surface. Relatedly, the analyses do not establish whether the reported effects are global across targets or disproportionately driven by the most distal locations. This limits the strength of inferences about "partitioning" or "reallocation" of representational space across biological and artificial segments. Some interpretive statements also appear stronger than the evidence supports (e.g., describing the stage 2 bio-extension map as "geometrically accurate", despite Bayes factors that provide only anecdotal support for no difference from true length). Extensive repeated judgements to a fixed set of locations may additionally stabilise response strategies or anchoring even without feedback, complicating the separation of body-representation change from task-specific calibration.

The manuscript would also benefit from clearer conceptual framing of what the device is and what its training probes are. The device is described variably as an "artificial finger" versus a rigid "finger extension," with different implications for perception and function. In addition, the training tasks appear to emphasise manipulation and dexterity more than scenarios requiring an extended reachable workspace (indeed, participants appear to have performed at least as well, if not better, in the control training), which brings into question whether participants explored the device's intended functionality and possible proprioceptive consequences. The control experiment is thoughtfully designed to test whether functional training contributes to the stage 3 changes, but because localisation is not performed while wearing the short device, the design does not resolve whether the stage 2 change and the post-removal aftereffect are specific to the augmentative extension versus more general consequences of wearing a device on the finger (and the following possible distorted distal cues).

Finally, the immediate post-removal aftereffects are intriguing, but the mechanistic interpretation remains underspecified. As presented within the internal model framework, the magnitude and consistency of the aftereffect following brief exposure are difficult to reconcile with the stability expected from a lifetime biological finger model, and because the aftereffect is assessed only immediately after removal, its time course and functional significance remain unclear.

Reviewer #3 (Public review):

Summary:

The study aims to investigate sensorimotor plasticity mechanisms by exposing a cohort of 20 subjects to manipulation activities while using wearable finger extensions. With a series of experiments involving localization and motor tasks, the authors provide evidence that the finger extensions are integrated into the body representation of the subjects.

Strengths:

The study deserves attention, and the psychophysical protocols are carefully designed, and the statistical analyses are solid.

Weaknesses:

However, the current version of the manuscript, in my opinion, makes an exaggerated use of the term plasticity, and this should be amended. This is because the authors support the plasticity claims with psychophysical experiments, without providing evidence of neural-plasticity mechanisms (e.g., neuroimaging methods are not used).

The authors are recommended to revise the wording of the manuscript and possibly perform additional experiments with brain imaging methods (e.g., EEG or fMRI).

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

This manuscript describes critical intermediate reaction steps of a HA synthase at the molecular level; specifically, it examines the 2nd step, polymerization, adding GlcA to GlcNAc to form the initial disaccharide of the repeating HA structure. Unlike the vast majority of known glycosyltransferases, the viral HAS (a convenient proxy extrapolated to resemble the vertebrate forms) uses a single pocket to catalyze both monosaccharide transfer steps. The authors' work illustrates the interactions needed to bind & proof-read the UDP-GlcA using direct and '2nd layer' amino acid residues. This step also allows the HAS to distinguish the two UDP-sugars; this is very important as the enzymes are not known or observed to make homopolymers of only GlcA or GlcNAc, but only make the HA disaccharide repeats GlcNAc-GlcA.

Strengths:

Overall, the strengths of this paper lie in its techniques & analysis.

The authors make significant leaps forward towards understanding this process using a variety of tools and comparisons of wild-type & mutant enzymes. The work is well presented overall with respect to the text and illustrations (especially the 3D representations), and the robustness of the analyses & statistics is also noteworthy.

Furthermore, the authors make some strides towards creating novel sugar polymers using alternative primers & work with detergent binding to the HAS. The authors tested a wide variety of monosaccharides and several disaccharides for primer activity and observed that GlcA could be added to cellobiose and chitobiose, which are moderately close structural analogs to HA disaccharides. Did the authors also test the readily available HA tetramer (HA4, [GlcA-GlcNAc]2) as a primer in their system? This is a highly recommended experiment; if it works, then this molecule may also be useful for cryo-EM studies of CvHAS as well.

The reviewer requested testing whether an HA tetratsaccharide could also serve as an glycosyl transfer acceptor for HAS. The commerically available HA tetrasaccharide (HA4) is terminated at its non-reducing end by GlcA, therein we proceeded to measure its effect on UDP-GlcNAc turnover kientics. Titration of HA4 failed to elicit any detectable change in UDP-GlcNAc turnover rate, indicating no priming. This is now mentioned in the main text and the data is shown in Fig. S9.

Weaknesses:

In the past, another report describing the failed attempt of elongating short primers (HA4 & chitin oligosaccharides larger than the cello- or chitobiose that have activity in this report) with a vertebrate HAS, XlHAS1, an enzyme that seems to behave like the CvHAS ( https://pubmed.ncbi.nlm.nih.gov/10473619/); this work should probably be cited and briefly discussed. It may be that the longer primers in the 1999 paper and/or the different construct or isolation specifics (detergent extract vs crude) were not conducive to the extension reaction, as the authors extracted recombinant enzyme.

We apologize for the oversight. This reference is now cited (ref. 18) together with the description of the failed elongation of HA4 by CvHAS.

There are a few areas that should be addressed for clarity and correctness, especially defining the class of HAS studied here (Class I-NR) as the results may (Class I-R) or may not (Class II) align (see comment (a) below), but overall, a very nicely done body of work that will significantly enhance understanding in the field.

Done as requested

Reviewer #2 (Public review):

Summary:

The paper by Stephens and co-workers provides important mechanistic insight into how hyaluronan synthase (HAS) coordinates alternating GlcNAc and GlcA incorporation using a single Type-I catalytic centre. Through cryo-EM structures capturing both "proofreading" and fully "inserted" binding poses of UDP-GlcA, combined with detailed biochemical analysis, the authors show how the enzyme selectively recognizes the GlcA carboxylate, stabilizes substrates through conformational gating, and requires a priming GlcNAc for productive turnover.

These findings clarify how one active site can manage two chemically distinct donor sugars while simultaneously coupling catalysis to polymer translocation.

The work also reports a DDM-bound, detergent-inhibited conformation that possibly illuminates features of the acceptor pocket, although this appears to be a purification artefact (it is indeed inhibitory) rather than a relevant biological state.

Overall, the study convincingly establishes a unified catalytic mechanism for Type-I HAS enzymes and represents a significant advance in understanding HA biosynthesis at the molecular level.

Strengths:

There are many strengths.

This is a multi-disciplinary study with very high-quality cryo-EM and enzyme kinetics (backed up with orthogonal methods of product analysis) to justify the conclusions discussed above.

Weaknesses:

There are few weaknesses.

The abstract and introduction assume a lot of detailed prior knowledge about hyaluronan synthases, and in doing so, risk lessening the readership pool.

A lot of discussion focuses on detergents (whose presence is totally inhibitory) and transfer to non-biological acceptors (at high concentrations). This risks weakening the manuscript.

The abstract and parts of the introduction have been revised to address the reviewer’s concerns.

Reviewer #1 (Recommendations for the authors):

(1) As noted above, please state in title, abstract & introduction that this work is focused on a "Class I-NR HAS" (as described in Ref. #4), and NOT all HAS families...this is truly essential to note as someone working with the Pasteurella HAS version (Class II) would be totally misled & at this point, no one knows the Streptococcus HAS (Class-IR) mechanistic details which could be different due to its inverse molecular directionality of elongation compared to the CvHAS Class I-NR enzyme.

Done as requested.

(2) Page 6 - for the usefulness of the HAS mutants as being folded correctly, it was stated these mutants are suitable since they all 'purify' similarly...the use of the more proper term should probably be 'chromatograph', similarly suggesting similar hydrodynamic radii without massive folding issues.

This has been revised to state that they all exhibited comparable size exclusion chromatography profiles.

“All mutants share similar size exclusion chromatography profiles with the WT enzyme, suggesting that the substitutions do not cause a folding defect (Fig. S3).”

(3) Page 7 - please check these sentences (& rest of paragraph?) as the meaning is not clear. "First, UDP-GlcNAc was titrated in the presence of excess UDP-GlcA, resulting in a response similar to the acceptor-free condition (Fig. 2C). However, the maximum reaction velocity at 20 mM UDP-GlcNAc was approximately 25% lower than that measured in the presence of UDP-GlcNAc only (Fig. 2C)."

The paragraph has been revised to avoid confusion.

(4) In Methods, please use an italicized 'g' for the centrifugation steps globally.

Changed as requested

(5) Please note the source/vendor for the HA standards on gels.

Done

(6) Page 35 - TLC section.

(a) 'n-butanol' (with italic n) is the most widespread chemical name (not butan-1-ol).

Done

(b) Also, for all of the TLC images, the origin and the solvent front should be marked.

Changed as suggested.

Reviewer #2 (Recommendations for the authors):

A number of minor issues should be addressed.

(1) Abstract

Two comments on the Abstract, which I found surprisingly weak given the quality of the work, and lacking a key detail.

A major conceptual contribution of this work is the demonstration of how a single Type-I catalytic centre discriminates, positions, and transfers two chemically distinct substrates in an alternating pattern. This distinguishes HAS from dual-active-site (Type-II) glycosyltransferases and is important for understanding HA polymerization.

However, this central point is not clearly articulated in the abstract. I suggest explicitly stating that HAS performs both GlcNAc and GlcA transfer reactions within a single catalytic site, and that the proofreading/inserted poses illuminate how this multifunctionality is achieved.

The abstract currently ends with the observation of a DDM-bound, detergent-inhibited state. While this is interesting, it absolutely does not represent the central conceptual advance of the study and gives the abstract an artefactual ending.

I strongly recommend revising the final sentences to emphasize the broader mechanistic insight and not an "artefact" (indeed, the enzyme is inactive in the presence of this detergent; it is thus a very unusual way to conclude an abstract).

That is, finish with the wider implications of how HAS coordinates alternating substrate use, proofreading, and polymer translocation. Ending on the main mechanistic or biological significance would make the abstract considerably stronger and more aligned with the main message of the paper.

The abstract has been revised thoroughly to reflect the important insights gained on CvHAS’ catalytic function and HA biogenesis in general.

(2) Introduction

The distinction between single active-centre enzymes, which transfer both sugars alternately, and twin catalytic domain enzymes that each perform one addition is surely central to the whole paper. But it is not discussed. Surely this has to be covered. There is a lot of work in this space, including, but not limited to:

https://doi.org/10.1093/glycob/cwg085

https://doi.org/10.1093/glycob/10.9.883

https://doi.org/10.1093/glycob/cwad075 (includes this author team)

Originally back to https://doi.org/10.1021/bi990270y

If the authors instead assume such a level of knowledge for the reader, then surely they are writing for a specialist audience, not consistent with the wider readership ambitions of eLife?

The Introduction has been revised as suggested by the reviewer, providing necessary background to frame our description of the Chlorella virus HAS. We made a deliberate effort to put new insights into a broader context.

(3) Results and Discussion

DDM "was observed for >50% of the analysed particles". I struggled with this. I couldn't understand how the authors selected particles that did or did not contain DDM. The main body text states: "To our surprise, careful sorting of the UDP-GlcA supplemented cryo EM dataset revealed a CvHAS subpopulation that was not bound to the substrate, but, instead, a DDM molecule near the active site (Fig 3A and S7). This was observed for >50% of the analyzed particles."

That reads like there is one sample with two populations. But the figures and the methods section suggest differently: they suggest two samples with different data-collection regimes. That does not match the main text. Could this be clarified?

Yes, that wasn’t explained well. We clarified the text to stress that the DDM-bound sample came from a dataset that was intended to resolve an UDP-GlcA-bound state, but instead revealed the inhibition by DDM.

Also in this space, in the modern world, "nominal magnification" has no real meaning, and calibrated pixel size would be more appropriate. Can this be given, please?

The relevant Methods section now states: “imaging of … was performed at a calibrated pixel size of 0.652 Å”.

The discovery of DDM in the active site is surprising. But it is an inhibitory artefact. Is this section pushed a little too hard? Also, "The coordination of DDM's maltoside moiety, an αlinked glucose disaccharide, is consistent with priming by cellobiose and chitobiose." I'm not sure why an α-linked maltose is consistent with the binding of a β-linked cellobiose. That makes no sense. There will be no other enzymes where starch and cellulose oligos are mutually accepted. Consider rewriting.

We like to stress the DDM coordination because it could lead to the development of compounds that can really function as inhibitors, either for HAS or other related enzymes. In the observed DDM binding pose, the alpha-linkage is not recognized. Instead, the reducing end glucosyl unit stacks against Trp342 while the non-reducing unit extends into the catalytic pocket. Hence, a similar binding pose is conceivable for cellobiose and potentially also for chitobiose. The relevant section has been reworded.

Author response:

The following is the authors’ response to the previous reviews

Public Review:

Reviewer #1 (Public review):

Ewing sarcoma is an aggressive pediatric cancer driven by the EWS-FLI oncogene. Ewing sarcoma cells are addicted to this chimeric transcription factor, which represents a strong therapeutic vulnerability. Unfortunately, targeting EWS-FLI has proven to be very difficult and better understanding how this chimeric transcription factor works is critical to achieving this goal. Towards this perspective, the group had previously identified a DBD-𝛼4 helix (DBD) in FLI that appears to be necessary to mediate EWS-FLI transcriptomic activity. Here, the authors used multi-omic approaches, including CUT&tag, RNAseq, and MicroC to investigate the impact of this DBD domain. Importantly, these experiments were performed in the A673 Ewing sarcoma model where endogenous EWS-FLI was silenced, and EWS-FLI-DBD proficient or deficient isoforms were re-expressed (isogenic context). They found that the DBD domain is key to mediate EWS-FLI cis activity (at msat) and to generate the formation of specific TADs. Furthermore, cells expressing DBD deficient EWS-FLI display very poor colony forming capacity, highlighting that targeting this domain may lead to therapeutic perspectives.

This new version of the study comprises as requested new data from an additional cell line. The new data has strengthened the manuscript. Nevertheless, some of the arguments of the authors pertaining to the limitations of immunoblots to assess stability of the DBD constructs or the poor reproducibility of the Micro C data remain problematic. While the effort to repeat MicroC in a different cell line is appreciated, the data are as heterogeneous as those in A673 and no real conclusion can be drawn. The authors should tone down their conclusions. If DBD has a strong effect on chromatin organization, it should be reproducible and detectable. The transcriptomic and cut and tag data are more consistent and provide robust evidence for their findings at these levels. 

We agree that the Micro-C data have more apparent heterogeneity within and across cell lines as compared to other analyses such as our included CUT&Tag and RNA-seq. We addressed the possible limitations of the technique as well as inherent biology that might be driving these findings in our previous responses. Despite the poor clustering on the PCA plots, our analysis on differential interacting regions, TADs and loops remain consistent across both cell lines. We are confident that these findings reflect the context of transcriptional regulation by the constructs, therefore the role of the alpha-helix in modulating chromatin organization. To address the concerns raised by the editors and reviewers for the strength of the conclusions we drew from the Micro-C findings we have made changes to the language used to describe them throughout the manuscript. Find these changes outlined below.

• On lines 70-71, "is required to restructure" was changed to "is implicated in restructuring of"

• On line 91, "is required for" was changed to "participates in"

• On line 98, "is required for" changed to "is potentially required for"

• On line 360-361, "is required for restructuring" changed to "participates in restructuring"

Concerning the issue of stability of the DBD and DBD+ constructs, a simple protein half-life assay (e.g. cycloheximide chase assay) could rule out any bias here and satisfactorily address the issue.

While we generally agree that a cycloheximide assay is a relatively simple approach to look at protein half-life, as we discussed last me the assays included in this paper are performed at equilibrium and rely on the concentration of protein at the me of the assay. This is particularly true for assays involving crosslinking, like Micro-C. As discussed in our prior response, western blots are semi quantitative at best, even when normalized to a housekeeping protein. In analyzing the relative protein concentration of DBD vs. DBD+ with relative protein intensities first normalized to tubulin and using the wildtype EWSR1::FLI1 rescue as a reference point, we find that there is no statistical difference in the samples used for micro-C here (Author responseimage 1A) or across all of the samples that we have used for publication (Author response image 1B). This does show that DBD generally has more variable expression levels relative to wildtype EWSR1::FLI1, and this is consistent with our experience in the lab.

Nonetheless, we did attempt to perform the requested cycloheximide chase experiment to determine protein stability. Unfortunately, despite an extensive number of troubleshooting attempts, we have not been able to get good expression of DBD for these experiments. The first author who performed this work has left the lab and we have moved to a new lab space since the benchwork was performed. We continue to try to troubleshoot to get this experimental system for DBD and DBD+ to work again. When we tried to look at stability of DBD+ following cycloheximide treatment, there did appear to be some difference in protein stability (Author response image 2). However, these conditions are not the same conditions as those we published, they do not meet our quality control standards for publication, and we are concerned about being close to the limit of detection for DBD throughout the later timepoints. Additional studies will be needed with more comparable expression levels between DBD and DBD+ to satisfactorily address the reviewer concerns.

Author response image 1.

Expression Levels of DBD and DBD+ Across Experiments. Expression levels of DBD and DBD+ protein based on western blot band intensity normalized by tubulin band intensity. Expression levels are relative to wildtype EWSR1::FLI1 rescue levels and are calculated for (A) A673 samples used for micro-C and (B) all published studies of DBD and DBD+. P-values were calculated with an unpaired t-test.

Author response image 2.

