Reviewer #2 (Public review):

Summary:

In this manuscript, Palo et al present a novel role for FRG1 as a multifaceted regulator of nonsense-mediated mRNA decay (NMD). Through a combination of reporter assays, transcriptome-wide analyses, genetic models, protein-protein interaction studies, ubiquitination assays, and ribosome-associated complex analyses, the authors propose that FRG1 acts as a negative regulator of NMD by destabilizing UPF1 and associating with spliceosomal, EJC, and translation-related complexes. Overall, the data, while consistent with the authors' central conclusions, are undermined by several claims-particularly regarding structural roles and mechanistic exclusivity. To really make the claims presented, further experimental evidence would be required.

Strengths:

(1) The integration of multiple experimental systems (zebrafish and cell culture).

(2) Attempts to go into a mechanistic understanding of the relationship between FGR1 and UPF1.

Weaknesses:

(1) Overstatement of FRG1 as a structural NMD component.

Although FRG1 interacts with UPF1, eIF4A3, PRP8, and CWC22, core spliceosomal and EJC interactions (PRP8-CWC22 and eIF4A3-UPF3B) remain intact in FRG1-deficient cells. This suggests that, while FRG1 associates with these complexes, this interaction is not required for their assembly or structural stability. Without further functional or reconstitution experiments, the presented data are more consistent with an interpretation of FRG1 acting as a regulatory or accessory factor rather than a core structural component.

(2) Causality between UPF1 depletion and NMD inhibition is not fully established.

While reduced UPF1 levels provide a plausible explanation for decreased NMD efficiency, the manuscript does not conclusively demonstrate that UPF1 depletion drives all observed effects. Given FRG1's known roles in transcription, splicing, and RNA metabolism, alterations in transcript isoform composition and apparent NMD sensitivity may arise from mechanisms independent of UPF1 abundance. To directly link UPF1 depletion to altered NMD efficiency, rescue experiments testing whether UPF1 re-expression restores NMD activity in FRG1-overexpressing cells would be important.

(3) Mechanism of FRG1-mediated UPF1 ubiquitination requires clarification.

The ubiquitination assays support a role for FRG1 in promoting UPF1 degradation; however, the mechanism underlying this remains unexplored. The relationship between FRG1-UPF1 what role FRG1 plays in this is unclear (does it function as an adaptor, recruits an E3 ubiquitin ligase, or influences UPF1 ubiquitination indirectly through transcriptional or signaling pathways?).

(4) Limited transcriptome-wide interpretation of RNA-seq data.

Although the RNA-seq data analysis relies heavily on a small subset of "top 10" genes. Additionally, the criteria used to define NMD-sensitive isoforms are unclear. A more comprehensive transcriptome-wide summary-indicating how many NMD-sensitive isoforms are detected and how many are significantly altered-would substantially strengthen the analysis.

(5) Clarification of NMD sensor assay interpretation.

The logic underlying the NMD sensor assay should be explained more clearly early in the manuscript, as the inverse relationship between luciferase signal and NMD efficiency may be counterintuitive to readers unfamiliar with this reporter system. Inclusion of a schematic or brief explanatory diagram would improve accessibility.

(6) Potential confounding effects of high MG132 concentration.

The MG132 concentration used (50 µM) is relatively high and may induce broad cellular stress responses, including inhibition of global translation (its known that proteosome inhibition shuts down translation). Controls addressing these secondary effects would strengthen the conclusion that UPF1 stabilization specifically reflects proteasome-dependent degradation would be essential.

(7) Interpretation of polysome co-sedimentation data.

While the co-sedimentation of FRG1 with polysomes is intriguing, this approach does not distinguish between direct ribosomal association and co-migration with ribosome-associated complexes. This limitation should be explicitly acknowledged in the interpretation.

(8) Limitations of PLA-based interaction evidence.

The PLA data convincingly demonstrate close spatial proximity between FRG1 and eIF4A3; however, PLA does not provide definitive evidence of direct interaction and is known to be susceptible to artefacts. Moreover, a distance threshold of ~40 nm still allows for proteins to be in proximity without being part of the same complex. These limitations should be clearly acknowledged, and conclusions should be framed accordingly.

Reviewer #3 (Public review):

The manuscript by Palo and colleagues demonstrates identification of FRG1 as a novel regulator of nonsense-mediated mRNA decay (NMD), showing that FRG1 inversely modulates NMD efficiency by controlling UPF1 abundance. Using cell-based models and a frg1 knockout zebrafish, the authors show that FRG1 promotes UPF1 ubiquitination and proteasomal degradation, independently of DUX4. The work further positions FRG1 as a structural component of the spliceosome and exon junction complex without compromising its integrity. Overall, the manuscript provides mechanistic insight into FRG1-mediated post-transcriptional regulation and expands understanding of NMD homeostasis. The authors should address the following issues to improve the quality of their manuscript.

(1) Figure 7A-D, appropriate positive controls for the nuclear fraction (e.g., Histone H3) and the cytoplasmic fraction (e.g., GAPDH or α-tubulin) should be included to validate the efficiency and purity of the subcellular fractionation.

(2) To strengthen the conclusion that FRG1 broadly impacts the NMD pathway, qRT-PCR analysis of additional core NMD factors (beyond UPF1) in the frg1⁻/⁻ zebrafish at 48 hpf would be informative.

(3) Figure labels should be standardized throughout the manuscript (e.g., consistent use of "Ex" instead of mixed terms such as "Oex") to improve clarity and readability.

(4) The methods describing the generation of the frg1 knockout zebrafish could be expanded to include additional detail, and a schematic illustrating the CRISPR design, genotyping workflow, and validation strategy would enhance transparency and reproducibility.

(5) As FRG1 is a well-established tumor suppressor, additional cell-based functional assays under combined FRG1 and UPF1 perturbation (e.g., proliferation, migration, or survival assays) could help determine whether FRG1 influences cancer-associated phenotypes through modulation of the NMD pathway.

(6) Given the claim that FRG1 inversely regulates NMD efficacy via UPF1, an epistasis experiment such as UPF1 overexpression in an FRG1-overexpressing background followed by an NMD reporter assay would provide stronger functional validation of pathway hierarchy.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this manuscript, Dixit and colleagues investigate the role of FRG1 in modulating nonsense-mediated mRNA decay using human cell lines and zebrafish embryos. They present data from experiments that test the effect of normal, reduced or elevated levels of FRG1 on NMD of a luciferase-based NMD reporter and on endogenous mRNA substrates of NMD. They also carry out experiments to investigate FRG1's influence on UPF1 mRNA and protein levels, with a particular focus on the possibility that FRG1 regulates UPF1 protein levels through ubiquitin-mediated proteolysis of UPF1. The experiments described also test whether DUX4's effect on UPF1 protein levels and NMD could be mediated through FRG1. Finally, the authors also present experiments that test for physical interaction between UPF1, the spliceosome and components of the exon junction complex.

Strengths:

A key strength of the work is its focus on an intriguing model of NMD regulation by FRG1, which is of particular interest as FRG1 is positively regulated by DUX4, which has been previously implicated in subjecting UPF1 to proteosome-mediated degradation and thereby causing NMD inhibition. The data that shows that DUX4-mediated effect on UPF1 levels is diminished upon FRG1 depletion suggests that DUX4's regulation of NMD could be mediated by FRG1.

Weaknesses:

A major weakness and concern is that many of the key conclusions drawn by the authors are not supported by the data, and there are also some significant concerns with experimental design. More specific comments below describe these issues:

(1) Multiple issues lower the confidence in the experiments testing the effect of FRG1 on NMD.

(a) All reporter assays presented in the manuscript are based on quantification of luciferase activity, and in most cases, the effect on luciferase activity is quite small. This assay is the key experimental approach throughout the manuscript. However, no evidence is provided that the effect captured by this assay is due to enhanced degradation of the mRNA encoding the luciferase reporter, which is what is implied in the interpretation of these experiments. Crucially, there is also no control for the reporter that can account for the effects of experimental manipulations on transcriptional versus post-transcriptional effects. A control reporter lacking a 3'UTR intron is described in Barid et al, where the authors got their NMD reporter from. Due to small effects observed on luciferase activity upon FRG1 depletion, it is necessary to not only measure NMD reporter mRNA steady state levels, but it will be equally important to ascertain that the effect of FRG1 on NMD is at the level of mRNA decay and not altered transcription of NMD substrates. This can be accomplished by testing decay rates of the beta-globin reporter mRNA.

We thank the reviewer for raising these points and for the careful evaluation of our experimental approach. Here we provide our response to comment (a) in three parts

Reliance on luciferase-based reporter assays

While luciferase-based NMD reporter assays represent an important experimental component of this study, our conclusions do not rely exclusively on this approach. The reporter-based findings are independently supported by RNA sequencing analyses of FRG1-perturbed cells, which demonstrate altered abundance of established PTC-containing NMD target transcripts. This genome-wide analysis provides an unbiased and physiologically relevant validation of FRG1 involvement in NMD regulation.

All reporter assays presented in the manuscript are based on quantification of luciferase activity, and in most cases, the effect on luciferase activity is quite small.

We respectfully disagree with the comment that the magnitude of the luciferase effects is low. Increased expression of FRG1, which leads to reduced UPF1 levels, results in a ~3.5-fold increase in relative luciferase activity (Fig. 1C), indicating a robust effect. Furthermore, in the in vivo zebrafish model, FRG1 knockout causes a pronounced decrease in relative luciferase activity (Fig. 1H), consistent with elevated UPF1 levels and enhanced NMD activity.

It is also important to note that FRG1 functions as a negative regulator of UPF1; therefore, its depletion is expected to increase UPF1 levels. However, excessive elevation of UPF1 is likely constrained by additional regulatory mechanisms, which may limit the observable effects of FRG1 knockdown or knockout. In line with this, our previous study (1) demonstrated that FRG1 positively regulates multiple NMD factors while exerting an inverse regulatory effect on UPF1. This dual role suggests that FRG1 may act as a compensatory modulator of the NMD machinery, which likely explains the relatively subtle net effects observed in FRG1 knockdown/knockout conditions in vitro (Fig. 1A and 1B). This interpretation is explicitly discussed in the manuscript (Discussion, paragraph para 4).

However, no evidence is provided that the effect captured by this assay is due to enhanced degradation of the mRNA encoding the luciferase reporter, which is what is implied in the interpretation of these experiments. Crucially, there is also no control for the reporter that can account for the effects of experimental manipulations on transcriptional versus post-transcriptional effects. A control reporter lacking a 3'UTR intron is described in Barid et al, where the authors got their NMD reporter from. Due to small effects observed on luciferase activity upon FRG1 depletion, it is necessary to not only measure NMD reporter mRNA steady state levels, but it will be equally important to ascertain that the effect of FRG1 on NMD is at the level of mRNA decay and not altered transcription of NMD substrates. This can be accomplished by testing decay rates of the beta-globin reporter mRNA.

Thank you for your suggestion. We will test decay rates of the beta-globin reporter mRNA.

(b) It is unusual to use luciferase enzymatic activity as a measurement of RNA decay status. Such an approach can at least be justified if the authors can test how many-fold the luciferase activity changes when NMD is inhibited using a chemical inhibitor (e.g., SMG1 inhibitor) or knockdown of a core NMD factor.

We respectfully disagree that the use of luciferase enzymatic activity as a readout for NMD is unusual. Multiple prior studies have successfully employed identical or closely related luciferase-based/fluorescence-based reporters to quantify NMD activity (2–5). Importantly, the goal of our study was not to measure RNA decay kinetics per se, but rather to assess how altered FRG1 levels influence the functional efficiency of the NMD pathway. Given that FRG1 is a structural component of the spliceosome C complex (6) and is previously indirectly linked to NMD regulation (1,7) this approach was well-suited to address our central question.

As suggested by the reviewer, we will also assess luciferase activity following pharmacological inhibition of NMD to further validate the reporter system's responsiveness.

(c) The concern about the direct effect of FRG1 on NMD is further amplified by the small effects of FRG1 knockout on steady-state levels of endogenous NMD targets (Figure 1A and B: ~20% reduction in reporter mRNA in MCF7 cells; Figure 1M, only 18 endogenous NMD targets shared between FRG1_KO and FRG1_KD).

The modest changes observed upon FRG1 loss do not preclude a direct role in NMD. As detailed in our response to comment (a) and discussed in paragraph 4 of the Discussion, limited effects on steady-state levels of endogenous NMD targets are expected given the buffering capacity of the NMD pathway and the contribution of compensatory regulatory mechanisms.

(d) The question about transcriptional versus post-transcriptional effects is also important in light of the authors' previous work that FRG1 can act as a transcriptional regulator.

We agree that distinguishing between transcriptional and post-transcriptional effects is important, particularly in light of our previous work demonstrating that FRG1 can function as a transcriptional regulator of multiple NMD genes (1). Consistent with this, the current manuscript shows that FRG1 influences the transcript levels of UPF1. In addition, we demonstrate that FRG1 regulates UPF1 at the protein level. We therefore conclude that FRG1 regulates UPF1 dually, at both transcriptional and post-transcriptional levels, supporting a dual role for FRG1 in the regulation of NMD.

This conclusion is further supported by prior studies indicating post-transcriptional functions of FRG1. FRG1 is a nucleocytoplasmic shuttling protein(8), interacts with the NMD factor ROD1 (7), and has been identified as a component of the spliceosomal C complex (6). FRG1 has also been reported to associate with the hnRNPK family of proteins (8), which participate in extensive protein–protein interaction networks. Collectively, these observations are consistent with a role for FRG1 in regulating NMD components at multiple levels.

(2) In the experiments probing the relationship between DUX4 and FRG1 in NMD regulation, there are some inconsistencies that need to be resolved.

(a) Figure 3 shows that the inhibition of NMD reporter activity caused by DUX4 induction is reversed by FRG1 knockdown. Although levels of FRG1 and UPF1 in DUX4 uninduced and DUX4 induced + FRG1 knockdown conditions are similar (Figure 5A), why is the reporter activity in DUX4 induced + FRG1 knockdown cells much lower than DUX4 uninduced cells in Figure 3?

We appreciate the reviewer’s comment. Figures 3 and 5A represent independent experiments in which FRG1 knockdown was achieved by transient transfection. As such, variability in transfection efficiency is expected and likely accounts for the quantitative difference. We want to highlight that compared to DUX4_induced lane (Fig. 5A, lane 2), when we knock down FRG1 on the DUX4_induced background, it shows a clear increase in the UPF1 level (Fig. 5A, lane 3). We will add one more replicate to 5 A with better FRG1_KD transfection to the experiment.

(b) In Figure 3, it is important to know the effect of FRG1 knockdown in DUX4 uninduced conditions.

We thank the reviewer for this thoughtful suggestion. The effect of FRG1 knockdown under DUX4-uninduced conditions is presented in Figure 1A, where FRG1 levels are reduced without altering DUX4 expression. In contrast, Figure 3 is specifically designed to assess the rescue effect—namely, how reduction of FRG1 expression under DUX4-induced conditions influences NMD efficiency. Therefore, inclusion of an FRG1 knockdown–only group in Figure 3 was not relevant to the objective of this experiment.

(c) On line 401, the authors claim that MG132 treatment leads to "time-dependent increase in UPF1 protein levels" in Figure 5C. However, upon proteasome inhibition, UPF1 levels significantly increase only at 8h time point, while the change at 12 and 24 hours is not significantly different from the control.

We thank the reviewer for this observation and agree that the statement of a “time-dependent increase in UPF1 protein levels” was inaccurate. A significant increase is observed only at the 8 h time point following MG132 treatment, with no significant changes at 12 h or 24 h. The text will be revised accordingly to reflect Figure 5C.

(3) There are multiple issues with experiments investigating ubiquitination of UPF1:

(a) Ubiquitin blots in Figure 6 are very difficult to interpret. There is no information provided either in the text or figure legends as to which bands in the blots are being compared, or about what the sizes of these bands are, as compared to UPF1. Also, the signal for Ub in most IP samples looks very similar to or even lower than the input.

We agree that the ubiquitin blots in Figure 6 require clearer presentation. In the revised figure, we will annotate the ubiquitin immunoblots to indicate the region corresponding to UPF1 (~140 kDa), which is the relevant molecular weight for interpretation. Because UPF1 is polyubiquitinated, ubiquitinated species are expected to appear as multiple bands rather than a single discrete signal; therefore, ubiquitination was assessed across the full blot. Importantly, interpretation is based on comparisons between UPF1 immunoprecipitated samples within each panel (Fig. 6C–F), rather than between input and IP lanes. For example, in Figure 6 C UPF1 IP FRG1_KD compared to UPF1 IP FRG1_Ex, in Figure 6 D UPF1 IP FRG1_WT compared to UPF1 IP FRG1_KO, in Figure 6 E UPF1 IP FRG1_KO compared to UPF1 IP FRG1_KO+FRG1_Ex, and in Figure 6 F UPF1 IP FRG1_Ex compared to UPF1 IP FRG1_Ex+MG132 TRT.

(b) Western blot images in Figure 6D appear to be adjusted for brightness/contrast to reduce background, but are done in such a way that pixel intensities are not linearly altered. This image appears to be the most affected, although some others have also similar patterns (e.g., Figure 5C).

We thank the reviewer for raising this point. The appearance noted in Figure 6D was not due to non-linear alteration of pixel intensities, but rather resulted from the poor quality of the ubiquitin antibody, which required prolonged exposure times. To address this, we replaced the antibody and repeated the ubiquitin immunoblots shown in Figures 6D, 6E, and 6F.

For Figure 5C, only uniform contrast adjustment was applied for clarity. Importantly, all adjustments were performed linearly and applied to the entire image. Raw, unprocessed images for all blots are provided in the Supplementary Information. Updated versions of Figures 5 and 6 will be included in the revised manuscript.

(4) The experiments probing physical interactions of FRG1 with UPF1, spliceosome and EJC proteins need to consider the following points:

(a) There is no information provided in the results or methods section on whether immunoprecipitations were carried out in the absence or presence of RNases. Each RNA can be bound by a plethora of proteins that may not be functionally engaged with each other. Without RNase treatment, even such interactions will lead to co-immunoprecipitation. Thus, experiments in Figure 6 and Figure 7A-D should be repeated with and without RNase treatment.

We thank the reviewer for this important point. The co-immunoprecipitation experiments shown in Figures 6 and 7A–D were performed in the absence of RNase treatment; this information was inadvertently omitted and will be added to the Methods section and the relevant figure legends. To directly assess whether the observed interactions are RNA-dependent, we will repeat the key co-immunoprecipitation experiments in the presence of RNase treatment and include these results in the revised manuscript.

(b) Also, the authors claim that FRG1 is a "structural component" of EJC and NMD complexes seems to be an overinterpretation. As noted in the previous comment, these interactions could be mediated by a connecting RNA molecule.

We thank the reviewer for this insightful comment. As noted, previous studies have suggested that FRG1 interacts with components of the EJC and NMD machinery. Specifically, Bertram et al. (6) identified FRG1 as a component of the spliceosomal C complex via Cryo-EM structural analysis, and pull-down studies have shown direct interaction between FRG1 and ROD1, a known EJC component (7). These findings support a protein-protein interaction rather than one mediated solely by RNA. To further address the reviewer’s concern, we will perform key co-immunoprecipitation experiments in the presence of RNase treatment to distinguish RNA-dependent from RNA-independent interactions.

(c) A negative control (non-precipitating protein) is missing in Figure 7 co-IP experiments.

We agree that including a non-precipitating protein as a negative control is important, and we will perform the co-IP experiment incorporating this control.

(d) Polysome analysis is missing important controls. FRG1 and EIF4A3 co-sedimentation with polysomes could simply be due to their association with another large complex (e.g., spliceosome), which will also co-sediment in these gradients. This possibility can at least be tested by Western blotting for some spliceosome components across the gradient fractions. More importantly, a puromycin treatment control needs to be performed to confirm that FRG1 and EIF4A3 are indeed bound to polysomes, which are separated into ribosome subunits upon puromycin treatment. This leads to a shift of the signal for ribosomal proteins and any polysome-associated proteins to the left.

As recommended, we will examine the distribution of a spliceosome component across the gradient fractions to assess potential co-sedimentation. Additionally, we will perform a puromycin treatment control to confirm that FRG1 and EIF4A3 are genuinely associated with polysomes.

Reviewer #2 (Public review):

Summary:

In this manuscript, Palo et al present a novel role for FRG1 as a multifaceted regulator of nonsense-mediated mRNA decay (NMD). Through a combination of reporter assays, transcriptome-wide analyses, genetic models, protein-protein interaction studies, ubiquitination assays, and ribosome-associated complex analyses, the authors propose that FRG1 acts as a negative regulator of NMD by destabilizing UPF1 and associating with spliceosomal, EJC, and translation-related complexes. Overall, the data, while consistent with the authors' central conclusions, are undermined by several claims-particularly regarding structural roles and mechanistic exclusivity. To really make the claims presented, further experimental evidence would be required.

Strengths:

(1) The integration of multiple experimental systems (zebrafish and cell culture).

(2) Attempts to go into a mechanistic understanding of the relationship between FGR1 and UPF1.

Weaknesses:

(1) Overstatement of FRG1 as a structural NMD component.

Although FRG1 interacts with UPF1, eIF4A3, PRP8, and CWC22, core spliceosomal and EJC interactions (PRP8-CWC22 and eIF4A3-UPF3B) remain intact in FRG1-deficient cells. This suggests that, while FRG1 associates with these complexes, this interaction is not required for their assembly or structural stability. Without further functional or reconstitution experiments, the presented data are more consistent with an interpretation of FRG1 acting as a regulatory or accessory factor rather than a core structural component.

We thank the reviewer for this clarification. We would like to emphasize that we do not claim FRG1 to be a core structural component of either the spliceosome or the EJC. Consistent with the reviewer’s interpretation, our data indicate that FRG1 deficiency does not disrupt the structural integrity of these complexes. Our intended conclusion is that FRG1 functions as a regulatory or accessory factor in NMD rather than being required for complex assembly or stability. We will carefully revise the manuscript to remove any language that could be interpreted as an overstatement. In addition, we are currently performing further experiments to better define the association of FRG1 with the EJC.

(2) Causality between UPF1 depletion and NMD inhibition is not fully established.

While reduced UPF1 levels provide a plausible explanation for decreased NMD efficiency, the manuscript does not conclusively demonstrate that UPF1 depletion drives all observed effects. Given FRG1's known roles in transcription, splicing, and RNA metabolism, alterations in transcript isoform composition and apparent NMD sensitivity may arise from mechanisms independent of UPF1 abundance. To directly link UPF1 depletion to altered NMD efficiency, rescue experiments testing whether UPF1 re-expression restores NMD activity in FRG1-overexpressing cells would be important.

As suggested, to directly test causality, we will perform rescue experiments to determine whether UPF1 re-expression restores NMD activity in FRG1-overexpressing MCF7 cells.

(3) Mechanism of FRG1-mediated UPF1 ubiquitination requires clarification.

The ubiquitination assays support a role for FRG1 in promoting UPF1 degradation; however, the mechanism underlying this remains unexplored. The relationship between FRG1-UPF1 what role FRG1 plays in this is unclear (does it function as an adaptor, recruits an E3 ubiquitin ligase, or influences UPF1 ubiquitination indirectly through transcriptional or signaling pathways?).

We agree with the reviewer that the precise mechanism by which FRG1 promotes UPF1 ubiquitination remains to be defined. Our ubiquitination assays support a role for FRG1 in facilitating UPF1 degradation; however, whether FRG1 functions directly as an adaptor or E3 ligase, or instead influences UPF1 stability indirectly, is currently unclear. Notably, a prior study by Geng et al. reported that DUX4 expression alters the expression of numerous genes involved in protein ubiquitination, including multiple E3 ubiquitin ligases (9), and FRG1 itself has been reported to be upregulated upon DUX4 expression in muscle cells. We will expand the Discussion to address these potential mechanisms and place our findings in the context of indirect transcriptional or signaling pathways that may regulate UPF1 proteolysis. A detailed mechanistic dissection of FRG1-mediated ubiquitination is beyond the scope of the present study.

(4) Limited transcriptome-wide interpretation of RNA-seq data.

Although the RNA-seq data analysis relies heavily on a small subset of "top 10" genes. Additionally, the criteria used to define NMD-sensitive isoforms are unclear. A more comprehensive transcriptome-wide summary-indicating how many NMD-sensitive isoforms are detected and how many are significantly altered-would substantially strengthen the analysis.

We thank the reviewer for this comment and agree that the current presentation may place a disproportionate emphasis on a limited subset of genes. These genes were selected as illustrative examples from an isoform-level analysis performed using IsoformSwitchAnalyzeR (ISAR) (10); however, we acknowledge that this approach does not fully convey the transcriptome-wide scope of the analysis.

Using quantified RNA-seq data, ISAR was employed to identify significant isoform switches and transcripts predicted to be NMD-sensitive. Isoforms were annotated using GENCODE v47, and NMD sensitivity was assigned based on the established 50-nucleotide rule, as described in the Materials and Methods. To address the reviewer’s concern, we will revise the Results section to include a transcriptome-wide summary derived from the ISAR analysis.

(5) Clarification of NMD sensor assay interpretation.

The logic underlying the NMD sensor assay should be explained more clearly early in the manuscript, as the inverse relationship between luciferase signal and NMD efficiency may be counterintuitive to readers unfamiliar with this reporter system. Inclusion of a schematic or brief explanatory diagram would improve accessibility.

We agree with the reviewer and would provide a schematic as well as the experimental setup diagram to improve accessibility to the readers.

(6) Potential confounding effects of high MG132 concentration.

The MG132 concentration used (50 µM) is relatively high and may induce broad cellular stress responses, including inhibition of global translation (its known that proteosome inhibition shuts down translation). Controls addressing these secondary effects would strengthen the conclusion that UPF1 stabilization specifically reflects proteasome-dependent degradation would be essential.