CHX chase assay to determine the stability of DBD and DBD+. (A) Knock-down of endogenous EWSR1::FLI1 detected with FLI1 ab and rescue with DBD and DBD+ detected with FLAG ab. (B) CHX chase assay to determine the stability of DBD and DBD+ in A-673 cells with quantification of the protein levels (n=3). Error bars represent standard deviation. The half-lives (t1/2) of DBD and DBD+ were listed in the table.

Suggestions:

The Reviewing Editor and a referee have considered the revised version and the responses of the referees. While the additional data included in the new version has consolidated many conclusions of the study, the MicroC data in the new cell line are also heterogeneous and as the authors argue, this may be an inherent limitation of the technique. In this situation, the best would be for the authors to avoid drawing robust conclusions from this data and to acknowledge its current limitations.

As discussed above, we have changed the language regarding our conclusions from micro-C data to soften the conclusions we draw per the Editor’s suggestion.

The referee and Reviewing Editor also felt that the arguments of the authors concerning a lack of firm conclusions on the stability of EWS-FLI1 under +/-DBD conditions could be better addressed. We would urge the authors to perform a cycloheximide chase type assay to assess protein half-life. These types of experiments are relatively simple to perform and should address this issue in a satisfactory manner.

As discussed above, we do not feel that differences in protein stability would affect the results here because the assays performed required similar levels of protein at equilibrium. Our additional analyses in this response shows that there are not significant differences between DBD and DBD+ levels in samples that pass quality control and are used in published studies. However, we attempted to address the reviewer and editor comments with a cycloheximide chase assay and were unable to get samples that would have passed our internal quality control standards. These data may suggest differences in protein stability, but it is unclear that these conditions accurately reflect the conditions of the published experiments, or that this would matter with equal protein levels at equilibrium.

The Two Parts of Likelihood: Likelihood isn't just one number. It is calculated by combining two factors:

Likelihood of Initiation/Occurrence: Will the enemy try to attack? (Or will the hurricane happen?)

Likelihood of Resulting in Adverse Impact: If they do attack, will they succeed? (Will our defenses stop them, or will they break through?)

Overall Likelihood: You combine those two factors to get the final score (e.g., if they are likely to try, AND likely to succeed, the Overall Likelihood is High).

to determine whether the strategy is on track

relate to an organization’s purpose and aspirations, and are typically communicated in some form of brief written statements

Overlap in developing strategy

vision statement, in contrast, is a future-oriented declaration of the organization’s purpose and aspirations.

mission statement communicates the organization’s reason for being, and how it aims to serve its key stakeholders.

Note d'Information : Priorités de la Protection de l’Enfance et Justice des Mineurs

Synthèse de l'Exécutif

Ce document synthétise les orientations stratégiques et les réformes engagées par le ministère de la Justice pour renforcer la protection de l’enfance et moderniser la justice des mineurs.

Les points clés incluent :

• Urgence et Rapidité : Réduction des délais de jugement (passés de 18 mois à 8,7 mois en quatre ans) et création d'une ordonnance de protection provisoire permettant au procureur de statuer en 72 heures.

• Refonte du Placement : Fermeture des Centres Éducatifs Fermés (CEF) publics au profit des Unités de Placement de la Jeunesse et de l'Éducation (UJPE), mettant l'accent sur la continuité pédagogique (52 semaines/an).

• Moyens Humains Massifs : Création de 1 600 postes au ministère de la Justice, dont 50 nouveaux cabinets de juges des enfants en deux ans et 70 postes à la Protection Judiciaire de la Jeunesse (PJJ).

• Évolutions Législatives : Soutien à l'imprescriptibilité des crimes sexuels sur mineurs, à la présence obligatoire de l'avocat pour l'enfant, et volonté de réformer l'« excuse de minorité » pour les crimes les plus graves.

• Protection contre les Fléaux Modernes : Lutte contre la prostitution des mineurs (6 prostituées sur 10 sont mineures), interdiction des téléphones portables en centres de placement, et encadrement du protoxyde d'azote.

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1. Renforcement de la Protection des Enfants Victimes

Urgence Judiciaire et Mesures de Sûreté

L'accent est mis sur la nécessité d'une justice qui s'adapte au rythme de l'enfant.

• Ordonnance de protection provisoire : Un nouveau dispositif permet au procureur d'agir en 72 heures pour protéger immédiatement un mineur, avec des interdictions de contact et l'attribution provisoire du logement au parent protecteur.

Le juge dispose ensuite de 8 jours pour être saisi et de 15 jours pour statuer.

• Loi du 18 mars 2024 : Prévoit le retrait automatique de l'autorité parentale pour les parents condamnés pour crime ou violence sexuelle sur leur enfant, ainsi que l'élargissement de la suspension de l'exercice de cette autorité dès la mise en examen.

Accompagnement et Droits des Mineurs

• Avocat pour l'enfant : Soutien à la présence obligatoire d'un avocat en assistance éducative.

Une expérimentation avec les barreaux est envisagée avant une généralisation législative.

• Unités d'Accueil Pédiatrique (UAPED) : Déploiement en cours sur tout le territoire pour améliorer le recueil de la parole et le soin des victimes.

• Chiens d'assistance judiciaire : Passage de 10 à une trentaine de chiens actuellement, avec un objectif de 100 chiens (un par département) d'ici un à deux ans pour apaiser les enfants lors des procédures.

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2. Réforme de la Justice Pénale des Mineurs

Équilibre entre Sanction et Éducation

La doctrine ministérielle refuse l'opposition entre ces deux concepts.

• La sanction comme acte éducatif : « La sanction fait partie de l'éducation. La sanction toute seule n'est pas un but en soi [...] et une éducation sans aucun interdit mène au n'importe quoi. »

• Efficacité du Code de la Justice Pénale des Mineurs (CJPM) : Les délais entre les faits et la sanction ont été divisés par deux en quatre ans (8,7 mois en 2024 contre 18 mois en 2020).

Transformation des Structures de Placement

Le constat sur les Centres Éducatifs Fermés (CEF) est jugé sévère : coût élevé (30 à 50 % de plus), taux de fugue identique aux centres classiques, et déshérence éducative (seulement 5 à 10 heures de cours par semaine).

• Création des UJPE : Ces nouvelles unités fusionnent les anciens foyers et les CEF pour garantir un parcours de reconstruction pédagogique.

• Recrutement de professeurs techniques : Réouverture d'un concours pour 40 professeurs dépendant directement du ministère de la Justice afin d'assurer 26 heures de cours par semaine, 52 semaines sur 52, y compris durant les vacances scolaires.

• Santé et Addictions : Recrutement de 60 infirmiers pour pallier les carences de soins psychiatriques et de prise en charge des addictions dans les centres de placement.

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3. Moyens et Organisation de la Justice

Augmentation des Effectifs

Le budget de la Justice permet une hausse inédite des moyens humains :

• Magistrature : Création de 50 cabinets de juges des enfants supplémentaires en deux ans (notamment à Bobigny, Cambrai, Alès).

Actuellement, certains cabinets gèrent entre 400 et 500 dossiers.

• PJJ : Recréation de 70 postes, permettant de renforcer les effectifs là où ils baissaient depuis 20 ans (ex: Marseille, Île-de-France).

• Milieu Ouvert : Réaffectation de 150 éducateurs vers le milieu ouvert pour ramener la charge de travail à environ 23 dossiers par agent (contre 25 auparavant).

Unité de Commandement

Le système actuel est jugé trop fragmenté (plusieurs ministères concernés, compétences partagées avec les départements pour l'ASE).

Une volonté de meilleure coordination, voire d'unité de responsabilité, est exprimée.

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4. Enjeux de Société et Nouvelles Menaces

Violences Sexuelles et Imprescriptibilité

• Fin de la prescription : Avis favorable pour l'imprescriptibilité des crimes sexuels sur mineurs, ainsi que pour les crimes de sang (assassinats).

• Prostitution des mineurs : Un constat alarmant montre que 60 % des prostituées en France sont mineures.

Des unités dédiées au sein de la PJJ sont opérationnelles depuis trois mois pour lutter contre ce fléau et les réseaux de proxénétisme.

Sécurité Numérique et Addictions

• Interdiction des téléphones : La nouvelle circulaire de politique éducative et pénale impose l'interdiction des téléphones portables dans les chambres des centres de placement pour protéger les mineurs des prédations numériques (trafiquants, proxénètes).

• Protoxyde d'azote : Soutien à la pénalisation du transport et de l'achat en ligne (en dehors du cadre médical), alors que les intoxications ont triplé entre 2020 et 2023.

Débats sur la Responsabilité Pénale

• Excuse de minorité : Position favorable à la fin de l'automatisme de l'atténuation de peine pour les crimes les plus graves (assassinats, tortures) commis par des mineurs de 13 à 15 ans.

Cela nécessiterait une évolution constitutionnelle tout en préservant la spécialisation du jugement des mineurs.

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5. Données Clés et Statistiques

| Indicateur | Donnée Source | | --- | --- | | Délai moyen de jugement (2020) | 18 mois | | Délai moyen de jugement (2024) | 8,7 mois | | Dossiers par cabinet de juge des enfants | 400 à 500 (moyenne) | | Proportion de mineurs parmi les prostitués | 60 % | | Nombre de mineurs à l'ASE | 400 000 (dont 200 000 placés) | | Heures de cours en CEF | < 10h/semaine (contre 26h en milieu classique) | | Placements chez des tiers de confiance | < 9 % (19 000 jeunes) |

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Citations Marquantes

« L'enfant ne vit pas au rythme d'un dossier administratif ou d'un dossier judiciaire. [...] 4 mois pour un mineur c'est une vie. »

« Nous devrions pouvoir en grande partie avoir honte de la façon dont on traite une partie de ces enfants notamment à l'aide sociale à l'enfance. »

« Le placement doit protéger et pas rendre encore plus vulnérable. »

« La sanction fait partie de l'éducation. [...] Une éducation sans jamais aucun interdit mène au n'importe quoi. »

Step 1: Prepare for Assessment Before you start, you need a plan. You align the assessment with the organization's goals. (Slide 2 explains this in detail).

Step 2: Conduct Assessment (The Core) This is the "Execution" phase. Memorize this sequence inside the gray box, as it is often a quiz question:

• Identify Threats: Who/what wants to attack us?
• Identify Vulnerabilities: Where are we weak?
• Determine Likelihood: What are the odds of this happening?
• Determine Impact: If it happens, how bad will it hurt?
• Determine Risk: Combine Likelihood and Impact to get a Risk score. Formula to remember: Risk = Likelihood × Impact

Step 3: Communicate Results Notice the arrows go both ways. You don't just report at the end; you talk to stakeholders during the process to ensure facts are correct.

Step 4: Maintain Assessment Risk assessment is not a one-time event. You must monitor and update it over time as technology changes.

implementation layer - where hardware and software live

on

Should this be formatted as code like elsewhere?

"On Atari," is better. Same with further on "while in the other two", should be "while on the other two"

The links didn't work for me

Gissar att vi borde ändra till "post"

We're all very sympathetic to Caliban, but he does attempt to use Miranda as a bargaining chip here, specifically selling her out for sexual purposes to Stephano. Its interesting that Miranda is made a sexual object by Caliban and Prospero, who hates Caliban for his attempted rape of her. Ferdinand isn't sexless, but is far less threatening as a white guy and prince. Prosper approves of him for those reasons, as well as the fact that he's in control, ]since he wants Ferdinand to fall in love with Miranda so he can exploit their courtship for power.

I would be interested to see the location of this study. Was it for an entire district? a city? a state?

Enrique's stake is a sense of identity as a Mexican American individual and safety when confronted at a border checkpoint. He was cautious and aware of the reality of his scenario. At the end, he showed greater clarity about his own identity and confidence in his voice to speak up. He had shown great change, and even though this experience wasn't pleasant, the ability to continue and speak up about a hurtful issue he was able to persevere after reflecting.

I chose this point specifically to remember that implicit bias is a real and big issue, and how the tiniest of things can escalate due to these types of things. I feel like this ties in with the theme that having an implicit bias can have long lasting effects to family's, which I believe why he thought back to this person, is because anything can happen regardless of status, implicit bias is still a prevalent thing.

The story centers on a moment where Kathi Kinnear is telling herself what she can’t do. She is limiting her capabilities in her own mind, which is a problem she was facing in the beginning of her hardship. The real stakes are whether she will accept those limitations or challenge them. If she fails to surpass her limitations, she reinforces doubt and dwells in that state of despair. If she succeeds in pushing past her own limits, she reshapes her identity. In the end, Kathi was able to see light through these dark times in her memory and persevere past what once restrained her.

On first read, I'm not seeing the direct connection that this is what I need to do to lean back in to my life (yet) -- might just mean switching this copy out and including it in this bigger text section

Notably, in focus groups 3/4 countries say dfisillusioned w/ politics IN GENRAL as oppsoed to only EU

In either case, MOST say they have no ideas how it works, SOME take conspiratorial attitude (not by today's standards, but still).

Défis et controverses de l'éducation des parents : Analyse et perspectives

Résumé exécutif

Le présent document synthétise l'analyse du sociologue Claude Martin concernant l'évolution des pratiques et des politiques d'éducation des parents en France.

Le constat initial révèle un « effet de ciseaux » alarmant : une explosion de la souffrance psychique chez les jeunes coïncidant avec un affaissement de l'offre de soins et de soutien humain.

L'analyse souligne un basculement paradigmatique majeur : le passage d'un déterminisme social (collectif et structurel) à un déterminisme parental (individuel et comportemental).

Cette évolution a favorisé l'émergence d'un marché du conseil aux parents et d'une « parentalité positive » qui, bien que prônant la bienveillance, impose de nouvelles injonctions de performance et de bonheur.

Le document explore également les usages politiques des neurosciences et les controverses actuelles entourant les méthodes éducatives, concluant sur le paradoxe du « double bind » (double contrainte) auquel les parents modernes sont confrontés.

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1. L'état des lieux : Une jeunesse en souffrance

La situation actuelle de l'enfance en France est marquée par une dégradation notable de la santé mentale, un phénomène antérieur à la pandémie de COVID-19 mais accentué par celle-ci.

L'effet de ciseaux

Le Haut Conseil de l'enfance, de la famille et de l'âge (HCFEA) alerte sur deux phénomènes concomitants :

• Explosion de la demande : Une hausse massive des manifestations de souffrance psychique chez les enfants et adolescents.

• Affaissement de l'offre : Une réduction drastique des moyens de prise en charge (thérapies de parole, lieux d'accueil) et une crise du secteur de la pédopsychiatrie.

La réponse médicamenteuse

Faute de structures d'accompagnement suffisantes, la réponse s'est déplacée vers la prescription de psychotropes, avec des augmentations spectaculaires entre 2014 et 2021 :

| Type de médicament | Augmentation de la prescription (2014-2021) | | --- | --- | | Antidépresseurs | \+ 63 % | | Psychostimulants | \+ 78 % | | Hypnotiques et sédatifs | \+ 155 % | | Antipsychotiques | \+ 50 % |

Le phénomène du retrait social

Le document identifie l'émergence en France du phénomène de retrait social (type Hikikomori), touchant principalement des garçons lycéens (15-17 ans).

Ce refus d'entrer dans la course à la réussite scolaire est parfois analysé, de manière controversée, à travers le prisme de l'influence parentale (notamment des mères jugées excessives ou intrusives).

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2. Le basculement des déterminismes

L'approche sociologique a radicalement changé de nature entre les années 1960 et aujourd'hui.

Du collectif à l'individuel

• Le déterminisme social (Années 60-70) : La réussite ou l'échec d'un enfant était perçu comme le résultat de l'appartenance à une classe sociale et de la reproduction des inégalités. C'était un enjeu de lutte collective et politique.

• Le déterminisme parental (Actuel) : La focale s'est déplacée vers le comportement individuel des parents.

Les difficultés de l'enfant (santé mentale, échec scolaire, comportement antisocial) sont désormais imputées à un déficit de « compétences parentales ».

La psychologisation des problèmes publics

Cette vision individualiste conduit à une responsabilisation accrue des parents, générant souvent un sentiment de culpabilité.

Des auteurs comme Frank Furedi (Paranoid Parenting) ou d'autres parlent de « parentalité narcissique », soulignant un manque de confiance des adultes dans le futur qui compromettrait leur capacité à éduquer.

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3. Évolution historique du contrôle de la fonction parentale

L'éducation des parents n'est pas un concept nouveau, mais elle a traversé plusieurs phases distinctes :

1. Fin XIXe - Début XXe siècle (Hygiénisme et Protection) : Lutte contre la mortalité infantile et protection contre les « classes dangereuses ».

Il s'agissait alors d'enseigner aux mères les soins de base et de limiter la puissance paternelle absolue.

2. L'après-guerre (Le marché du conseil) : Émergence de manuels à succès (Benjamin Spock, Laurence Pernoud, Françoise Dolto).

Ce secteur économique puissant prospère sur l'inquiétude des parents : plus ils consomment de conseils, plus ils se sentent déroutés, alimentant une consommation accrue.

3. Années 1990 (L'invention de la « Parentalité ») : Le terme parenting (centré sur l'acte et le comportement plutôt que sur le statut) est traduit par « parentalité ».

Cela devient un segment à part entière de l'action publique, visant à « soutenir » les parents, mais les prenant en réalité comme cibles d'intervention.

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4. Neurosciences et "Neuro-parenting"

L'usage des neurosciences dans l'éducation des parents fait l'objet de critiques importantes, notamment concernant la surinterprétation de données scientifiques.

• Le mythe des trois premières années : Une fascination scientiste pour l'imagerie cérébrale a conduit à l'idée d'une « fenêtre d'opportunité » unique durant les trois premières années de vie.

Cette vision déterministe présente le bébé comme un « petit ordinateur » dont le câblage dépendrait entièrement des stimuli parentaux.