We acknowledge the reviewer’s concern regarding the relatively high concentration of MG132 used in this study. While proteasome inhibition can indeed induce global translation inhibition, our interpretation is based on the specific stabilization of UPF1 observed under these conditions. Since inhibition of global translation would generally reduce protein levels rather than cause selective accumulation, the observed increase in UPF1 is unlikely to result from translational effects. To address this point, we plan to repeat selected experiments using a lower MG132 concentration to further confirm that UPF1 stabilization reflects proteasome-dependent degradation.

(7) Interpretation of polysome co-sedimentation data.

While the co-sedimentation of FRG1 with polysomes is intriguing, this approach does not distinguish between direct ribosomal association and co-migration with ribosome-associated complexes. This limitation should be explicitly acknowledged in the interpretation.

We acknowledge that polysome co-sedimentation alone cannot definitively distinguish between direct ribosomal binding and co-migration with ribosome-associated complexes. Importantly, our interpretation does not rely solely on this assay; when combined with co-immunoprecipitation and proximity ligation assay results, the data consistently support an association of FRG1 with the exon junction complex. We are also conducting additional experiments with appropriate controls to further validate the specificity of FRG1’s association with ribosomes and to address the possibility of nonspecific co-migration.

(8) Limitations of PLA-based interaction evidence.

The PLA data convincingly demonstrate close spatial proximity between FRG1 and eIF4A3; however, PLA does not provide definitive evidence of direct interaction and is known to be susceptible to artefacts. Moreover, a distance threshold of ~40 nm still allows for proteins to be in proximity without being part of the same complex. These limitations should be clearly acknowledged, and conclusions should be framed accordingly.

We thank the reviewer for highlighting this important point. We agree that PLA indicates close spatial proximity but does not constitute definitive evidence of direct interaction and can be susceptible to artefacts. We will explicitly acknowledge this limitation in the revised manuscript. Importantly, our conclusions are not solely based on PLA data; they are supported by complementary co-immunoprecipitation and polysome co-sedimentation assays, which provide biochemical evidence consistent with an association between FRG1 and eIF4A3.

Reviewer #3 (Public review):

The manuscript by Palo and colleagues demonstrates identification of FRG1 as a novel regulator of nonsense-mediated mRNA decay (NMD), showing that FRG1 inversely modulates NMD efficiency by controlling UPF1 abundance. Using cell-based models and a frg1 knockout zebrafish, the authors show that FRG1 promotes UPF1 ubiquitination and proteasomal degradation, independently of DUX4. The work further positions FRG1 as a structural component of the spliceosome and exon junction complex without compromising its integrity. Overall, the manuscript provides mechanistic insight into FRG1-mediated post-transcriptional regulation and expands understanding of NMD homeostasis. The authors should address the following issues to improve the quality of their manuscript.

(1) Figure 7A-D, appropriate positive controls for the nuclear fraction (e.g., Histone H3) and the cytoplasmic fraction (e.g., GAPDH or α-tubulin) should be included to validate the efficiency and purity of the subcellular fractionation.

We thank the reviewer for the suggestion. We will include appropriate positive controls for the nuclear fraction (Histone H3) and the cytoplasmic fraction (GAPDH or α-tubulin) in Figure 7A–D to validate the efficiency and purity of the subcellular fractionation.

(2) To strengthen the conclusion that FRG1 broadly impacts the NMD pathway, qRT-PCR analysis of additional core NMD factors (beyond UPF1) in the frg1⁻/⁻ zebrafish at 48 hpf would be informative.

We appreciate the reviewer’s insightful comment. We will perform qRT-PCR analysis of additional core NMD factors in the frg1⁻/⁻ zebrafish at 48 hpf to further strengthen the conclusion that FRG1 broadly impacts the NMD pathway.

(3) Figure labels should be standardized throughout the manuscript (e.g., consistent use of "Ex" instead of mixed terms such as "Oex") to improve clarity and readability.

We thank the reviewer for noticing the inconsistency. We will ensure that all figure labels are standardized throughout the manuscript (e.g., using “Ex” consistently) to improve clarity and readability.

(4) The methods describing the generation of the frg1 knockout zebrafish could be expanded to include additional detail, and a schematic illustrating the CRISPR design, genotyping workflow, and validation strategy would enhance transparency and reproducibility.

We appreciate the reviewer’s suggestion and will expand the Methods section to provide additional detail on the generation of the frg1 knockout zebrafish. A schematic illustrating the CRISPR design, genotyping workflow, and validation strategy will also be included to enhance transparency and reproducibility.

(5) As FRG1 is a well-established tumor suppressor, additional cell-based functional assays under combined FRG1 and UPF1 perturbation (e.g., proliferation, migration, or survival assays) could help determine whether FRG1 influences cancer-associated phenotypes through modulation of the NMD pathway.

We thank the reviewer for this thoughtful and constructive suggestion. While FRG1 is indeed a well-established tumor suppressor, incorporating additional cell-based functional assays under combined FRG1 and UPF1 perturbation would significantly broaden the scope of the current study. The present work is focused on elucidating the molecular relationship between FRG1 and the NMD pathway. Investigation of downstream cancer-associated phenotypes represents an important and interesting direction for future studies, but is beyond the scope of the current manuscript.

(6) Given the claim that FRG1 inversely regulates NMD efficacy via UPF1, an epistasis experiment such as UPF1 overexpression in an FRG1-overexpressing background followed by an NMD reporter assay would provide stronger functional validation of pathway hierarchy.

We agree with the reviewer’s suggestion. To strengthen the functional validation of the proposed pathway hierarchy, we will perform an epistasis experiment by overexpressing UPF1 in an FRG1-overexpressing background and assess NMD activity using an established NMD reporter assay. The results of this experiment will be included in the revised manuscript.

References

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(6) Bertram K, El Ayoubi L, Dybkov O, Agafonov DE, Will CL, Hartmuth K, et al. Structural Insights into the Roles of Metazoan-Specific Splicing Factors in the Human Step 1 Spliceosome. Mol Cell. 2020 Oct 1;80(1):127-139.e6.

(7) Brazão TF, Demmers J, van IJcken W, Strouboulis J, Fornerod M, Romão L, et al. A new function of ROD1 in nonsense-mediated mRNA decay. FEBS Lett. 2012 Apr 24;586(8):1101–10.

(8) Sun CYJ, van Koningsbruggen S, Long SW, Straasheijm K, Klooster R, Jones TI, et al. Facioscapulohumeral muscular dystrophy region gene 1 is a dynamic RNA-associated and actin-bundling protein. J Mol Biol. 2011 Aug 12;411(2):397–416.

(9) Geng LN, Yao Z, Snider L, Fong AP, Cech JN, Young JM, et al. DUX4 activates germline genes, retroelements, and immune mediators: implications for facioscapulohumeral dystrophy. Dev Cell. 2012 Jan 17;22(1):38–51.

(10) Vitting-Seerup K, Sandelin A. The Landscape of Isoform Switches in Human Cancers. Mol Cancer Res MCR. 2017 Sep;15(9):1206–20.

Reviewer #1 (Public review):

Summary:

During the earliest stages of mouse development, the zygote and 2-cell (2C) embryo are totipotent, capable of generating all embryonic and extra-embryonic lineages, and they transiently express a distinctive set of "2C-stage" genes, many driven by MERVL long terminal repeat (LTR) promoters. Although activation of these transcripts is a normal feature of totipotency, they must be rapidly silenced as development proceeds to the 4-cell and 8-cell stages; failure to shut down the 2C program results in developmental arrest. This study examines the role of maternal SETDB1, a histone H3K9 methyltransferase, in suppressing the 2C transcriptional network. Using an oocyte-specific conditional knockout that removes maternal Setdb1 while leaving the paternal allele intact, the authors demonstrate that embryos lacking maternal SETDB1 arrest during cleavage, with very few progressing beyond the 8-cell stage and no morphologically normal blastocysts forming. Transcriptomic analyses reveal persistent expression of MERVL-LTR-driven transcripts and other totipotency markers, indicating a failure to terminate the totipotent state. Together, the data demonstrate that maternally deposited SETDB1 is required to silence the MERVL-driven 2C program and enable the transition from totipotency to pluripotency. More broadly, the work identifies maternal SETDB1 as a key chromatin repressor that deposits repressive H3K9 methylation to shut down the transient 2C gene network and to permit normal preimplantation development.

Strengths:

(1) Closes a key knowledge gap.

The study tackles a central open question - how embryos exit the totipotent 2-cell (2C) state - and provides direct in vivo evidence that epigenetic repression is required to terminate the 2C program for development to proceed. By identifying maternal SETDB1 as the responsible factor, the work substantially advances our understanding of the maternal-to-zygotic transition and early lineage specification.

(2) Clean genetics paired with rigorous genomics.

An oocyte-specific Setdb1 knockout cleanly isolates a maternal-effect requirement, ensuring that early phenotypes arise from loss of maternal protein. The resulting cleavage-stage arrest is unambiguous (most embryos stall before or around the 8-cell stage). State-of-the-art single-embryo RNA-seq across stages - well-matched to low-cell-number constraints - captures genome-wide mis-expression, including persistent 2C transcripts in mutants, strongly supporting the conclusions.

(3) Compelling molecular linkage to phenotype.

Transcriptome data show that without maternal SETDB1, embryos fail to repress a suite of 1-cell/2C-specific genes by the 8-cell stage. The tight correlation between continued activation of the MERVL-driven totipotency network and developmental arrest provides a specific molecular explanation for the observed failure to progress.

(4) Mechanistic insight grounded in chromatin biology.

SETDB1, a H3K9 methyltransferase classically linked to heterochromatin and transposon repression, targets MERVL LTRs and MERVL-driven chimeric transcripts in early embryos. Bioinformatic evidence indicates that these loci normally acquire H3K9me3 during the 2C→4C transition. The data articulate a coherent mechanism: maternal SETDB1 deposits repressive H3K9me3 at 2C gene loci to shut down the totipotency network, extending observations from ESC systems to bona fide embryos.

(5) Broad implications for development and stem-cell biology.

By pinpointing a maternal gatekeeper of the totipotent-to-pluripotent transition, the work suggests that some cases of cleavage-stage arrest (e.g., in IVF) may reflect faulty epigenetic silencing of transposon-driven genes. It also informs stem-cell efforts to control totipotent-like states in vitro (e.g., 2C-like cells), linking epigenetic reprogramming, transposable-element regulation, and developmental potency.

Weaknesses:

(1) Causality not directly demonstrated.

The link among loss of SETDB1, persistence of 2C transcripts, and developmental arrest is compelling but remains correlative. No rescue experiments test whether dampening the 2C/MERVL program restores development. Targeted interventions-e.g., knocking down key 2C drivers (such as Dux) or pharmacologically curbing MERVL-linked transcription in maternal Setdb1 mutants-would strengthen the claim that unchecked 2C activity is causal rather than a by-product of other SETDB1 functions.

(2) Limited mechanistic resolution of SETDB1 targeting.

The study establishes a requirement for maternal SETDB1 but does not define how it is recruited to MERVL loci. Given SETDB1's canonical cooperation with TRIM28/KAP1 and KRAB-ZNFs, upstream sequence-specific factors and/or pre-existing chromatin features likely guide targeting. Direct occupancy and mark-placement evidence (e.g., SETDB1/TRIM28 CUT&RUN or ChIP, and H3K9me3 profiling at MERVL LTRs during the 2C→4C window) would convert inferred mechanisms into demonstrated ones.

(3) Narrow scope on MERVL; broader epigenomic consequences underexplored.

Maternal SETDB1 may restrain additional repeat classes or genes beyond the 2C network. A systematic repeatome analysis (LINEs/SINEs/ERV subfamilies) would clarify specificity versus a general loss of heterochromatin control. Moreover, potential effects on imprinting or DNA methylation balance are not examined; perturbations there could also contribute to arrest. Bisulfite-based DNA methylation maps at imprinted loci and allele-specific expression analyses would help rule in/out these mechanisms.

(4) Phenotype quantitation and transcriptomic breadth could be clearer.

The developmental phenotype is described qualitatively ("very few beyond 8-cell") without precise stage-wise arrest rates or representative morphology. Tabulated counts (2C/4C/8C/blastocyst), images, and statistics would increase clarity. On the RNA-seq side, the narrative emphasizes known 2C markers; reporting novel/unannotated misregulated transcripts, as well as downregulated pathways (e.g., failure to activate normal 8-cell programs, metabolism, or early lineage markers), would present a fuller portrait of the mutant state.

Reviewer #2 (Public review):

Zeng et al. report that Setdb1-/- embryos fail to extinguish the 1- and 2-cell embryo transcriptional program and have permanent expression of MERVL transposable elements. The manuscript is technically sound and well performed, but, in my opinion, the results lack conceptual novelty.

(1) The manuscript builds on previous observations that: 1, Setbd1 is necessary for early mouse development, with knockout embryos rarely reaching the 8-cell stage; 2, SETB1 mediates H3K9me3 deposition at transposable elements in mouse ESCs; 3, SETB1silences MERVLs to prevent 2CLC-state acquisition in mouse ESCs. The strength of the current work is the demonstration that this is not due to a general transcriptional collapse; but otherwise, the findings are not surprising. The well-known (several Nature papers of years ago) crosstalk between m6A RNA modification and H3K9me3 in preventing 2CLC generation also partly compromises the novelty of this work.

(2) The conclusions regarding H3K9me3 deposition are inferred based on previously reported datasets, but there is no direct demonstration.

(3) The detection of chimeric transcripts is somewhat unreliable using short-read sequencing.

Author response:

eLife Assessment 

This study presents a valuable finding on maternal SETDB1 as a key chromatin repressor that shuts down the 2C gene program and enables normal mouse embryonic development. The evidence supporting the claims of the authors is solid, although the inclusion of a causality test, a mechanistic understanding of SETDB1 targeting, and phenotypic quantification would have greatly strengthened the study. The work will be of broad interest to biologists working on embryonic development, stem cells and gene regulation.

Thank you for this positive evaluation of our work. Please find the point-by point responses to the Reviewer’s comments below.

Public Reviews:

Reviewer #1 (Public review):

Summary: 

During the earliest stages of mouse development, the zygote and 2-cell (2C) embryo are totipotent, capable of generating all embryonic and extra-embryonic lineages, and they transiently express a distinctive set of "2C-stage" genes, many driven by MERVL long terminal repeat (LTR) promoters. Although activation of these transcripts is a normal feature of totipotency, they must be rapidly silenced as development proceeds to the 4-cell and 8-cell stages; failure to shut down the 2C program results in developmental arrest. This study examines the role of maternal SETDB1, a histone H3K9 methyltransferase, in suppressing the 2C transcriptional network. Using an oocyte-specific conditional knockout that removes maternal Setdb1 while leaving the paternal allele intact, the authors demonstrate that embryos lacking maternal SETDB1 arrest during cleavage, with very few progressing beyond the 8-cell stage and no morphologically normal blastocysts forming. Transcriptomic analyses reveal persistent expression of MERVL-LTR-driven transcripts and other totipotency markers, indicating a failure to terminate the totipotent state. Together, the data demonstrate that maternally deposited SETDB1 is required to silence the MERVL-driven 2C program and enable the transition from totipotency to pluripotency. More broadly, the work identifies maternal SETDB1 as a key chromatin repressor that deposits repressive H3K9 methylation to shut down the transient 2C gene network and to permit normal preimplantation development. 

Strengths: 

(1) Closes a key knowledge gap. 

The study tackles a central open question - how embryos exit the totipotent 2-cell (2C) state - and provides direct in vivo evidence that epigenetic repression is required to terminate the 2C program for development to proceed. By identifying maternal SETDB1 as the responsible factor, the work substantially advances our understanding of the maternal-to-zygotic transition and early lineage specification. 

(2) Clean genetics paired with rigorous genomics. 

An oocyte-specific Setdb1 knockout cleanly isolates a maternal-effect requirement, ensuring that early phenotypes arise from loss of maternal protein. The resulting cleavage-stage arrest is unambiguous (most embryos stall before or around the 8-cell stage). State-of-the-art single-embryo RNA-seq across stages - well-matched to low-cell-number constraints - captures genome-wide mis-expression, including persistent 2C transcripts in mutants, strongly supporting the conclusions. 

(3) Compelling molecular linkage to phenotype. 

Transcriptome data show that without maternal SETDB1, embryos fail to repress a suite of 1-cell/2C-specific genes by the 8-cell stage. The tight correlation between continued activation of the MERVL-driven totipotency network and developmental arrest provides a specific molecular explanation for the observed failure to progress. 

(4) Mechanistic insight grounded in chromatin biology. 

SETDB1, a H3K9 methyltransferase classically linked to heterochromatin and transposon repression, targets MERVL LTRs and MERVL-driven chimeric transcripts in early embryos. Bioinformatic evidence indicates that these loci normally acquire H3K9me3 during the 2C→4C transition. The data articulate a coherent mechanism: maternal SETDB1 deposits repressive H3K9me3 at 2C gene loci to shut down the totipotency network, extending observations from ESC systems to bona fide embryos. 

(5) Broad implications for development and stem-cell biology. 

By pinpointing a maternal gatekeeper of the totipotent-to-pluripotent transition, the work suggests that some cases of cleavage-stage arrest (e.g., in IVF) may reflect faulty epigenetic silencing of transposon-driven genes. It also informs stem-cell efforts to control totipotent-like states in vitro (e.g., 2C-like cells), linking epigenetic reprogramming, transposable-element regulation, and developmental potency.

We thank Reviewer 1 for recognizing the strengths in our work and for the suggestions below.

Weaknesses: 

(1) Causality not directly demonstrated. 

The link among loss of SETDB1, persistence of 2C transcripts, and developmental arrest is compelling but remains correlative. No rescue experiments test whether dampening the 2C/MERVL program restores development. Targeted interventions-e.g., knocking down key 2C drivers (such as Dux) or pharmacologically curbing MERVL-linked transcription in maternal Setdb1 mutants-would strengthen the claim that unchecked 2C activity is causal rather than a by-product of other SETDB1 functions.

We agree that rescue experiments might strengthen causality. Those experiments, however, would be extremely challenging technically because the knockdowns would need to be precisely timed to follow (and not prevent) the wave of 2c-specific activation. Knocking down 2c drivers in the zygote, for example, may prevent switching on the totipotency program. In addition, while sustained MERVL expression—such as that induced by forced DUX expression—disrupts totipotency exit and embryo development (1, 2), derepression of transcription is very broad in Setdb1^mat-/+ embryos and knocking down individual 2C drivers may not be sufficient to rescue development or restore the exit from totipotency.

(2) Limited mechanistic resolution of SETDB1 targeting. 

The study establishes a requirement for maternal SETDB1 but does not define how it is recruited to MERVL loci. Given SETDB1's canonical cooperation with TRIM28/KAP1 and KRAB-ZNFs, upstream sequence-specific factors and/or pre-existing chromatin features likely guide targeting. Direct occupancy and mark-placement evidence (e.g., SETDB1/TRIM28 CUT&RUN or ChIP, and H3K9me3 profiling at MERVL LTRs during the 2C→4C window) would convert inferred mechanisms into demonstrated ones.

We do show H3K9me3 patterns at MERVL LTRs during the early2c-late2c-2c-4c-8c-morula window from a published dataset. Please see the genome browser images in Figures 4C, 4D, 4E, 6D, 6E and Figure S6. We agree that mapping of SETDB1/TRIM28 to those locations would strengthen the mechanistic insight. However, ChIPseq or CUT&RUN of those proteins in preimplantation embryos are not technically feasible. We do provide genetic evidence for the collaboration between SETDB1 and DUXBL, a DNA-binding factor, by showing that DUXBL cannot switch off its top targets without SETDB1 (Figure 6). Future studies will characterize the molecular mechanisms underlying this (likely indirect) collaboration. We do not think that DUXBL and SETDB1 directly interact, because such interaction was not detected by DUXBL IP-MS (3).

(3) Narrow scope on MERVL; broader epigenomic consequences underexplored. 

Maternal SETDB1 may restrain additional repeat classes or genes beyond the 2C network. A systematic repeatome analysis (LINEs/SINEs/ERV subfamilies) would clarify specificity versus a general loss of heterochromatin control. Moreover, potential effects on imprinting or DNA methylation balance are not examined; perturbations there could also contribute to arrest. Bisulfite-based DNA methylation maps at imprinted loci and allele-specific expression analyses would help rule in/out these mechanisms.

We did examine genes and repeat elements beyond the 2c network. We evaluated gene and TE expression changes using four-way comparisons. Please find the results regarding gene expression in Figure 1C-J, Figure S2, Figure S3, Figure S4., Table S2, Table S3, and Table S4. Please find results on TE expression in Figure S5. Table S6, Table S7, and Table S8 and in the text. We agree that DNA methylation may be altered in Setdb1^mat-/+ embryos. In our hands, evaluating this possibility using bisulfite sequencing requires a larger number of embryos than what we can feasibly obtain (the number of obtained mutant embryos is very small). Regarding imprinted gene expression, one cannot fully assess and interpret imprinted gene expression in preimplantation stage embryos before the maternally deposited transcripts are gone. We reported earlier that clear somatic parental-specific patterns of imprinted gene expression may only start later in development, around 8.5 dpc (4).

(4) Phenotype quantitation and transcriptomic breadth could be clearer. 

The developmental phenotype is described qualitatively ("very few beyond 8-cell") without precise stage-wise arrest rates or representative morphology. Tabulated counts (2C/4C/8C/blastocyst), images, and statistics would increase clarity. On the RNA-seq side, the narrative emphasizes known 2C markers; reporting novel/unannotated misregulated transcripts, as well as downregulated pathways (e.g., failure to activate normal 8-cell programs, metabolism, or early lineage markers), would present a fuller portrait of the mutant state.

Tabulated counts are displayed in Figure 1A, and morphology is shown in Figure S1A. We do say that 4% Setdb1^mat-/+ embryos reached the 8-cel stage by 2.5 dpc. We recovered zero Setdb1^mat-/+ blastocysts at 4.5 dpc (not shown). On the RNA-seq side we do report a more global assessment of transcription of genes and TEs (please see above at point 3), including novel chimeric transcripts (Table S6). Developmental pathways are shown in Figure S3 and Figure S4. Metabolic pathways are displayed in Figure S2.

Reviewer #2 (Public review): 

Zeng et al. report that Setdb1-/- embryos fail to extinguish the 1- and 2-cell embryo transcriptional program and have permanent expression of MERVL transposable elements. The manuscript is technically sound and well performed, but, in my opinion, the results lack conceptual novelty.

(1) The manuscript builds on previous observations that: 1, Setbd1 is necessary for early mouse development, with knockout embryos rarely reaching the 8-cell stage; 2, SETB1 mediates H3K9me3 deposition at transposable elements in mouse ESCs; 3, SETB1silences MERVLs to prevent 2CLC-state acquisition in mouse ESCs. The strength of the current work is the demonstration that this is not due to a general transcriptional collapse; but otherwise, the findings are not surprising. The well-known (several Nature papers of years ago) crosstalk between m6A RNA modification and H3K9me3 in preventing 2CLC generation also partly compromises the novelty of this work.

We thank the Reviewer for appreciating the technical quality of our work. Regarding novelty, please consider that prior work in ES cells included contradictory findings (please see our Introduction). Prior embryology work (please see our Introduction) did not explain the preimplantation-stage phenotype. We highly appreciate those earlier works. Our work here answers the expectations drawn from prior studies and unequivocally shows that SETDB1 carries out the developmentally essential function of suppressing MERVLs and the 2-cell program in the mouse embryo.

(2) The conclusions regarding H3K9me3 deposition are inferred based on previously reported datasets, but there is no direct demonstration.

Dynamic H3K9me3 deposition is displayed at MERVL LTRs during the early2c-late2c-2c-4c-8c-morula window (Figures 4C, 4D, 4E, 6D, 6E and Figure S6) from a published work that has very high-quality data. We agree that demonstrating loss off H3K9me3 in Setdb1^mat-/+ embryos would confirm that the H3K9me3 histone methyltransferase function of SETDB1 (as opposed to any, yet unidentified, non-HMT specific activity of SETDB1) is responsible for shutting down MERVL LTRs. However, ChIP-seq, CUT&RUN, or similar assays are not feasible due to the rarity of Setdb1^mat-/+ embryos.

(3) The detection of chimeric transcripts is somewhat unreliable using short-read sequencing.

We used single embryo total RNA-seq and we report detecting chimeric transcripts (Table S6), which is considered more reliable than mRNA-seq for detecting chimeric transcripts, because many are not polyadenylated. We acknowledge, however, that long-read sequencing, which recently is becoming available, but which is still very expensive, is currently the most powerful method for detecting chimeric transcripts. This, however, does not affect the major conclusions or the significance of our work.

Author response:

The following is the authors’ response to the original reviews

Comment to both reviewers:

We are very grateful for the thoughtful and constructive comments from both reviewers. During the revision, and in direct response to these comments, we performed additional control experiments for the cellular fluorescence measurements. These new data revealed that the weak increase in green fluorescence reported in our original submission does not depend on retron-expressed Lettuce RT-DNA or the DFHBI-1T fluorophore, but instead reflects stress-induced autofluorescence of E. coli (e.g. upon inducer and antibiotic treatment).

We also benchmarked the fluorogenic properties of Lettuce against the RNA FLAP Broccoli and found that Lettuce is ~100-fold less fluorogenic under optimal in vitro conditions. Consequently, with the currently available, in vitro- but not in vivo-optimized Lettuce variants, intracellular fluorescence cannot be reliably detected by microscopy or flow cytometry. We have therefore removed the original flow cytometry / and in-culture-fluorescence data and no longer claim detectable intracellular Lettuce fluorescence.

In the revised manuscript, we now directly demonstrate that retron-produced Lettuce RT-DNA can be purified from cells and remains functional ex vivo with a gel-based fluorophore-binding assays. Together, these data clarify the current limitations of DNA-based FLAPs for in vivo imaging, while still establishing retrons as a viable platform for intracellular production of functional DNA aptamers.

Reviewer #1 (Public Review):

Summary:

The authors use an interesting expression system called a retron to express single-stranded DNA aptamers. Expressing DNA as a single-stranded sequence is very hard - DNA is naturally double-stranded. However, the successful demonstration by the authors of expressing Lettuce, which is a fluorogenic DNA aptamer, allowed visual demonstration of both expression and folding. This method will likely be the main method for expressing and testing DNA aptamers of all kinds, including fluorogenic aptamers like Lettuce and future variants/alternatives.