• L'adolescent stigmatisé : À l'inverse de la vision « mine d'or » du cerveau du nourrisson, le cerveau de l'adolescent est souvent présenté par les politiques publiques comme « mal foutu » ou intrinsèquement problématique, justifiant des interventions urgentes.

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5. La parentalité positive : Entre bienveillance et injonction

La « parentalité positive » est devenue un courant dominant, porté par un lobbying actif auprès des pouvoirs publics.

La controverse du "Time Out"

Une polémique oppose actuellement deux visions :

• Les partisans du cadre : Préconisent des méthodes simples comme le « Time Out » (envoyer l'enfant dans sa chambre) pour gérer les crises.

• Les radicaux de la bienveillance : Assimilent le « Time Out » à une « violence éducative ordinaire », créant une continuité entre ces pratiques et des dérives graves comme l'infanticide.

L'injonction au bonheur

La parentalité moderne impose une « norme sous la peau » : les mères ne doivent pas seulement bien agir, elles doivent être « authentiquement heureuses ». Un faux sourire est perçu comme dangereux pour l'enfant, créant une pression psychologique insoutenable pour les parents.

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6. Conclusion : Le paradoxe de la mission parentale

Le document conclut sur l'impasse du « double bind » parental actuel :

• D'un côté : Les parents qui « n'en font pas assez » sont désignés comme irresponsables ou absents.

• De l'autre : Les « parents hélicoptères » (parentalité intensive) sont critiqués pour générer une dépendance problématique chez l'enfant.

L'analyse de Claude Martin suggère que la politique de parentalité devrait redevenir un soutien collectif et générationnel plutôt qu'une focalisation sur les comportements individuels.

L'éducation est une improvisation située historiquement ; les modèles parentaux ne peuvent être des invariants déconnectés du contexte social et des limites de chaque génération.

Decline in Golden Eagle prey habitat = decline in Golden Eagle population and therefore an overall disturbance to the ecosystem.

Warm-dry sagebrush steppe is a critically important habitat for Golden Eagles, serving as a nesting habitat, hunting ground (and primary habitat for prey). The health of this ecosystem is a key indicator of the Golden Eagle population's survival.

mean proportion of unburned shrubland decreased from 0.73 to 0.22 inches in 2014

Dossier de Synthèse : L'Implication des Usagers dans les Structures d'Exercice Coordonné

Synthèse

Ce document synthétise les enseignements du webinaire régional concernant l'indicateur « Implication des usagers » pour les Maisons de Santé Pluriprofessionnelles (MSP) et les Centres de Santé (CdS).

Initialement centré sur la satisfaction des patients, cet indicateur a évolué pour devenir un levier global de transformation du système de santé, incitant les structures à passer d'une logique de soin « pour » le patient à une logique de soin « avec » le patient.

Bien qu'optionnel, cet indicateur est considéré comme un objectif structurant pour l'exercice coordonné, conditionnant une partie du financement par l'Assurance Maladie via l'Accord Cadre Interprofessionnel (ACI).

En 2024, plus de 70 % des structures ont atteint le niveau 1 de cet indicateur, démontrant une maturité croissante.

Le passage au niveau 2, qui implique une co-décision et un partenariat pérenne, reste le défi majeur pour les équipes de soins primaires.

1. Cadre Stratégique et Enjeux de l'Indicateur

L'implication des usagers n'est plus perçue comme un objectif isolé, mais comme une démarche transversale visant à améliorer l'efficacité des soins et l'adéquation de l'offre de santé aux besoins réels des territoires.

Objectifs de la démarche

• Améliorer la qualité des soins : En intégrant l'expertise de vie du patient (maladie, handicap).

• Renforcer la démocratie en santé : Donner une voix légitime aux usagers dans la co-construction des actions de santé.

• Évolution du projet de santé : Utiliser les retours des usagers pour faire évoluer de manière vivante le projet de la structure.

• Qualité de vie au travail (QVT) : Le partenariat est identifié comme un levier d'amélioration du quotidien des professionnels.

Financement et Justification

Le financement par l'Assurance Maladie est conditionné par la fourniture de justificatifs probants.

Cette exigence est présentée non pas comme une suspicion, mais comme une garantie de transparence dans la gestion des fonds publics.

• Nouveauté : Les négociations en cours suggèrent une évolution du modèle pour supprimer les niveaux de complexité, tout en maintenant l'évaluation de la satisfaction et la co-décision.

• Dynamisme : Pour être rémunérée, une structure doit démontrer une progression ou une révision de ses outils d'une année sur l'autre.

2. La Philosophie du Partenariat en Santé

Le passage au partenariat repose sur un changement de paradigme, souvent appelé le « modèle de Montréal ».

| Modèle | Approche | Position de l'usager | | --- | --- | --- | | Paternaliste | Pour le patient | Objet de soin, passif. | | Centré sur le patient | Pour le patient | Au centre des préoccupations, mais exclu des décisions d'équipe. | | Partenariat | Avec le patient | Membre de l'équipe, reconnaissance de ses savoirs expérientiels. |

Le Continuum de l'Engagement

L'implication se décline en quatre étapes progressives :

1. Information : Diffusion de données de santé publique ou de fonctionnement de la structure.

2. Consultation : Recueil d'avis (questionnaires de satisfaction, boîtes à idées).

3. Collaboration : Travail conjoint sur des projets ponctuels (création d'une affiche, soirée thématique).

4. Partenariat : Co-construction, co-décision et co-réalisation sur le long terme.

3. Niveaux d'Atteinte et Justificatifs Requis

L'indicateur se structure en deux niveaux cumulatifs pour l'octroi de la rémunération.

Niveau 1 : Information et Consultation

• Actions : Mise en place d'outils pour évaluer la satisfaction et recueillir les besoins.

• Justificatifs : Exemplaires des questionnaires, synthèse des résultats, plan d'action découlant des retours usagers.

• Évolution annuelle : Si la structure reste au niveau 1, elle doit prouver que l'outil a été révisé ou analysé à nouveau.

Niveau 2 : Collaboration et Partenariat

• Actions : Intégration pérenne des usagers dans la gouvernance ou les groupes de travail.

• Justificatifs : Désignation d'un référent usager, compte-rendu de réunions de co-construction, description de l'apport réel de l'usager dans les décisions.

• Exemple de dynamique : « Si l'année suivante la structure reste au niveau 2, elle doit évaluer ce qui a été fait l'année précédente dans le cadre de la collaboration. »

4. Les Acteurs du Partenariat

La diversité des profils permet d'adapter l'implication selon les besoins du projet de santé.

• L'Usager : Patient, personne accompagnée ou proche-aidant.

• Le Patient Partenaire / Expert : Individu ayant développé des compétences suite à sa maladie et pouvant intervenir en Éducation Thérapeutique du Patient (ETP) ou en recherche.

• Le Représentant des Usagers (RU) : Membre d'une association agréée, formé au système de santé et siégeant dans des instances officielles.

• Le Citoyen Engagé : Habitant du quartier souhaitant contribuer à la vie de la structure de proximité.

• Le Médiateur en Santé : Facilite le lien dans les salles d'attente ou lors de l'accueil.

Donnée clé (Enquête BVA 2021) : 80 % des habitants d'Occitanie souhaitent le développement des regroupements de professionnels et 47 % se disent prêts à s'impliquer auprès de ces équipes.

5. Exemples Concrets et Ressources

Le webinaire a mis en avant des initiatives réussies illustrant la mise en œuvre de l'indicateur :

• Éducation Thérapeutique (ETP) : Une MSP a intégré un patient expert pour reconstruire totalement son programme diabète, augmentant significativement la satisfaction de la patientèle.

• Groupes de parole : En Haute-Garonne, une patiente partenaire et une psychologue co-animent mensuellement un groupe de parole sur le cancer.

• Gouvernance : Bien que les SISA (Sociétés Interprofessionnelles de Soins Ambulatoires) soient juridiquement limitées aux professionnels, des comités d'usagers peuvent être créés pour influencer les décisions stratégiques.

• Communication : Utilisation de lettres d'information, de panneaux en salle d'attente ou de vidéos "ambassadeurs" où des patients expliquent l'offre de soins de la structure à leurs pairs.

Ressources Disponibles

• COPS (Centre Opérationnel du Partenariat en Santé) : Dispositif financé par l'ARS Occitanie offrant des fiches pratiques, un répertoire de patients partenaires et des compagnonnages.

• France Assos Santé : Propose des formations gratuites pour les usagers souhaitant s'impliquer.

• Haute Autorité de Santé (HAS) : Guide sur l'engagement des usagers dans les structures de soins primaires.

6. Points de Vigilance et Obstacles

• Statut juridique et financier : Il n'existe pas encore de statut de « métier » pour le patient partenaire. La rémunération reste complexe (micro-entreprise ou bénévolat avec défrayage).

• Recrutement : Il est conseillé de recruter un patient partenaire « comme un collaborateur », sur la base de ses compétences, de son savoir-être et de valeurs partagées avec l'équipe.

• Représentativité : Il est illusoire de chercher une représentativité statistique parfaite. L'objectif est de combiner une diversité de visions et de compétences.

• Accompagnement : Compte tenu de l'absence de cadre légal rigide, les structures sont encouragées à se faire accompagner par des tiers facilitateurs pour sécuriser leurs projets.

Lady guest reinforced the place of arthur as a welshman through her translation. however, did such a translation into english, and her setting into the 'broader context of medieval European romance', reduce the extent that arthur belonged to the welsh?

L’Attention aux Vulnérabilités : Une Priorité Éthique et Pédagogique

Résumé Exécutif

Ce document de synthèse examine le rôle critique de l'attention aux vulnérabilités dans le milieu scolaire, positionnant cette approche non seulement comme une obligation éthique, mais aussi comme un facteur déterminant de l'efficacité pédagogique.

L'analyse souligne que la relation enseignant-élève est intrinsèquement asymétrique, plaçant l'élève dans une position d'exposition aux risques — de la blessure émotionnelle au décrochage scolaire.

Les points clés abordés incluent :

• La redéfinition de la vulnérabilité : Elle n'est plus perçue comme un état permanent de la personne, mais comme une situation (momentanée ou durable) affectant jusqu'à la moitié des effectifs scolaires sur une année.

• L'impact des besoins fondamentaux : La satisfaction des besoins de compétence, d'autonomie et d'affiliation est essentielle à la sécurité relationnelle.

• La lutte contre la « Violence Pédagogique Ordinaire » : L'identification et l'élimination des micro-violences (verbales, comportementales) sont impératives.

• Le passage à la bienveillance active : L'adoption de gestes professionnels ciblés, tels que le feedback positif et l'exigence bienveillante, corrèle directement avec la réussite des élèves.

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1. La Nature de la Relation Pédagogique : Une Asymétrie Fondamentale

La relation éducative est définie par une asymétrie structurelle. L'enseignant détient la maîtrise des compétences, du statut, des objectifs pédagogiques, de l'espace et du temps, tandis que l'élève évolue dans une position de dépendance et de moindre conscience des enjeux.

La Vulnérabilité comme Situation

Le terme vulnérabilité (du latin vulnus, la blessure) désigne une fragilité qui expose l'élève à des risques de blessures concernant ses droits, sa dignité ou, plus fréquemment, ses besoins fondamentaux.

• Évolution conceptuelle : La recherche actuelle privilégie la notion de « situations de vulnérabilité » plutôt que de « personnes vulnérables ».

• Typologie des situations :

◦ Durables : Élèves en situation de handicap ou à besoins éducatifs particuliers (environ 470 000 à 800 000 élèves incluant les profils neurodéveloppementaux, haut potentiel et allophones).   

◦ Momentanées : Élèves traversant des crises familiales (séparation), économiques (perte d'emploi des parents), affectives ou liées au parcours migratoire.

On estime que près de 50 % des élèves vivent de telles phases chaque année.

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2. Cartographie des Besoins Fondamentaux en Milieu Scolaire

Pour garantir une relation éthique, l'enseignant doit répondre à une nomenclature de besoins multidimensionnels.

| Catégorie de Besoin | Composantes Clés | | --- | --- | | Besoins de base (Deci & Ryan) | Compétence, Autonomie, Affiliation. | | Sécurité et Confiance | Sécurité relationnelle, confiance en soi, confiance en l'adulte et en l'institution. | | Socialisation et Équité | Appartenance au groupe, besoin de justice, respect et considération. | | Accompagnement | Besoin d'aide, besoin de temps, besoin de dialogue avec l'adulte. |

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3. Gestes Professionnels et Leviers de Réussite

La recherche, notamment les méta-analyses de John Hattie, démontre que les facteurs relationnels ont un impact supérieur à la moyenne sur la réussite scolaire (coefficients de corrélation supérieurs à 0,7, là où le seuil de significativité est à 0,4).

Levier Majeur : Le Feedback

Le feedback positif agit comme un levier fondamental pour nourrir le besoin d'estime et de sécurité de l'élève. Il doit être intégré dans les moments pédagogiques critiques :

• L'accueil des élèves.

• La mise en activité.

• Les phases d'évaluation (annonce, correction, exploitation).

• La gestion des obstacles et des erreurs (dédramatisation).

Communication et Posture

La communication se divise en trois dimensions :

1. Verbale : Les mots utilisés.

2. Non-verbale : Gestes, mimiques, posture spatiale.

3. Paraverbale : Ton, volume et débit de la voix (cruciaux pour la perception de la satisfaction de l'enseignant par l'élève).

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4. La Violence Pédagogique Ordinaire (VPO)

La VPO regroupe des micro-violences souvent inconscientes mais délétères, désormais interdites par la loi du 10 juillet 2019.

• Manifestations : Cris, moqueries, intimidations, stigmatisations, discriminations sociales, comparaisons excessives ou injonctions paradoxales.

• Conséquences : Stress, mal-être, conduites antisociales et agressivité. Ces comportements ajoutent une vulnérabilité supplémentaire à celle déjà présente, créant un cercle vicieux de l'échec.

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5. Vers une Éthique de la Bienveillance Active

L'éthique est ici définie comme une disposition psychique visant à rechercher le comportement le plus juste pour l'élève.

Distinction entre Bienveillances

Le passage d'une posture passive à une posture active est nécessaire :

• Bienveillance Passive (ou minimale) : Se limiter à ne pas blesser l'élève et le laisser affronter seul ses difficultés par manque de temps ou de ressources.

• Bienveillance Active : Caractérisée par une qualité de présence, un soutien de proximité, des exigences adaptées et un intérêt réel pour la personne de l'élève au-delà de ses résultats.

Les 5 Modes d'Expression (selon Gwénola Reto)

1. S'intéresser à l'élève : Encourager sa pensée et accepter ses divergences.

2. Prendre en compte les besoins : Identifier les besoins cognitifs et fondamentaux.

3. Se soucier de son bien-être : Veiller à son intérêt et sa motivation.

4. Valoriser la personne : Distinguer l'individu de ses résultats normatifs lors des évaluations.

5. Manifester de la compassion : Montrer une sensibilité face aux difficultés rencontrées par l'élève.

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Conclusion

L'attention aux vulnérabilités ne doit pas être perçue comme une baisse d'exigence, mais comme une exigence bienveillante.

En sécurisant le cadre relationnel et en répondant aux besoins psycho-affectifs, l'enseignant rend l'exigence scolaire acceptable et fructueuse, garantissant ainsi que l'élève reste « dans le jeu de la réussite ».

Reviewer #3 (Public review):

Summary:

Combining electrophysiological recording, circuit tracing, single cell RNAseq, and optogenetic and chemogenetic manipulation, Howe and colleagues have identified a graded division between anterior and posterior plCoA and determined the molecular characteristics that distinguish the neurons in this part of the amygdala. They demonstrate that the expression of slc17a6 is mostly restricted to the anterior plCoA whereas slc17a7 is more broadly expressed. Through both anterograde and retrograde tracing experiments, they demonstrate that the anterior plCoA neurons preferentially projected to the MEA whereas those in the posterior plCoA preferentially innervated the nucleus accumbens. Interestingly, optogenetic activation of the aplCoA drives avoidance in a spatial preference assay whereas activating the pplCoA leads to preference. The data support a model that spatially segregated and molecularly defined populations of neurons and their projection targets carry valence specific information for the odors. Moreover, the intermingling of neurons in the plCoA is consistent with prior observations. The presence of a gradient rather than a distinct separation of the cells fits the model being proposed. The discoveries represent a conceptual advance in understanding plCoA function and innate valence coding in the olfactory system.

Strengths:

The strongest evidence supporting the model comes from single-cell RNASeq, genetically facilitated anterograde and retrograde circuit tracing, and optogenetic stimulation. The evidence clear demonstrates two molecularly defined cell populations with differential projection targets. Stimulating the two populations produced opposite behavioral responses.

Weaknesses:

The weaknesses noted in primary review have all been addressed adequately.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

This study by Howe and colleagues investigates the role of the posterolateral cortical amygdala (plCoA) in mediating innate responses to odors, specifically attraction and aversion. By combining optogenetic stimulation, single-cell RNA sequencing, and spatial analysis, the authors identify a topographically organized circuit within plCoA that governs these behaviors. They show that specific glutamatergic neurons in the anterior and posterior regions of plCoA are responsible for driving attraction and avoidance, respectively, and that these neurons project to distinct downstream regions, including the medial amygdala and nucleus accumbens, to control these responses.

Strengths:

The major strength of the study is the thoroughness of the experimental approach, which combines advanced techniques in neural manipulation and mapping with high-resolution molecular profiling. The identification of a topographically organized circuit in plCoA and the connection between molecularly defined populations and distinct behaviors is a notable contribution to understanding the neural basis of innate motivational responses. Additionally, the use of functional manipulations adds depth to the findings, offering valuable insights into the functionality of specific neuronal populations.

Weaknesses:

There are some weaknesses in the study's methods and interpretation. The lack of clarity regarding the behavior of the mice during head-fixed imaging experiments raises the possibility that restricted behavior could explain the absence of valence encoding at the population level.

We agree with idea that head-fixation may alter the state of the animal and the neural encoding of odor. To address this, we have provided further analysis of walking behavior during the imaging sessions, which is provided in Figure S2. Overall, we could not identify any clear patterns in locomotor behavior that are odor-specific. Moreover, when neural activity was sorted depending on the behavioral state (walking, pausing or fleeing) we didn’t observe any apparent patterns in odor-evoked neural activity. This is now discussed in the Results and Limitations sections of the manuscript.

Furthermore, while the authors employ chemogenetic inhibition of specific pathways, the rationale for this choice over optogenetic inhibition is not fully addressed, and this could potentially affect the interpretation of the results.

The rationale was logistical. First, inhibition of over a timescale of minutes is problematic with heat generation during prolonged optical stimulation. Second, our behavioral apparatus has a narrow height between the ceiling and floor, making tethering difficult. This is now explained the results section. The trade-off of using chemogenetics is that we are silencing neurons and not specific projections. However, because we find that NAc- and MeA- projecting neurons have little shared collateralization, we believe the conclusion of divergent pathways still stands. This is now discussed in the Limitations section.

Additionally, the choice of the mplCoA for manipulation, rather than the more directly implicated anterior and posterior subregions, is not well-explained, which could undermine the conclusions drawn about the topographic organization of plCoA.

We targeted the middle region of plCoA because it contains a mixture of cell types found in both the anterior and posterior plCoA, allowing us to test the hypothesis that cell types, not intra plCoA location, elicit different responses. Had we targeted the anterior or posterior regions, we would expect to simply recapitulate the result from activation of random cells in each region. As a result, we think stimulation in the middle plCoA is a better test for the contribution of cell types. We have now clarified this in the text.

Despite these concerns, the work provides significant insights into the neural circuits underlying innate behaviors and opens new avenues for further research. The findings are particularly relevant for understanding the neural basis of motivational behaviors in response to sensory stimuli, and the methods used could be valuable for researchers studying similar circuits in other brain regions. If the authors address the methodological issues raised, this work could have a substantial impact on the field, contributing to both basic neuroscience and translational research on the neural control of behavior.

Reviewer #2 (Public review):

Summary:

The manuscript by the Root laboratory and colleagues describes how the posterolateral cortical amygdala (plCoA) generates valenced behaviors. Using a suite of methods, the authors demonstrate that valence encoding is mediated by several factors, including spatial localization of neurons within the plCoA, glutamatergic markers, and projection. The manuscript shows convincingly that multiple features (spatial, genetic, and projection) contribute to overall population encoding of valence. Overall, the authors conduct many challenging experiments, each of which contains the relevant controls, and the results are interpreted within the framework of their experiments.

Strengths:

- For a first submission the manuscript is well constructed, containing lots of data sets and clearly presented, in spite of the abundance of experimental results.

- The authors should be commended for their rigorous anatomical characterizations and posthoc analysis. In the field of circuit neuroscience, this is rarely done so carefully, and when it is, often new insights are gleaned as is the case in the current manuscript.

- The combination of molecular markers, behavioral readouts and projection mapping together substantially strengthen the results.

- The focus on this relatively understudied brain region in the context is valence is well appreciated, exciting and novel.

Weaknesses:

- Interpretation of calcium imaging data is very limited and requires additional analysis and behavioral responses specific to odors should be considered. If there are neural responses behavioral epochs and responses to those neuronal responses should be displayed and analyzed.

We have now considered this, see response above.

- The effect of odor habituation is not considered.

We considered this, but we did not find any apparent differences in valence encoding as measured by the proportion of neurons with significant valence scores across trials (see Figure 1J).

- Optogenetic data in the two subregions relies on very careful viral spread and fiber placement. The current anatomy results provided should be clear about the spread of virus in A-P, and D-V axis, providing coordinates for this, to ensure readers the specificity of each sub-zone is real.

We were careful to exclude animals for improper targeting. The spread of virus is detailed in Figures S3, S8 & S9.

- The choice of behavioral assays across the two regions doesn't seem balanced and would benefit from more congruency.

The choice of the 4-quadrant assay was used because this study builds off of our prior experiments that demonstrate a role for the plCoA in innate behavior. It is noteworthy that the responses to odor seen in this assay are generally in agreement with other olfactory behavioral assays, so one wouldn’t predict a different result. Moreover, the approach and avoidance responses measured in this assay are precisely the behaviors we wish to understand. We did examine other non-olfactory behavioral readouts (Figures S3, S8), and didn’t observe any effect of manipulation of these pathways.

- Rationale for some of the choices of photo-stimulation experiment parameters isn't well defined.

The parameters for photo-stimulation were based on those used in our past work (Root et al., 2014). We used a gradient of frequency from 1-10 Hz based on the idea that odor likely exists in a gradient and this was meant to mimic a potential gradient, though we don’t know if it exists. The range in stimulation frequencies appears to align with the actual rate of firing of plCoA neurons (Iurilli et al., 2017).

Reviewer #3 (Public review):

Summary:

Combining electrophysiological recording, circuit tracing, single cell RNAseq, and optogenetic and chemogenetic manipulation, Howe and colleagues have identified a graded division between anterior and posterior plCoA and determined the molecular characteristics that distinguish the neurons in this part of the amygdala. They demonstrate that the expression of slc17a6 is mostly restricted to the anterior plCoA whereas slc17a7 is more broadly expressed. Through both anterograde and retrograde tracing experiments, they demonstrate that the anterior plCoA neurons preferentially projected to the MEA whereas those in the posterior plCoA preferentially innervated the nucleus accumbens. Interestingly, optogenetic activation of the aplCoA drives avoidance in a spatial preference assay whereas activating the pplCoA leads to preference. The data support a model that spatially segregated and molecularly defined populations of neurons and their projection targets carry valence specific information for the odors. The discoveries represent a conceptual advance in understanding plCoA function and innate valence coding in the olfactory system.

Strengths:

The strongest evidence supporting the model comes from single cell RNASeq, genetically facilitated anterograde and retrograde circuit tracing, and optogenetic stimulation. The evidence clear demonstrates two molecularly defined cell populations with differential projection targets. Stimulating the two populations produced opposite behavioral responses.

Weaknesses:

There are a couple of inconsistencies that may be addressed by additional experiments and careful interpretation of the data.

Stimulating aplCoA or slc17a6 neurons results in spatial avoidance, and stimulating pplCoA or slc17a7 neurons drives approach behaviors. On the other hand, the authors and others in the field also show that there is no apparent spatial bias in odor-driven responses associated with odor valence. This discrepancy may be addressed better. A possibility is that odor-evoked responses are recorded from populations outside of those defined by slc17a6/a7. This may be addressed by marking activated cells and identifying their molecular markers. A second possibility is that optogenetic stimulation activates a broad set of neurons that and does not recapitulate the sparseness of odor responses. It is not known whether sparsely activation by optogenetic stimulation can still drive approach of avoidance behaviors.

We agree that marking specific genetic or projection defined neurons could help to clarify if there are some neurons have more selective valence responses. However, we are not able to perform these experiments at the moment. We have included new data demonstrating that sparser optogenetic activation evokes behaviors similar in magnitude as the broader activation (see Figure S4).

The authors show that inhibiting slc17a7 neurons blocks approaching behaviors toward 2-PE. Consistent with this result, inhibiting NAc projection neurons also inhibits approach responses. However, inhibiting aplCOA or slc17a6 neurons does not reduce aversive response to TMT, but blocking MEA projection neurons does. The latter two pieces of evidence are not consistent with each other. One possibility is that the MEA projecting neurons may not be expressing slc17a6. It is not clear that the retrogradely labeling experiments what percentage of MEA- and NACprojecting neurons express slc17a6 and slc17a7. It is possible that neurons expressing neither VGluT1 nor VGluT2 could drive aversive or appetitive responses. This possibility may also explain that silencing slc17a6 neurons does not block avoidance.

We have now performed RNAscope staining on retrograde tracing to better define this relationship. Although the VGluT1 and VGluT2 neurons have biased projections to the MeA and NAc, respectively, there is some nuance detailed in Figure S10. Generally, MeA projecting neurons are predominately VGluT2+, whereas NAc projecting have about 20% that express both. Some (less than 35%) retrogradely labeled neurons were not detected as VGluT1 or VGluT2 positive, suggesting that other populations could also contribute. We agree that the discrepancy between MeA-projection and VGluT2 silencing is likely due to incomplete targeting of the MeA-projecting population with the VGluT2-cre line. This is included in the Discussion section.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Main:

(1) For the head-fixed imaging experiments, what is the behavior of the mice during odor exposure? Could the weak reliability of individual neurons be due to a lack of approach or avoidance behavior? Could restricted behavior also explain the lack of valence encoding at the population level?

We agree that this is a limitation of head-fixed recordings. In the revised manuscript we did attempt to characterize their behavioral response, and look for correlations in odor representation. Although we did find different patterns of odor-evoked walking behavior, these patterns were not reliable or specific to particular odors (Figure S2). For example, one might expect aversive odors to pause walking or elicit a fast fleeing-like response, but we did not observe any apparent differences for locomotion between odors as all odors evoked a mixture of responses (Figure S2A-D, text lines 208-232). We then examined responses to odor depending on the behavioral state (walking, pausing or fleeing) and didn’t observe any apparent patterns in odor responses (Figure S2E,F). Lastly, we acknowledge in the text that the lack of valence encoding may be an artifact of head-fixation (see lines 849-857).

(2) For the optogenetic manipulations of Vglut1 and Vglut2 neurons, why was the injection and fiber targeted to the medial portion of the plCoA, if the hypothesis was that these glutamatergic neuron populations in different regions (anterior or posterior) are responsible for approach and avoidance? 

We targeted the middle region of plCoA because it contains a mixture of cell types found in both the anterior and posterior plCoA, allowing us to test the hypothesis that cell types, not intraplCoA location, elicit different responses. Had we targeted the anterior or posterior regions, we would expect to simply recapitulate the result from activation of random cells in each region. As a result, we think stimulation in the middle plCoA is a better test for the contribution of cell types. We have clarified this in the text (Lines 417-419).

Could this explain the lack of necessity with the DREADD experiments? 

For the loss of function experiments, a larger volume of virus was injected to cover a larger area and we did confirm targeting of the appropriate areas. Though, it is always possible that the lack of necessity is due to incomplete silencing.

Further, why was an optogenetic inhibition approach not utilized? 

Although optogenetic inhibition could have plausibly been used instead, we chose chemogenetic inhibition for two reasons: First, for minutes-long periods of inhibition, optical illumination poses the risk of introducing heat related effects (Owen et al., 2019). In fact, we first tried optical inhibition but controls were exhibited unusually large variance. Second, it is more feasible in our assay as it has a narrow height between the floor and lid that complicates tethering to an optic fiber. Past experiments overcame this with a motorized fiber retraction system (Root et al., 2014), but this is highly variable with user-dependent effects, so we found chemogenetics to be a more practical strategy. We have added a sentence to explain the rationale (see lines 561-563).

(3) The specific subregion of the nucleus accumbens that was targeted should be named, as distinct parts of the nucleus accumbens can have very different functions. 

We attempted to define specific subregions of the nucleus accumbens and found that plCoA projection is not specific to the shell or core, anterior or posterior, rather it broadly innervates the entire structure. We have added a note about this in manuscript (see lines 470-471). Given that we did not find notable subregion-specific outputs within the NAc, targeting was directed to the middle region of NAc, with coordinates stated in the methods. 

(4) Why was an intersectional DREADD approach used to inhibit the projection pathways, as opposed to optogenetic inhibition? The DREADD approach could potentially affect all projection targets, and the authors might want to address how this could influence the interpretation of the results.

This is partly addressed above in point 2. As for interpretation, we acknowledge that the intersectional approach silences the neurons projecting to a given target and not the specific projection and we have been careful with the wording. Although this may complicate the conclusion, we did map the collaterals for NAc and MeA projecting neurons and find that neurons do not appreciably project to both targets and have minimal projections to other targets. We have now taken care to state that we silence the neurons projecting to a structure, not silencing the projection, and we acknowledge this caveat. However, since the MeA- and NAcprojecting neurons appear to be distinct from each other (largely not collateralizing to each other), the conclusion that these divergent pathways are required still stands. We have added discussion of this in the Limitations section (see lines 859-863).

Minor:

(1) Line 402 needs a reference.

We have added the missing reference (now line 441).

(2) The Supplemental Figure labeling in the main text should be checked carefully.

Thank you for pointing this out. We have fixed the prior errors.

(3) Panel letter D is missing from Figure 2.

This has been fixed.

Reviewer #2 (Recommendations for the authors):

Major Concerns, additional experiments:

- In the calcium imaging experiments mice were presented with the same odor many times. Overall responses to odor presentations were quite variable and appear to habituate dramatically (Figure S1F). The general conclusion from these experiments are a lack of consistent valence-specific responses of individual neurons, but I wonder if this conclusion is slightly premature. A few potential explanatory factors that may need additional attention are: -First, despite recording video of the mouse's face during experiments, no behavioral response to any odor is described. Is it possible these odors when presented in head-fixed conditions do not have the same valence?

Yes, we agree that this is a possibility. We have added a discussion in the Limitations section (see lines 849-857). We have also added additional behavioral analysis discussed below.

On trials with neural responses are there behavioral responses that could be quantified? 

We have now added data in which we attempt to characterize their behavioral response, to look for correlations in odor representation (see lines 208-228). Although we did observe different patterns of odor-evoked walking behavior, these patterns were not reliable or specific to particular odors (Figure S2). One might expect aversive odors to pause walking or elicit a fast fleeing-like response, but we did not observe any apparent differences for locomotion between odors (Figure S2A-D). Next, we examined responses to odor depending on the behavioral state (walking, pausing or fleeing) and didn’t observe any meaningful differences in odor responses (Figure S2E,F). Lastly, we acknowledge that the odor representation may be different in freely moving animals that exhibit dynamic responses to odor (see lines 859-857).

- Habituation seems to play a prominent role in the neural signals, is there a larger contribution of valence if you look only at the first delivery (or some subset of the 20 presentations) of an odor type for a given trial? 

Indeed, we considered this, but we did not find any apparent differences in valence encoding as measured by the proportion of neurons with significant valence scores across trials (see Figure 1J).

- Is it reasonable to exclude valence encoding as a possibility when largely neurons were unresponsive to the positive valence odors (2PE and peanut) chosen when looking at the average cluster response (Figure 1F)? 

It is true that we see fewer neurons responding to the appetitive odors (Figure 1H) and smaller average responses within the cluster, but some neurons do respond robustly. If these were valence responses, we would predict that neural responses should be similarly selective, but we do not observe any such selectivity. The sparseness of responses to appetitive odors does cause the average cluster analysis (Figure 1F) to show muted responses to these odors, consistent with the decreased responsivity to appetitive odors. Moreover, single neuron response analysis reveals that a given neuron is not more likely to respond to appetitive or aversive odors with any selectivity greater than chance. For these reasons, we think it is reasonable to conclude an absence of valence responses, which is consistent with the conclusion from another report (Iurilli et al., 2017).

- While the preference and aversion assay with 4 corners is an interesting set-up and provides a lot of data for this particular manuscript. It would be helpful to test additional behaviors to determine whether these circuits are more conserved. As it stands the current manuscript relies on very broad claims using a single behavioral readout. Some attempts to use head-fixed approaches with more defined odor delivery timelines and/or additional valenced behavioral readouts is warranted.

We appreciate the suggestion, but are not able to perform these experiments at the moment. The choice of the 4-quadrant assay was used because it built off of our prior experiments that demonstrate a role for the plCoA in innate behavior. It is noteworthy that the responses to odor seen in this assay are generally in agreement with other olfactory behavioral assays, so one wouldn’t predict a different result. The approach and avoidance responses measured in this assay are precisely the behaviors we wish to understand. Moreover, we did examine other nonolfactory behavioral readouts (Figures S3, S8), and didn’t observe any effect of manipulation of these pathways. Lastly, we have tried to define parameters for head-fixed behavior that would permit correlation of neural responses with behavior, including longer stimulations and closed loop locomotion control of odor concentration, but were unsuccessful at establishing parameters that generated reliable behavioral responses. We acknowledge that one limitation of the study is the limited behavioral tests with two odors and whether the circuits are more broadly necessary for other odors. 

Minor comments:

• Please define PID in the Results when it is first introduced.

Done (see line 154)

• Line 412 Figure S5C-N should be Figure S6C-N.

Fixed. Now Figure S8C-N due to additional figures (see line 451).

• Throughout the Discussion it would be helpful if the authors referred to specific Figure panels that support their statements (e.g. lines 654-656 "[...] which is supported by other findings presented here showing that both VGluT2+ and VGluT1+ neurons project to MeA, while the projection to NAc is almost entirely composed of VGluT1+ neurons".

Thank you for the suggestion. We have added figure references in the discussion.

• Line 778 "producing" should be "produce".

Corrected (see line 840)

• The figures are very busy, especially all the manipulations. The authors are commended for including each data point, but they might consider a more subtle design (translucent lines only for each animal, and one mean dot for the SEM), just to reduce the overall clutter of an already overwhelming figure set. But this is ultimately left to the authors to resolve and style to their liking. 