Strengths:

This has an overall simplicity which will lead to ready adoption. I am very excited about this work. People will be able to express other fluorogenic aptamers or DNA aptamers tagged with Lettuce with this system.

We thank the reviewer for their thoughtful assessment and appreciate their encouraging remarks.

Weaknesses:

Several things are not addressed/shown:

(1) How stable are these DNA in cells? Half-life?

We thank the reviewer for this insightful question.

Retron RT-DNA forms a phage surveillance complex with the associated RT and effector protein[1-4]. Moreover, considering the unique ‘closed’ structure of RT-DNA[5] (with the ends of msr and msd bound either by 2’-5’ linkage and base paired region) and its noncoding function, we hypothesized that the RT-DNA must be exceptionally stable. Nevertheless, we attempted to determine half-life of the RT-DNA using qPCR for Eco2 RT-DNA. To this end, we designed an assay where we would first induce RT-DNA expression, use the induced cells to start a fresh culture without the inducers. We would then take aliquots from this fresh culture at different timepoints and determine RT-DNA abundance by qPCR.

We induced RT-DNA expression of retron Eco2 in BL21AI cells as described in the Methods. After overnight induction, cells were washed to remove IPTG and arabinose, diluted to OD₆₀₀ = 0.2 into fresh LB without inducers, and grown at 37°C. At the indicated time points, aliquots corresponding to OD₆₀₀ = 0.1 were boiled (95°C, 5 min), and 1 µL of the lysate was used as template in 20 µL qPCR reactions (see revised Methods for details).

Assuming RT-DNA degradation would occur by active degradation mechanisms (nuclease-mediated degradation) and dilution (cell growth and division), we determined the rate of degradation by the following equation

where  is the degradation rate constant and the ratio is the dilution factor which takes into account dilution by cell division. OD₆₀₀(t) was determined by fitting the OD₆₀₀ measurements by the following the equation describing logistic growth:

Which yields the plots shown in Figure 2–figure supplement 1.

After substituting OD₆₀₀(t) by the function in equation (2), we fit the experimental data for the fold-change of the RT-DNA to equation (1). Interestingly, the best fit (red) was obtained with a  converging towards zero suggesting that the half-life of the RT-DNA is beyond the detection limit of our assay. To showcase typical half-lives of RNA, which are in the range of minutes in growing E. coli cells[6], we refitted the data using constant half-life of 15 and 30 minutes. In both cases, simulated curve deviated significantly from the experimental data further confirming that the half-life of the RT-DNA is probably orders of magnitude higher than the doubling time of E. coli under these optimal conditions. While we cannot exclude that the RT-DNA is still produced as a result of promotor leakiness, but we expect this effect to be low as the expression of RT-DNA in E. coli AI cells requires both the presence of IPGT and arabinose, which were thoroughly removed before inoculating the growth media with the starter culture. Overall, our data therefore argues for an exceptional stability of the RT-DNA in growing bacterial cells.

We have now included this new experimental data in the supplementary information.

(2) What concentration do they achieve in cells/copy numbers? This is important since it relates to the total fluorescence output and, if the aptamer is meant to bind a protein, it will reveal if the copy number is sufficient to stoichiometrically bind target proteins. Perhaps the gels could have standards with known amounts in order to get exact amounts of aptamer expression per cell?

The copy number of RT-DNA can be estimated based on the qPCR experiments. We use a pET28a plasmid, which is low-copy with typical copy number 15-20 per cell[7]. We determined the abundance of RT-DNA over plasmid/RT-DNA, upon induction, to be 8-fold, thereby indicating copy number of Eco2 RT-DNA to be roughly around 100-200. Assuming an average aqueous volume of E. coli of 1 femtoliter[6], the concentration of RT-DNA is ~250-500 nM. We have added this information to the revised version of the manuscript.

(3) Microscopic images of the fluorescent E. coli - why are these not shown (unless I missed them)? It would be good to see that cells are fluorescent rather than just showing flow sorting data.

In the original submission, we used flow cytometry as an orthogonal method to quantify the fluorescence output of intracellularly expressed Lettuce aptamer, anticipating that it would provide high-throughput, quantitative information on a large population of cells. During the revision, additional controls revealed that the weak increase in fluorescence we had previously attributed to Lettuce expression was in fact a stress-induced autofluorescence signal that occurred independently of retron RT-DNA and DFHBI-1T. We have therefore removed these data from the manuscript and no longer claim detectable intracellular Lettuce fluorescence.

To understand this limitation, we compared the in vitro fluorescence of Lettuce with that of the RNA FLAP Broccoli, which is commonly used for RNA live-cell imaging. Under optimal in vitro conditions, Lettuce shows ~100-fold lower fluorescence output than Broccoli (new Figure 3–figure supplement 5). Given this poor fluorogenicity and the low intracellular concentration of retron RT-DNA (now derived from the qPCR experiments), we conclude that the current Lettuce variants are below the detection threshold for in vivo imaging in our system. We now explicitly discuss this limitation and the need for further (in vivo) evolution of DNA-based FLAPs in the revised manuscript.

(4) I would appreciate a better Figure 1 to show all the intermediate steps in the RNA processing, the subsequent beginning of the RT step, and then the final production of the ssDNA. I did not understand all the processing steps that lead to the final product, and the role of the 2'OH.

We thank the referee for this comment. We have now made changes to Figure 1, showing the intermediate steps as well as a better illustration of the 2’-5’ linkage.

(5) I would like a better understanding or a protocol for choosing insertion sites into MSD for other aptamers - people will need simple instructions.

We appreciate the reviewer for bringing up this important point. We simulated the ssDNA structure using Vienna RNA fold with DNA parameters. Based on the resulting structure, we inserted Lettuce sequence in the single stranded and/or loop regions to minimise interference with the native msd fold. We have now included this information in the description of Figure 3.

(6) Can the gels be stained with DFHBI/other dyes to see the Lettuce as has been done for fluorogenic RNAs?

Yes. We have now included experiments where we performed in-gel staining with DFHBI-1T for both chemically synthesized Eco2-Lettuce surrogates as well as the heterologously expressed Eco2-Lettuce RT-DNA. We have added this data to the revised Figure 3 (panel C and E).

(7) Sometimes FLAPs are called fluorogenic RNA aptamers - it might be good to mention both terms initially since some people use fluorogenic aptamer as their search term.

We thank the referee for this useful suggestion. We have now included both terms in the introduction of the revised version.

(8) What E coli strains are compatible with this retron system?

Experimental and bioinformatic analysis have shown that retrons abundance varies drastically across different strains of E. coli[8-10]. For example, in an experimental investigation of 113 independent clinical isolates of E. coli, only 7 strains contained RT-DNA[8]. In our experiments, we have found that BL21AI strain is compatible with plasmid-borne Eco2. The fact that this strain has a native retron system (Eco1) allowed us to use it as internal standard. However, we were also able express Eco2 RT-DNA in conventional lab strains such as E. coli Top 10 (data not shown), indicating both ncRNA and the RT alone are sufficient for intracellular RT-DNA synthesis.

(9) What steps would be needed to use in mammalian cells?

We appreciate the reviewer’s thoughtful inquiry. Expression of retrons has been demonstrated in mammalian cells by Mirochnitchenko et al[11] and Lopez et al[12]. For example, Lopez et al demonstrate expression of retrons in mammalian cell lines using the Lipofectamine 3000 transfection protocol (Invitrogen) and a PiggyBac transposase system[12]. We also mention this in the discussion section of the revised manuscript. Expression of retron-encoded DNA aptamers in mammalian cells should be possible with these systems.

(10) Is the conjugated RNA stable and does it degrade to leave just the DNA aptamer?

We are grateful to the reviewer for their perceptive question. This usually depends on the specific retron system. For example, in case of certain retron systems such as retron Sen2, Eco4 and Eco7, the RNA is cleaved off, leaving behind just the ssDNA. In our case, with retron Eco2, the RNA remains stably bound to the ssDNA, thereby maintaining a stable hybrid RNA-DNA structure[10,13]. During the extraction of RT-DNA, the conjugated RNA is degraded during the RNase digestion step, and therefore is not visible in the gel images.

Reviewer #2 (Public Review):

Summary:

This manuscript explores a DNA fluorescent light-up aptamer (FLAP) with the specific goal of comparing activity in vitro to that in bacterial cells. In order to achieve expression in bacteria, the authors devise an expression strategy based on retrons and test four different constructs with the aptamer inserted at different points in the retron scaffold. They only observe binding for one scaffold in vitro, but achieve fluorescence enhancement for all four scaffolds in bacterial cells. These results demonstrate that aptamer performance can be very different in these two contexts.

Strengths:

Given the importance of FLAPs for use in cellular imaging and the fact that these are typically evolved in vitro, understanding the difference in performance between a buffer and a cellular environment is an important research question.

The return strategy utilized by the authors is thoughtful and well-described.

The observation that some aptamers fail to show binding in vitro but do show enhancement in cells is interesting and surprising.

We appreciate the reviewer’s thorough assessment.

Weaknesses:

This study hints toward an interesting observation, but would benefit from greater depth to more fully understand this phenomenon. Particularly challenging is that FLAP performance is measured in vitro by affinity and in cells by enhancement, and these may not be directly proportional. For example, it may be that some constructs have much lower affinity but a greater enhancement and this is the explanation for the seemingly different performance.

We thank the reviewer for this insightful comment. In response, we conducted a series of additional control experiments to better understand the apparent discrepancy between the in vitro and in vivo data. These experiments revealed that the previously reported increase in intracellular green fluorescence is independent of retron-expressed Lettuce RT-DNA and DFHBI-1T, and instead reflects stress-induced autofluorescence of E. coli upon inducer and antibiotic treatment. Our original negative controls (empty wild-type Eco2, uninduced cells in the presence of DFHBI-1T) were therefore not sufficient to rule out this effect.

As a consequence, we have removed the earlier FACS data from the manuscript and no longer claim detectable intracellular Lettuce fluorescence. The reviewer’s comment prompted us to re-examine the fluorogenicity of our constructs in vitro. We found that the 4Lev4 construct folds poorly and produces very low signal in in-gel staining assays with DFHBI-1T. In contrast, the 8LE variant (8-nt P1 stem at position v4) shows the highest fluorescence in these in-gel assays (new Figure 3C). Nevertheless, even this construct remains 100-fold less fluorogenic than the RNA-based FLAP Broccoli (new Figure 3–figure supplement 5), and we were unable to detect its intracellular fluorescence above background (new Figure 3–figure supplement 4).

To still directly demonstrate that retron-embedded Lettuce domains that are synthesized under intracellular conditions are functional, we modified our strategy in the revision and purified the expressed RT-DNA from E. coli, followed by in-gel staining with DFHBI-1T (new Figure 3E). Despite the challenge of obtaining sufficient amounts of ssDNA, this ex vivo approach clearly shows that the retron-produced Lettuce RT-DNA retains fluorogenic activity.

The authors only test enhancement at one concentration of fluorophore in cells (and this experimental detail is difficult to find and would be helpful to include in the figure legend). This limits the conclusions that can be drawn from the data and limits utility for other researchers aiming to use these constructs.

We appreciate this excellent suggestion. In the original experiments, the DFHBI-1T concentration in cells was chosen based on published conditions for live-cell imaging of the Broccoli RNA aptamer[14], which is substantially more fluorogenic than Lettuce. Motivated by the reviewer’s comment, we explored different fluorophore concentrations and additional controls to optimize the in vivo readout. These experiments showed that the weak intracellular fluorescence signal is dominated by stress-induced autofluorescence[15] (possibly due to the weaker antitoxin activity of the modified msd) and does not depend on the presence of Lettuce RT-DNA or DFHBI-1T.

Given the combination of low Lettuce fluorogenicity and low intracellular RT-DNA levels, we concluded that varying the fluorophore concentration alone does not provide a meaningful way to deconvolute these confounding factors in cells. Instead, we shifted our focus to a more direct assessment of Lettuce activity: we now demonstrate that retron-produced Lettuce RT-DNA can be purified from E. coli and retains fluorogenic activity in an in-gel staining assay with DFHBI-1T (new Figure 3E). We believe this revised strategy provides a clearer and more quantitative characterization of the system’s capabilities and limitations than the initial in vivo fluorescence measurements.

The FLAP that is used seems to have a relatively low fluorescence enhancement of only 2-3 fold in cells. It would be interesting to know if this is also the case in vitro. This is lower than typical FLAPs and it would be helpful for the authors to comment on what level of enhancement is needed for the FLAP to be of practical use for cellular imaging.

In the revised manuscript, we directly address this point by comparing the in vitro fluorescence of Lettuce (DNA) and Broccoli (RNA) under optimized buffer conditions. These experiments show that Broccoli is nearly two orders of magnitude more fluorogenic than Lettuce (new Figure 3-figure supplement 5). Thus, the low enhancement observed for Lettuce in cells is consistent with its intrinsically poor fluorogenicity in vitro.

Based on this comparison and on reported properties of RNA FLAPs such as Broccoli, we conclude that robust cellular imaging typically requires substantially higher fluorogenicity and dynamic range than currently provided by DNA-based Lettuce. In other words, under our conditions, Lettuce is close to or below the practical detection limit for in vivo imaging, whereas Broccoli performs well. We now explicitly state in the Discussion that further evolution and optimization of DNA FLAPs will be required to achieve fluorescence enhancements that are suitable for routine cellular imaging, and we position our work as a first demonstration that functional DNA aptamers can be produced in cells via retrons, while also delineating the current sensitivity limits.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Addgene accession numbers are not listed - how is this plasmid obtained?

The sequence was obtained from Millman et al[16], and ordered as gblock from IDT. The gblock was then cloned into a pET28a vector by Gibson assembly. We have now included this in the methods section.

Reviewer #2 (Recommendations For The Authors):

Page 2, line 40 - FLAPS should be FLAPs

We have corrected this typo in the revised version.

References

(1) Rousset, F. & Sorek, R. The evolutionary success of regulated cell death in bacterial immunity. Curr. Opin. Microbiol. 74, 102312; 10.1016/j.mib.2023.102312 (2023).

(2) Gao, L. et al. Diverse enzymatic activities mediate antiviral immunity in prokaryotes. Science 369, 1077–1084; 10.1126/science.aba0372 (2020).

(3) Carabias, A. et al. Retron-Eco1 assembles NAD+-hydrolyzing filaments that provide immunity against bacteriophages. Mol. Cell 84, 2185-2202.e12; 10.1016/j.molcel.2024.05.001 (2024).

(4) Wang, Y. et al. DNA methylation activates retron Ec86 filaments for antiphage defense. Cell Rep. 43, 114857; 10.1016/j.celrep.2024.114857 (2024).

(5) Wang, Y. et al. Cryo-EM structures of Escherichia coli Ec86 retron complexes reveal architecture and defence mechanism. Nat. Microbiol. 7, 1480–1489; 10.1038/s41564-022-01197-7 (2022).

(6) Milo, R. & Phillips, R. Cell biology by the numbers (Garland Science Taylor & Francis Group, New York NY, 2016).

(7) Sathiamoorthy, S. & Shin, J. A. Boundaries of the origin of replication: creation of a pET-28a-derived vector with p15A copy control allowing compatible coexistence with pET vectors. PLOS ONE 7, e47259; 10.1371/journal.pone.0047259 (2012).

(8) Sun, J. et al. Extensive diversity of branched-RNA-linked multicopy single-stranded DNAs in clinical strains of Escherichia coli. Proc. Natl. Acad. Sci. U. S. A. 86, 7208–7212; 10.1073/pnas.86.18.7208 (1989).

(9) Rice, S. A. & Lampson, B. C. Bacterial reverse transcriptase and msDNA. Virus Genes 11, 95–104; 10.1007/BF01728651 (1995).

(10) Simon, A. J., Ellington, A. D. & Finkelstein, I. J. Retrons and their applications in genome engineering. Nucleic Acids Res. 47, 11007–11019; 10.1093/nar/gkz865 (2019).

(11) Mirochnitchenko, O., Inouye, S. & Inouye, M. Production of single-stranded DNA in mammalian cells by means of a bacterial retron. J. Biol. Chem. 269, 2380–2383; 10.1016/S0021-9258(17)41956-9 (1994).

(12) Lopez, S. C., Crawford, K. D., Lear, S. K., Bhattarai-Kline, S. & Shipman, S. L. Precise genome editing across kingdoms of life using retron-derived DNA. Nat. Chem. Biol. 18, 199–206; 10.1038/s41589-021-00927-y (2022).

(13) Lampson, B. C. et al. Reverse transcriptase in a clinical strain of Escherichia coli: production of branched RNA-linked msDNA. Science 243, 1033–1038; 10.1126/science.2466332 (1989).

(14) Filonov, G. S., Moon, J. D., Svensen, N. & Jaffrey, S. R. Broccoli: rapid selection of an RNA mimic of green fluorescent protein by fluorescence-based selection and directed evolution. J. Am. Chem. Soc. 136, 16299–16308; 10.1021/ja508478x (2014).

(15) Renggli Sabine, Keck Wolfgang, Jenal Urs & Ritz Daniel. Role of Autofluorescence in Flow Cytometric Analysis of Escherichia coli Treated with Bactericidal Antibiotics. J. Bacteriol. 195, 4067–4073; 10.1128/jb.00393-13. (2013).

(16) Millman, A. et al. Bacterial Retrons Function In Anti-Phage Defense. Cell 183, 1551-1561.e12; 10.1016/j.cell.2020.09.065 (2020).

Briefing : Apprivoiser les écrans et accompagner l'enfant (Repères 3-6-9-12+)

Ce document synthétise les interventions de Serge Tisseron, psychiatre et docteur en psychologie, concernant l'introduction et la régulation des outils numériques dans la vie des enfants.

Il détaille la méthodologie des balises « 3-6-9-12+ » et analyse les enjeux sociétaux, psychologiques et éducatifs liés aux écrans.

Résumé Exécutif

L'omniprésence des écrans ne doit pas être abordée sous l'angle de la simple interdiction, mais sous celui de l'apprivoisement et de la médiation.

La méthode « 3-6-9-12+ » propose des repères chronologiques pour adapter l'usage des outils numériques au développement de l'enfant. Les points clés sont les suivants :

• La relation humaine prime sur l'outil : Le danger n'est pas l'écran en soi, mais la carence d'interactions humaines et le défaut d'attention parentale (notamment via l'usage excessif du smartphone par les adultes).

• L'autorégulation : L'objectif éducatif est d'apprendre à l'enfant à gérer son temps et ses frustrations, sur le modèle de l'éducation alimentaire.

• Responsabilité collective : La gestion des écrans relève des parents, mais aussi de l'école (éducation aux médias), des industriels (captologie) et des politiques publiques (offres d'activités alternatives).

• Hygiène de vie : La préservation du sommeil (absence d'écrans dans la chambre la nuit) et des moments d'échanges (repas sans écran) est impérative.

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1. Mutations générationnelles et culturelles

L'analyse de Serge Tisseron distingue deux vagues majeures de transformation liées au numérique :

Les Millennials (nés entre 1980 et 1995)

Ils ont introduit des changements structurels dans le rapport au savoir et à l'identité :

• Collaboration : Émergence de la construction collaborative des savoirs (type Wikipédia).

• Hyper-attention : Développement d'une attention très concentrée et éphémère, propre aux jeux vidéo, opposée à l'attention lente de la lecture.

• Fluidité identitaire : Capacité à gérer des identités multiples via des avatars dans les mondes virtuels.

• Sociabilité d'intérêt : Les liens se construisent désormais davantage par centres d'intérêt partagés que par proximité physique.

La Génération Z (née entre 1995 et 2010)

Cette génération grandit avec un smartphone en poche, ce qui modifie radicalement son rapport à la famille, au travail et à la politique.

Elle est la cible privilégiée de l'économie de l'attention et de la captologie, discipline utilisant les biais cognitifs pour maximiser le temps passé sur les plateformes.

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2. Le cadre des balises 3-6-9-12+

Ce système repose sur trois principes : l'alternance (activités avec/sans écran), l'accompagnement et l'apprentissage de l'autorégulation.

| Âge | Recommandations Clés | Objectifs et Logique | | --- | --- | --- | | Avant 3 ans | Éviter la télévision. Écrans interactifs uniquement si accompagnés et brefs. | Le cerveau du bébé ne « digère » pas la télévision. Besoin prioritaire de mimiques et de voix humaines réelles. | | De 3 à 6 ans | Temps d'écran fixe à heure régulière. Choix de programmes de qualité. | Apprendre à attendre (retarder la satisfaction) pour développer l'autorégulation. | | De 6 à 9 ans | Introduction d'activités créatives (ex: photographie numérique). | Passer du statut de consommateur à celui d'acteur/créateur d'images. | | De 9 à 12 ans | Écrans partagés. Dialogue sur le fonctionnement d'Internet. | Partager une culture commune (films, jeux collaboratifs type Minecraft). Prévenir les risques (données, pornographie). | | Après 12 ans | Surveillance du sommeil et des réseaux sociaux. | Responsabilisation face aux algorithmes et aux fake news. Éducation à la « grammaire d'Internet ». |

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3. Les dangers de la « Technoférence » parentale

Un point majeur du discours de Serge Tisseron concerne l'impact de l'usage des écrans par les adultes sur le développement de l'enfant :

• Appauvrissement relationnel : Un parent sur son smartphone pendant qu'il s'occupe de son bébé réduit ses mimiques, utilise des phrases plus courtes et moins d'émotions.

• Conséquences neurologiques : Pour l'enfant, cette inattention parentale peut avoir les mêmes effets délétères qu'un abandon devant un écran : troubles du développement et sentiment d'insécurité.

• Risques physiques : Augmentation du risque d'accidents dans les espaces publics (jardins, parcs) par manque de vigilance des accompagnateurs connectés.

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4. Analyse des mésusages : Responsabilités et Facteurs de risque

Le document identifie plusieurs causes aux usages problématiques des écrans, au-delà de la simple volonté individuelle :

1. Les quiproquos numériques : La communication par écran est dénuée de mimiques et d'intonations, créant des malentendus qui peuvent dégénérer en violence physique lors du retour en présentiel (notamment le lundi à l'école après un week-end d'échanges numériques).

2. Inégalités sociales : Les familles favorisées peuvent offrir des alternatives (sport, musique, théâtre). Dans les milieux défavorisés, l'écran est souvent la seule distraction accessible, faute de politiques de la ville adaptées.

3. Fragilités psychiques : Les enfants ayant subi des traumatismes ou souffrant d'un défaut d'estime de soi peuvent utiliser le numérique pour reproduire des violences ou s'isoler.

4. Stratégies industrielles : Les plateformes contournent les régulations pour instaurer des habitudes comportementales fortes, bien que le terme médical d'« addiction » soit scientifiquement réservé par l'OMS au seul trouble du jeu vidéo (sous conditions strictes).

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5. Recommandations pratiques pour les familles

Pour une gestion saine du numérique au quotidien, deux règles d'or sont préconisées :

• Le repas du soir sans écran : Instituer le dîner comme un moment d'échange exclusif. Cela crée une « fenêtre temporelle » où l'enfant sait qu'il peut parler s'il rencontre un problème (harcèlement, inquiétude).

• Pas d'écran dans la chambre la nuit : Les écrans sont les ennemis du sommeil en raison de la lumière bleue qui perturbe la mélatonine et de l'excitation cognitive.

L'utilisation d'un réveil classique est conseillée pour tous, parents compris.

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6. Citations et Réflexions Clés

« Le problème ce n’est pas l’écran, c’est le défaut de relation humaine. »

« On ne met pas de bifteck et de frites dans le biberon [...] de la même manière, le bébé ne digère pas la télé. »

« La culture des jeunes d'aujourd'hui, c'est la culture des adultes de demain. »

« Apprendre à attendre, c'est la première marche sur la voie de l'apprentissage de l'autorégulation. »

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7. Perspectives institutionnelles et éducatives

Serge Tisseron appelle à une mobilisation dépassant le cadre familial :

• Politique de la ville : Créer des activités sportives et culturelles gratuites ou à prix réduit pour offrir des alternatives réelles à l'enfermement numérique.

• Éducation nationale : Passer d'une simple fourniture de tablettes à une véritable formation aux pratiques collaboratives. La tablette doit servir à créer ensemble et non à isoler chaque élève.

• Régulation européenne : Légiférer sur les plateformes pour obliger à la régulation des contenus et protéger les données (RGPD).

Document de Synthèse : L'Approche Stratégique du Harcèlement Scolaire par Emmanuelle Piquet

Résumé Exécutif

Le harcèlement scolaire est qualifié de véritable "fléau" sociétal, touchant potentiellement l'ensemble des 12 millions d'élèves français, que ce soit en tant que victimes, agresseurs ou témoins.

Face à l'inefficacité relative des sanctions traditionnelles et des interventions d'adultes agissant comme "gardes du corps", l'approche systémique de l'école de Palo Alto, portée par Emmanuelle Piquet, propose un changement de paradigme radical.

Plutôt que de tenter de modifier moralement le harceleur ou de surprotéger la victime, cette méthode vise à "outiller" l'enfant harcelé pour qu'il puisse, par lui-même, briser la dynamique d'emprise.

Le pivot central de cette stratégie est le "virage à 180 degrés" : cesser de fuir ou de demander l'arrêt des violences pour affronter l'agresseur avec une répartie stratégique et de l'autodérision, déplaçant ainsi l'inconfort de l'épaule de la victime vers celle du harceleur.

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1. État des Lieux et Ampleur du Phénomène

Le harcèlement en milieu scolaire n'est plus un sujet tabou mais une urgence nationale reconnue par la loi (délit pénal depuis mars 2021) et le plan national "phARE".

• Statistiques clés :

◦ 1 enfant sur 10 est victime de harcèlement à l'école.   

◦ 1 enfant sur 4 est victime de cyber-harcèlement.   

◦ 100 % des élèves sont concernés, incluant les harceleurs, les harcelés et les témoins (passifs ou non).