Thank you for the suggestion. We have tried some different styles but like the original best.

Reviewer #3 (Recommendations for the authors):

If within reach, I suggest that the author determine the percentage of retrogradely labeled neurons to NAc or MEA that expresses GluT1 and GluT2. 

We have done this for the middle region plCoA that has the greatest mixture of cell types (See Figure S10, lines 504-517). We find that the MeA projecting neurons are mostly VGluT2+ with a minority that express both VGluT1 and VGlut2. NAc-projecting neurons are primarily VGluT1+ with about 20% expressing VGlut2 as well.

It would also be nice to sparse label of aplCoA and pplCoA using ChR2 to see if sparse activation drives approach or avoidance. 

We agree that it would be useful to vary the sparseness of the ChR2 expression, to see if produces similar results. We examined this using sparsely labeled odor ensembles, as previously done (Root et al., 2014). Briefly, we used the Arc-CreER mouse to label TMT responsive neurons with a cre-dependent ChR2 AAV vector targeted to the anterior or posterior regions, while previously we had broadly targeted the entirety of plCoA. We had established that this labeling method captures about half of the active cells detected by Arc expression, which is on the order of hundreds of neurons rather than thousands by broad cre-independent expression. Remarkably, we get effects similar in magnitude that are not significantly different from that with broader activation of the anterior or posterior domains (see new Figure S4, lines 267-288). It still remains possible that there is a threshold number of neurons that are necessary to elicit behavior, but that is beyond the scope of the current study. However, these data indicate that the effect of activating anterior and posterior domains is not an artifact of broad stimulation.

Reviewer #1 (Public review):

Summary:

This study set out to investigate potential pharmacological drug-drug interactions between the two most common antimalarial classes, the artemisinins and quinolines. There is strong rationale for this aim, because drugs from these classes are already widely-used in Artemisinin Combination Therapies (ACTs) in the clinic, and drug combinations are an important consideration in the development of new medicines. Furthermore, whilst there is ample literature proposing many diverse mechanisms of action and resistance for the artemisinins and quinolines, it is generally accepted that the mechanisms for both classes involve heme metabolism in the parasite, and that artemisinin activity is dependent on activation by reduced heme. The study was designed to measure drug-drug interactions associated with a short pulse exposure (4 h) that is reminiscent of the short duration of artemisinin exposure obtained after in vivo dosing. Clear antagonism was observed between dihydroartemisinin (DHA) and chloroquine, which became even more extensive in chloroquine-resistant parasites. Antagonism was also observed in this assay for the more clinically-relevant ACT partner drugs piperaquine and amodiaquine, but not for other ACT partners mefloquine and lumefantrine, which don't share the 4-aminoquinoline structure or mode of action. Interestingly, chloroquine induced an artemisinin resistance phenotype in the standard in vitro Ring-stage Survival Assay, whereas this effect was not as extensive for piperaquine.

The authors also utilised a heme-reactive probe to demonstrate that the 4-aminoquinolines can inhibit heme-mediated activation of the probe within parasites, which suggests that the mechanism of antagonism involves the inactivation of heme, rendering it unable to activate the artemisinins. Measurement of protein ubiquitination showed reduced DHA-induced protein damage in the presence of chloroquine, which is also consistent with decreased heme-mediated activation, and/or with decreased DHA activity more generally.

Overall, the study clearly demonstrates a mechanistic antagonism between DHA and 4-aminoquinoline antimalarials in vitro. It is interesting that this combination is successfully used to treat millions of malaria cases every year, which may raise questions about the clinical relevance of this finding. However, the conclusions in this paper are supported by multiple lines of evidence and the data is clearly and transparently presented, leaving no doubt that DHA activity is compromised by the presence of chloroquine in vitro. It is perhaps fortunate the that the clinical dosing regimens of 4-aminoquinoline-based ACTs have been sufficient to maintain clinical efficacy despite the non-optimal combination. Nevertheless, optimisation of antimalarial combinations and dosing regimens is becoming more important in the current era of increasing resistance to artemisinins and 4-aminoquinolines. Therefore, these findings should be considered when proposing new treatment regimens (including Triple-ACTs) and the assays described in this study should be performed on new drug combinations that are proposed for new or existing antimalarial medicines.

Strengths:

This manuscript is clearly written and the data presented is clear and complete. The key conclusions are supported by multiple lines of evidence, and most findings are replicated with multiple drugs within a class, and across multiple parasite strains, thus providing more confidence in the generalisability of these findings across the 4-aminoquinoline and peroxide drug classes.

A key strength of this study was the focus on short pulse exposures to DHA (4 h in trophs and 3 h in rings), which is relevant to the in vivo exposure of artemisinins. Artemisinin resistance has had a significant impact on treatment outcomes in South-East Asia, and is now emerging in Africa, but is not detected using a 'standard' 48 or 72 h in vitro growth inhibition assay. It is only in the RSA (a short pulse of 3-6 h treatment of early ring stage parasites) that the resistance phenotype can be detected in vitro. Therefore, assays based on this short pulse exposure provide the most relevant approach to determine whether drug-drug interactions are likely to have a clinically-relevant impact on DHA activity. These assays clearly showed antagonism between DHA and 4-aminoquinolines (chloroquine, piperaquine, amodiaquine and ferroquine) in trophozoite stages. Interestingly, whilst chloroquine clearly induced an artemisinin-resistant phenotype in the RSA, piperaquine only had a minor impact on the early ring stage activity of DHA, which may be fortunate considering that piperaquine is a currently recommended DHA partner drug in ACTs, whereas chloroquine is not.

The evaluation of additional drug combinations at the end of this paper is a valuable addition, which increases the potential impact of this work. The finding of antagonism between piperaquine and OZ439 in trophozoites is consistent with the general interactions observed between peroxides and 4-aminoquinolines, and it may be interesting to see whether piperaquine impacts the ring-stage activity of OZ439.

The evaluation of reactive heme in parasites using a fluorescent sensor, combined with the measurement of K48-linked ubiquitin, further support the findings of this study, providing independent read-outs for the chloroquine-induced antagonism.<br /> The in-depth discussion of the interpretation and implications of the results are an additional strength of this manuscript. Whilst the discussion section is rather lengthy, there are important caveats to the interpretation of some of these results, and clear relevance to the future management of malaria that require these detailed explanations.

Overall, this is a high quality manuscript describing an important study that has implications for the selection of antimalarial combinations for new and existing malaria medicines.

Weaknesses:

This study is an in vitro study of parasite cultures, and therefore caution should be taken when applying these findings to decisions about clinical combinations. The drug concentrations and exposure durations in these assays are intended to represent clinically relevant exposures, although it is recognised that the in vitro system is somewhat simplified and there may be additional factors that influence in vivo activity. This limitation is reasonably well acknowledged in the manuscript.

It is also important to recognise that the majority of the key findings regarding antagonism are based on trophozoite-stage parasites, and one must show caution when generalising these findings to other stages or scenarios. For example, piperaquine showed clear antagonism in trophozoite stages, but minimal impact in ring stages under these assay conditions.

A key limitation is the interpretation of the mechanistic studies that implicate heme-mediated artemisinin activation as the mechanism underpinning antagonism by chloroquine. This study did not directly measure the activation of artemisinins. The data obtained from the activation of the fluorescent probe are generally supportive of chloroquine suppressing the heme-mediated activation of artemisinins, and I think this is the most likely explanation, but there are significant caveats to consider. Primarily, the inconsistency between the fluorescence profile in the chemical reactions and the cell-based assay raise questions about the accuracy of this readout. In the chemical reaction, mefloquine and chloroquine showed identical inhibition of fluorescence, whereas piperaquine had minimal impact. On the contrary, in the cell, chloroquine and piperaquine had similar impacts on fluorescence, but mefloquine had minimal impact. This inconsistency indicates that the cellular fluorescence based on this sensor does not give a simple direct readout of the reactivity of ferrous heme, and therefore, these results should be interpreted with caution. Indeed, the correlation between fluorescence and antagonism for the tested drugs is a correlation, not causation. There could be several reasons for the disconnect between the chemical and biological results, either via additional mechanisms that quench fluorescence, or the presence of biomolecules that alter the oxidation state or coordination chemistry of heme or other potential catalysts of this sensor. It is possible that another factor that influences the H-FluNox fluorescence in cells also influences the DHA activity in cells, leading to the correlation with activity. It should be noted that H-FluNox is not a chemical analogue of artemisinins. It's activation relies on Fenton-like chemistry, but with a N-O rather that O-O bond, and it possesses very different steric and electronic substituents around the reactive centre, which are known to alter reactivity to different iron sources. Despite these limitations, the authors have provided reasonable justification for the use of this probe to directly visualise heme reactivity in cells, and the results are still informative.

Another interesting finding that was not elaborated by the authors is the impact of chloroquine in the DHA dose-response curves from the ring stage assays. Detection of artemisinin resistance in the RSA generally focuses on the % survival at high DHA concentrations (700 nM) as there is minimal shift in the IC50 (see Fig 2), however, chloroquine clearly induces a shift in the IC50 (~5-fold), where the whole curve is shifted to the right, whereas the increase in % survival is relatively small. This different profile suggests that the mechanism of chloroquine-induced antagonism may be different to the mechanism of artemisinin resistance. Current evidence regarding the mechanism of artemisinin resistance generally points towards decreased heme-mediated drug activation due to a decrease in hemoglobin uptake, which should be analogous to the decrease in heme-mediated drug activation caused by chloroquine. However, these different dose response curves suggest different mechanisms are primarily responsible. Additional mechanisms have been proposed for artemisinin resistance, involving redox or heat stress responses, proteostatic responses, mitochondrial function, dormancy and PI3K signalling among others. Whilst the H-FluNox probe generally supports the idea that chloroquine suppresses heme-mediated DHA activation, it remains plausible that chloroquine could induce these, or other, cellular responses that suppress DHA activity.

Impact:

This study has important implications for the selection of drugs to form combinations for the treatment of malaria. The overall findings of antagonism between peroxide antimalarials and 4-aminoquinolines in the trophozoite stage are robust, and the this carries across to the ring stage for chloroquine.

The manuscript also provides a plausible mechanism to explain the antagonism, although future work will be required to further explore the details of this mechanism and to rule out alternative factors that may contribute.

Overall, this is an important contribution to the field and provides a clear justification for the evaluation of potential drug combinations in relevant in vitro assays before clinical testing.

Reviewer #3 (Public review):

Summary:

The authors present an in vitro evaluation of drug-drug interactions between artemisinins and quinoline antimalarials, as an important aspect for screening the current artemisinin-based combination therapies for Plasmodium falciparum. Using a revised pulsing assay, they report antagonism between dihydroartemisinin (DHA) and several quinolines, including chloroquine, piperaquine (PPQ), and amodiaquine. This antagonism is increased in CQ-resistant strains in isobologram analyses. Moreover, CQ co-treatment was found to induce artemisinin resistance even in parasites lacking K13 mutations during the ring-stage survival assay. This implies that drug-drug interactions, not just genetic mutations, can influence resistance phenotypes. By using a chemical probe for reactive heme, the authors demonstrate that quinolines inhibit artemisinin activation by rendering cytosolic heme chemically inert, thereby impairing the cytotoxic effects of DHA. The study also observed negative interactions in triple-drug regimens (e.g., DHA-PPQ-Mefloquine) and in combinations involving OZ439, a next-generation peroxide antimalarial. Taken together, these findings raise significant concerns regarding the compatibility of artemisinin and quinoline combinations, which may promote resistance or reduce efficacy.

With the additive profile as the comparison and a lack of synergistic effect in any of the comparisons, it is hard to contextualize the observed antagonism. Including a known synergistic pair (e.g., artemisinin + lumefantrine) would have provided a useful benchmark to assess the relative impact of the drug interactions described.

Strengths:

This study demonstrates the following strengths:

• The use of a pulsed in vitro assay that is more physiologically relevant over the traditional 48h or 72h assays

• Small molecule probes, H-FluNox, and Ac-H-FluNox to detect reactive cytosolic heme, demonstrating that quinolines render heme inert and thereby block DHA activation.

• Evaluates not only traditional combinations but also triple-drug combinations and next-generation artemisinins like OZ439. This broad scope increases the study's relevance to current treatment strategies and future drug development.

• By using the K13 wild-type parasites, the study suggests that resistance phenotypes can emerge from drug-drug interactions alone, without requiring genetic resistance markers.

Weaknesses:

• The study would benefit from a future characterization of the molecular basis for the observed heme inactivation by quinolines to support this hypothesis - while the probe experiments are valuable, they do not fully elucidate how quinolines specifically alter heme chemistry at the molecular level.

• Suggestion of alternative combinations that show synergy could have improved the significance of the work. The invitro study did not include pharmacokinetic/pharmacodynamic modeling, hence it leaves questions about how the observed antagonism would manifest under real-world dosing conditions, necessitating furture work based on these findings.

Author response:

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

eLife Assessment

We appreciate the positive assessment. We recognize that since all of the work in this manuscript was done in vitro, there are reasonable concerns about the translatability of these data to clinical settings. These results should not directly inform malaria policy, but we hope that these data bring new considerations to the approach for choosing strategic antimalarial combinations. We have modified the manuscript to clarify this distinction.

Public Reviews

Reviewer #1 (Public Review):

We thank the reviewer for their thoughtful summary of this manuscript. It is important to note that DHA-PPQ did show antagonism in RSAs. In this modified RSA, 200 nM PPQ alone inhibited growth of PPQ-sensitive parasites approximately 20%. If DHA and PPQ were additive, then we would expect that addition of 200 nM PPQ would shift the DHA dose response curve to the left and result in a lower DHA IC50. Please refer to Figure 4a and b as examples of additive relationships in dose-response assays. We observed no significant shift in IC50 values between DHA alone and DHA + PPQ. This suggests antagonism, albeit not to the extent seen with CQ. We have modified the manuscript to emphasize this point. As the reviewer pointed out, it is fortunate that despite being antagonistic, clinically used artemisinin-4-aminoquinoline combinations are effective, provided that parasites are sensitive to the 4-aminoquinoline. It is possible that superantagonism is required to observe a noticeable effect on treatment efficacy (Sutherland et al. 2003 and Kofoed et al. 2003), but that classical antagonism may still have silent consequences. For example, if PPQ blocks some DHA activation, this might result in DHA-PPQ acting more like a pseudo-monotherapy. However, as the reviewer pointed out, while our data suggest that DHA-PPQ and AS-ADQ are “non-optimal” combinations, the clinical consequences of these interactions are unclear. We have modified the manuscript to emphasize the later point.

While the Ac-H-FluNox and ubiquitin data point to a likely mechanism for DHA-quinoline antagonism, we agree that there are other possible mechanisms to explain this interaction.  We have addressed this limitation in the discussion section. Though we tried to measure DHA activation in parasites directly, these attempts were unsuccessful. We acknowledge that the chemistry of DHA and Ac-H-FluNox activation is not identical and that caution should be taken when interpreting these data. Nevertheless, we believe that Ac-H-FluNox is the best currently available tool to measure “active heme” in live parasites and is the best available proxy to assess DHA activation in live parasites. These points are now addressed in the discussion section. Both in vitro and in parasite studies point to a roll for CQ in modulating heme, though an exact mechanism will require further examination. Similar to the reviewer, we were perplexed by the differences observed between in vitro and in parasite assays with PPQ and MFQ. We proposed possible hypotheses to explain these discrepancies in the discussion section. Interestingly, our data corelate well with hemozoin inhibition assays in which all three antimalarials inhibit hemozoin formation in solution, but only CQ and PPQ inhibit hemozoin formation in parasites. In both assays, in-parasite experiments are likely to be more informative for mechanistic assessment.

It remains unclear why K13 genotype influences RSA values, but not early ring DHA IC50 values. In K13^WT parasites, both RSA values and DHA IC50 values were increased 3-5 fold upon addition of CQ. This suggests that CQ-mediated resistance is more robust than that conferred by K13 genotype. However, this does not necessarily suggest a different resistance mechanism. We acknowledge that in addition to modulating heme, it is possible that CQ may enhance DHA survival by promoting parasite stress responses. Future studies will be needed to test this alternative hypothesis. This limitation has been acknowledged in the manuscript. We have also addressed the reviewer’s point that other factors, including poor pharmacokinetic exposure, contributed to OZ439-PPQ treatment failure.

Reviewer #2 (Public Review):

We appreciate the positive feedback. We agree that there have been previous studies, many of which we cited, assessing interactions of these antimalarials. We also acknowledge that previous work, including our own, has shown that parasite genetics can alter drug-drug interactions. We have included the author’s recommended citations to the list of references that we cited. Importantly, our work was unique not only for utilizing a pulsing format, but also for revealing a superantagonistic phenotype, assessing interactions in an RSA format, and investigating a mechanism to explain these interactions. We agree with the reviewer that implications from this in vitro work should be cautious, but hope that this work contributes another dimension to critical thinking about drug-drug interactions for future combination therapies. We have modified the manuscript to temper any unintended recommendations or implications.

The reviewer notes that we conclude “artemisinins are predominantly activated in the cytoplasm”. We recognize that the site of artemisinin activation is contentious. We were very clear to state that our data combined with others suggest that artemisinins can be activated in the parasite cytoplasm. We did not state that this is the primary site of activation. We were clear to point out that technical limitations may prevent Ac-H-FluNox signal in the digestive vacuole, but determined that low pH alone could not explain the absence of a digestive vacuole signal.

With regard to the “reproducibility” and “mechanistic definition” of superantagonism, we observed what we defined as a one-sided superantagonistic relationship for three different parasites (Dd2, Dd2 PfCRT^Dd2, and Dd2 K13^R539T) for a total of nine independent replicates. In the text, we define that these isoboles are unique in that they had mean ΣFIC50 values > 2.4 and peak ΣFIC50 values >4 with points extending upward instead of curving back to the axis. As further evidence of the reproducibility of this relationship, we show that CQ has a significant rescuing effect on parasite survival to DHA as assessed by RSAs and IC50 values in early rings.

Reviewer #3 (Public Review):

We thank the reviewer for their positive feedback. We acknowledge that no combinations tested in this manuscript were synergistic. However, two combinations, DHA-MFQ and DHA-LM, were additive, which provides context for contextualizing antagonistic relationships. We have previously reported synergistic and additive isobolograms for peroxide-proteasome inhibitor combinations using this same pulsing format (Rosenthal and Ng 2021). These published results are now cited in the manuscript.

We believe that these findings are specific to 4-aminoquinoline-peroxide combinations, and that these findings cannot be generalized to antimalarials with different mechanisms of action. Note that the aryl amino alcohols, MFQ and LM, were additive with DHA. Since the mechanism of action of MFQ and LM are poorly understood, it is difficult to speculate on a mechanism underlying these interactions.

We agree with the reviewer that while the heme probe may provide some mechanistic insight to explain DHA-quinoline interactions, there is much more to learn about CQ-heme chemistry, particularly within parasites.

The focus of this manuscript was to add a new dimension to considerations about pairings for combination therapies. It is outside the scope of this manuscript to suggest alternative combinations. However, we agree that synergistic combinations would likely be more strategic clinically.

An in vitro setup allows us to eliminate many confounding variables in order to directly assess the impact of partner drugs on DHA activity. However, we agree that in vivo conditions are incredibly more complex, and explicitly state this.

We agree that in the future, modeling studies could provide insight into how antagonism may contribute to real-world efficacy. This is outside the scope of our studies.

Recommendations for the Authors:

Reviewer #1 (Recommendations for the Authors):

The key weaknesses identified in this manuscript are described in the 'weaknesses' section of the public review. The major one is the inconsistency around the H-FluNox response in the chemical vs biological experiments. I can't think of a simple experiment to resolve this issue, but it is good that this data is openly provided in the manuscript. I believe there could be more discussion to clarify this limitation with the current study, and the conclusions, and particularly the title, should be softened regarding the mechanism of antagonism being based on heme reactivity.

We have softened the title and conclusions to take into account the limitations of our studies.

(1) Please double-check the definitions for isobologram interpretation. In most antimicrobial interaction studies, I see the threshold for antagonism at sumFIC50 of 1.5, or even 2. 1.25 is often interpreted as additive in many studies.

We acknowledge that different studies use various cutoff values. Our interpretations for additive versus antagonistic versus superantagonistic were based not only on mean ΣFIC50 values, but also isobologram shape. For example, the flat isoboles for MFQ-DHA were clearly distinct from the curved isoboles of PPQ-DHA. It is unclear what cutoff value(s) would be most clinically relevant.

(2) For the MFQ-PPQ interaction study, please make it clear that these drugs have very long half-lives (weeks), so the 4 h pulse assay isn't really relevant to their overall activity. It probably shows a slower onset of action, but there is plenty of drug remaining for many days in the clinical scenario, so perhaps the data from the traditional 48h assay is more relevant. The same consideration applies to OZ439, which may impact the interpretation of that data.

We have now included the half-lives of these compounds in the discussion section. Our intent was to use a pulsing format to make these isobolograms comparable with the other assays. It is important to note that pulses can reveal stronger phenotypes that might be missed with traditional methods. Thus, while 48 h assays may better mimic in vivo conditions, they could also mask important phenotypes.

Reviewer #3 (Recommendations for the Authors):

I have included most of my concerns in the public review. Below are some additional specific points for consideration:

(1) It is expected to include a synergistic combination as a control (e.g., artemisinin + lumefantrine) to contextualize the degree of antagonism observed. The experimental design should show some synergistic profiles in comparison. Adding a few experiments by including a synergistic control is needed.

Both MFQ-DHA and LM-DHA combinations were additive, which provides context for antagonistic combinations. This is now stated in the results section pertaining to Figure 1. We have also included a reference to our previous publication in which we demonstrated that proteasome inhibitor-peroxide combinations are synergistic to additive using this same pulsing format.

(2) Consider in vivo validation or pharmacokinetic/pharmacodynamic modeling to strengthen the translational relevance of the findings when it comes to doses and the IC50 correlations.

We agree that this would be useful to do in future, but it is outside the scope of the current study.

(3) It would be beneficial to include a discussion section on how the findings are generalizable to different Plasmodium falciparum genotypes (3D7, Dd2, MRA-1284) and their relevance.

Findings were consistent across three parasite backgrounds depending on PfCRT genotype. This point has been included in the discussion section. The background of these parasites is also provided in Table 1.

(4) Potential evaluation criteria to understand where certain combinations should be reconsidered can be included as a suggestion for the wider audience.

Our in vitro studies suggest that pulsing isobolograms would be a useful assay to include when evaluating combination therapies. While we believe that synergistic combinations would be more strategic than antagonistic combinations, we cannot provide evaluation criteria or make recommendations for reconsidering currently used combinations.

(5) Further elaborate on the mechanistic basis of heme inactivation by quinolines. If data are available, please include more data on the specificity of the process.

Despite our best efforts, we were unable to evaluate quinoline-heme interactions in parasites. Even in vitro, this interaction has remined elusive for decades. We agree that this would be an important future step towards supporting a specific mechanism for quinoline-DHA antagonism.

Reviewer #2 (Public review):

Summary:

In this study, the authors identify a previously uncharacterised regulator of mitochondrial function using a genetic screen and propose a role for this protein in supporting mitochondrial protein production. They provide evidence that the protein localises to mitochondria, interacts with components of the mitochondrial translation machinery, and is required for normal heart function in an animal model.

Strengths:

A major strength of the work is the use of multiple independent approaches to assess mitochondrial activity and protein production, which together provide support for the central conclusions. The in vivo data linking loss of this factor to impaired heart function are particularly compelling and elevate the relevance of the study beyond a purely cell-based context.

Weaknesses:

Given prior reports placing this protein outside mitochondria, its mitochondrial localisation would benefit from more rigorous and quantitative validation, and the proposed mechanism of the interaction with the mitochondrial translation machinery remains only partially explored. In addition, the physiological analysis is largely limited to the heart, leaving open questions about how broadly this pathway operates across tissues.

Major comments:

(1) Evidence for mitochondrial localization of EOLA1<br /> EOLA1 has previously been reported as a nuclear and cytosolic protein and is not annotated in MitoCarta 3.0, making rigorous validation of its mitochondrial localization particularly important. Although the authors provide several lines of evidence, interpretation is complicated by the use of different cell lines across localization, interaction, and functional experiments. Greater consistency in the cellular models used would strengthen the conclusions. The immunofluorescence analysis of tagged EOLA1 would also benefit from quantification across more cells and the inclusion of an additional mitochondrial marker (e.g., an outer membrane marker such as TOM20), as HSP60 staining can vary with mitochondrial state.

(2) Normalization of OCR measurements<br /> Clarification of how Seahorse oxygen consumption rate measurements were normalized (e.g., cell number or protein content) would aid interpretation, particularly given potential effects of Eola1 loss on cell growth.

(3) Linking interaction data to functional phenotypes<br /> Loss-of-function analyses are performed in mouse cell lines, whereas localization and interactome studies are conducted in human HEK293T cells. The absence of a human EOLA1 knockout model makes it difficult to directly connect the interaction data to the observed functional phenotypes. Additional validation or discussion of species conservation would improve clarity.

(4) Mechanistic interpretation of the EOLA1-TUFM-12S rRNA interaction<br /> The identification of TUFM and 12S mt-rRNA as EOLA1 interactors is an interesting finding; however, the basis for prioritizing TUFM among the many mitochondrial proteins identified in the interactome is not fully explained. Providing enrichment statistics and functional categorization of mitochondrial interactors would increase transparency. In addition, the proposed role of the ASCH domain in RNA binding would be strengthened by structure-informed or mutational analysis of the conserved RNA-binding motif.

(5) Interpretation of mitochondrial translation and protein abundance data<br /> Several assays supporting impaired mitochondrial translation would benefit from additional controls and quantification. The de novo mitochondrial translation assay (Fig. 3h) is not quantified, making it difficult to assess the magnitude and reproducibility of the effect. In addition, western blots showing reduced levels of mitochondrially encoded OXPHOS subunits (Figure 3g) lack a mitochondrial loading control (e.g., TOM20 or VDAC). Since loss of EOLA1 may affect mitochondrial mass, normalization to a mitochondrial marker is necessary. Relatedly, it would be informative to assess whether steady-state levels of mitoribosomal proteins (e.g., MRPS15, MRPL37) and nuclear-encoded OXPHOS subunits are altered upon Eola1 loss, both in knockout cell lines and in the knockout mouse.

(6) Physiological scope of the in vivo analysis<br /> The cardiac phenotype observed in the whole-body Eola1 knockout mouse is compelling, but the focus on a single tissue limits interpretation of EOLA1's broader physiological role. Examination of additional high-energy-demand tissues would help clarify whether the observed effects are heart-specific or more general. In addition, the presence of residual EOLA1 protein bands in western blots (Figure 4a) and remaining Eola1 transcripts in qRT-PCR analyses (Extended Figure 4e) from knockout tissues should be addressed. The authors should clarify whether these signals reflect incomplete knockout, alternative isoforms, antibody cross-reactivity, or technical background.

(7) Relationship to previously reported MT2A interaction<br /> Given prior reports of EOLA1 interaction with MT2A, a brief comment on whether MT2A was detected in the authors' co-immunoprecipitation experiments and how this relates to the proposed mitochondrial role would be useful.

Reviewer #3 (Public review):

The authors identified EOLA1 in a CRISPR/Cas9 screen for essential mitochondrial genes in a mouse B16-F10 cell line; however, no information on the library used for this screen or the list of all identified essential genes is provided. What was the p-value for EOLA1 in Figure 1b?

The authors show that EOLA1 is indeed a mitochondrial protein (using both mouse and human cell lines). It is valuable that the authors use different cell lines to investigate the function of this protein; however, this also presents a challenge, as four different cell lines (two mouse and two human) are used across individual experiments, with no consistency between them. Knock-out (KO) experiments were performed in mouse cell lines only, and human cell lines were used in overexpression experiments, in which EOLA1 was tagged with FLAG-HA. It would be beneficial if a knock-out were also generated in a human cell line to confirm the effect on the expression of mitochondria-encoded proteins, along with a rescue experiment in which the EOLA1 protein is reintroduced into KO cells.

Functional analysis of EOLA1: The authors performed affinity immunoprecipitation of FLAG-HA-tagged EOLA1 from stably overexpressing cells, and identified 202 co-immunoprecipitating proteins, of which 71 were known mitochondrial proteins; however, no list of these proteins is provided. Why did the authors choose TUFM? Were any mitochondrial ribosomal proteins co-immunoprecipitated, if EOLA1 is suggested to regulate translation? Were levels of TUFM affected in EOLA1-KO cells?

The authors continued to analyze mitochondrial ribosomes using sucrose gradient fractionation and in-vitro mitochondrial translation. However, there are several technical problems with the presented data: It has been established that mitochondrial ribosomes do not form polysomes in mammalian cells but rather perform translation as monosomes. The authors indirectly confirm this: almost no 12S or 16S rRNA (Fig. 3f) or MRP proteins (Extended data 3c) are present in "polysome" fractions. Although indeed 12S and 16S rRNAs are decreased in monosome fractions, the levels of mRNAs are not different between KO and WT cells, and neither is the migration of mitochondrial ribosomal proteins. As there is no loading control provided for the sucrose gradients blots (such as SDHA, VDAC), it is not possible to assess the overall levels of mitochondrial ribosomes. The gel presented for mitochondrial translation is of poor quality, as it is impossible to identify any of the expected 13 polypeptides. Although the intensity of the signal is weaker for KO, so is the intensity in the portion of Coomassie stained gel. A better-quality gel and quantification need to be provided to support the claims.

What is the difference between endogenous and exogenous RIP-qPCR? EOLA1 pulled down 12S rRNA without cross-linking (Figure 3d) or with UV-crosslinking (Figure 3e), however, both 12S and 16S rRNAs were enriched in UV-crosslinked cells (Figure 3c) and by UV-RIP-seq (Extended data 3b; although no control is provided here). Is no discussion offered for this observation? Is it possible that EOLA1 plays a role in the maturation of the mito-ribosome, rather than translation? Does EOLA1 co-migrate with the mito-ribosome on sucrose gradients?

Altogether, there is insufficient evidence to support the conclusion that EOLA1 plays a role in mitochondrial translation.

To investigate EOLA1 biological function, the authors created a whole-body EOLA1-/- mouse that exhibited no overall developmental abnormalities; however presented with an abnormal cardiac function. This is an ideal model to confirm prior observations in cellular models; however, apart from one western-blot for three mitochondrial encoded subunits, no other experiments were provided (such as measurements of the levels of 12S, or 16S rRNA, TUFM levels, ribosomes profile, mitochondrial translation, OXPHOS assembly, respirometry).

In Figure 2 g-i: TEM images are presented, but the method is not described, nor is any information on the cells used provided, nor is it clear how the circularity was determined. KO cells certainly look abnormal; however, are the authors sure that the indicated structures are mitochondria? They rather resemble autophagosomes/lysosomes with lamellar inclusions.

Yo como lector reflexiono que: "La escritura es el monumento de la memoria", porque genera un registro de lo que vamos viviendo, construyendo o pensando, pero que al final de cuentas es un producto industrializado porque esta es una representa idealista sobre nuestro pensamiento que no tiene un contexto y no se puede complejizar tanto como nuestros pensamientos. Porque para mi, la escritura es el resultado escrito de nuestras reflexiones, pero para poder llegar a concretar esas ideas especificas que me parecieron valiosas para comunicarlas de forma escrita, tuve que haber tenido un proceso de pensamiento de dias, semanas o meses que no fue registrado. La escritura es el único lugar donde pueden existir las palabras habladas.

Aquí se da el desarrollo de lo argumentos por la frase de platon donde menciona que: "es inhumana al pretender establecer fuera del pensamiento lo que en realidad solo puede existir dentro de él". Argumentos que sostienen esta frase: 1. Es un objeto, un producto manufacturado. Lo mismo se dice de las computadoras 2. La escritura destruye la memoria. Coloca el ejemplo de que la invención de las calculadoras es un recurso externo que produjo que las tablas aprendidas de memoria que son un recurso interno sean olvidadas. 3. Un texto escrito no produce respuestas, si se le pregunta a una persona que explica sus palabras presencialmente este podría hacerlo, pero en un texto escrito no se podría porque no tenemos al autor en frente. 4. El hecho de que la palabra escrita no puede defender como es capaz de hacerlo la palabra hablada natural. Menciona que el habla y pensamiento real está medida por un contexto de ida y vuelta, pero en la palabra escrita es pasiva, es decir, fuera de dicho contexto, irreal y artificial al igual que las computadoras.

Document de Synthèse : Le Projet FUSÉ – Une Approche Structurelle pour la Réussite des Élèves Fragilisés

Résumé Exécutif

Le projet FUSÉ (Formation à l’utilisation de stratégies efficaces pour l’engagement) est une initiative novatrice mise en œuvre à l’école secondaire Carrefour (Centre de services scolaire des Draveurs) pour contrer le décrochage scolaire précoce.

Ce projet cible les élèves du premier cycle du secondaire en situation de grande vulnérabilité, particulièrement ceux ayant des acquis de 6e année mais se trouvant en échec dans plusieurs matières à sanction.

Partant du constat que le redoublement traditionnel ne produisait aucun résultat positif (33 % de taux de sortie sans diplôme), la direction a instauré une structure rigoureuse remplaçant la culture du redoublement par un accompagnement intensif basé sur l’autodétermination et la réussite immédiate.

Après une année d'application, les résultats sont probants : sur 39 élèves ciblés, 20 ont réussi leur passage en secondaire 3, évitant ainsi des trajectoires de formation moins qualifiantes.

Le projet repose sur une mobilisation des services complémentaires, une réorganisation des horaires et l'utilisation d'un quartier général dédié : le « Bistrado ».

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1. Contexte et Problématique

1.1 Un constat d'échec systémique

L'école secondaire Carrefour, située en milieu urbain défavorisé, accueille environ 2 000 élèves. Avant l'implantation de FUSÉ, l'école faisait face à des défis majeurs :

• Taux de décrochage élevé : 33 % de sorties sans diplôme au régulier, contre une moyenne québécoise de 24,6 % pour des milieux équivalents.

• Décrochage précoce : Le profil type du décrocheur se dessinait dès l'âge de 15 ans, souvent suite à une reprise de la première année du secondaire.

• Inefficacité du redoublement : Les données montraient que les élèves reprenant leur secondaire 1 obtenaient des résultats inférieurs à leur première tentative, tout en développant des problèmes de comportement et de motivation accrus.

1.2 L'urgence d'agir

En mars 2024, les prévisions indiquaient que 42 élèves sur 200 au régulier étaient en échec dans au moins trois matières à sanction.

Face à la pression du personnel pour un redoublement massif ou un transfert en adaptation scolaire (non justifié par les acquis académiques), la direction a choisi de rompre avec les pratiques établies.

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2. Fondements et Vision du Projet FUSÉ

Le projet s'appuie sur une philosophie de « création du possible » lorsque les méthodes traditionnelles échouent.