• Universalité du fléau : Les données cliniques des centres "Chagrin Scolaire" montrent que le phénomène est homogène sur le territoire (zones rurales, grandes villes comme Paris ou Genève) et traverse tous les milieux socio-professionnels, à l'instar des violences conjugales.

• Le moteur du harcèlement : Le carburant principal du harceleur est la souffrance exprimée par la victime. Percevoir l'impact émotionnel de ses actes procure à l'agresseur un plaisir lié à l'emprise et au pouvoir.

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2. Analyse de la Vulnérabilité et Mécanismes d'Emprise

L'agresseur ne choisit pas sa cible au hasard ; il utilise un "radar" pour détecter une vulnérabilité présumée.

• Définition de la vulnérabilité : Une fragilité repérable par ceux qui veulent asseoir leur pouvoir. Elle peut être ponctuelle (deuil, maladie d'un proche, changement de situation).

• Profils à risque : Les enfants très couvés par les parents ou scrutés avec inquiétude par les enseignants dégagent un "anneau d'inquiétude" qui peut attirer les harceleurs en quête de popularité.

• Le rôle du témoin : Les témoins n'interviennent souvent pas par peur d'être les prochains sur la liste. Ils oscillent entre la peur (plus forte) et la culpabilité.

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3. L'Échec des Solutions Conventionnelles : "Le Problème est la Solution"

Selon l'école de Palo Alto, ce sont souvent les tentatives de résolution qui alimentent le problème.

| Type d'intervention | Mécanisme | Conséquence négative | | --- | --- | --- | | L'adulte "Garde du corps" | L'adulte intervient massivement à la place de l'enfant. | Confirme l'incapacité de l'enfant à se défendre ; pousse le harcèlement à devenir souterrain (zones grises). | | La médiation classique | Confrontation entre harceleur et harcelé devant un adulte. | Le harceleur peut feindre le remords puis se venger violemment une fois hors de vue ("poucave"). | | Le conseil de l'indifférence | Dire à l'enfant : "Fais comme si tu n'entendais pas". | Impossible à réaliser pour un enfant ; peut transformer la victime en "poupée cassée" isolée de son environnement. | | Le changement d'école | Soustraire l'enfant à l'écosystème toxique. | 60 à 70 % de récidive, car l'enfant n'a pas appris à gérer la personnalité du harceleur, qu'il retrouvera ailleurs. |

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4. La Méthodologie du "Virage à 180 Degrés"

L'objectif est de transformer l'enfant de "proie" en "os relationnel".

A. La stratégie de résistance

Plutôt que de demander l'arrêt du harcèlement (ce qui excite l'agresseur), la victime doit envoyer le message : "Continue si tu veux, mais contemple les conséquences pour toi".

B. Le cas pratique de "Jean-Paul" (12 ans, HPI)

• Situation : Jean-Paul est moqué pour son prénom, son poids (32 kg) et ses excellentes notes. Lucas, le harceleur populaire, lui assène des coups de coude invisibles pour les adultes.

• La riposte stratégique : Au lieu de fuir aux toilettes pour pleurer, Jean-Paul utilise l'autodérision et la flèche de "l'absence de vie sociale" du harceleur : "C'est vrai que je n'ai pas de vie sociale, mais j'ai l'impression que toi sans moi, tu n'en as pas non plus vu que tu es tout le temps sur moi."

• Résultat : L'inversion de l'inconfort. Le harceleur, déstabilisé par cette "morsure d'agneau", perd son piédestal devant ses pairs.

C. Les trois étapes de la riposte

1. Attendre l'attaque : Ne plus fuir, mais attendre activement l'agression. Dans 50 % des cas, le simple changement de posture de l'enfant suffit à stopper le harceleur.

2. L'autodérision (La flèche) : Utiliser une répartie qui valide la moquerie pour la rendre inefficace.

3. Constater l'effet boomerang : Observer le déplacement de la gêne chez l'agresseur.

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5. Perspectives sur les Nouvelles Formes de Harcèlement

• Le Cyber-harcèlement : Il est rarement isolé. Il agit comme une "caisse de résonance" du harcèlement vécu en personne.

Il est particulièrement douloureux car il pénètre dans l'intimité du foyer, sans répit temporel.

• L'ostracisme (Le "harcèlement intelligent") : De plus en plus fréquent, il consiste à isoler totalement un enfant (ne pas lui parler, ne pas l'inviter).

C'est une forme de violence difficilement sanctionnable par l'institution car on ne peut forcer personne à être ami avec un autre.

• La Sanction : Les études cliniques montrent qu'elle n'a aucun impact durable sur les harceleurs. Elle est souvent perçue comme un "galon" ou une simple règle du jeu à contourner.

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6. Conseils aux Parents et Équipes Éducatives

• Libérer la parole sans trahir : Ne jamais agir sans l'accord de l'enfant.

Si un parent intervient contre la volonté de l'enfant, celui-ci ne lui fera plus jamais confiance.

• Identifier les signaux faibles : Baisse des notes, maux de ventre le dimanche soir, irritabilité accrue avec la fratrie, isolement dans la cour.

• Valider les compétences : Il faut cesser de nier les compétences sociales des enfants harcelés et les aider à développer leur propre "carquois" de défense.

• L'entraînement : Dans les cabinets de thérapie ou les ateliers de résistance, les enfants sont entraînés physiquement et verbalement à tenir une posture de résistance crédible.

Citation clé : "Un des carburants essentiels du harcèlement, c'est la souffrance exprimée. [...] Notre proposition consiste à outiller les enfants pour qu'ils puissent résister par eux-mêmes au moment de l'action." — Emmanuelle Piquet.

Briefing : La Parentalité à l’Ère de la « Famille Tout Écran »

Ce document de synthèse analyse les enjeux de la parentalité numérique tels qu'exposés par Marie Pierrotte, professeure d'histoire-géographie et de géopolitique, ainsi que référente académique pour l'Éducation aux Médias et à l'Information (EMI) au sein du CLEMI.

Résumé Exécutif

L'omniprésence des écrans au sein des foyers — avec une moyenne de neuf écrans par famille — a radicalement transformé les dynamiques éducatives.

Le concept de « Famille Tout Écran », développé par le CLEMI, souligne que la parentalité numérique est avant tout une question de parentalité classique adaptée à un nouvel environnement.

Les points clés identifiés sont :

• L'exemplarité parentale : Les parents doivent prendre conscience de leur propre dépendance aux écrans, car ils servent de modèles à leurs enfants.

• Le passage du contrôle au dialogue : La surveillance restrictive doit céder le pas à un accompagnement actif et à une compréhension des usages des jeunes.

• La littératie médiatique : Les compétences numériques ne sont pas innées chez les « digital natives » ; elles nécessitent un apprentissage structuré de la vérification des sources et des mécanismes algorithmiques.

• Les défis institutionnels : Entre l'émergence de l'IA (ChatGPT) et les propositions législatives sur l'âge d'accès aux réseaux sociaux, l'éducation reste le levier le plus efficace face aux limites des solutions purement restrictives.

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1. État des Lieux : Le Foyer Numérique en Chiffres et Concepts

L'environnement familial a été bouleversé par l'arrivée de la génération « Alpha » (enfants nés après 2010), qui n'a jamais connu de monde sans écrans.

• Saturation technologique : Un foyer avec deux enfants possède en moyenne neuf écrans. Ce phénomène est transversal à tous les milieux sociaux.

• Paradoxe parental : Les parents équipent massivement leurs enfants, percevant l'ordinateur comme un outil de réussite scolaire, tout en exprimant une profonde anxiété face aux contenus consommés.

• Absence de repères historiques : Les parents actuels ne peuvent pas s'appuyer sur l'éducation qu'ils ont reçue, leurs propres parents n'ayant pas eu à gérer de tels outils.

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2. Les Quatre Piliers de la Régulation Familiale

Pour structurer la pratique numérique, Marie Pierrotte propose une analyse selon quatre axes thématiques majeurs :

| Thème | Enjeux Clés | | --- | --- | | L'Équipement | Le choix du matériel et l'âge du premier équipement (souvent vers 9 ans pour le portable, entrée en 6ème pour l'ordinateur). | | La Temporalité | La conscience du temps passé. Les usages peuvent atteindre 3 heures par jour, souvent de manière inconsciente. | | La Spatialisation | Le lieu d'usage au sein de la maison (chambre isolée vs lieux communs comme le salon pour maintenir un lien visuel). | | Le Contenu | La nature de ce qui est consulté. Il est recommandé de s'intéresser aux activités des enfants sans tomber dans le dénigrement. |

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3. Information, Réseaux Sociaux et Algorithmes

La consommation d'information par les jeunes a évolué, s'éloignant des médias traditionnels pour se concentrer sur des plateformes comme Instagram, TikTok, WhatsApp ou Twitch.

Le Phénomène Hugo Décrypte

Cette chaîne est citée comme un modèle de succès. Bien qu'elle soit perçue comme la production d'un jeune « éclairé », elle repose sur une structure économique solide (15 salariés, journalistes, monteurs).

C'est un outil précieux pour le décryptage, mais dont les adolescents doivent comprendre les dessous économiques.

Bulles Cognitives et Désinformation

• Biais cognitifs : Le cerveau a tendance à ne retenir que les informations confirmant des opinions préexistantes.

• Algorithmes d'enfermement : Des plateformes comme TikTok utilisent des algorithmes pour proposer des contenus similaires à ceux déjà consultés, enfermant l'utilisateur dans une « bulle cognitive ».

L'exemple de l'attaque du Capitole illustre le danger de cette déconnexion de la réalité factuelle.

• Modèle social chinois vs américain : Le document distingue le contrôle social explicite via le numérique en Chine (système de points) de l'exploitation commerciale des données (RGPD en Europe vs modèles américains).

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4. Défis Éducatifs et Institutionnels

L'Intelligence Artificielle (ChatGPT)

L'IA est perçue comme un défi pour l'évaluation scolaire (rédaction de lettres de motivation, devoirs).

Cependant, Marie Pierrotte souligne qu'un travail non intégré personnellement ne construit aucune compétence. L'accent doit être mis sur la sincérité de l'apprentissage.

Le Cadre Légal et l'École

• Âge numérique : La loi fixe l'accès aux réseaux sociaux à 13 ans, mais l'âge réel du premier portable en France est de 9 ans.

• Proposition de loi Marcangeli : Vise à porter cet âge à 15 ans.

Le document reste sceptique sur l'efficacité d'une telle mesure face à des multinationales américaines, privilégiant la formation des parents.

• Le portable en classe : Au collège, l'interdiction est la règle. Au lycée, des stratégies de contournement apparaissent (le « leurre », où l'élève rend un vieux téléphone et en garde un second pour tricher).

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5. Risques Comportementaux et Santé Mentale

Jeux Vidéo et « Hormone de la Joie »

Le jeu vidéo stimule la sécrétion de dopamine (comparée à la satisfaction d'un aliment sucré ou du sport), ce qui explique la difficulté extrême pour un enfant de « décrocher » au moment des repas, générant des conflits familiaux.

• Conseil pratique : Respecter la signalétique PEGI et définir le temps par « nombre de parties » plutôt que par minutes.

Brutalisation des Échanges

L'anonymat ou la distance numérique favorise une brutalité verbale (sexisme, racisme, homophobie).

Les jeunes ont tendance à oublier que les règles de respect de la « vie réelle » s'appliquent aussi sur WhatsApp ou les réseaux sociaux.

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6. Outils et Bonnes Pratiques

Le document mentionne des solutions concrètes pour une transition numérique apaisée :

1. L'Application "Forest" : Permet de relever des défis de concentration. Si l'utilisateur n'utilise pas son téléphone pendant un temps défini (ex: 2h), un arbre réel est planté.

2. L'Heure sans Écran : Éviter les écrans une heure avant le coucher pour préserver le sommeil.

3. La Portabilité des Données : Utiliser le RGPD pour demander aux plateformes l'intégralité des données collectées (souvent des centaines de pages) afin de sensibiliser sur la vie privée.

4. L'Éducation aux Médias par la Pratique : Encourager la création (webradio, rédaction d'articles) pour comprendre comment l'information est construite.

Briefing : Santé, Besoins et Développement de l'Enfant

Ce document de synthèse s'appuie sur l'intervention de Marie-Paule Desanti, psychologue clinicienne à la Protection Maternelle et Infantile (PMI) de Corse, lors du webinaire « L'instant parents ».

Il détaille les enjeux de la santé globale de l'enfant, les étapes charnières de son développement et les besoins fondamentaux nécessaires à son épanouissement.

Résumé Exécutif

Le développement de l'enfant ne doit pas être perçu comme une simple progression organique, mais comme une évolution globale intégrant la santé physique, le bien-être psychologique et l'insertion sociale.

Les points saillants de cette analyse incluent :

• La conception globale de la santé : Elle dépasse l'absence de maladie pour englober un équilibre social et psychologique.

• L'importance des 1000 premiers jours : Une période de vulnérabilité et de plasticité neuronale extraordinaire (du 4ème mois de grossesse aux 2 ans) où l'environnement et l'attachement jouent un rôle déterminant.

• La trajectoire de développement : Un processus non linéaire marqué par des étapes clés (9 mois, 24 mois, 4 ans) validées par des certificats de santé obligatoires.

• Les besoins fondamentaux : Articulés autour de cinq piliers (sécurité, exploration, cadre/limites, identité, valorisation), leur satisfaction est la condition sine qua non d'une maturation réussie.

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I. Le Rôle de la Protection Maternelle et Infantile (PMI)

Issue d'une ordonnance de 1945 visant à redresser l'état sanitaire post-guerre, la PMI a évolué d'une mission purement médicale vers un accompagnement global de la parentalité.

• Missions principales : Prévention sanitaire, protection de l'enfance, et accompagnement médico-social des femmes enceintes et des enfants de moins de 6 ans.

• Services proposés : Consultations de nourrissons, bilans de santé en école maternelle, agrément des assistantes maternelles et accueil en structures petite enfance.

• Caractéristiques : C'est un service public de proximité, gratuit et ouvert à tous, composé d'équipes pluridisciplinaires (médecins, sages-femmes, puéricultrices, psychologues, éducatrices).

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II. Analyse Chronologique du Développement

L'évolution de l'enfant est suivie à travers trois certificats de santé obligatoires (8 jours, 9 mois, 24 mois) et un bilan en école maternelle (4 ans).

1. Le cap des 9 mois : L'individuation

• Auto-apaisement : À cet âge, l'enfant commence à acquérir la capacité de se calmer seul, notamment durant la nuit.

Faire ses nuits signifie ici ne plus solliciter les parents lors des réveils nocturnes.

• Peur de l'étranger et séparation : Ces réactions marquent la construction de l'enfant en tant qu'individu séparé (« dé-fusionné ») de sa figure d'attachement.

• Permanence de l'objet : L'enfant intériorise l'image de ses parents, ce qui lui permet de se rassurer en leur absence.

2. Le cap des 24 mois : L'explosion motrice et l'indépendance

• Autonomie et opposition : L'utilisation du « non » est un signe de maturation ; l'enfant affirme qu'il est un sujet distinct de ses parents.

• Capacités d'empathie : Début de la reconnaissance des émotions d'autrui et des gestes de consolation.

• Symbolisation : Apparition des jeux de « faire semblant » (bercer une poupée), témoignant de la mise en place de représentations mentales.

• Langage : Émergence d'un langage capable de combiner deux ou trois mots.

3. Le cap des 4 ans : L'entrée dans le monde social

• Décentration : Avec l'école, l'enfant réalise qu'il n'est plus le centre du monde et doit s'ouvrir aux autres.

• Monde interne : Capacité d'exprimer des émotions complexes (peur, joie, tristesse) et accès à l'auto-réflexion via l'utilisation du « Je ».

• Compétences cognitives : Développement du graphisme (dessin du bonhomme) et structuration du récit.

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III. Les 1000 Premiers Jours et la Théorie de l'Attachement

Cette période (grossesse jusqu'à 2 ans) est qualifiée d'« époustouflante » par les experts en raison de la vitesse de maturation cérébrale (200 000 connexions neuronales par minute).

• L'influence de l'environnement : Si la génétique fournit l'encodage, l'environnement (nutrition, sommeil, qualité des interactions) façonne le cerveau.

• L'attachement sécure : Un besoin vital. La réponse chaleureuse et adéquate de l'adulte aux besoins de l'enfant crée un lien de confiance.

Ce lien sécurisé permet à l'enfant d'explorer le monde et de faire face aux difficultés futures de manière adaptée.

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IV. Typologie des Besoins Fondamentaux

L'analyse de Marie-Paule Desanti, basée sur les définitions de la Haute Autorité de Santé (HAS), distingue cinq catégories de besoins :

| Catégorie de Besoin | Composantes Essentielles | | --- | --- | | Sécurité (Physiologique et Affective) | Nutrition, hygiène, sommeil régulier, protection contre le froid. Relation affective stable et cohérente avec des adultes disponibles. | | Exploration | Liberté de mouvement, exercice du corps, jeux imaginatifs, immersion dans le langage, accès à la culture (ludothèques, crèches). Droit à l'ennui et au rêve. | | Cadre et Limites | Apprentissage des codes sociaux et des valeurs. Régulation émotionnelle (reconnaître et nommer une émotion sans passer à l'acte agressif). Cohérence de l'adulte. | | Identité | Inscription dans une filiation et une génération. Reconnaissance des multiples facettes (sexe, culture, spiritualité, appartenance à un groupe). | | Valorisation | Besoin d'être reconnu comme un être singulier, irremplaçable et nécessaire à la société. Importance du regard de l'autre. |

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V. Conclusion et Perspectives Éducatives

Le développement harmonieux de l'enfant repose sur une vigilance constante lors des moments de rupture ou de changement (retour de maternité, entrée en crèche, arrivée d'un nouveau membre dans la fratrie).

L'objectif final de l'accompagnement parental et éducatif est de garantir un cadre qui soit simultanément :

1. Bienveillant : Acceptation de tous les ressentis de l'enfant (ex: accepter qu'il exprime ne pas aimer son frère tout en interdisant l'agression physique).

2. Sécurisant : Présence d'adultes modélisants et cohérents.

3. Contenant : Capacité de l'environnement à structurer et à donner du sens aux expériences de l'enfant.

Comme le souligne Desanti, « la santé ne se limite pas à l'absence de pathologie » ; elle est le fruit d'une attention portée à tous les axes de développement : moteur, relationnel, social et affectif.

Reviewer #2 (Public review):

The authors report results from behavioral data, fMRI recordings, and computer simulations during a conceptual navigation task. They report 3-fold symmetry in behavioral and simulated model performance, 3-fold symmetry in hippocampal activity, and 6-fold symmetry in entorhinal activity (all as a function of movement directions in conceptual space). The analyses seem thoroughly done, and the results and simulations are very interesting.

[Editors' note: this version was assessed by the editors without consulting the reviewers further.]

Author response:

The following is the authors’ response to the previous reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

Zhang and colleagues examine neural representations underlying abstract navigation in entorhinal cortex (EC) and hippocampus (HC) using fMRI. This paper replicates a previously identified hexagonal modulation of abstract navigation vectors in abstract space in EC in a novel task involving navigating in a conceptual Greeble space. In HC, the authors identify a three-fold signal of the navigation angle. They also use a novel analysis technique (spectral analysis) to look at spatial patterns in these two areas and identify phase coupling between HC and EC. Interestingly, the three-fold pattern identified in the hippocampus explains quirks in participants' behavior where navigation performance follows a three-fold periodicity. Finally, the authors propose a EC-HPC PhaseSync Model to understand how the EC and HC construct cognitive maps. The wide array and creativity of the techniques used is impressive but because of their unique nature, the paper would benefit from more details on how some of these techniques were implemented.

Comments on revisions:

Most of my concerns were adequately addressed, and I believe the paper is greatly improved. I have two more points. I noticed that the legend for Figure 4 still refers to some components of the previous figure version, this should be updated to reflect the current version of the figure. I also think the paper would benefit from more details regarding some of the analyses.

Specifically, the phase-amplitude coupling analysis should have a section in the methods which should be sure to clarify how the BOLD signals were reconstructed.

(1)“…I noticed that the legend for Figure 4 still refers to some components of the previous figure version, this should be updated to reflect the current version of the figure…”.

Thank you for pointing this out. We have revised the legend of Figure 4 by removing the significance notation “***: p < 0.001”, which referred to elements from a previous version of the figure.

(2)“…I also think the paper would benefit from more details regarding some of the analyses. Specifically, the phase-amplitude coupling analysis should have a section in the methods which should be sure to clarify how the BOLD signals were reconstructed”.

We agree and appreciate the reviewer’s helpful suggestion. We have added a dedicated subsection entitled “Phase–amplitude coupling” to the Materials and Methods, in which we provide a detailed description of how the EC and HPC BOLD signals were reconstructed and how the coupling analysis was implemented. Correspondingly, we refined the description of this analysis in the Results section under “Phase synchronization between the HPC and EC activity”. The revised sections have been included below for your convenience. 

Materials and Methods: Phase–amplitude coupling

To quantify the spatial peak relationship between EC and HPC BOLD activity, we implemented a cross-frequency amplitude–phase coupling analysis in the directional space (Canolty et al., 2006). Rather than analyzing raw BOLD signals, we reconstructed 6-fold EC activity and 3-fold HPC activity in each voxel using sinusoidal modulation weights (β_sine and β_cosine) estimated from the raw BOLD signals. Specifically, activity was modeled as β_cosinecos(kθ) + β_sinesin(kθ), where k denotes the rotational symmetry. This approach selectively captures the hypothesized spatial symmetries of neural activity (e.g., 6-fold or 3-fold periodicity) as a function of movement direction. For this coupling analysis, we used participants’ original movement directions (i.e., without applying orientation calibration). The reconstructed 6-fold EC and 3-fold HPC activity were then converted into analytic representations using the Hilbert transform, yielding the instantaneous phase of the HPC (ϕ_HPC) and the amplitude envelope of the EC (A_ERC). HPC phases were classified into nine bins. The composite analytic signal, defined as z = A_ERCe^iϕHPC, was used to compute the modulation index M (Canolty et al., 2006), defined as the absolute value of the mean of z values, quantifying the scalar coupling strength between EC amplitude and HPC phase within each bin. A surrogate dataset, a null distribution of the modulation indices (M^-), was generated by spatially offsetting the EC amplitude relative to the HPC phase across all possible spatial lags. The mean of this surrogate distribution was used as the baseline reference against which the observed coupling strength was compared.

Results: Phase synchronization between the HPC and EC activity

To examine whether the spatial phase structure in one region could predict that in another, we tested whether the orientations of the 6-fold EC and 3-fold HPC periodic activities, estimated from odd-numbered sessions using sinusoidal modulation with rotationally symmetric parameters, were correlated across participants. A cross-participant circular correlation was conducted between the spatial phases of the two areas to quantify the spatial correspondence of their activity patterns (EC: purple dots; HPC: green dots) (Jammalamadaka & Sengupta, 2001). The analysis revealed a significant circular correlation (Fig. 4a; r = 0.42, p < 0.001), as reflected by the continuous color progression across the participants (i.e., the colored lines connecting each pair of the EC and HPC dots in Fig. 4a), suggesting that participants with smaller hippocampal phases (green, outer ring) tended to have smaller entorhinal phases (purple, inner ring), and vice versa.

In addition to the across-participant phase correlation, we further examined the spatial alignment between the 6-fold EC and 3-fold HPC activity patterns. Given that the spatial phase of the HPC is hypothesized to depend on EC projections, particularly along the three primary axes of the hexagonal code, we examined whether the periodic activities of the EC and HPC were spatially peak-aligned. Notably, unlike previous studies that focused on temporal coherence of neural oscillations (Buzsaki, 2006; Maris et al., 2011; Friese et al., 2013), our analysis focused on periodic coupling between brain areas in the directional space. To test spatial peak alignment between EC and HPC, a cross-frequency spatial coupling analysis (adapted from the amplitude–phase coupling framework; Canolty et al., 2006) was employed to identify at which HPC phase the EC exhibited maximal amplitude modulation. If the activities of both areas were peak-aligned (i.e., no peak offset), a strong coupling at phase 0 of the HPC would be expected as shown by the one-cyclebased schema in Fig. 4b. In doing so, the instantaneous phase of the HPC and the amplitude envelope of the EC were extracted from the reconstructed activity using the Hilbert transform (see methods for details). HPC phases were classified into nine bins, and the modulation index (M), quantifying the scalar coupling strength between EC amplitude and HPC phase, was computed within each bin. As a result, significant coupling was observed in the bin centered at phase 0 of the HPC (Fig. 4c; t(32) = 2.57, p = 0.02, Bonferroni-corrected across tests; Cohen’s d = 0.45). In contrast, no significant coupling was found in other bins (p > 0.05). To rule out the possibility that the observed coupling was driven by a potential harmonic (integer multiple) relationship between the 3-fold and 6-fold periodicities, we additionally conducted control analyses using 9-fold and 12-fold EC components. However, no significant coupling was observed in these controls (Fig. 4c; p > 0.05). Together, these results confirmed selective alignments of spatial peaks between the 6fold EC and 3-fold HPC periodicity in the conceptual direction domain.

Reviewer #2 (Public review):

The authors report results from behavioral data, fMRI recordings, and computer simulations during a conceptual navigation task. They report 3-fold symmetry in behavioral and simulated model performance, 3-fold symmetry in hippocampal activity, and 6-fold symmetry in entorhinal activity (all as a function of movement directions in conceptual space). The analyses seem thoroughly done, and the results and simulations are very interesting.

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

We thank both reviewers again for their constructive and insightful feedback, which has substantially strengthened the manuscript.

Note de Synthèse : La Logique Émotionnelle chez l'Enfant

Ce document de synthèse analyse les interventions de Catherine Aimelet-Perrisol, médecin et psychothérapeute, concernant la nature des émotions enfantines et la posture parentale requise pour les accompagner.

Il repose sur l'approche de la « logique émotionnelle », qui s'éloigne d'une vision purement psychologique pour embrasser une compréhension biologique de l'émotion.

Résumé Exécutif

L’émotion ne doit pas être perçue comme un débordement à gérer ou à réprimer, mais comme un mouvement vital (e-movere) et un langage biologique signalant un besoin d'existence.

Fondée sur les travaux du professeur Henri Laborit, cette approche postule que chaque émotion (peur, colère, tristesse, joie) répond à un code biologique précis visant la survie et l'affirmation de soi.

Pour le parent, l'enjeu n'est pas de calmer l'enfant par la coercition, mais d'écouter ce que l'émotion dit de son besoin de sécurité, d'identité ou de sens.

Le rôle éducatif évolue ainsi d'un cadre rigide vers une structure souple et une enveloppe sécurisante, permettant à l'enfant de transformer ses émotions en solutions adaptatives plutôt qu'en problèmes comportementaux.

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1. L’Émotion : Un Processus Biologique et Vital

L'émotion est étymologiquement un « mouvement vers l'extérieur ». Loin d'être un simple phénomène psychologique, elle est une réaction cellulaire et neuronale ancrée dans le vivant.

• L’intention vitale : L'émotion manifeste l'élan vital de l'enfant. Lorsqu'un enfant crie ou s'agite, il exprime fondamentalement : « J'existe ».

• La rupture avec la « gestion » : Vouloir « gérer » ou contrôler les émotions est jugé contre-productif.

L'émotion est un mécanisme de régime biologique qui s'impose à l'individu ; elle est donc « vraie » par définition, même si la réaction semble inadéquate aux yeux des adultes.

• Un langage à décrypter : L'émotion est le langage utilisé par l'enfant, souvent avant même la maîtrise des mots, pour dire quelque chose de sa propre existence et de son rapport au monde.

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2. Le Code Émotionnel : Les Quatre Catégories Fondamentales

Selon la logique émotionnelle, chaque émotion est un signal spécifique répondant à un besoin précis. Le consensus identifie quatre grandes catégories :

| Émotion | Besoin sous-jacent | Perception de la situation | Comportement associé | | --- | --- | --- | --- | | Peur | Sécurité | Danger perçu | Fuite ou évitement | | Colère | Identité / Estime de soi | Menace ou agression | Lutte ou confrontation | | Tristesse | Sens / Compréhension | Chaos ou privation de sens | Repli sur soi / Bulle de protection | | Joie | Expansion / Vitalité | Opportunité / Récompense | Externalisation / Explosion de vie |

Focus sur les fonctions spécifiques :

• La Peur : Elle permet d'anticiper le pire pour s'y préparer. Elle devient une solution si le parent aide l'enfant à élaborer une stratégie face au danger ressenti.

• La Colère : Elle sert d'exutoire pour protéger le « moi ». L'enfant cherche à se faire entendre et à affirmer son identité dans la relation.

• La Tristesse : Elle crée une bulle de protection (souvent observée durant la période du COVID-19) face à un monde extérieur devenu incompréhensible.

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3. La Posture Parentale : Présence, Structure et Enveloppement

Le parent est invité à passer d'un rôle de « sauveur » ou de « contrôleur » à celui d'accompagnateur.

L'écoute et la restitution

Au lieu d'évaluer le comportement, le parent doit s'intéresser au « comment » :

• Observation : Regarder comment l'enfant s'y prend pour dessiner ou apprendre (ex: une lune carrée n'est pas une erreur, mais une expression de ce que l'enfant a vu ou imaginé).

• Restitution : Redonner à l'enfant ses propres outils en lui montrant qu'on a perçu sa démarche (« Je vois que tu apprends mieux en marchant »). Cela renforce sa sécurité intérieure.

Structure vs Cadre

Le concept de « cadre » est souvent perçu comme restrictif ou source de conflit. On lui préfère deux autres notions :

1. La Structure (ou Architecture) : Une colonne vertébrale à la fois souple et solide. C'est la « droiture » qui permet à l'enfant de s'élever et de découvrir ses propres règles.

2. L'Enveloppement : Une protection nécessaire lorsque l'enfant est démuni ou traversé par un chagrin immense. C'est une présence qui dit : « Je suis là, je t'écoute ».

L'Éducation comme Conduite

L'éducation (ducere) consiste à apprendre à l'enfant comment « se conduire » plutôt que de lui imposer une conduite.

Questionner un enfant sur la façon dont il compte se comporter dans une situation donnée stimule ses neurones et développe son sens de la responsabilité.

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4. Le Mystère du Développement et de l'Apprentissage

Chaque enfant naît avec une « tonalité émotionnelle » singulière (plutôt inquiet, batailleur ou joyeux).

• L'influence de l'environnement : La culture familiale peut favoriser ou restreindre certaines émotions (ex : « chez nous, on ne pleure pas »).

L'enfant s'adapte ou entre en résistance, ce qui constitue une part du mystère de sa personnalité.

• L'apprentissage comme chemin vers la sécurité : Il n'existe pas d'enfant qui ne veuille pas apprendre.

Comprendre un concept ou réussir un apprentissage est une source majeure de sécurité intérieure.

• La loi commune : Si le « comment » (la méthode) est libre et appartient à l'enfant, le « quoi » (la nécessité d'apprendre la leçon, de respecter les règles sociales) relève de la loi et de l'ordre collectif, qui ne sont pas négociables.

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Conclusion : L’Émotion comme Solution

L'approche de Catherine Aimelet-Perrisol conclut que l'émotion n'est jamais un problème en soi.

Elle est une solution biologique que le corps trouve pour exprimer un besoin non satisfait.

En validant le ressenti de l'enfant (« Ton corps dit vrai ») sans nécessairement valider toutes ses interprétations factuelles, le parent crée une relation « gagnant-gagnant » fondée sur la reconnaissance de l'existence de l'autre.

L'Éducation Positive : Au-delà des Clichés et des Dogmes

Résumé Exécutif

Ce document de synthèse analyse les interventions de Béatrice Kammerer, journaliste spécialisée en éducation, lors du webinaire « L’Instant Parents ».

L'objectif est de clarifier le concept d'éducation positive, souvent mal compris ou réduit à des slogans marketing.

Les points clés à retenir sont :

• Absence de définition théorique stricte en France : Contrairement aux pays anglo-saxons, l'éducation positive en France est davantage un mouvement éditorial qu'un concept universitaire précis.

• Idéalisation excessive : La définition du Conseil de l'Europe fixe un standard très élevé (le « plein développement » de l'enfant), créant un idéal parfois inatteignable pour les parents.

• Le paradoxe de l'efficacité : Il existe un décalage entre les promesses marketing (fin des conflits) et les valeurs réelles du courant (épanouissement et autonomie), qui n'excluent pas les comportements enfantins naturels.

• Réhabilitation du « parent bricoleur » : Face aux injonctions des experts, il est crucial de redonner confiance aux parents dans leur capacité d'adaptation et leur droit à l'erreur (essai-erreur).

• Enjeux sociétaux : Le débat doit dépasser la sphère privée pour intégrer les inégalités de genre (charge mentale des mères) et les violences systémiques (pauvreté, exclusion) trop souvent ignorées par ce courant.

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I. Définitions et Cadre Conceptuel

1. Un malentendu géographique et sémantique

L'éducation positive ne recouvre pas la même réalité selon les régions du monde :

• Aux États-Unis : Elle s'appuie sur la « discipline positive » (Jane Nelsen, années 80), visant à remplacer les punitions par la responsabilisation, ou sur la « psychologie positive » (Martin Seligman), axée sur les déterminants du bien-être.

• En France : Le terme n'est pas issu du monde académique ou de la recherche. C'est une « bannière » utilisée par l'édition pour regrouper des aspirations diverses visant à contester l'éducation autoritaire traditionnelle.

2. La définition du Conseil de l'Europe (2006)

C'est la seule référence institutionnelle majeure. Elle définit la parentalité positive comme un comportement fondé sur :

• L'intérêt supérieur de l'enfant.

• Un environnement non violent.

• La reconnaissance et l'assistance.

• L'établissement de repères pour le plein développement.

Analyse : Béatrice Kammerer souligne que cette définition est à la fois consensuelle et « complètement idéalisée », plaçant la barre si haut qu'elle devient une source potentielle de pression pour les parents.

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II. Les Piliers de l'Éducation Positive

Bien que floue, l'éducation positive repose sur trois objectifs transversaux identifiés chez la plupart des auteurs :

| Pilier | Description et Objectifs | | --- | --- | | Non-violence éducative | Questionner la légitimité de la force. Supprimer les punitions, les récompenses et la coercition non nécessaire. | | Amélioration de la communication | Favoriser l'écoute mutuelle, l'expression des besoins sans jugement et l'attention portée aux émotions. | | Reconnaissance des spécificités | Prendre en compte l'immaturité cognitive et affective de l'enfant. Voir le "mauvais" comportement comme l'expression d'un besoin plutôt que comme une attaque personnelle. |

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III. Fondements Historiques et Psychologiques

1. L'évolution du statut de l'enfant

Le XXe siècle a marqué une rupture majeure :

• Baisse de la mortalité infantile : Les progrès médicaux (vaccins, antibiotiques) ont permis un investissement affectif plus intense.

• Changement de paradigme philosophique : Passage de l'enfant « marqué par le péché » (à civiliser) à l'enfant « jardin d'Eden » (Rousseau, romantisme) dont il faut préserver l'innocence.

• Éducation nouvelle : Des figures comme Maria Montessori ou Célestin Freinet ont déplacé l'objectif de la transmission brute de savoirs vers le développement de la personne.

2. Les théories de l'attachement

L'éducation positive puise largement dans les travaux de :

• John Bowlby (années 40) : Identification des comportements de proximité comme vitaux pour le bébé.

• René Spitz : Concept de l'hospitalisme, démontrant que l'absence de lien affectif peut mener au dépérissement et à la mort du nourrisson.

• Mary Ainsworth (années 70) : Mise en évidence du besoin d'une « base de sécurité ». Paradoxalement, c'est parce que l'enfant se sent sécure et soutenu qu'il peut devenir autonome.

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IV. Critiques et Zones d'Ombre du Courant

1. Le piège du dogme et de l'injonction

Béatrice Kammerer dénonce une dérive autoritaire dans la diffusion des conseils :

• Recettes miracles : Les livres proposent des tutoriels rigides (« dites ceci, pas cela ») qui ignorent la singularité de chaque relation.

• Culpabilisation : Si la méthode échoue, le parent se sent en échec personnel, pensant avoir mal appliqué les consignes des experts.

• Figures de proue « gourous » : Certains discours deviennent intouchables, où critiquer la méthode revient à être accusé de promouvoir la maltraitance.

2. Le parent « thérapeute » et le contrôle émotionnel

Une attente inhumaine pèse sur les parents : on leur demande d'être des réceptacles calmes et empathiques en permanence (similaires à des thérapeutes en séance), tout en leur interdisant d'exprimer leur propre colère ou fatigue.

3. Les angles morts sociaux

Le courant est critiqué pour son manque d'engagement sur des sujets critiques :

• Pauvreté et exclusion : Peu d'intérêt pour les enfants dormant à la rue ou vivant sous le seuil de pauvreté.

• Inégalités de genre : L'éducation positive reste largement portée par les femmes (95% des participants aux ateliers). Les mères demeurent les « parents par défaut » gérant la charge mentale, tandis que les pères sont souvent perçus comme des « seconds couteaux ».

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V. Débats Contemporains : La Famille Démocratique et le "Time Out"

1. Le mythe de l'enfant roi

Béatrice Kammerer conteste l'idée que nous vivons sous la domination des enfants. Elle préfère le concept de famille démocratique (François de Singly) :

• L'enfant est « ambivalent » : petit par ses besoins de protection, mais grand par ses droits.

• Le défi est de l'associer aux décisions sans lui faire porter une responsabilité de choix démesurée pour son âge.

2. La controverse Caroline Goldman vs Recherche scientifique

Le débat actuel sur le « Time Out » (mise à l'écart) est marqué par des confusions :

• La « mise au point » (Goldman) : Prône une mise à l'écart longue (30 min à 1h) dès 1 an, avec une dimension punitive et pénitente.

• Le « Time Out » scientifique : Il s'agit d'un temps mort très court (quelques minutes), non chargé émotionnellement, visant simplement le retour au calme.

Il reste, sous cette forme, compatible avec les principes de l'éducation positive.

Conclusion : Pour une compétence de bricolage

L'expertise réelle n'appartient pas aux livres, mais aux parents qui vivent avec l'enfant.

L'éducation doit être vue comme un processus d'ajustement permanent.

Le « parent bricoleur », qui essaie, se trompe et répare, offre à l'enfant un modèle d'humanité et d'amour bien plus précieux qu'une application parfaite de méthodes standardisées.

L’Éducation Efficace : Synthèse de la Méthode de Laurence Dudek

Ce document présente une analyse détaillée des principes de la méthode « Éducation efficace » développée par Laurence Dudek, psychopédagogue, lors d'un webinaire organisé par le Réseau Canopé de Corse.

La méthode repose sur l'idée que la non-violence n'est pas seulement une valeur morale, mais le levier le plus performant pour l'apprentissage et le développement de l'enfant.

Résumé Exécutif

L'éducation efficace se définit par un postulat simple : ce qui est bienveillant est ce qui fonctionne.

Contrairement aux méthodes coercitives (punitions et récompenses) qui visent l'obéissance à court terme au détriment de la relation, cette approche privilégie l'attachement sécure et l'exemple comme moteurs principaux d'apprentissage. Les points critiques à retenir sont :

• Le primat de l'exemple : L'enfant apprend par imitation et expérience, non par des injonctions verbales ou des explications rationnelles (inefficaces avant l'âge de 7 ans).

• L'émotion comme obstacle : La peur, la honte et le rejet sont des « encombrants cognitifs » qui saturent le cerveau et empêchent tout apprentissage réel.

• La redéfinition de l'erreur : L'échec n'est pas un manque de compétence, mais une étape nécessaire du développement qui doit être accueillie avec confiance.

• L'inefficacité de la force : Aucune violence n'est éducative. La contrainte brise le lien de confiance, moteur essentiel de la transmission entre mammifères.

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I. Les Fondements de l’Éducation Efficace

Définition et Objectifs

Laurence Dudek récuse les termes « éducation positive » ou « bienveillante » qui peuvent induire un jugement de valeur (positif vs négatif).

Elle choisit le terme efficace car il est neutre : une méthode est efficace si elle produit les résultats escomptés (apprentissage, autonomie) sans détruire la relation.

| Éducation Coercitive | Éducation Efficace | | --- | --- | | Basée sur la force (punition/récompense). | Basée sur la non-violence et l'attachement. | | Vise l'obéissance immédiate. | Vise l'apprentissage à long terme et l'autonomie. | | Génère un lien d'attachement insécure. | Favorise un lien d'attachement sécure. | | Utilise la peur, la honte et le rejet. | Utilise l'exemple, l'expérience et la confiance. |

Le rôle de l'attachement

Pour les mammifères humains, le lien d'attachement est la condition sine qua non de l'apprentissage.

Un enfant qui craint une réaction imprévisible de son parent (punition, claque, colère) entre dans un état de vigilance qui paralyse ses capacités cognitives.

L'enseignant ou le parent efficace est celui qui sait instaurer un respect mutuel et une disponibilité sécurisante.

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II. La Clé n°1 : La Valeur et le Pouvoir de l'Exemple

Le levier principal de l'apprentissage est l'imitation de la figure d'attachement.

L'inefficacité du discours rationnel

Une erreur courante consiste à surinvestir l'explication verbale chez les jeunes enfants.

• Avant 7 ans : Les liens indirects de cause à effet (ex: « ne mange pas de bonbons, tu auras mal aux dents plus tard ») n'ont aucun sens pour le cerveau de l'enfant.

Seul le lien direct et immédiat est intégré (ex: « c'est chaud, ça brûle »).

• Injonctions contradictoires : Dire « fais ce que je dis, pas ce que je fais » est une impasse.

Un parent qui utilise son téléphone toute la journée ne peut pas exiger de son enfant qu'il s'en détache.

Le miroir du comportement

Si un enfant adopte un comportement inadapté, le parent doit d'abord se demander : « Où a-t-il appris cela ? ». L'enfant reflète les informations et le contexte fournis par l'adulte.

La distinction entre réflexe et violence

Chez les tout-petits (jusqu'à 4 ans), certains comportements dits « violents » (mordre, griffer) sont des réactions réflexes de défense.

Si un adulte entrave physiquement un enfant de manière coercitive, le cerveau archaïque de l'enfant interprète la situation comme une prédation.

L'enfant ne choisit pas d'être violent ; il réagit à un contexte perçu comme hostile.

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III. L'Impact des Émotions sur l'Apprentissage

Les émotions douloureuses sont qualifiées d'encombrants cognitifs.

1. Saturation cérébrale : Lorsqu'un enfant a peur, ressent de la honte ou se sent rejeté, son cerveau est entièrement consacré à la gestion de cette douleur interne. La concentration est rompue.

2. Ancrages sensoriels négatifs : Si un apprentissage est imposé par la force ou la menace, le cerveau de l'enfant associe durablement le sujet (ex: les devoirs, les repas) à la douleur, cherchant ensuite à l'éviter systématiquement.

3. Les trois leviers de la coercition : La peur (menaces), la honte (moqueries, culpabilisation) et le rejet (mise à distance) sont les outils d'une éducation qui sacrifie la confiance au profit d'un résultat immédiat et fragile.

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IV. Application Pratique et Autonomie

La gestion de l'erreur (Clé n°1 et 3)

L'erreur est une étape biologique du développement.

• Validation de l'apprentissage : On ne sait vraiment faire une chose que lorsqu'on a expérimenté le fait de ne pas savoir la faire.

• Posturale parentale : Accueillir l'erreur positivement (« C'est bien, tu es en train d'apprendre ») renforce la confiance.

Sanctionner l'erreur stoppe le processus naturel de recherche et de correction.

Le cas des règles sociales (Exemple des repas)

Le webinaire illustre la méthode à travers l'exemple d'un enfant de 9 ans préférant manger avec les doigts.

• L'adulte n'a pas réponse à tout : Si l'enfant a les informations (l'exemple des parents utilisant des couverts) mais choisit de faire autrement, il exerce ses habiletés sociales.

• Contexte vs Obéissance : Forcer l'usage des couverts chez les proches crée un rejet de la relation (peur de retourner chez les grands-parents).

Dudek suggère de faire confiance à l'enfant : si l'exemple est donné, il saura s'adapter en société par imitation, comme il le fait déjà à la cantine.

La perfection parentale

La violence éducative surgit souvent lorsque le parent est lui-même soumis à des injonctions de perfection ou de stress (ex: peur d'être en retard).

L'urgence sociale (horaires) prend alors le pas sur la relation. La méthode suggère de prioriser le lien : il est moins grave d'être en retard que de briser la sécurité émotionnelle de l'enfant par une crise de colère.

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V. Enjeux Sociétaux et Institutionnels

• L'entrée précoce à l'école : Le sevrage naturel chez les primates humains se situe vers 5 ans (entre 2,5 et 7,5 ans).

Envoyer des enfants non sevrés à l'école dès 3 ans génère un stress de séparation massif qui peut placer l'enfant en état de « sidération » ou de veille prolongée, ralentissant les apprentissages sociaux.

• Conditionnement et déconstruction : Environ 60 % de la population revendique encore le droit à la violence éducative, tandis que seuls 20 % conscientisent une approche non violente.

Pour ces derniers, le défi majeur est de déconstruire leurs propres automatismes coercitifs hérités de leur enfance.

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Citations Clés

« Efficace, ça veut juste dire que ça marche. Ce n'est pas un jugement de valeur, c'est un constat. »

« La bienveillance n'est pas un but en soi, c'est un moyen. C'est le moyen d'être efficace et d'obtenir une meilleure transmission. »

« Aucune violence n'est éducative. Absolument aucune. »

« Ce qui va partir à la poubelle en premier [avec la punition], c'est la confiance, c'est la relation. »

« Une erreur, c'est une étape du développement des apprentissages. »

Briefing : La Sophrologie Ludique et le Renforcement du Lien Parent-Enfant

Synthèse de la session "Instant Parent" avec Claire Lise de Zerbi

Ce document de synthèse analyse les interventions de Claire Lise de Zerbi, sophrologue et chargée de mission, concernant la pratique de la sophrologie ludique. Il explore comment cette discipline, adaptée aux enfants et aux adolescents, constitue un levier pour le développement personnel, la réussite éducative et la consolidation des liens affectifs au sein de la famille et de la société.

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Résumé Exécutif

La sophrologie ludique est une adaptation de la sophrologie classique destinée aux enfants (prioritairement de 3 à 11 ans) et aux adolescents.

Elle se distingue par une approche pédagogique fondée sur le jeu, l'imaginaire et l'interaction directe entre le parent et l'enfant.

Points clés à retenir :

• Objectif central : Développer une conscience accrue du corps, de l'esprit et des émotions pour favoriser l'épanouissement et l'estime de soi.

• Le binôme parent-enfant : Contrairement à une posture d'observation, le parent est un acteur à part entière de la séance, créant un univers de complicité et de confiance mutuelle.

• Applications multiples : La pratique s'étend du cadre familial aux milieux scolaires et aux quartiers prioritaires, visant à améliorer le "vivre ensemble" et à démocratiser l'accès au bien-être.

• Posture de non-jugement : L'absence d'attente de résultat ou de "bonne réponse" permet de lever les pressions sociales et scolaires, particulièrement chez les adolescents.

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1. Fondements et Méthodologie de la Sophrologie Ludique

Définition et approche

La sophrologie ludique est décrite comme une succession progressive d'activités mettant en jeu le corps et la sensibilité. Elle s'articule autour de deux facettes du "monde intérieur" :

1. La prise de conscience du corps : Habiter son corps et comprendre sa motricité en mouvement.

2. La gestion des émotions : Ressentir, identifier et poser des mots sur ses états internes.

Structure d'une séance type

Une séance ne suit pas un schéma rigide mais s'adapte à la "matière vivante" apportée par les participants. Elle comprend généralement :

• Un rituel d'accueil : Présentation et échanges sur les événements récents.

• L'alternance activité/repos : Des phases de mouvement intense (jeux de rôles, mimes) suivies de moments de calme.

• La "pause réflexive" : Un temps d'introspection et de verbalisation pour analyser ce qui a été fait, ressenti et pensé.

• La relaxation profonde : Utilisation de supports sensoriels (musique, plumes, foulards) pour la détente et le contact physique.

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2. Le Rôle Pivot du Parent dans l'Atelier

L'une des spécificités de cette approche est l'implication totale du parent. L'atelier transforme la dynamique habituelle :

• Participation active : Le parent joue, mime des animaux et adopte des postures rigolotes, ce qui stimule et amuse l'enfant.

• Inversion des rôles : Dans les exercices de souplesse ou de créativité, l'enfant est souvent plus performant que l'adulte, ce qui valorise ses capacités.

• Création d'un univers commun : L'expérience partagée renforce la complicité et la confiance. Le regard valorisant du parent est essentiel pour la constitution de l'estime de soi de l'enfant.

• Espace de liberté : Pour les familles nombreuses, c'est un moment privilégié où l'enfant bénéficie de l'attention exclusive de son parent.

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3. Objectifs et Compétences Développées

La sophrologie ludique s'aligne sur les compétences psychosociales référencées par l'OMS.

| Domaine | Objectifs Spécifiques | | --- | --- | | Conscience corporelle | Passer de "avoir un corps" à "être ce corps" ; habiter son corps consciemment. | | Imaginaire et Créativité | Développer la pensée symbolique via des images mentales (ex: marcher sur le feu, imaginer être un poisson). | | Gestion Émotionnelle | Identifier les sensations liées aux émotions ; apprendre à canaliser les débordements. | | Valeurs et Citoyenneté | Explorer des thèmes comme la justice, l'amitié et la coopération à travers des contes et fables. | | Estime de Soi | Valoriser la parole et le vécu sans jugement ; réduire l'anxiété par le renforcement de la confiance. |

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4. Applications Scolaires et Sociales

En milieu scolaire

Les enseignants sollicitent ces interventions car un climat de classe serein est un préalable nécessaire aux apprentissages fondamentaux (mathématiques, français). La sophrologie aide à :

• Développer l'empathie et le respect mutuel.

• Réduire l'agitation par la ritualisation et le ralentissement du geste.

• Favoriser le "vivre ensemble".

Dans les quartiers prioritaires

La proposition d'ateliers dans les centres sociaux vise à :

• Démocratiser la pratique : Rendre accessible une technique souvent coûteuse.

• Créer du lien social : Permettre à des parents de cultures différentes de partager des problématiques communes et de sortir de l'isolement.

• Soutien à la parentalité : Reconnaître et accepter les forces et les faiblesses de chacun dans sa fonction parentale.

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5. Spécificités pour les Adolescents

Chez les adolescents, la pratique diffère car le regard de l'autre devient un enjeu majeur.

• Format : Ateliers en petits groupes (maximum 8 élèves) basés sur le volontariat.

• Besoin de décompression : Les adolescents utilisent la sophrologie comme une "bulle" pour échapper aux pressions multiples (familiales, sociales, scolaires).

• Absence de performance : Il n'y a pas de "mauvaise séance". L'acceptation de ses propres pensées parasites est considérée comme une réussite.

• Défis : La verbalisation des émotions est souvent plus complexe pour ce public que pour les jeunes enfants, surtout si la culture familiale n'y prédispose pas.

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6. Conseils Pratiques pour une Mise en Œuvre à Domicile

L'intervenante souligne que la sophrologie est avant tout une posture plutôt qu'une simple mallette d'outils. Elle propose des pistes pour intégrer cette conscience au quotidien :

1. Exploiter le quotidien : Utiliser des activités existantes (cuisine, lecture, massage) comme supports de pleine conscience.

2. Verbaliser les sensations : Lors d'une activité (ex: faire un gâteau), poser des mots sur le toucher, les émotions et le plaisir partagé.

3. Apprivoiser la respiration : Apprendre à l'enfant à situer sa respiration (narines, gorge, ventre) pour en faire une alliée contre le stress.