2.1 Objectifs centraux

• Maintenir la trajectoire scolaire : Éviter que les élèves ne soient dirigés prématurément vers des parcours comme la FMS (Formation menant à l'exercice d'un métier semi-spécialisé).

• Favoriser l'autodétermination : Baser l'intervention sur les besoins fondamentaux d'appartenance, de relation et de compétence.

• Inverser l'effort : Faire en sorte que l'élève devienne l'acteur principal de sa réussite, plutôt que de voir les adultes « travailler plus fort que l'élève ».

2.2 Cadre théorique et leviers

Le projet s'inspire de modèles existants tels que :

• L'approche Check & Connect (utilisée au 2e cycle sous le nom de « Boussole éducative »).

• Le Plan d'intervention autodéterminé, soutenu par une formation de la conseillère pédagogique du centre de services.

• L'utilisation de données probantes pour identifier les facteurs de risque et de protection.

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3. Structure Opérationnelle et Mise en Œuvre

La réussite de FUSÉ repose sur une structure « bétonnée » plutôt que sur un simple changement de culture imposé au personnel enseignant.

3.1 Le « Bistrado » : Le Quartier Général

Le bistro étudiant de l'école est transformé chaque matin en centre de services centralisé pour les élèves FUSÉ. C'est un lieu sécurisant, loin de l'agitation des classes, où s'effectue l'accueil quotidien.

3.2 L'Intervenant Pivot

Chaque élève est lié à un intervenant pivot (agent de réadaptation, orthopédagogue, enseignant ressource ou intervenant en toxicomanie). Ce dernier :

• Centralise les communications.

• Assure un accueil quotidien (les « Soleils FUSÉ »).

• Suit les objectifs personnels de l'élève.

3.3 Analyse des données et sous-groupes

Les élèves sont regroupés selon la nature de leurs besoins, tout en restant intégrés dans leur profil ou programme d'origine (pas de classes fermées) :

| Profil de sous-groupe | Nature des difficultés | | --- | --- | | Comportement | Manifestations comportementales perturbatrices. | | Motivation / Assiduité | Taux d'absentéisme élevé, désengagement. | | Apprentissage | Lacunes académiques graves en français ou mathématiques. |

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4. L'Expérience Élève et Engagement

4.1 Le contrat d'engagement

La participation est volontaire. L'élève doit signer un contrat d'engagement. Si l'engagement fait défaut, l'élève peut être retiré du projet, avec la possibilité d'y revenir lorsqu'il se sent prêt.

4.2 Le déroulement quotidien

• Période solée (8h40 - 9h00) : Accueil au Bistrado, petit-déjeuner pour les élèves en milieu défavorisé, et fixation d'objectifs quotidiens ou hebdomadaires (ex: arriver à l'heure, participer en classe).

• Suivi des objectifs : Les réussites sont soulignées par des « billets de tirage » et des certificats de reconnaissance, favorisant l'émulation.

• Horaire différencié : Pour certains élèves, des matières comme les arts, l'anglais ou le CCQ sont temporairement allégées pour permettre des périodes de rattrapage intensif en français et mathématiques avec des enseignants ressources.

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5. Résultats et Impact

5.1 Statistiques de la première cohorte (39 élèves)

Les résultats ont surpassé les attentes initiales de la direction :

• 20 élèves ont intégré le secondaire 3 régulier.

• 4 élèves ont été dirigés vers la FMS.

• 2 élèves vers le Pré-DEP.

• 1 élève vers la formation générale des adultes.

• 8 élèves ont repris leur secondaire 2 (mais avec un meilleur accompagnement).

• Seulement 4 abandons (dont 2 en cours d'année).

5.2 Gains qualitatifs

• Amélioration du lien école-famille : Les parents, souvent découragés, ont retrouvé de l'espoir grâce à une communication axée sur le positif.

• Cohérence organisationnelle : Le personnel partage désormais un langage commun autour de l'autodétermination.

• Épanouissement social : Participation à des activités d'émulation (ex: sorties au théâtre) et implication bénévole des élèves au sein de l'école.

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6. Évolution : FUSÉ 2.0 et Perspectives

Fort de son succès, le projet entame sa deuxième année avec des ajustements majeurs :

1. Enseignement multiniveaux : Création de groupes en français et mathématiques pour les élèves ayant des lacunes profondes (niveau 5e année primaire), tout en évitant le cloisonnement.

2. Expansion au secondaire 1 : Identification précoce des élèves fragiles dès la rentrée pour prévenir l'échec.

3. Intégration systémique : Fusion de l'approche FUSÉ dans la « Boussole éducative » globale de l'école pour assurer une transition fluide entre le premier et le deuxième cycle.

4. Adaptation scolaire : Réflexion sur l'application de l'approche fusée pour les élèves en adaptation afin de viser une progression constante plutôt que la simple réussite de fin d'année.

Le projet FUSÉ démontre qu'en réallouant les ressources existantes et en structurant rigoureusement l'accompagnement, il est possible de modifier radicalement la trajectoire d'élèves que le système considérait autrefois comme perdus.

Plotová pole Grafické šablony pro plotová pole stany ve všech velikostech

The EECC is a directive, and transposition in MS took very long. 3 by the 2020/12 deadline. All only by 2024/08, with EC support.

Cadre de référence sur les mesures de contrôle en milieu scolaire : Note de synthèse

https://www.youtube.com/watch?v=D43t0L_G7-Y

Résumé exécutif

Ce document de référence, fruit d'une collaboration entre le ministère de l’Éducation (MEQ) et la Fédération des centres de services scolaires du Québec (FCSSQ), définit les orientations nationales concernant l’utilisation des mesures de contrôle — contention et isolement — dans les établissements d'enseignement.

La prémisse fondamentale est que ces mesures ne doivent être envisagées qu'en dernier recours, exclusivement dans des situations d'urgence où la sécurité de l'élève ou d'autrui est menacée de façon imminente.

Le cadre privilégie une approche préventive et éducative, structurée autour du Système de soutien à paliers multiples (SSPM), visant à réduire au minimum le recours à la force ou à la contrainte.

Il clarifie les responsabilités légales et professionnelles, notamment depuis les modifications réglementaires d'octobre 2023 habilitant certains professionnels (psychologues et psychoéducateurs) à décider de l’utilisation de mesures de contention.

La mise en œuvre repose sur une démarche rigoureuse en cinq étapes, incluant l'élaboration de protocoles spécifiques (école ou élève) et l'application de modalités postsituationnelles pour assurer le bien-être et la réévaluation constante des pratiques.

1. Fondements et principes directeurs

Le recours aux mesures de contrôle est strictement encadré par des références légales (Charte des droits et libertés, Code civil, Loi sur l'instruction publique) et doit respecter les principes de dignité, d'intégrité et de sécurité de l'élève.

Principes fondamentaux de l'intervention :

• Dernier recours : Utilisé uniquement lorsque les interventions préventives et les mesures alternatives ont échoué.

• Danger imminent : La menace doit être caractérisée par sa prévisibilité, son immédiateté et la gravité de ses conséquences.

• Contrainte minimale : La mesure doit être la moins restrictive possible et durer le moins longtemps possible (cesser dès que le danger est écarté).

• Respect et dignité : L'intervention doit être empreinte de bienveillance et de chaleur humaine, sous une surveillance constante.

• Suivi obligatoire : Chaque application doit faire l'objet d'un suivi postsituationnel pour évaluer l'efficacité et réguler les futures interventions.

2. Définitions des mesures de contrôle

Le cadre distingue plusieurs types d'interventions pour assurer une compréhension commune au sein du réseau scolaire.

| Type de mesure | Description | Exemples | | --- | --- | --- | | Contention physique | Utilisation de la force humaine pour immobiliser ou diriger un élève contre son gré. | Tenir le bras d'un élève qui résiste ou le maintenir s'il frappe. | | Contention mécanique | Emploi d'un équipement ou de matériel pour limiter le mouvement. | Mitaines de sécurité, vestes de retenue dans le transport scolaire. | | Retrait de matériel | Confiscation d'un appareil palliant normalement un handicap. | Retirer les freins d'un fauteuil roulant ou confisquer une marchette. | | Isolement | Confinement de l'élève dans un lieu d'où il ne peut sortir librement. | Tenir la poignée d'une porte fermée ou bloquer physiquement l'accès. |

Note : L'administration de substances chimiques à des fins de contrôle nécessite une prescription médicale et n'est pas traitée dans ce document.

3. Cadre opérationnel : Intervention planifiée vs non planifiée

Le cadre distingue deux contextes d'application, impactant directement les responsabilités professionnelles.

| Caractéristique | Intervention Non Planifiée | Intervention Planifiée | | --- | --- | --- | | Contexte | Comportement inhabituel et imprévisible. | Comportement connu et susceptible de se répéter. | | Outil de gestion | Protocole-école (universel). | Protocole-élève (personnalisé, lié au Plan d'intervention). | | Décision (Contention) | Activité non réservée (urgence). | Activité réservée aux professionnels habilités. | | Décision (Isolement) | Activité non réservée. | Activité non réservée (mais encadrée). | | Application | Activité non réservée. | Activité non réservée. |

4. La démarche d'intervention en cinq étapes

Pour assurer la sécurité et le respect des droits, une structure systématique est proposée :

1. Élaboration du protocole : Mise en place préventive de balises (comité-école pour le protocole-école ; équipe-école et parents pour le protocole-élève).

2. Application des interventions préventives et alternatives : Utilisation de stratégies éducatives pour éviter la crise (diversion, sécurisation de l'environnement).

3. Évaluation du danger : Analyse rigoureuse de la situation selon les critères de prévisibilité, d'immédiateté et de gravité.

4. Application de la mesure de contrôle : Mise en œuvre selon les balises du protocole et les recommandations professionnelles.

5. Modalités postsituationnelles : Retour sur l'événement, établissement des faits, soutien aux témoins (élèves et adultes) et révision du protocole.

5. Prévention et climat scolaire

La prévention est la "première voie d'action". Le document souligne l'importance du Système de soutien à paliers multiples (SSPM) :

• Palier 1 (Universel) : Soutien proactif pour tous les élèves (climat sain, règles claires, relations positives).

• Palier 2 (Ciblé) : Soutien supplémentaire pour les élèves à risque (autorégulation, habiletés sociales).

• Palier 3 (Intensif) : Interventions individualisées pour les difficultés graves ou persistantes.

Le modèle "3 x 3" du CSSMB est cité en exemple, croisant l'intensité de l'intervention avec les sphères individuelle, scolaire et familiale.

6. Rôles et responsabilités clés

Le succès de ce cadre repose sur une responsabilité partagée :

• Direction d'établissement : Coordonne l'élaboration des protocoles, assure la formation du personnel et veille au bien-être physique et psychologique de tous.

• Personnel professionnel habilité (Ergothérapeutes, infirmiers, médecins, physiothérapeutes, psychoéducateurs, psychologues) : Réalise l'évaluation clinique, décide de la mesure en contexte planifié et émet des recommandations.

• Intervenants scolaires : Collaborent à l'analyse des comportements, appliquent les mesures en suivant les protocoles et informent la direction.

• Parents et élèves : Doivent être impliqués activement dans l'élaboration du protocole-élève. Un consentement libre et éclairé est requis pour toute mesure planifiée.

Citations et informations critiques

« Une mesure de contrôle [...] est une intervention de dernier recours qui devrait être réalisée exclusivement en situation d’urgence, c’est-à-dire lorsque la sécurité du personnel ou des élèves est menacée. » — Bernard Drainville, Ministre de l'Éducation

« L’utilisation d’une mesure de contrôle n’est pas préconisée en milieu scolaire. [...] Elle ne doit jamais être employée comme mesure éducative ou punitive ou encore pour faciliter la surveillance de l’élève. » — Source Contextuelle, Section 1.1

« Le recours aux mesures de contrôle est susceptible d’entraîner des blessures physiques et psychologiques qui peuvent avoir des implications à long terme. » — Source Contextuelle, Section 1

Das

Gedankenstriche rausnehmen

Warum ist das wichtig? Kurz erklären

Das, dies ist unsauber

die Gedankenstriche geben ChatGPT-Vibes. Vielleicht lieber Kommas?

Finde ich gut

Das ist sehr heruntergebrochen, vielleicht in einem Halbsatz erwähnen

Hier sieht man auch schön, den Zusammenhang zwischen Text und Tabelle. Es lässt sich nicht immer so leicht trennen

Mega!

Sind die open-source? Oxygen zumindest nicht. Ein Hinweis dazu wäre vielleicht sinnvoll

Sehr schön, gern mehr Beispiele einbauen

stellen wir...

streichen

werden...

Korpus ist synonym zu Textkorpus?

Was meint ihr mit Datensammlungen

Deutlicher Machen. Korpora sind sehr grundlegend für das OER. Das sollte auf jeden Fall hängen bleiben

richtiger Begriff? Vielleicht lieber entwickelt?

Sehr schön mit dem "Wir"

Vielleicht etwas dazu schreiben, dass auch traditionelle Methoden Einschränkungen mit sich bringen. Sonst bleibt hängen, dass DH super kritisch ist und der Rest nicht

Retail strategy is essentially the game plan that a retailer adopts in order to be successful. It describes how the business will react to the environment, how it will satisfy the needs of the customers, and how it will differentiate itself from other retailers. It links the overall understanding of retailing to the operational decisions such as merchandise and store operations. A retail strategy has three key components: target market, retail format, and competitive advantage.

These statements regarding the little progress made in wage equality and equal representation was discussed in module 3 in the You Tube video Democracy in the Workplace. The video reminds us of the growth of corporations and capitalism in the 1970's, leaving workers behind, particularly people of color. As productivity grew salaries did not. Little compasion was shown for workers, this article highlights that this remains the same.

id = identité et href = hypertext reference.

Pour faire le dièse sur Windows: Alt GR + touche 3 du clavier central.

Respondents to the Musicians’ Union survey noted “a lack of confidence from employers in [female musicians’] abilities” and that “very often women were asked if they were fans, rather than musicians or it was assumed they must be singers not instrumentalists."

Women also face less opportunities, are given less moral support, and unequal pay.

Percentage of Women Artists by Performer Type:

-Table

-Percentages

-Sample sizes

Discussion of table 2

Only 3 women songwriters who worked as often as 6 of the 15-most working men.

I would add something like 'or other more in-depth approaches'

This is a bit too simple. Note WELLBY, as used in the simplest approaches, mainly requires differences to be comparable-- and even linear -- across individuals. Moving from 1 to 3 is equally valued as moving from 4 to 6 or 8 to 10, and gets twice the value in this measure as moving 2 people from 3 to 4.

Le Partenariat en Santé : Synthèse de trois Expériences de Terrain

Ce document de synthèse analyse les interventions de trois équipes lors d'une session organisée par le Centre Opérationnel du Partenariat en Santé (COPS).

Il explore la mise en œuvre concrète du partenariat en santé à travers les secteurs des soins primaires, du sanitaire et du médico-social.

Résumé Exécutif

L'intégration du patient et de ses proches comme partenaires actifs transforme durablement les pratiques de soin.

Les retours d'expérience mettent en lumière une transition fondamentale : passer d'une logique de « faire pour » le patient à une logique de « faire avec » lui.

Points clés à retenir :

• Diversité des modèles : Le partenariat s'adapte à différents contextes, de la gouvernance des structures territoriales (CPTS) à la co-construction de parcours hospitaliers spécifiques ou au soutien à domicile.

• Défis opérationnels : Le recrutement des patients partenaires, l'acculturation des professionnels, la gestion du temps commun et la pérennisation des financements constituent les principaux obstacles.

• Bénéfices mutuels : Le partenariat améliore la pertinence des soins, réduit l'isolement des familles et renforce le sens du travail pour les professionnels de santé, contribuant ainsi à une meilleure qualité de vie au travail (QVT).

• Rigueur méthodologique : Pour éviter le « tokenisme » (participation de façade), une méthodologie rigoureuse et une coordination dédiée sont essentielles.

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1. Expérience en Soins Primaires : La CPTS du Grand Pic Saint-Loup

Les Communautés Professionnelles Territoriales de Santé (CPTS) regroupent des acteurs de santé libéraux pour mener des actions de santé publique. Dans cette expérience, le partenariat est envisagé comme une confrontation de « morceaux de réalité ».

Niveaux d'implication

Le partenariat au sein de la CPTS se décline sur plusieurs strates :

• Consultation : Réalisation d'enquêtes sur l'expérience des patients dans les lieux de soins non programmés pour comprendre leurs motivations de déplacement.

• Parcours de soins : Implication de patients experts dans des groupes de travail pluriprofessionnels (insuffisance cardiaque, diabète, santé orale).

• Gouvernance : Création d'un collège spécifique au sein de l'association ouvert aux patients, élus et habitants, disposant de voix consultatives au conseil d'administration.

Freins et leviers identifiés

| Catégorie | Détails | | --- | --- | | Freins | Confusion sémantique (multiplicité des termes : patient expert, traceur, coach) ; Difficulté de recrutement local ; Absence de statut administratif (SIRET) pour rémunérer les patients sans association. | | Leviers | Appui des médecins spécialistes hospitaliers déjà acculturés ; Création d'espaces de rencontre hors cabinets médicaux (ex: dépistage en centre commercial). |

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2. Expérience en Secteur Sanitaire : Polyclinique Saint-Roch

Le projet « Au cœur des soins », mené avec l'association Tremplin, porte sur le parcours des enfants porteurs de fentes faciales. Il repose sur une collaboration étroite entre parents partenaires et soignants.

Objectifs et Méthodologie

L'ambition est de promouvoir une relation de soin partenariale dès le diagnostic.