4. Ritualiser : Instauration d'un moment qualitatif hebdomadaire dédié à l'attention soutenue et à l'échange, loin de l'urgence du quotidien.

5. Horizontalité : L'adulte doit accepter de se "mettre à nu" et de partager ses propres ressentis pour encourager l'enfant.

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Conclusion

La sophrologie ludique se présente comme un "moment de liberté" sans pression de résultat.

En permettant de "plonger en soi" et de redécouvrir ses ressources internes, elle offre aux enfants et aux parents les clés d'une meilleure connaissance de soi et d'une relation plus harmonieuse, ancrée dans le moment présent et l'acceptation de la complexité humaine.

Comprendre et Accompagner l'Adolescence : Analyse de la Crise et des Signes d'Alerte

Ce document de synthèse s'appuie sur l'expertise de Sophie Ettori, psychologue clinicienne à la Maison des Adolescents de Porto-Vecchio, pour explorer les mécanismes de l'adolescence, identifier les signes de souffrance psychique et définir les modalités d'accompagnement optimales par les parents et les professionnels.

Synthèse opérationnelle

L'adolescence est un processus dynamique de "l'entre-deux", une transition de 10 à 15 ans entre l'enfance et l'âge adulte.

Elle se caractérise par un bouleversement biologique et neurologique majeur : le cerveau adolescent, mature à 80 %, possède un système émotionnel (limbique) suractivé tandis que ses capacités de régulation (lobes frontaux) sont encore immatures.

Points clés à retenir :

• La "Crise" est un processus sain : L'opposition et la recherche d'identité sont nécessaires pour permettre la séparation d'avec les parents.

• Santé mentale : Environ 15 % des adolescents présentent un trouble psychique (soit 4 élèves par classe de 28).

• Signes d'alerte : Une irritabilité constante ou une colère persistante peuvent masquer une dépression.

• Réseaux sociaux : Ils constituent de nouveaux espaces de socialisation (le "skate park" numérique), mais peuvent exacerber des troubles préexistants, notamment alimentaires.

• Intervention précoce : Une prise en charge rapide, notamment pour les troubles psychotiques, améliore drastiquement le pronostic de vie sociale et professionnelle à long terme.

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1. Les mécanismes de la mutation adolescente

Un bouleversement neurologique et biologique

L'adolescence n'est pas qu'une construction sociale, c'est une réalité physiologique. Le cerveau subit une transformation radicale :

• La métaphore de la Ferrari : Le cerveau adolescent est comparé à "une Ferrari sans freins".

Le moteur (le système limbique, siège des émotions et de la mémoire) gronde à plein régime, tandis que les freins (les lobes frontaux et pariétaux, responsables de la logique et de la temporisation) sont encore en développement.

• Efficacité des connexions : On observe une augmentation de la substance blanche (myélinisation), ce qui accélère la transmission de l'information. C'est le passage du "56k à la fibre".

• L'élagage synaptique : Le cerveau devient plus performant mais plus sélectif, délaissant certains centres d'intérêt pour en privilégier d'autres, nécessaires à la survie et à l'autonomie.

Les finalités psychologiques

Le processus adolescent vise deux objectifs majeurs :

1. La constitution de l'identité : Une recherche qui peut être plurielle et transitoire.

2. La séparation-individuation : L'adolescent doit quitter l'espace parental pour écrire sa propre histoire. Cela passe souvent par la transgression (du latin transgreddi : traverser, franchir).

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2. Identifier les signes de basculement

Il est parfois complexe de distinguer une crise "normale" d'une souffrance réelle, car l'adolescent masque souvent son mal-être derrière un "masque de normalité".

Signes de vigilance pour l'entourage

| Type de comportement | Manifestations normales (Crise saine) | Signes d'alerte (Souffrance) | | --- | --- | --- | | Émotions | Labilité émotionnelle, fleur de peau. | Irritabilité constante, colère incontrôlable, tristesse profonde. | | Social | Besoin accru d'intimité, retrait dans la chambre. | Repli sur soi total, perte d'intérêt pour les amis et les plaisirs (anhédonie). | | Opposition | Changement de style (vêtements noirs), opposition verbale. | Mises en danger réelles, conduites à risques extrêmes. | | Alimentation | Préoccupations esthétiques passagères. | Perte/prise de poids rapide, rituels restrictifs, contrôle excessif. |

Les troubles de santé mentale

• Dépression : Chez l'adolescent, elle ne ressemble pas toujours au tableau clinique de l'adulte et peut se manifester uniquement par une agressivité permanente.

• Troubles du Comportement Alimentaire (TCA) : Souvent déclenchés par un régime banal, ils peuvent rapidement devenir graves et nécessitent un double suivi (nutritionnel et psychologique).

• Psychoses (Schizophrenie) : Elles émergent généralement entre 15 et 25 ans. Les premiers signes sont souvent ténus : anxiété forte, discours décousu, bizarreries dans les centres d'intérêt ou perte de contact avec la réalité.

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3. L'univers numérique : Opportunités et Risques

Le rapport à l'écran est un prolongement identitaire ("exposer, c'est exister").

• Aspects positifs : Les serveurs (type Discord) ou forums spécialisés permettent une socialisation par intérêt (gaming, musique) hors du regard parental. Pour certains adolescents, c'est un refuge salvateur qui facilite la sociabilisation.

• Aspects négatifs :

◦ Consommation vide : Le "scrollage compulsif" sur TikTok peut nuire au potentiel de l'adolescent par surstimulation immédiate.  

◦ Désinformation : Les adolescents suivent des influenceurs généralistes dont les conseils en santé (santé mentale, nutrition) sont souvent non sourcés ou commerciaux.  

◦ Renforcement des troubles : Les réseaux peuvent enfermer un adolescent fragile dans des communautés valorisant des comportements pathologiques (notamment pour les TCA).

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4. Accompagnement et Ressources

Le rôle des parents

La crise d'adolescence est aussi une "crise des parents" qui doivent accepter la perte de l'enfant idéal pour découvrir l'adulte en devenir.

• Communication "élastique" : Le cadre doit être souple, fondé sur une adaptation perpétuelle plutôt que sur une rigidité aveugle.

• Préparation précoce : L'habitude de communiquer doit être instaurée dès l'enfance pour que le terrain soit prêt au moment de la tempête adolescente.

• Intérêt pour leur monde : Participer à leurs jeux (Minecraft, Mario Kart) construit une relation de confiance et légitime leur univers.

Les structures d'aide

En cas de doute, il est impératif de consulter, même pour une difficulté qui semble mineure (prévention).

| Structure | Caractéristiques | | --- | --- | | Maison des Adolescents (MDA) | Accueil des 11-25 ans. Gratuit, anonyme, confidentiel. Aucun accord parental requis, ce qui facilite l'accès pour les jeunes en rébellion. | | Centres Médico-Psychologiques (CMP) | Soins gratuits, axés sur le suivi psychiatrique et pédopsychiatrique sur le long terme. | | Milieu scolaire | Infirmières scolaires, assistants sociaux et psychologues de l'Éducation nationale sont des relais de proximité essentiels. |

Note sur le secret professionnel : En Maison des Adolescents, le secret est la règle.

Il n'est levé qu'en cas de danger grave pour l'adolescent ou de révélations de violences subies (obligation de signalement pour protéger le mineur).

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DOI: 10.1007/s00210-025-04893-3

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DOI: 10.1007/s00210-025-04893-3

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DOI: 10.1007/s00210-025-04893-3

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DOI: 10.1007/s00210-025-04893-3

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DOI: 10.1007/s00210-025-04893-3

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DOI: 10.64898/2026.01.19.699861

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DOI: 10.1111/bph.70321

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What is this?

DOI: 10.1038/s41586-025-10003-3

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DOI: 10.1038/s41586-025-10003-3

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DOI: 10.1038/s41586-025-10003-3

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DOI: 10.1038/s41586-025-10003-3

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What is this?

I find this to be super cool, since I feel that seeing archives in person is actually amazing, since your seeing everything in real life.

I find Malala to be a huge inspiration to me, and I feel that she is strong for what she has done and I look up to her.

I feel that the setting gives the reader a better imagination of what's going on in my opinion.

I can agree with this, since I feel that people have their own preferences to characters.

I can relate with this, since when I was writing my college essay, I felt that I was very comfortable with others reading that essay.

This shows that anyone can share their story about learning literacy in the DALN. I like that it includes all kinds of people and experiences, making it a place to learn from others and see different ways people read, write, and communicate.

This part explains that every story has a problem that drives the action and a solution that finishes it. It helps me understand how to make my own stories interesting by showing challenges and how they get solved.

I like how this part shows that good storytelling uses our senses to make the story feel real. It helps us picture and experience what’s happening, not just read about it. I can see how adding these details can make my own writing more engaging.

For me this means we speak and write differently depending on where we are or who we are talking to. In school and work, we’ll learn the right way to use language in each situation. It shows that knowing when to use different styles is part of becoming a good speaker.

This part shows that literacy has changed over time. Now it means being able to communicate well in many ways, not just through books and writing. This connects to how we learn and share information today.

Reviewer #1 (Public review):

Summary:

The authors develop a multivariate extension of SEM models incorporating transmitted and non-transmitted polygenic scores to disentangle genetic and environmental intergenerational effects across multiple traits. Their goal is to enable unbiased estimation of cross-trait vertical transmission, genetic nurture, gene-environment covariance, and assortative mating within a single coherent framework. By formally deriving multivariate path-tracing rules and validating the model through simulation, they show that ignoring cross-trait structure can severely bias both cross- and within-trait estimates. The proposed method provides a principled tool for studying complex gene-environment interplay in family genomic data.

Strengths:

It has become apparent in recent years that multivariate processes play an important role in genetic effects that are studied (e.g., Border et al., 2022), and these processes can affect the interpretation of these studies. This paper develops a comprehensive framework for polygenic score studies using trio data. Their model allows for assortative mating, vertical transmission, gene-environment correlation, and genetic nurture. Their study makes it clear that within-trait and cross-trait influences are important considerations. While their exposition and simulation focus on a bivariate model, the authors point out that their approach can be easily extended to higher-dimensional applications.

Weaknesses:

(1) My primary concern is that the paper is very difficult to follow. Perhaps this is inevitable for a model as complicated as this one. Admittedly, I have limited experience working with SEMs, so that might be partly why I really struggled with this paper, but I ultimately still have many questions about how to interpret many aspects of the path diagram, even after spending a considerable amount of time with it. Below, I will try to point out the areas where I got confused (and some where I still am confused). If the authors choose to revise the paper, clarifying some of these points would substantially broaden the paper's accessibility and impact.

(1a) Figure 1 contains a large number of paths and variable names, and it is not always apparent which variables correspond to which paths. For example, at a first glance, the "k + g_c" term next to the "T_m" box could arguably correspond to any of the four paths near it. Disentangling this requires finding other, more reasonable variables for the other lines and sifting through the 3 pages of tables describing the elements of the figure.

(1b) More hand-holding, describing the different parameters in the model, would help readers who don't have experience with SEMs. For example, many parameters show up several times (e.g., delta, a, g_c, i_c, w) and describing what these parameters are and why they show up several times would help. Some of this information is found in the tables (e.g., "Note: [N]T denotes either NT or T, as both share the same matrix content"), though I don't believe it is explained what it means to "share the same matrix content."

(1c) Relatedly, descriptions of the path tracing were very confusing to me. I was relieved to see the example on the bottom of page 10 and top of page 11, but then as I tried to follow the example, I was again confused. Because multiple paths have the same labels, I was not able to follow along which exact path from Figure 1 corresponded to the elements of the sum that made up Theta_{Tm}. Also, based on my understanding of the path-tracing rules described, some paths seemed to be missing. After a while, I think I decided that these paths were captured by the (1/2)*w term since that term didn't seem to be represented by any particular path in the figure, but I'm still not confident I'm right. In this example, rather than referring to things like "four paths through the increased genetic covariance from AM", it might be useful to identify the exact paths represented by indicating the nodes those paths go through. If there aren't space constraints, the authors might even consider adding a figure which just contains the relevant paths for the example

(1d) The paper has many acronyms and variable names that are defined early in the paper and used throughout. Generally, I would limit acronyms wherever possible in a setting like this, where readers are not necessarily specialists. For the variables, while the definitions are technically found in the paper, it would be useful to readers if they were reminded what the variables stood for when they are referred to later, especially if that particular variable hasn't been mentioned for a while. As I read, I found myself constantly having to scroll back up to the several pages of figures and tables to remind myself of what certain variables meant. Then I would have to find where I was again. It really made a dense paper even harder to follow.

(1e) Relatedly, on page 13, the authors make reference to a parameter eta, and I don't see it in Figure 1 or any of the tables. What is that parameter?

(2) This point may be related to me misunderstanding the model, but if LT_p represent the actual genetic factors for the two traits for variants that are transmitted to the child, and T_p represents the PGS of for transmitted variants, shouldn't their be a unidirectional arrow from LT_p to T_p (since the genetic factor affects the PGS and not the other way around) and shouldn't there be no arrow from T_p to Y_0 (since the entire effect of the transmitted SNPs is represented by the arrow from LT_p to Y_0)? If I'm mistaken here, it would be useful to explain why these arrows are necessary.

(3) Some explanation of how the interpretation of the coefficients differs in a univariate model versus a bivariate model would be useful. For example, in a univariate model, the delta parameter represents the "direct effect" of the PGI on the offspring's outcome (roughly corresponding to a regression of the offspring's outcome onto the offspring's PGI and each parent's PGI). Does it have the same interpretation in the bivariate case, or is it more closely related to a regression of one of the outcomes onto the PGIs for both traits?

(4) It appears from the model that the authors are assuming away population stratification since the path coefficient between T_m and T_m is delta (the same as the path coefficient between T_m and Y_0). Similarly, I believe the effect of NT_m on Y_0 only has a genetic nurture interpretation if there is no population stratification. Some discussion of this would be valuable.

References:

Border, R., Athanasiadis, G., Buil, A., Schork, AJ, Cai, N., Young, AI, ... & Zaitlen, N.A. (2022). Cross-trait assortative mating is widespread and inflates genetic correlation estimates. Science , 378 (6621), 754-761.

Reviewer #2 (Public review):

(1) Summary and overall comments:

This is an impressive and carefully executed methodological paper developing an SEM framework with substantial potential. The manuscript is generally very well written, and I particularly appreciated the pedagogical approach: the authors guide the reader step by step through a highly complex model, with detailed explanations of the structure and the use of path tracing rules. While this comes at the cost of length, I think the effort is largely justified given the technical audience and the novelty of the contribution.

The proposed SEM aims to estimate cross-trait indirect genetic effects and assortative mating, using genotype and phenotype data from both parents and one offspring, and builds on the framework introduced by Balbona et al. While I see the potential interest of the model, it is still a bit unclear in which conditions I could use it in practice. However, this paper made a clear argument for the need for cross-traits models, which changed my mind on the topic (I would have accommodated myself with univariate models and only interpreted in the light of likely pleiotropy, but I am now excited by the potential to actually disentangle cross-traits effects).

The paper is written in a way that makes me trust the authors' thoroughness and care, even when I do not fully understand every step of the model. I want to stress that I am probably not well-positioned to identify technical errors in the implementation. My comments should therefore be interpreted primarily from the perspective of a potential user of the method: I focus on what I understand, what I do not, and where I see (or fail to see) the practical benefits.

For transparency, here is some context on my background. I have strong familiarity with the theoretical concepts involved (e.g., genetic nurture, gene-environment covariance, dynastic effects), and I have worked on those with PGS regressions and family-based comparison designs. My experience with SEM is limited to relatively simple models, and I have never used OpenMx. Reading this paper was therefore quite demanding for me, although still a better experience than many similarly technical papers, precisely because of the authors' clear effort to explain the model in detail. That said, keeping track of all moving parts in such a complex framework was difficult, and some components remain obscure to me.

(2) Length, structure, and clarity:

I do not object in principle to the length of the paper. This is specialized work, aimed at a relatively narrow audience, and the pedagogical effort is valuable. However, I think the manuscript would benefit from a clearer and earlier high-level overview of the model and its requirements. I doubt that most readers can realistically "just skim" the paper, and without an early hook clearly stating what is estimated and what data are required, some readers may disengage.

In particular, I would suggest clarifying early on:

• What exactly is estimated?

For example, in the Discussion, the first two paragraphs seem to suggest slightly different sets of estimands: "estimate the effects of both within- and cross-trait AM, genetic nurture, VT, G-E covariance, and direct genetic effects." versus "model provides unbiased estimates of direct genetic effects (a and δ), VT effects (f), genetic nurture effects (ϕ and ρ), G-E covariance w and v, AM effects (μ), and other parameters when its assumptions are met." A concise and consistent summary of parameters would be helpful.

• What data are strictly required?

At several points, I thought that phenotypes for both parents were required, but later in the Discussion, the authors consider scenarios where parental phenotypes are unavailable. I found this confusing and would appreciate a clearer statement of what is required, what is optional, and what changes when data are missing.

• Which parameters must be fixed by assumption, rather than estimated from the data?

Relatedly, in the Discussion, the authors mention the possibility of adding an additional latent shared environmental factor across generations. It would help to clearly distinguish: - the baseline model, - the model actually tested in the paper, and - possible extensions.

Making these distinctions explicit would improve accessibility.

This connects to a broader concern I had when reading Balbona et al. (2021): at first glance, the model seemed readily applicable to commonly available data, but in practice, this was not the case. I wondered whether something similar applies here. A clear statement of what data structures realistically allow the model to be fitted would be very useful.

I found the "Suggested approach for fitting the multivariate SEM-PGS model" in the Supplementary Information particularly helpful and interesting. I strongly encourage highlighting this more explicitly in the main manuscript. If the authors want the method to be widely used, a tutorial or at least a detailed README in the GitHub repository would greatly improve accessibility.

Finally, while the pedagogical repetition can be helpful, there were moments where it felt counterproductive. Some concepts are reintroduced several times with slightly different terminology, which occasionally made me question whether I had misunderstood something earlier. Streamlining some explanations and moving more material to the SI could improve clarity without sacrificing rigor.

(3) Latent genetic score (LGS) and the a parameter

I struggled to understand the role of the latent genetic score (LGS), and I think this aspect could be explained more clearly. In particular, why is this latent genetic factor necessary? Is it possible to run the model without it?

My initial intuition was that the LGS represents the "true" underlying genetic liability, with the PGS being a noisy proxy. Under that interpretation, I expected the i matrix to function as an attenuation factor. However, i is interpreted as assortative-mating-induced correlation, which suggests that my intuition is incorrect. Or should the parameter be interpreted as an attenuation factor?

Relatedly, in the simulation section, the authors mention simulating both PGS and LGS, which confused me because the LGS is not a measured variable. I did not fully understand the logic behind this simulation setup.

Finally, I was unsure whether the values simulated for parameter a in Figures 8-9 are higher than what would typically be expected given the current literature, though this uncertainty may reflect my incomplete understanding of a itself. I appreciated the Model assumptions section of the discussion, and I wonder if this should not be discussed earlier.

(4) Vertical transmission versus genetic nurture

I am not sure I fully understand the distinction between vertical transmission (VT) and genetic nurture as defined in this paper. From the Introduction, I initially had the impression that these concepts were used almost interchangeably, but Table 3 suggests they are distinct.

Relatedly:

• Why are ϕ and ρ not represented in the path diagram?

• Are these parameters estimated in the model?

The authors also mention that these parameters target different estimands compared to other approaches. It would be helpful to elaborate on this point. Relatedly, where would the authors expect dynastic effects to appear in this framework?

(5) Univariate model and misspecification

In the simulations where a univariate model is fitted to data generated under a true bivariate scenario, I have a few clarification questions.

What is the univariate model used (e.g., Table 5)? Is it the same as the model described in Balbona et al. (2025)? Does it include an LGS?

If the genetic correlation in the founder generation is set to zero, does this imply that all pleiotropy arises through assortative mating? If so, is this a realistic mechanism, and does it meaningfully affect the interpretation of the results?

(6) Simulations

Overall, I found the simulations satisfying to read; they largely test exactly the kinds of issues I would want them to test, and the rationale for these tests is clear.

That said, I was confused by the notation Σ and did not fully understand what it represents.

In the Discussion, the authors mention testing the misspecification of social versus genetic homogamy, but I do not recall this being explicitly described in the simulation section. They also mention this issue in the SI ("Suggested approach for fitting..."). I think it would be very helpful to include an example illustrating this form of misspecification.

(7) Cross-trait specific limitations

I am wondering - and I don't think this is addressed - what is the impact of the difference in the noisiness and the heritability of the traits used for this multivariate analysis?

Using the example, the authors mention of BMI and EA, one could think that these two traits have different levels of noise (maybe BMI is self-reported and EA comes from a registry), and similarly for the GWAS of these traits, let's say one GWAS is less powered than the other ones. Does it matter? Should I select the traits I look at carefully in function of these criteria? Should I interpret the estimates differently if one GWAS is more powered than the other one?

Reviewer #2 (Public review):

Summary:

This manuscript by Choubani et al presents a technically strong analysis of A/B compartment dynamics across interphase using cell-cycle-resolved Hi-C. By combining the elegant Fucci-based staging system with in situ Hi-C, the authors achieve unusually fine temporal resolution across G1, S, and G2, particularly within the short G1 phase of mESCs. The central finding that A/B compartment strength increases abruptly at the G1/S transition, stabilizes during S phase, and subsequently weakens toward G2 challenges the prevailing view that compartmentalization strengthens monotonically throughout interphase. The authors further propose that this "compartment maturation" is triggered by S-phase entry but occurs independently of active DNA synthesis, and that it involves a consolidation and large-scale reorganization of A-compartment domains.

Strengths:

Overall, this is a thoughtfully executed study that will be of broad interest to the 3D genome community. The data are of high quality, and the analyses are extensive, albeit not completely novel. In particular, previous work (Nagano et al 2017 and Zhang et al 2019) has shown that compartments are re-established after mitosis and strengthened during early interphase, and single-cell Hi-C studies have reported changes in compartment association across S phase. In particular, Nagano et al show that DNA replication correlates with a build-up of compartments, similar to what is presented here, with the authors' conclusion that compartment strength peaks in early S. The idea that it weakens toward G2, rather than continuing to strengthen, appears to be novel and differs from the prevailing framing in the literature.

Weaknesses:

That said, several aspects of the conceptual framing and interpretation would also benefit from further clarification, and the mechanistic interpretation of the reported compartment dynamics requires more careful positioning relative to established models of genome organization. Specific concerns are outlined below:

(1) One of the major conclusions of the study is that compartment maturation does not require ongoing DNA replication. However, the interpretation would benefit from more precise wording. Thymidine arrest still permits licensing, replisome assembly, and other S-phase-associated chromatin changes upstream of bulk DNA synthesis. Therefore, their data, as presented, demonstrate independence from DNA synthesis per se, but not necessarily from the broader replication program. Please clarify this distinction in the text and interpretations throughout the manuscript.

(2) A major conceptual issue that is not addressed at all is the well-established anti-correlation between cohesin-mediated loop extrusion and A/B compartmentalization. Numerous studies have shown that loss of cohesin or reduced loop extrusion leads to stronger compartment signals, whereas increased cohesin residence or enhanced extrusion weakens compartmentalization. Given this framework, an obvious alternative explanation for the authors' observations is that the abrupt increase in compartment strength at G1/S, and its decline toward G2, could reflect cell-cycle-dependent modulation of cohesin activity rather than a compartment-intrinsic "maturation" program.

The manuscript does not explicitly consider this possibility, nor does it examine loop extrusion-related features (such as loop strength, insulation, or stripe patterns) across the same cell-cycle stages. Without discussing or analyzing this widely accepted model, it is difficult to distinguish whether the reported compartment dynamics represent a novel architectural mechanism or an indirect consequence of known changes in extrusion behavior during the cell cycle. I strongly encourage the authors to analyze their data to determine if they observe anti-correlated loop changes at the same time they observe compartment changes. Ideally, the authors would remove loop extrusion during interphase using well-established cohesin degrons available in mESCs and determine if the relative differences in compartment dynamics persist.

(3) The proposed "peninsula-like" A-domain structures are inferred from ensemble Hi-C data and polymer modeling, rather than directly observed physical conformations. That is, single-cell imaging data clearly have shown that Hi-C (especially ensemble Hi-C) cannot uniquely specify physical conformations and that different underlying structures can produce similar contact patterns. The "peninsula" language, as written, risks being interpreted as a literal structural model rather than a conceptual visualization. Instead of risking this as just another nuanced Hi-C feature in the field, the authors could strengthen the manuscript by either (i) explicitly framing the peninsula model as a heuristic description of contact redistribution rather than a definitive physical architecture, or (ii) discussing alternative structural scenarios that could give rise to similar Hi-C patterns. Clarifying this distinction would improve the rigor and help readers better understand what aspects of A-compartment consolidation are directly supported by the data versus model-based extrapolations. For example, it would be useful to clarify whether the observed increase in long-range A-A contacts reflects spatial extension of internal A regions, changes in loop extrusion dynamics, increased compartment mixing within the A state, or population-averaged heterogeneity across alleles.

(4) The extension of the analysis to additional cell types using HiRES single-cell data is a valuable addition and supports the idea that compartment maturation is not unique to mESCs. However, the limitations of these data, in particular, the limited phase resolution, in addition to the pseudo-bulk aggregation and variable coverage, should be emphasized more clearly in the main text. Framing these results as evidence for conservation in principle, rather than definitive proof of identical dynamics across tissues, would be a more appropriate framing.

Reviewer #1 (Public review):

Giordano et al. demonstrate that yeast cells expressing separated N- and C-terminal regions of Tfb3 are viable and grow well. Using this creative and powerful tool, the authors effectively uncouple CTD Ser5 phosphorylation at promoters and assess its impact on transcription. This strategy is complementary to previous approaches, such as Kin28 depletion or the use of CDK7 inhibitors. The results are largely consistent with earlier studies, reinforcing the importance of the Tfb3 linkage in mediating CTD Ser5 phosphorylation at promoters and subsequent transcription.

Notably, the authors also observe effects attributable to the Tfb3 linker itself, beyond its role as a simple physical connection between the N- and C-terminal domains. These findings provide functional insight into the Tfb3 linker, which had previously been observed in structural studies but lacked clear functional relevance. Overall, I am very positive about this manuscript and offer a few minor comments below that may help to further strengthen the study.

(1) Page 4

PIC structures show the linker emerging from the N-terminal domain as a long alpha-helix running along the interface between the two ATPase subunits, followed by a turn and a short stretch of helix just N-terminal to a disordered region that connects to the C-terminal region (see schematic in Figure 1A).

The linker helix was only observed in the poised PIC (Abril-Garrido et al., 2023), not in other fully-engaged PIC structures.

(2) Page 8

Recent structures (reviewed in (Yu et al., 2023)) show that the Kinase Module would block interactions between the Core Module and other NER factors. Therefore, TFIIH either enters into the NER complex as the free Core Module, or the Kinase Module must dissociate soon after.

To my knowledge, this is still controversial in the NER field. I note the potential function of the kinase module is likely attributed to the N-terminal region of Tfb3 through its binding to Rad3. Because the yeast strains used in Figure 6 retain the N-terminal region of Tfb3, the UV sensitivity assay presented here is unlikely to directly address the contribution of the kinase module to NER.

(3) Page 11

Notably, release of the Tfb3 Linker contact also results in the long alpha-helix becoming disordered (Abril-Garrido et al., 2023), which could allow the kinase access to a far larger radius of area. This flexibility could help the kinase reach both proximal and distal repeats within the CTD, which can theoretically extend quite far from the RNApII body.

Although the kinase module was resolved at low resolution in all PIC-Mediator structures, these structural studies consistently reveal the same overall positioning of the kinase module on Mediator, indicating that its localization is constrained rather than variable. This observation suggests that the linker region may help position the kinase module at this specific site, likely through direct interactions with the PIC or Mediator. This idea is further supported by numerous cross-links between the linker region and Mediator (Robinson et al., 2016).

Reviewer #3 (Public review):

Summary:

Eukaryotic gene transcription requires a large assemblage of protein complexes that govern the molecular events required for RNA Polymerase II to produce mRNAs. One of these complexes, TFIIH, comprises two modules, one of which promotes DNA unwinding at promoters, while the other contains a kinase (Kin28 in yeast) that phosphorylates the repeated motif at the C-terminal domain (CTD) of the largest subunit of Pol II. Kin28 phosphorylation of Ser5 in the YSPTSPS motif of the CTD is normally highly localized at promoter regions, and marks the beginning of a cycle of phosphorylation events and accompanying protein association with the CTD during the transition from initiation to elongation.

The two modules of TFIIH are linked by Tfb3. Tfb3 consists of two globular regions, an N-terminal domain that contacts the Core module of TFIIH and a C-terminal domain that contacts the kinase module, connected by a linker. In this paper, Giordano et al. test the role of Tfb3 as a connector between the two modules of TFIIH in yeast. They show that while no or very slow growth occurs if only the C-terminal or N-terminal region of Tfb3 is present, near normal growth is observed when the two unlinked regions are expressed. Consistent with this result, the separate domains are shown to interact with the two distinct TFIIH modules. ChIP experiments show that the Core module of TFIIH maintains its localization at gene promoters when the Tfb3 domains are separated, while localization of the kinase module and of Ser5 phosphorylation on the CTD of Pol II is disrupted. Finally, the authors examine the effect of separating the Tfb3 domains on another function of TFIIH, namely nucleotide excision repair, and find little or no effect when only the N-terminal region of Tfb3 or the two unlinked domains are present.

Strengths:

Experiments involving expression of Tfb3 domains in yeast are well-controlled, and the data regarding viability, interaction of the separate Tfb3 domains with TFIIH modules, genome-wide localization of the TFIIH modules and of phosphorylated Ser5 CTDs, and of effects on NER, are convincing. The experiments are consistent with current models of TFIIH structure and function and support a model in which Tfb3 tethers the kinase module of TFIIH close to initiation sites to prevent its promiscuous action on elongating Pol II.

Weaknesses:

(1) The work is limited in scope and does not provide any major insights into the mechanism of transcription. One indication of this limitation is that in the Discussion, published structural and functional results on transcription are used to support the interpretations of the results here more than current results inform previous models or findings.

(2) The first described experiment, which purports to show that three kinases cannot function in place of Kin28 when tethered (by fusion) to Tfb3, is missing the crucial control of showing that Kin28 can support viability in the same context. This result also does not connect with the rest of the manuscript.

(3) Finally, the authors present the interesting and reasonable speculation that the TFIIH complex and connecting Tfb3 found in mammals and yeast may have evolved from an earlier state in which the two TFIIH subdomains were present as unconnected, distinct enzymes. This idea is supported by a single example from the literature (T. brucei). A more thorough evolutionary analysis could have tested this idea more rigorously.

TLDR 1) Glutamate is released and binds to both AMPA and NMDA receptor 2) AMPA receptor opens to allow entry of sodium. NMDA cannot open due to magnesium blockage. 3)Influx of sodium via AMPA receptor causes depolarisation which removes magnesium blockage 4)NMDA channel opens and sodium and calcium can enter 5)Influx of calcium results in activation of kinases that travel to nucleus, leading to upregulation of AMPA receptor in the post synaptic cell or increased glutamate released.

gồm 8 loại hiệu suất: 1. Loại bỏ async waterfalls 2. Bundle size optimization (Tối ưu hóa kích thước gói ) 3. Server-side performance (Hiệu suất phía máy chủ ) 4. Client-side data fetching (Tìm nạp dữ liệu phía máy khách) 5. Re-render optimization (Tối ưu hóa kết xuất lại ) 6. Rendering performance( Hiệu suất kết xuất ) 7. Advanced patterns (Mẫu nâng cao) 8. JavaScript performance (Hiệu suất JavaScript )

Reinforcing the 3 focuses of the information sciences: people, techonology and information

Reviewer #1: Evidentiary Rating: Potentially Informative

Written Review: The manuscript presents an ultradeep, untargeted wastewater metagenomic survey and makes several key claims about pathogen detection, viral seasonality, and the discovery of emerging viruses. Below, we evaluate these claims and the evidence provided. 1. Highly pathogenic viruses, including H5N1, are present in Missouri wastewater

The authors claim that highly pathogenic viruses like the H5N1 are escaping ordinary surveillance and being picked up by the authors’ ultradeep metagenomic surveillance, requires further scrutiny. In order to be sure that these viruses are really present in the sewage, it is not sufficient to present read counts or BLAST-based classifications alone. Notably, influenza H5N1 was detected only by the less conservative BLAST-based NVD pipeline, while the more stringent GOTTCHA2 method does not find it. 2. Ultradeep Untargeted approach can reveal many remerging and novel viruses

In order to show that it is the “ultra-deepness” that allows us to see new viruses, the authors should present rarefaction curves. However it is of course not a very controversial claim. One can point to other papers and to the general understanding of microbial and viral community sampling, where increased sequencing depth greatly improves the detection of low-abundance taxa.

We agree that the sequencing and wet lab procedure is indeed suited to finding novel viruses, but the bioinformatic workflow that the authors choose to implement, is not. Retaining only reads that share k-mers with known viruses in databases, will remove very novel viruses. Furthermore, we expect that the viral assemblies will be more fragmented, when all reads with unknown kmers are removed. 3. Many coronaviruses, rhino viruses and influenza show distinct and reproducible seasonality in the ultradeep metagenomes

The study analyses samples from a single site spanning a single year. In order to claim that the seasonality is “reproducible”, data from multiple years (several winters) or multiple cities would be useful. The different viruses do indeed seem to have different peaks and can thus be considered distinct from each other. We agree with the authors on this.

Additional comments: 1. It would be helpful if the authors presented their Kraken2 results in the supplementary material to see both classifiers side by side, as “unique in the GOTTCHA2 database” does not necessarily mean it is unique in the wastewater microbiome community, where many species can share sharing genomic regions. 2. We would recommend an alternative way of visualising the seasonal trends of the viruses as it is quite difficult to see it on the heatmaps. Lineplots for some specific genomes of interest could be helpful. 3. The comparison between metagenomic SARS-CoV-2 counts and PCR measurements shows only a modest correlation, which could also originate from the two workflows that process the samples in different ways. 4. The authors might consider treating the data compositionally (see CLR and ALR transformations), as recommended by Gloor et al1. If a lot of additional microbes are added to a sample, the relative abundance of everything else will go down. 1. Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome Datasets Are Compositional: And This Is Not Optional. Front. Microbiol. 8, (2017).

Reviewer #3: Evidentiary Rating: Strong

Written Review: The authors showcase an impressive ultra-deep sequencing effort and in the manuscript documenting their work address several central points and goals, such as to “test the feasibility of viral surveillance using unbiased metagenomic sequencing”; that their approach can detect novel viruses and is unbiased and scalable; the ability to detect low abundance viruses such as influenza A. Critical thoughts about these claims are developed in the following paragraphs.

In principle, the feasibility of the presented metagenomics approach is well demonstrated, but I think a clearer communication of what the authors’ notion of feasibility is exactly, and how well the experimental approach performs with regard to such feasibility goals would drive that point home even better. For example, most researchers don’t have resources for routine ultra-deep sequencing - could one sequence less deep and still achieve enough statistical power to detect low abundance viruses?

While the exact definition of terms like “scalable” in this manuscript remains vague, one might argue that the presented approach is, due to its cost, currently not scalable, and probably will not be so in the midterm, even if sequencing costs continue to drop. Whether the approach is scalable to more than one location or along other axes is not explicitly evaluated in the manuscript.

Similarly, the authors use “unbiased” but also use “minimally biased” throughout the manuscript. I would recommend a unified use and an explicit discussion of the mentioned minimal biases. Further, a RNA virus workflow is presented, without DNase treatment, implying that DNA viruses are equally represented - but the ability to capture different virus types is not addressed in the manuscript. Has the workflow been optimized to represent DNA and RNA viruses equally, or are DNA viruses in the library just a useful “side-effect”? This challenges the claim of being unbiased.

Similarly, the authors claim being able to detect “novel” viruses and list relying on a priori knowledge of genetic makeup as a disadvantage of probe-capture approaches, but clearly also rely on a priori knowledge by basing detection filters on public databases. Being more explicit about what “novel” means in their study would make the manuscript more comprehensive. Additionally, this also impacts the notion of “unbiased” (see above).

The authors validate SARS-CoV-2 detection with digital PCR. A similar validation approach for viruses which are “unexpected” and/or occur in low abundance would strengthen the claim of detecting epidemiological relevant virus surges. Especially the subtyping of influenza down to the genotype would benefit greatly from documenting how the authors came to that conclusion, for example with influenza virus segment coverage plots.

Viral detection is validated via digital PCR for SARS-CoV-2. I would appreciate more information about the capability of ultra-deep sequencing to detect viruses, by establishing e.g. a limit of detection. In that context, the absolute number of reads attributed to SARS-CoV-2 or other viruses (depicted in Fig 4 & 5) is unclear to me, since the denominator of the normalization is not explicitly stated, i.e. the number or fraction of reads mapped to viral contigs. This together with better documentation of assembly statistics would enhance the manuscript and provide a clearer picture about the method’s capability in detecting low abundance or unexpected viruses.

Overall, the manuscript is well written, explains methods in great detail and documents well this impressive approach of ultra-deep sequencing. I believe that an associated publication will greatly benefit the community. Publishing a negative finding such as rRNA removal not significantly improving viral detection is appreciated. Using SQL to query the large amounts of generated data is a great approach and shows the intention and ambition of building a routine surveillance pipeline.

An old friend of mine published their poetry and offered physical copies. I had asked about this because I still thought you had to order, for example, a thousand copies and then hope you would sell them all. It is wonderful that this technology has came to be for writers because now you don't have to worry about being left with an excessive amount of copies. This is especially good for people with small followings or who are new to self-publishing. I believe the more accessible things like this become, the more we can hear from voices who can't afford printing thousands of copies.

When I was a teen, a lot of people in my social circles wrote books on free websites. It was very nice, in my opinion. People would write books on their phone when they had free time, and would go home and edit them on a laptop or continue on their phone. During this time, I felt people's vocabulary got better. Although the popularization of this form of writing and publishing books might devalue physical books or purchasable e-books, this accessibility encouraged many to write. As a result of being able to write and access books without payment, people were reading and learning more.

Less than 5% seems like a really small number, but when put into comparison to just how many books have been written and released physically over time, it's actually really impressive that e-books have made a decent dent in the book market. Especially given the short amount of time e-books have been released.

I remember growing up when the tablet or iPad was released, the Kindle was completely forgotten. This might have just been in my community, but they were seen as foolish to buy since all they could do was read books. People would buy a tablet instead since it was able to do more than a Kindle. I do wonder if maybe a Kindle would encourage reading, because most people I knew who bought tablets were not reading on them.

Reliable, trustworthy, competent.

Honest, accountable, and trustworthy.

The outcome that I want to achieve is to directly portray to my boss that I have food poisoning and that it randomly came about. The relationship I want to have with my Boss is a professional relationship but also I would want him to get to know me on a personal level and how I am a hard worker and this project meant a lot to me. 3-5 adjectives I would want my receiver to describe me as is Formal, responsible, sympathetic, hard working, and effective communication

Many changes have been made since the Immigration and Nationality Act was passed in 1952. Over the decades different presidential administrations made big changes. The introduction of DACA made the initial recognition of the rights of undocumented children and gave them a possibility to avoid deportation and work legally.

tiene que verificar en cola de peristencia y cola de redis para que no se vuelva a ejecutar.

tenerlo en cuenta para usar un flujo de alertas para nosotros en n8n, capturando el evento.

3. website format and accessibility - good practice

youtube is a very simple and easy to navigate website. The search bar is accessible and easy to use/find. The other feature tabs are all laid out on the side (settings, subscriptions, history, account). There aren't links that redirect users to separate web pages, which can be confusing for some.

A magical genie is a human that gets trapped and it forced to give our 3 wishes to break there curse

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

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Reply to the reviewers

We are grateful to the Review Commons reviewers for their constructive feedback, which has significantly strengthened the manuscript. In response, we have performed additional experiments, revised and expanded multiple figures, incorporated new statistical and functional analyses, and carefully edited the text to improve clarity and precision. A detailed point-by-point response to all reviewer comments, together with a summary of revised figures, is provided.

To address the reviewers' suggestions, we have conducted additional experiments that are now incorporated into new figures, or we have added new images to several existing figures where appropriate.

For this reason, please note that all figures have been renumbered to improve clarity and facilitate cross-referencing throughout the text. As recommended by Referee #3, all figure legends have been thoroughly revised to reflect these updates and are now labeled following the standard A-Z panel format, enhancing readability and ensuring easier identification. In addition, all figure legends now include the sample size for each statistical analysis.

For clarity and ease of reference, we provide below a comprehensive list of all figures included in the revised version. Figures that have undergone modifications are underlined.

Figure 1____. The first spermatogenesis wave in prepuberal mice.

This figure now includes amplified images of representative spermatocytes and a summary schematic illustrating the timeline of spermatogenesis. In addition, it now presents the statistical analysis of spermatocyte quantification to support the visual data.

__Figure 2.____ Cilia emerge across all stages of prophase I in spermatocytes during the first spermatogenesis wave. __

The images of this figure remain unchanged from the original submission, but all the graphs present now the statistical analysis of spermatocyte quantification.

Figure 3. Ultrastructure and markers of prepuberal meiotic cilia.

This figure remains unchanged from the original submission; however, we have replaced the ARL3-labelled spermatocyte image (A) with one displaying a clearer and more representative signal.

__Figure 4. Testicular tissue presents spermatocyte cysts in prepuberal mice and adult humans. __

This figure remains unchanged from the original submission.

__Figure 5. Cilia and flagella dynamics are correlated during prepuberal meiosis. __

This figure remains unchanged from the original submission.

__Figure 6. Comparative proteomics identifies potential regulators of ciliogenesis and flagellogenesis. __

This figure remains unchanged from the original submission.

Figure 7.____ Deciliation induces persistence of DNA damage in meiosis.

This figure has been substantially revised and now includes additional experiments analyzing chloral hydrate treatment, aimed at more accurately assessing DNA damage under both control and treated conditions. Images F-I and graph J are new.

Figure 8____. Aurora kinase A is a regulator of cilia disassembly in meiosis.

This figure is remodelled as the original version contained a mistake in previous panel II, for this, graph in new Fig.8 I has been corrected. In addition, it now contains additional data of αTubulin staining in arrested ciliated metaphases I after AURKA inhibition (new panel L1´).

__Figure 9. Schematic representation of the prepuberal versus adult seminiferous epithelium. __

This figure remains unchanged from the original submission.

__Supplementary Figure 1. Meiotic stages during the first meiotic wave. __

This figure remains unchanged from the original submission.

__Supplementary Figure 2 (new)____. __

This is a new figure that includes additional data requested by the reviewers. It includes additional markers of cilia in spermatocytes (glutamylated Tubulin/GT335), and the control data of cilia markers in non-ciliated spermatocytes. It also includes now the separated quantification of ciliated spermatocytes for each stage, as requested by reviewers, complementing graphs included in Figure 2.

Please note that with the inclusion of this new Supplementary Figure 2, the numbering of subsequent supplementary figures has been updated accordingly.

Supplementary Figure 3 (previously Suppl. Fig. 2)__. Ultrastructure of prophase I spermatocytes. __

This figure is equal in content to the original submission, but some annotations have been included.

Supplementary Figure 4 (previously Suppl. Fig. 3).__ Meiotic centrosome under the electron microscope. __

This figure remains unchanged from the original submission, but additional annotations have been included.

Supplementary Figure 5 (previously Suppl. Fig. 4)__. Human testis contains ciliated spermatocytes. __

This figure has been revised and now includes additional H2AX staining to better determine the stage of ciliated spermatocytes and improve their identification.

Supplementary Figure 6 (previously Suppl. Fig. 5). GLI1 and GLI3 readouts of Hedgehog signalling are not visibly affected in prepuberal mouse testes.

This figure has been remodeled and now includes the quantification of GLI1 and GLI3 and its corresponding statistical analysis. It also includes the control data for Tubulin, instead of GADPH.

Supplementary Figure 7 (previously Suppl. Fig. 6)__. CH and MLN8237 optimization protocol. __

This figure has been remodeled to incorporate control experiments using 1-hour organotypic culture treatment.

Supplementary Figure 8 (previously Suppl. Fig. 7)__. Tracking first meiosis wave with EdU pulse injection during prepubertal meiosis. __This figure remains unchanged from the original submission.

Supplementary Figure 9 (previously Suppl. Fig. 8)__. PLK1 and AURKA inhibition in cultured spermatocytes. __

This figure has been remodeled and now includes additional data on spindle detection in control and AURKA-inhibited spermatocytes (both ciliated and non ciliated).

DETAILED POINT-BY-POINT RESPONSE TO THE REVIEWERS

We will submit both the PDF version of the revised manuscript and the Word file with tracked changes relative to the original submission. Each modification made in response to reviewers' suggestions is annotated in the Word document within the corresponding section of the text. all new figures have also been uploaded to the system.

Response to the Referee #1

In this manuscript by Perez-Moreno et al., titled "The dynamics of ciliogenesis in prepubertal mouse meiosis reveal new clues about testicular maturation during puberty", the authors characterize the development of primary cilia during meiosis in juvenile male mice. The authors catalog a variety of testicular changes that occur as juvenile mice age, such as changes in testis weight and germ cell-type composition. They next show that meiotic prophase cells initially lack cilia, and ciliated meiotic prophase cells are detected after 20 days postpartum, coinciding with the time when post-meiotic spermatids within the developing testes acquire flagella. They describe that germ cells in juvenile mice harbor cilia at all substages of meiotic prophase, in contrast to adults where only zygotene stage meiotic cells harbor cilia. The authors also document that cilia in juvenile mice are longer than those in adults. They characterize cilia composition and structure by immunofluorescence and EM, highlighting that cilia polymerization may initially begin inside the cell, followed by extension beyond the cell membrane. Additionally, they demonstrate ciliated cells can be detected in adult human testes. The authors next perform proteomic analyses of whole testes from juvenile mice at multiple ages, which may not provide direct information about the extremely small numbers of ciliated meiotic cells in the testis, and is lacking follow up experiments, but does serve as a valuable resource for the community. Finally, the authors use a seminiferous tubule culturing system to show that chemical inhibition of Aurora kinase A likely inhibits cilia depolymerization upon meiotic prophase I exit and leads to an accumulation of metaphase-like cells harboring cilia. They also assess meiotic recombination progression using their culturing system, but this is less convincing.

Author response: We sincerely thank Ref #1 for the thorough and thoughtful evaluation of our manuscript. We are particularly grateful for the reviewer's careful reading and constructive feedback, which have helped us refine several sections of the text and strengthen our discussion. All comments and suggestions have been carefully considered and addressed, as detailed below.

__Major comments: __

1. There are a few issues with the experimental set up for assessing the effects of cilia depolymerization on DNA repair (Figure 7-II). First, how were mid pachytene cells identified and differentiated from early pachytene cells (which would have higher levels of gH2AX) in this experiment? I suggest either using H1t staining (to differentiate early/mid vs late pachytene) or the extent of sex chromosome synapsis. This would ensure that the authors are comparing similarly staged cells in control and treated samples. Second, what were the gH2AX levels at the starting point of this experiment? A more convincing set up would be if the authors measure gH2AX immediately after culturing in early and late cells (early would have higher gH2AX, late would have lower gH2AX), and then again after 24hrs in late cells (upon repair disruption the sampled late cells would have high gH2AX). This would allow them to compare the decline in gH2AX (i.e., repair progression) in control vs treated samples. Also, it would be informative to know the starting gH2AX levels in ciliated vs non-ciliated cells as they may vary.

Response:

We thank Ref #1 for this valuable comment, which significantly contributed to improving both the design and interpretation of the cilia depolymerization assay.

Following this suggestion, we repeated the experiment including 1-hour (immediately after culturing), and 24-hour cultures for both control and chloral hydrate (CH)-treated samples (n = 3 biological replicates). To ensure accurate staging, we now employ triple immunolabelling for γH2AX, SYCP3, and H1T, allowing clear distinction of zygotene (H1T−), early pachytene (H1T−), and late pachytene (H1T+) cells. The revised data (Figure 7) now provide a more complete and statistically robust analysis of DNA damage dynamics. These results confirm that CH-induced deciliation leads to persistence of the γH2AX signal at 24 hours, indicating impaired DNA repair progression in pachytene spermatocytes. The new images and graphs are included in the revised Figure 7.

Regarding the reviewer's final point about the comparison of γH2AX levels between ciliated and non-ciliated cells, we regret that direct comparison of γH2AX levels between ciliated and non-ciliated cells is not technically feasible. To preserve cilia integrity, all cilia-related imaging is performed using the squash technique, which maintains the three-dimensional structure of the cilia but does not allow reliable quantification of DNA damage markers due to nuclear distortion. Conversely, the nuclear spreading technique, used for DNA damage assessment, provides optimal visualization of repair foci but results in the loss of cilia due to cytoplasmic disruption during the hypotonic step. Given that spermatocytes in juvenile testes form developmentally synchronized cytoplasmic cysts, we consider that analyzing a statistically representative number of spermatocytes offers a valid and biologically meaningful measure of tissue-level effects.

In conclusion, we believe that the additional experiments and clarifications included in revised Figure 7 strengthen our conclusion that cilia depolymerization compromises DNA repair during meiosis. Further functional confirmation will be pursued in future works, since we are currently generating a conditional genetic model for a ciliopathy in our laboratory.

The authors analyze meiotic progression in cells cultured with/without AURKA inhibition in Figure 8-III and conclude that the distribution of prophase I cells does not change upon treatment. Is Figure 8-III A and B the same data? The legend text is incorrect, so it's hard to follow. Figure 8-III A shows a depletion of EdU-labelled pachytene cells upon treatment. Moreover, the conclusion that a higher proportion of ciliated zygotene cells upon treatment (Figure 8-II C) suggests that AURKA inhibition delays cilia depolymerization (page 13 line 444) does not make sense to me.

Response:

We thank Ref#1 for identifying this issue and for the careful examination of Figure 8. We discovered that the submitted version of Figure 8 contained a mismatch between the figure legend and the figure panels. The legend text was correct; however, the figure inadvertently included a non-corresponding graph (previously panel II-A), which actually belonged to Supplementary Figure 7 in the original submission. We apologize for this mistake.

This error has been corrected in the revised version. The updated Figure 8 now accurately presents the distribution of EdU-labelled spermatocytes across prophase I substages in control and AURKA-inhibited cultures (previously Figure 8-II B, now Figure 8-A). The corrected data show no significant differences in the proportions of EdU-labelled spermatocytes among prophase I substages after 24 hours of AURKA inhibition, confirming that meiotic progression is not delayed and that no accumulation of zygotene cells occurs under this treatment. Therefore, the observed increase in ciliated zygotene spermatocytes upon AURKA inhibition (new Figure 8 H-I) is best explained by a delay in cilia disassembly, rather than by an arrest or slowdown in meiotic progression. The figure legend and main text have been revised accordingly.

How do the authors know that there is a monopolar spindle in Figure 8-IV treated samples? Perhaps the authors can use a different Tubulin antibody (that does not detect only acetylated Tubulin) to show that there is a monopolar spindle.

Response:

We appreciate Ref#1 for this excellent suggestion. In the original submission (lines 446-447), we described that ciliated metaphase I spermatocytes in AURKA-inhibited samples exhibited monopolar spindle phenotypes. This description was based on previous reports showing that AURKA or PLK1 inhibition produces metaphases with monopolar spindles characterized by aberrant yet characteristic SYCP3 patterns, abnormal chromatin compaction, and circular bivalent alignment around non-migrated centrosomes (1). In our study, we observed SYCP3 staining consistent with these characteristic features of monopolar metaphases I.