1. Recueil de l'expérience : Écoute du vécu des parents.

2. Approfondissement : Identification précise des besoins.

3. Co-construction : Utilisation d'outils participatifs nouveaux en milieu hospitalier.

L'ingrédient secret : Une coordination dédiée (représentant 2/3 des fonds du projet) pour organiser les espaces de dialogue et garantir la rigueur de la démarche, évitant ainsi d'utiliser les patients pour la simple forme.

Impacts observés

• Pour les familles : Reconnaissance de leur rôle d'acteur et réduction du sentiment d'isolement.

• Pour les professionnels : Meilleure compréhension des besoins réels. Une orthophoniste témoigne : « J'ai le sentiment d'aller plus vite, d'être plus efficace... la charge mentale est aussi vraiment moindre. »

• Relationnel : Établissement d'une horizontalité dans les échanges, permettant aux patients de comprendre aussi les contraintes des soignants.

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3. Expérience en Secteur Médico-Social : Association AA

L'association AA (Aide et Soins à domicile) s'est engagée dans le partenariat suite à une crise majeure : un conflit délétère entre une équipe de soins et une aidante, ayant entraîné un épuisement professionnel massif (10 arrêts de travail sur 10 salariés).

Évolution de la démarche

Initialement, l'association a commis l'erreur de construire la démarche entre professionnels uniquement. Le « rétropédalage » a été nécessaire pour intégrer réellement des aidants et des personnes accompagnées dans les groupes de travail en 2025.

Les témoignages clés recueillis :

• Mme Isabelle (personne accompagnée) : Souligne l'importance d'être attentif à la demande : « Parfois le salarié agit comme il souhaite mais pas comme la personne le souhaite. »

• M. Marc (aidant) : Note que les soignants doivent accepter les conseils des tiers lorsqu'ils manquent de connaissance sur le patient spécifique.

Défis spécifiques au domicile

• Fatigabilité : La participation des usagers est contrainte par leur état de santé ou leur charge d'aidant.

• Changement de paradigme : Abandonner le terme de « prise en charge » (jugé passif) au profit de « prendre en soin » ou « accompagner ».

• Transparence : Accepter de recevoir des critiques directes et parfois dures sur la qualité de l'accompagnement.

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4. Analyse Transversale : Obstacles, Leviers et Perspectives

L'analyse comparée des trois interventions permet de dégager des constantes dans la mise en œuvre du partenariat en santé.

Synthèse des obstacles communs

1. L'Acculturation : Le niveau de maturité face au partenariat est très hétérogène. Certains professionnels y voient une remise en question de leur autorité, d'autres une perte de temps.

2. Le Recrutement : Trouver le « bon patient pour le bon parcours », disponible et prêt à s'investir dans la durée, reste complexe.

3. La Temporalité : Aligner les agendas des professionnels libéraux, des salariés hospitaliers et des patients (souvent fatigués) est un défi logistique permanent.

4. Le Financement : La pérennisation des ressources pour rémunérer le temps de coordination et l'expertise des patients est cruciale.

Facteurs de succès

• Volonté Institutionnelle : Un engagement fort de la direction et des cadres est indispensable pour lever les résistances.

• Savoirs Expérientiels : Reconnaître que le savoir issu du vécu de la maladie est complémentaire au savoir scientifique et clinique.

• Évaluation de l'impact : Bien que difficile, la mesure de l'amélioration de l'expérience patient et de la qualité des soins est nécessaire pour valider la démarche à long terme.

Conclusion sur la Qualité de Vie au Travail (QVT)

Une observation majeure émerge : le partenariat en santé est un levier puissant de bien-être au travail.

En améliorant la compréhension des besoins et en réduisant les situations conflictuelles, il redonne du sens aux missions des professionnels et diminue leur charge mentale, malgré l'investissement temporel initial requis pour sa mise en place.

Briefing : Perspectives et Jalons du Partenariat en Santé

Synthèse Sommaire

Ce document de synthèse détaille les perspectives post-événement de la journée régionale consacrée au partenariat en santé en Occitanie.

Il s'articule autour de l'action coordonnée de trois entités clés : la Structure Régionale d'Appui (SRA), France Assos Santé Occitanie et le Centre Opérationnel du Partenariat en Santé (COPS).

L'objectif central est de transformer les réflexions de la journée en actions concrètes par le biais de la formation, de l'accompagnement méthodologique et de la mise à disposition de ressources structurantes.

Les points saillants incluent l'intégration du partenariat dans l'évaluation des pratiques professionnelles à l'horizon 2026, le renforcement de la synergie entre représentants des usagers et patients partenaires, et le déploiement d'outils numériques pour faciliter le maillage territorial des projets de santé.

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1. Orientations Stratégiques de la Structure Régionale d'Appui (SRA)

La SRA réaffirme son ambition d'agir collectivement pour l'amélioration des parcours de santé à travers huit thématiques prioritaires, dont le partenariat fait partie intégrante.

Modalités d'Intervention

L'action de la SRA se déploie selon plusieurs axes opérationnels :

• Information et Sensibilisation : Organisation de journées régionales annuelles.

• Formation et Enseignement : Participation à l'enseignement universitaire et à la recherche.

• Accompagnement Méthodologique : Soutien à l'évaluation des pratiques et des organisations sur le terrain, sans se substituer aux acteurs locaux.

• Production de Données : Publication de travaux de recherche dans le domaine de la santé.

Prospective : L'Horizon 2026

Une ambition majeure a été annoncée pour l'année 2026 : l'intégration de la thématique du partenariat en santé au cœur de l'évaluation des pratiques professionnelles (EPP). Ce sujet, reconnu comme complexe et passionnant, fera l'objet d'un appel à manifestation d'intérêt pour les acteurs souhaitant approfondir cette réflexion.

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2. France Assos Santé Occitanie : Mobilisation et Formation

En tant qu'union d'associations agréées, France Assos Santé joue un rôle de fédérateur tant au niveau régional que national.

Structure et Représentation

| Niveau | Volume d'associations | Domaines couverts | | --- | --- | --- | | Régional | 70 associations | Personnes malades, situation de handicap, consommateurs, santé environnementale, associations familiales, précarité. | | National | ~100 associations | Identiques au niveau régional. |

Missions et Ressources pour les Usagers

• Information et Veille : Observation du bon fonctionnement du système de santé et interventions médiatiques.

• Accès aux données : Mise à disposition d'un site web (régional et national) et d'un extranet dédié aux représentants des usagers (RU) comprenant fiches pratiques et guides.

• Guide de référence : Co-construction avec "Savoir Patient" d'un guide sur les facettes de l'engagement de l'usager partenaire (pair-aidance, recherche, formation des professionnels).

Dispositif de Formation

L'organisme propose un parcours structuré pour accompagner les mandats des RU :

• Volume : 41 jours de formation dispensés l'an passé dans 7 départements.

• Formation "RU et Patients Partenaires" : Un module spécifique visant à améliorer la collaboration et la connaissance mutuelle entre ces deux types d'acteurs de l'engagement.

• Accessibilité : Formations disponibles en présentiel et en distanciel via un catalogue dédié.

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3. Le Centre Opérationnel du Partenariat en Santé (COPS) : Appui Opérationnel

Le COPS se définit comme un facilitateur de projets de partenariat, agissant concrètement auprès des structures et des acteurs.

Accompagnement de Projets

Le COPS intervient en binôme (incluant un chargé de projet et une perspective professionnelle) sur sollicitation via une plateforme dédiée. Les domaines d'appui incluent :

• Le médico-social et les soins primaires.

• La co-construction de parcours (ex: hospitalisation à domicile - HAD, oncologie, santé mentale).

• L'accompagnement stratégique et la qualité.

Outils et Plateforme Collaborative

La plateforme participative du COPS offre plusieurs services en libre accès :

• Répertoire et Cartographie : Outils permettant d'identifier des patients partenaires ou des structures porteuses de projets pour favoriser le réseautage autonome.

• Ressources Multimédia : "Copcasts" (podcasts), webinaires, supports de présentation et guides (dont la fiche "Engager" pour l'implication des patients).

• Formation : Offre de e-learning certifiée Qualiopi, avec des formats "à la carte" pour les équipes projets.

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4. Jalons et Événements à Venir

Le calendrier institutionnel prévoit plusieurs étapes clés pour maintenir la dynamique du partenariat en santé :

| Date / Période | Événement / Action | Thématique | | --- | --- | --- | | 9 décembre | Webinaire | Lien entre partenariat en santé et Qualité de Vie au Travail (QVT). | | Prochainement | Soirée départementale | Déplacement dans le Lot (actions "aller vers"). | | 1er Trimestre 2026 | Soirée départementale | Rencontre dans les Pyrénées-Orientales (PO). | | Courant 2026 | Nouveaux formats | Groupes d'analyse de pratiques (mixtes, patients et professionnels) et ateliers de co-développement. |

5. Synthèse Éthique

L'Espace de réflexion éthique Occitanie, représenté par le Professeur Michel Clanet, assure une fonction de "grand témoin".

Son rôle est d'analyser la place du partenariat en santé dans la démarche éthique globale, soulignant que l'engagement des partenaires n'est pas seulement une modalité organisationnelle, mais une réflexion profonde sur la pratique du soin et le respect des parties prenantes.

What are Ptf1a Cre; Sox9 OE mice?

These are mice that were engineered so that:

Ptf1a-Cre turns on genetic changes specifically in pancreatic acinar cells (the enzyme-producing cells).

Sox9 OE means Sox9 is overexpressed (OE = overexpression), meaning the Sox9 gene is artificially turned on at higher-than-normal levels.

The system likely includes:

An HA-tag (a small detectable protein tag used to track the overexpressed protein)

RFP (red fluorescent protein) as a marker for cells that did not undergo recombination.

🔬 What did they observe?

Sox9 overexpression happened mostly in acinar cells

These cells expressed:

The HA-tag

Extra Sox9

This confirms the genetic system worked where expected.

Duct cells and endocrine cells mostly did NOT change

They remained unrecombined

They still expressed RFP

Meaning the genetic modification did not activate in those cells.

So the gene change was specific to acinar cells, which is what the researchers intended.

🐭 What about the pancreas itself?

In 3-week-old modified mice:

Pancreas weight → normal

Tissue structure (morphology) → normal

Blood glucose levels → normal

When compared to control mice, there were no obvious differences.

📌 What’s the conclusion?

Even though Sox9 was artificially overexpressed in acinar cells, it:

Did not disrupt pancreatic development

Did not change pancreas size

Did not affect blood sugar levels

In short:

For early development (up to 3 weeks), forcing Sox9 expression in acinar cells does not cause obvious problems in the pancreas.

Sox9+ state associated with PDA inititiation.

PDA and PanIN 2-3 show strong and negative SOX9 expression.

Sécuriser l'apprentissage et l'épanouissement : Mettre fin à la violence dans et par l'éducation

Synthèse de haut niveau

La violence en milieu éducatif constitue une crise mondiale d'une ampleur alarmante, touchant environ un milliard d'enfants chaque année.

Loin d'être des incidents isolés, ces violences — qu'elles soient physiques, sexuelles ou psychologiques — s'inscrivent dans un continuum qui entrave le droit fondamental à l'éducation et compromet le développement des sociétés.

L'impact économique est colossal, avec une perte estimée à 11 000 milliards de dollars en revenus futurs à l'échelle mondiale.

Le présent document souligne l'impératif de passer d'interventions fragmentées à une approche holistique et systémique.

L'éducation ne doit plus seulement être vue comme un lieu où la violence se produit, mais comme le levier principal pour la prévenir.

Pour transformer durablement les écoles en sanctuaires de sécurité, il est impératif d'intégrer la prévention et la réponse à la violence au cœur même des systèmes éducatifs, et non comme une simple responsabilité additionnelle.

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I. État des lieux : Les multiples visages de la violence

La violence en milieu éducatif est un phénomène complexe qui dépasse largement le cadre des agressions physiques visibles. Elle se manifeste sous plusieurs formes interdépendantes :

1. Typologie de la violence envers les apprenants

• Violence physique : Inclut les bagarres, les attaques et les châtiments corporels. Plus d'un tiers des élèves ont été impliqués dans une bagarre physique au cours de l'année écoulée.

• Violence psychologique : Humiliation, intimidation, insultes et exclusion sociale. À titre d'exemple, 42 % des jeunes LGBTQ+ rapportent avoir été ridiculisés ou menacés à l'école.

• Violence sexuelle : Harcèlement, attouchements et rapports forcés. Jusqu'à 25 % des adolescents subissent des violences sexuelles, dont 40 % se produisent dans l'enceinte scolaire.

• Harcèlement (Bullying) : Caractérisé par un déséquilibre de pouvoir, il touche 1 apprenant sur 3 chaque mois à travers le monde.

• Violence facilitée par la technologie : Le cyberharcèlement et l'exploitation en ligne amplifient la portée des agressions au-delà des murs de l'école.

2. Violence institutionnelle et structurelle

La violence ne provient pas uniquement des individus ; elle peut être intégrée au système lui-même via :

• Des politiques discriminatoires (ex: codes vestimentaires biaisés).

• Des méthodes d'enseignement inéquitables ou un curriculum excluant certains groupes.

• La normalisation de la violence comme outil de discipline.

3. Violence contre le personnel éducatif

Le personnel n'est pas épargné. Une enquête révèle que près de 80 % des enseignants ont subi une forme de violence à l'école au cours d'une année scolaire, ce qui dégrade leur bien-être et leur efficacité pédagogique.

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II. Analyse des moteurs de la violence : Une approche intersectionnelle

La violence est alimentée par une interaction complexe de facteurs à plusieurs niveaux. L'identité de l'apprenant (genre, handicap, race, orientation sexuelle) détermine souvent la nature et l'intensité de la violence subie.

| Niveau de facteur | Exemples de moteurs identifiés | | --- | --- | | Individuel | Antécédents de violence domestique, manque de sensibilisation aux droits. | | Interpersonnel | Mauvaise gestion des conflits, absence de modèles adultes positifs. | | Systémique | Manque de formation sur la discipline positive, absence de protocoles de signalement. | | Communautaire | Normalisation des châtiments corporels, influence des gangs ou des conflits locaux. | | Sociétal | Inégalités socio-économiques, cadres juridiques faibles ou inexistants. | | Normatif | Normes de genre néfastes (valorisation de la dureté masculine, soumission féminine). |

La dimension de genre (SRGBV)

La violence de genre en milieu scolaire (SRGBV) est omniprésente. Les filles sont plus exposées au harcèlement sexuel et aux grossesses précoces forcées, tandis que les garçons subissent davantage de châtiments corporels et de violences physiques, souvent au nom de normes de masculinité rigides.

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III. Les répercussions : Au-delà de l'enceinte scolaire

Les conséquences de la violence sont profondes et durables, affectant non seulement l'individu mais aussi la société entière :

• Impact éducatif : Les élèves victimes sont trois fois plus susceptibles de se sentir aliénés et deux fois plus enclins à manquer l'école. Cela mène à une baisse des résultats en lecture et calcul, et souvent au décrochage scolaire.

• Santé mentale : Anxiété, dépression, perte d'estime de soi et comportements d'automutilation.

• Santé physique : Risques accrus de VIH, d'infections sexuellement transmissibles et de grossesses non planifiées (facteur majeur de décrochage chez les adolescentes).

• Coût économique : La violence entrave le développement du capital humain, entraînant des pertes de revenus massives sur toute une vie.

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IV. Le cadre d'action : Une approche holistique

Pour mettre fin à la violence, l'UNESCO et ses partenaires préconisent une transformation radicale basée sur six piliers fondamentaux :

1. Curriculum et apprentissage : Intégrer des programmes d'éducation sexuelle complète (ESC), d'apprentissage socio-émotionnel (SEL) et de prévention de la violence pour transformer les attitudes dès le plus jeune âge.

2. Environnement scolaire : Créer des espaces physiques sûrs (toilettes séparées, éclairage) et promouvoir une culture de "discipline positive" qui exclut tout châtiment corporel.

3. Mécanismes de signalement : Mettre en place des systèmes confidentiels, accessibles et adaptés aux enfants (lignes d'assistance, boîtes aux lettres, focal points).

4. Politiques et lois : Adopter des législations nationales interdisant explicitement les châtiments corporels (comme au Pérou en 2015) et promouvoir l'inclusion radicale (comme en Sierra Leone).

5. Partenariats et mobilisation : Collaborer avec les syndicats d'enseignants, les parents, les leaders communautaires et les entreprises technologiques.

6. Données et preuves : Utiliser des outils de diagnostic et des enquêtes numériques (ex: système Ma’An en Jordanie) pour orienter les interventions de manière factuelle.

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V. Perspectives pour un changement durable

La réussite de cette transformation repose sur quatre principes transversaux :

• Centrage sur l'apprenant : Prioriser la sécurité et le "ne pas nuire" (do no harm).

• Sensibilité aux traumatismes : Éviter la re-traumatisation lors du soutien aux victimes.

• Adaptation au contexte : Reconnaître que les solutions en zone de conflit diffèrent de celles en zone urbaine stable.

• Transformation du rôle de l'enseignant : Soutenir les enseignants non seulement comme protecteurs, mais aussi comme individus ayant besoin de protection et de formation continue.

Citation clé

"Puisque les guerres prennent naissance dans l'esprit des hommes et des femmes, c'est dans l'esprit des hommes et des femmes que doivent être élevées les défenses de la paix." — Acte constitutif de l'UNESCO

En conclusion, mettre fin à la violence dans l'éducation n'est pas seulement une obligation morale et légale, c'est une condition sine qua non pour bâtir une société juste, inclusive et prospère.

L'heure est à l'action collective et systématique pour faire de chaque école un véritable havre de paix.