However, we agree with Ref #1 that this could be better sustained with data. Following the reviewer's suggestion, we performed additional immunostaining using α-Tubulin, which labels total microtubules rather than only the acetylated fraction. For clarity purposes, the revised Figure 8 now includes α-Tubulin staining in the same ciliated metaphase I cells shown in the original submission, confirming the presence of defective microtubule polymerization and defective spindle organization. For clarity, we now refer to these ciliated metaphases I as "arrested MI". This new data further support our conclusion that AURKA inhibition disrupts spindle bipolarization and prevents cilia depolymerization, indicating that cilia maintenance and bipolar spindle organization are mechanistically incompatible events during male meiosis. The abstract, results, and discussion section has been expanded accordingly, emphasizing that the persistence of cilia may interfere with microtubule polymerization and centrosome separation under AURKA inhibition. The Discussion has been expanded to emphasize that persistence of cilia may interfere with centrosome separation and microtubule polymerization, contrasting with invertebrate systems -e.g. Drosophila (2) and P. brassicae (3)- in which meiotic cilia persist through metaphase I without impairing bipolar spindle assembly.

1. Alfaro, et al. EMBO Rep 22, (2021). DOI: 15252/embr.202051030 (PMID: 33615693)
2. Riparbelli et al . Dev Cell (2012) DOI: 1016/j.devcel.2012.05.024 (PMID: 22898783)
3. Gottardo et al, Cytoskeleton (Hoboken) (2023) DOI: 1002/cm.21755 (PMID: 37036073)

The authors state in the abstract that they provide evidence suggesting that centrosome migration and cilia depolymerization are mutually exclusive events during meiosis. This is not convincing with the data present in the current manuscript. I suggest amending this statement in the abstract.

Response:

We thank Ref#1 for this valuable observation, with which we fully agree. To avoid overstatement, the original statement has been removed from the Abstract, Results, and Discussion, and replaced with a more accurate formulation indicating that cilia maintenance and bipolar spindle formation are mutually exclusive events during mouse meiosis.

This revised statement is now directly supported by the new data presented in Figure 8, which demonstrate that AURKA inhibition prevents both spindle bipolarization and cilia depolymerization. We are grateful to the reviewer for highlighting this important clarification.

Minor comments:

The presence of cilia in all stages of meiotic prophase I in juvenile mice is intriguing. Why is the cellular distribution and length of cilia different in prepubertal mice compared to adults (where shorter cilia are present only in zygotene cells)? What is the relevance of these developmental differences? Do cilia serve prophase I functions in juvenile mice (in leptotene, pachytene etc.) that are perhaps absent in adults?

Related to the above point, what is the relevance of the absence of cilia during the first meiotic wave? If cilia serve a critical function during prophase I (for instance, facilitating DSB repair), does the lack of cilia during the first wave imply differing cilia (and repair) requirements during the first vs latter spermatogenesis waves?

In my opinion, these would be interesting points to discuss in the discussion section.

Response:

We thank the reviewer for these thoughtful observations, which we agree are indeed intriguing.

We believe that our findings likely reflect a developmental role for primary cilia during testicular maturation. We hypothesize that primary cilia at this stage might act as signaling organelles, receiving cues from Sertoli cells or neighboring spermatocytes and transmitting them through the cytoplasmic cysts shared by spermatocytes. Such intercellular communication could be essential for coordinating tissue maturation and meiotic entry during puberty. Although speculative, this hypothesis aligns with the established role of primary cilia as sensory and signaling hubs for GPCR and RTK pathways regulating cell differentiation and developmental patterning in multiple tissues (e.g., 1, 2). The Discussion section has been expanded to include these considerations.

1. Goetz et al, Nat Rev Genet (2010)- DOI: 1038/nrg2774 (PMID: 20395968)
2. Naturky et al , Cell (2019) DOI: 1038/s41580-019-0116-4 (PMID: 30948801) Our study focuses on the first spermatogenic wave, which represents the transition from the juvenile to the reproductive phase. It is therefore plausible that the transient presence of longer cilia during this period reflects a developmental requirement for external signaling that becomes dispensable in the mature testis. Given that this is only the second study to date examining mammalian meiotic cilia, there remains a vast area of research to explore. We plan to address potential signaling cascades involved in these processes in future studies.

On the other hand, while we cannot confirm that the cilia observed in zygotene spermatocytes persist until pachytene within the same cell, it is reasonable to speculate that they do, serving as longer-lasting signaling structures that facilitate testicular development during the critical pubertal window. In addition, the observation of ciliated spermatocytes at all prophase I substages at 20 dpp, together with our proteomic data, supports the idea that the emergence of meiotic cilia exerts a significant developmental impact on testicular maturation.

In summary, although we cannot yet define specific prophase I functions for meiotic cilia in juvenile spermatocytes, our data demonstrate that the first meiotic wave differs from later waves in cilia dynamics, suggesting distinct regulatory requirements between puberty and adulthood. These findings underscore the importance of considering developmental context when using the first meiotic wave as a model for studying spermatogenesis.

The authors state on page 9 lines 286-288 that the presence of cytoplasmic continuity via intercellular bridges (between developmentally synchronous spermatocytes) hints towards a mechanism that links cilia and flagella formation. Please clarify this statement. While the correlation between the timing of appearance of cilia and flagella in cells that are located within the same segment of the seminiferous tubule may be hinting towards some shared regulation, how would cytoplasmic continuity participate in this regulation? Especially since the cytoplasmic continuity is not between the developmentally distinct cells acquiring the cilia and flagella?

Response:

We thank Ref#1 for this excellent question and for the opportunity to clarify our statement.

The presence of intercellular bridges between spermatocytes is well known and has long been proposed to support germ cell communication and synchronization (1,2) as well as sharing mRNA (3) and organelles (4). A classic example is the Akap gene, located on the X chromosome and essential for the formation of the sperm fibrous sheath; cytoplasmic continuity through intercellular bridges allows Akap-derived products to be shared between X- and Y-bearing spermatids, thereby maintaining phenotypic balance despite transcriptional asymmetry (5). In addition, more recent work has further demonstrated that these bridges are critical for synchronizing meiotic progression and for processes such as synapsis, double-strand break repair, and transposon repression (6).

In this context, and considering our proteomic data (Figure 6), our statement did not intend to imply direct cytoplasmic exchange between ciliated and flagellated cells. Although our current methods do not allow comprehensive tracing of cytoplasmic continuity from the basal to the luminal compartment of the seminiferous epithelium, we plan to address this limitation using high-resolution 3D and ultrastructural imaging approaches in future studies.

Based on our current data, we propose that cytoplasmic continuity within developmentally synchronized spermatocyte cysts could facilitate the coordinated regulation of ciliogenesis, and similarly enable the sharing of regulatory factors controlling flagellogenesis within spermatid cysts. This coordination may occur through the diffusion of centrosomal or ciliary proteins, mRNAs, or signaling intermediates involved in the regulation of microtubule dynamics. However, we cannot exclude the possibility that such cytoplasmic continuity extends across all spermatocytes derived from the same spermatogonial clone, potentially providing a larger regulatory network.]] This mechanism could help explain the temporal correlation we observe between the appearance of meiotic cilia and the onset of flagella formation in adjacent spermatids within the same seminiferous segment.

We have revised the Discussion to explicitly clarify this interpretation and to note that, although hypothetical, it is consistent with established literature on cytoplasmic continuity and germ cell coordination.

1. Dym, et al. * Reprod.*(1971) DOI: 10.1093/biolreprod/4.2.195 (PMID: 4107186)
2. Braun et al. Nature. (1989) DOI: 1038/337373a0 (PMID: 2911388)
3. Greenbaum et al. * Natl. Acad. Sci. USA*(2006). DOI: 10.1073/pnas.0505123103 (PMID: 16549803)
4. Ventelä et al. Mol Biol Cell. (2003) DOI: 1091/mbc.e02-10-0647 (PMID: 12857863)
5. Turner et al. Journal of Biological Chemistry (1998). DOI: 1074/jbc.273.48.32135 (PMID: 9822690)
6. Sorkin, et al. Nat Commun (2025). DOI: 1038/s41467-025-56742-9 (PMID: 39929837) *note: due to manuscript-length limitations, not all cited references can be included in the text; they are listed here to substantiate our response.

Individual germ cells in H&E-stained testis sections in Figure 1-II are difficult to see. I suggest adding zoomed-in images where spermatocytes/round spermatids/elongated spermatids are clearly distinguishable.

Response:

Ref#1 is very right in this suggestion. We have revised Figure 1 to improve the quality of the H&E-stained testis sections and have added zoomed-in panels where spermatocytes, round spermatids, and elongated spermatids are clearly distinguishable. These additions significantly enhance the clarity and interpretability of the figure.

In Figure 2-II B, the authors document that most ciliated spermatocytes in juvenile mice are pachytene. Is this because most meiotic cells are pachytene? Please clarify. If the data are available (perhaps could be adapted from Figure 1-III), it would be informative to see a graph representing what proportions of each meiotic prophase substages have cilia.

Response:

We thank the reviewer for this valuable observation. Indeed, the predominance of ciliated pachytene spermatocytes reflects the fact that most meiotic cells in juvenile testes are at the pachytene stage (Figure 1). We have clarified this point in the text and have added a new supplementary figure (Supplementary Figure 2, new figure) presenting a graph showing the proportion of spermatocytes at each prophase I substage that possess primary cilia. This visualization provides a clearer quantitative overview of ciliation dynamics across meiotic substages.

I suggest annotating the EM images in Sup Figure 2 and 3 to make it easier to interpret.

Response:

We thank the reviewer for this helpful suggestion. We have now added annotations to the EM images in Supplementary Figures 3 and 4 to facilitate their interpretation. These visual guides help readers more easily identify the relevant ultrastructural features described in the text.

The authors claim that the ratio between GLI3-FL and GLI3-R is stable across their analyzed developmental window in whole testis immunoblots shown in Sup Figure 5. Quantifying the bands and normalizing to the loading control would help strengthen this claim as it hard to interpret the immunoblot in its current form.

Response:

We thank the reviewer for this valuable suggestion. Following this recommendation, Supplementary Figure 5 has been revised to include quantification of GLI1 and GLI3 protein levels, normalized to the loading control.

After quantification, we observed statistically significant differences across developmental stages. Specifically, GLI1 expression is slightly higher at 21 dpp compared to 8 dpp. For GLI3, we performed two complementary analyses:

• Total GLI3 protein (sum of full-length and repressor forms normalized to loading control) shows a progressive decrease during development, with the lowest levels at 60 dpp (Supplementary Figure 5D).
• GLI3 activation status, assessed as the GLI3-FL/GLI3-R ratio, is highest during the 19-21 dpp window, compared to 8 dpp and 60 dpp. Although these results suggest a possible transient activation of GLI3 during testicular maturation, we caution that this cannot automatically be attributed to increased Hedgehog signaling, as GLI3 processing can also be affected by other processes, such as changes in ciliogenesis. Furthermore, because the analysis was performed on whole-testis protein extracts, these changes cannot be specifically assigned to ciliated spermatocytes.

We have expanded the Discussion to address these findings and to highlight the potential involvement of the Desert Hedgehog (DHH) pathway, which plays key roles in testicular development, Sertoli-germ cell communication, and spermatogenesis (1, 2, 3). We plan to investigate these pathways further in future studies.

1. Bitgood et al. Curr Biol. (1996). DOI: 1016/s0960-9822(02)00480-3 (PMID: 8805249)
2. Clark et al. Biol Reprod. (2000) DOI: 1095/biolreprod63.6.1825 (PMID: 11090455)
3. O'Hara et al. BMC Dev Biol. (2011) DOI: 1186/1471-213X-11-72 (PMID: 22132805) *note: due to manuscript-length limitations, not all cited references can be included in the text; they are listed here to substantiate our response.

There are a few typos throughout the manuscript. Some examples: page 5 line 172, Figure 3-I legend text, Sup Figure 5-II callouts, Figure 8-III legend, page 15 line 508, page 17 line 580, page 18 line 611.

Response:

We thank the reviewer for detecting this. All typographical errors have been corrected, and figure callouts have been reviewed for consistency.

Response to the Referee #2

This study focuses on the dynamic changes of ciliogenesis during meiosis in prepubertal mice. It was found that primary cilia are not an intrinsic feature of the first wave of meiosis (initiating at 8 dpp); instead, they begin to polymerize at 20 dpp (after the completion of the first wave of meiosis) and are present in all stages of prophase I. Moreover, prepubertal cilia (with an average length of 21.96 μm) are significantly longer than adult cilia (10 μm). The emergence of cilia coincides temporally with flagellogenesis, suggesting a regulatory association in the formation of axonemes between the two. Functional experiments showed that disruption of cilia by chloral hydrate (CH) delays DNA repair, while the AURKA inhibitor (MLN8237) delays cilia disassembly, and centrosome migration and cilia depolymerization are mutually exclusive events. These findings represent the first detailed description of the spatiotemporal regulation and potential roles of cilia during early testicular maturation in mice. The discovery of this phenomenon is interesting; however, there are certain limitations in functional research.

We thank Referee #2 for their careful reading of the manuscript and for highlighting important limitations regarding functional interpretation.

Our primary objective in this study was to provide a rigorous structural, temporal, and developmental characterization of meiotic ciliogenesis in the mammalian testis, a process for which almost no prior data exist. Given this lack of foundational information, we focused on establishing when, where, and in which meiotic stages primary cilia form during prepubertal development, and on identifying candidate regulatory pathways using complementary imaging, proteomic, and pharmacological approaches.

We agree that genetic ablation models would provide the most direct means to test ciliary function during spermatogenesis. However, we believe that such functional analyses must be preceded by a detailed developmental and phenotypic framework, which was previously unavailable. The present study therefore represents a necessary first step, defining the dynamics, ultrastructure, and molecular context of meiotic cilia during the transition from juvenile to adult spermatogenesis. We are currently generating conditional genetic models to directly address functional mechanisms in future work.

Regarding the temporal coincidence between the emergence of meiotic cilia and the onset of flagellogenesis, we do not interpret this observation as evidence of stochastic or non-functional protein expression. Rather, we present it as a developmental correlation that may reflect shared regulatory constraints on axonemal assembly during testicular maturation. We have clarified in the revised manuscript that this relationship is descriptive and hypothesis-generating, and we avoid assigning direct causal roles.

With respect to the proteomic analysis, we agree that proteomics alone cannot establish function. Our intent was not to assign causality, but to provide a developmental, hypothesis-generating dataset identifying candidate regulators that are enriched at the precise developmental window when both meiotic cilia and spermatid flagella first emerge. We have revised the text to explicitly frame these data as a resource for future mechanistic studies, rather than as direct functional evidence.

Taken together, we believe that the revised manuscript now more accurately reflects the scope and limitations of the study, while providing a robust and much-needed developmental framework for future genetic and functional analyses of meiotic ciliogenesis in mammals. We would be happy to further clarify any aspect of these interpretations if the reviewer or editor considers it helpful.

Major points:

1. The prepubertal cilia in spermatocytes discovered by the authors lack specific genetic ablation to block their formation, making it impossible to evaluate whether such cilia truly have functions. Because neither in the first wave of spermatogenesis nor in adult spermatogenesis does this type of cilium seem to be essential. In addition, the authors also imply that the formation of such cilia appears to be synchronized with the formation of sperm flagella. This suggests that the production of such cilia may merely be transient protein expression noise rather than a functionally meaningful cellular structure.

Response:

We agree that a genetic ablation model would represent the ideal approach to directly test cilia function in spermatogenesis. However, given the complete absence of prior data describing the dynamics of ciliogenesis during testis development, our priority in this study was to establish a rigorous structural and temporal characterization of this process in the main mammalian model organism, the mouse. This systematic and rigorous phenotypic characterization is a necessary first step before any functional genetics could be meaningfully interpreted.

To our knowledge, this study represents the first comprehensive analysis of ciliogenesis during prepubertal mouse meiosis, extending our previous work on adult spermatogenesis (1). Beyond these two contributions, only four additional studies have addressed meiotic cilia-two in zebrafish (2, 3), with Mytlys et al. also providing preliminary observations relevant to prepubertal male meiosis that we discuss in the present work, one in Drosophila (4) and a recent one in butterfly (5). No additional information exists for mammalian gametogenesis to date.

1. López-Jiménez et al. Cells (2022) DOI: 10.3390/cells12010142 (PMID: 36611937)
2. Mytlis et al. Science (2022) DOI: 10.1126/science.abh3104 (PMID: 35549308)
3. Xie et al. J Mol Cell Biol (2022) DOI: 10.1093/jmcb/mjac049 (PMID: 35981808)
4. Riparbelli et al . Dev Cell (2012) DOI: 10.1016/j.devcel.2012.05.024 (PMID: 22898783)
5. Gottardo et al, Cytoskeleton (Hoboken) (2023) DOI: 10.1002/cm.21755 (PMID: 37036073) We therefore consider this descriptive and analytical foundation to be essential before the development of functional genetic models. Indeed, we are currently generating a conditional genetic model for a ciliopathy in our laboratory. These studies are ongoing and will directly address the type of mechanistic questions raised here, but they extend well beyond the scope and feasible timeframe of the present manuscript.

We thus maintain that the present work constitutes a necessary and timely contribution, providing a robust reference dataset that will facilitate and guide future functional studies in the field of cilia and meiosis.

Taking this into account, we would be very pleased to address any additional, concrete suggestions from Ref#2 that could further strengthen the current version of the manuscript

The high expression of axoneme assembly regulators such as TRiC complex and IFT proteins identified by proteomic analysis is not particularly significant. This time point is precisely the critical period for spermatids to assemble flagella, and TRiC, as a newly discovered component of flagellar axonemes, is reasonably highly expressed at this time. No intrinsic connection with the argument of this paper is observed. In fact, this testicular proteomics has little significance.

Response:

We appreciate this comment but respectfully disagree with the reviewer's interpretation of our proteomic data. To our knowledge, this is the first proteomic study explicitly focused on identifying ciliary regulators during testicular development at the precise window (19-21 dpp) when both meiotic cilia and spermatid flagella first emerge.

While Piprek et al (1) analyzed the expression of primary cilia in developing gonads, proteomic data specifically covering the developmental transition at 19-21 dpp were not previously available. Furthermore, a recent cell-sorting study (2), detected expression of cilia proteins in pachytene spermatocytes compared to round spermatids, but did not explore their functional relevance or integrate these data with developmental timing or histological context.

In contrast, our dataset integrates histological staging, high-resolution microscopy, and quantitative proteomics, revealing a set of candidate regulators (including DCAF7, DYRK1A, TUBB3, TUBB4B, and TRiC) potentially involved in cilia-flagella coordination. We view this as a hypothesis-generating resource that outlines specific proteins and pathways for future mechanistic studies on both ciliogenesis and flagellogenesis in the testis.

Although we fully agree that proteomics alone cannot establish causal function, we believe that dismissing these data as having little significance overlooks their value as the first molecular map of the testis at the developmental window when axonemal structures arise. Our dataset provides, for the first time, an integrated view of proteins associated with ciliary and flagellar structures at the developmental stage when both axonemal organelles first appear. We thus believe that our proteomic dataset represents an important and novel contribution to the understanding of testicular development and ciliary biology.

Considering this, we would again welcome any specific suggestions from Ref#2 on additional analyses or clarifications that could make the relevance of this dataset even clearer to readers.

1. Piprek et al. Int J Dev Biol. (2019) doi: 10.1387/ijdb.190049rp (PMID: 32149371).
2. Fang et al. Chromosoma. (1981) doi: 10.1007/BF00285768 (PMID: 7227045). Response to the Referee #3

In "The dynamics of ciliogenesis in prepubertal mouse meiosis reveals new clues about testicular development" Pérez-Moreno, et al. explore primary cilia in prepubertal mouse spermatocytes. Using a combination of microscopy, proteomics, and pharmacological perturbations, the authors carefully characterize prepubertal spermatocyte cilia, providing foundational work regarding meiotic cilia in the developing mammalian testis.

Response: We sincerely thank Ref#3 for their positive assessment of our work and for the thoughtful suggestions that have helped us strengthen the manuscript. We are pleased that the reviewer recognizes both the novelty and the relevance of our study in providing foundational insights into meiotic ciliogenesis during prepubertal testicular development. All specific comments have been carefully considered and addressed as detailed below.

Major concerns:

1. The authors provide evidence consistent with cilia not being present in a larger percentage of spermatocytes or in other cells in the testis. The combination of electron microscopy and acetylated tubulin antibody staining establishes the presence of cilia; however, proving a negative is challenging. While acetylated tubulin is certainly a common marker of cilia, it is not in some cilia such as those in neurons. The authors should use at least one additional cilia marker to better support their claim of cilia being absent.

Response:

We thank the reviewer for this helpful suggestion. In the revised version, we have strengthened the evidence for cilia identification by including an additional ciliary marker, glutamylated tubulin (GT335), in combination with acetylated tubulin and ARL13B (which were included in the original submission). These data are now presented in the new Supplementary Figure 2, which also includes an example of a non-ciliated spermatocyte showing absence of both ARL13B and AcTub signals.

Taken together, these markers provide a more comprehensive validation of cilia detection and confirm the absence of ciliary labelling in non-ciliated spermatocytes.

The conclusion that IFT88 localizes to centrosomes is premature as key controls for the IFT88 antibody staining are lacking. Centrosomes are notoriously "sticky", often sowing non-specific antibody staining. The authors must include controls to demonstrate the specificity of the staining they observe such as staining in a genetic mutant or an antigen competition assay.

Response:

We appreciate the reviewer's concern and fully agree that antibody specificity is critical when interpreting centrosomal localization. The IFT88 antibody used in our study is commercially available and has been extensively validated in the literature as both a cilia marker (1, 2), and a centrosome marker in somatic cells (3). Labelling of IFT88 in centrosomes has also been previously described using other antibodies (4, 5). In our material, the IFT88 signal consistently appears at one of the duplicated centrosomes and at both spindle poles-patterns identical to those reported in somatic cells. We therefore consider the reported meiotic IFT88 staining as specific and biologically reliable.

That said, we agree that genetic validation would provide the most definitive confirmation. We would like to inform that we are currently since we are currently generating a conditional genetic model for a ciliopathy in our laboratory that will directly assess both antibody specificity and functional consequences of cilia loss during meiosis. These experiments are in progress and will be reported in a follow-up study.

1. Wong et al. Science (2015). DOI: 1126/science.aaa5111 (PMID: 25931445)
2. Ocbina et al. Nat Genet (2011). DOI: 1038/ng.832 (PMID: 21552265)
3. Vitre et al. EMBO Rep (2020). DOI: 15252/embr.201949234 (PMID: 32270908)
4. Robert A. et al. J Cell Sci (2007). DOI: 1242/jcs.03366 (PMID: 17264151)
5. Singla et al, Developmental Cell (2010). DOI: 10.1016/j.devcel.2009.12.022 (PMID: 20230748) *note: due to manuscript-length limitations, not all cited references can be included in the text; they are listed here to substantiate our response.

There are many inconsistent statements throughout the paper regarding the timing of the first wave of spermatogenesis. For example, the authors state that round spermatids can be detected at 21dpp on line 161, but on line 180, say round spermatids can be detected a 19dpp. Not only does this lead to confusion, but such discrepancies undermine the validity of the rest of the paper. A summary graphic displaying key events and their timing in the first wave of spermatogenesis would be instrumental for reader comprehension and could be used by the authors to ensure consistent claims throughout the paper.

Response:

We thank the reviewer for identifying this inconsistency and apologize for the confusion. We confirm that early round spermatids first appear at 19 dpp, as shown in the quantitative data (Figure 1J). This can be detected in squashed spermatocyte preparations, where individual spermatocytes and spermatids can be accurately quantified. The original text contained an imprecise reference to the histological image of 21 dpp (previous line 161), since certain H&E sections did not clearly show all cell types simultaneously. However, we have now revised Figure 1, improving the image quality and adding a zoomed-in panel highlighting early round spermatids. Image for 19 dpp mice in Fig 1D shows early, yet still aflagellated spermatids. The first ciliated spermatocytes and the earliest flagellated spermatids are observed at 20 dpp. This has been clarified in the text.

In addition, we also thank the reviewer for the suggestion of adding a summary graphic, which we agree greatly facilitates reader comprehension. We have added a new schematic summary (Figure 1K) illustrating the key stages and timing of the first spermatogenic wave.

In the proteomics experiments, it is unclear why the authors assume that changes in protein expression are predominantly due to changes within the germ cells in the developing testis. The analysis is on whole testes including both the somatic and germ cells, which makes it possible that protein expression changes in somatic cells drive the results. The authors need to justify why and how the conclusions drawn from this analysis warrant such an assumption.

Response:

We agree with the reviewer that our proteomic analysis was performed on whole testis samples, which contain both germ and somatic cells. Although isolation of pure spermatocyte populations by FACS would provide higher resolution, obtaining sufficient prepubertal material for such analysis would require an extremely large number of animals. To remain compliant with the 3Rs principle for animal experimentation, we therefore used whole-testis samples from three biological replicates per age.

We acknowledge that our assumption-that the main differences arise from germ cells-is a simplification. However, germ cells constitute the vast majority of testicular cells during this developmental window and are the population undergoing major compositional changes between 15 dpp and adulthood. It is therefore reasonable to expect that a substantial fraction of the observed proteomic changes reflects alterations in germ cells. We have clarified this point in the revised text and have added a statement noting that changes in somatic cells could also contribute to the proteomic profiles.

The authors should provide details on how proteins were categorized as being involved in ciliogenesis or flagellogenesis, specifically in the distinction criteria. It is not clear how the categorizations were determined or whether they are valid. Thus, no one can repeat this analysis or perform this analysis on other datasets they might want to compare.

Response:

We thank the reviewer for this opportunity to clarify our approach. The categorization of protein as being involved in ciliogenesis or flagellogenesis was based on their Gene Ontology (GO) cellular component annotations obtained from the PANTHER database (Version 19.0), using the gene IDs of the Differentially Expressed Proteins (DEPs). Specifically, we used the GO terms cilium (GO:0005929) and motile cilium (GO:0031514). Since motile cilium is a subcategory of cilium, proteins annotated only with the general cilium term, but not included under motile cilium, were considered to be associated with primary cilia or with shared structural components common to different types of cilia. These GO terms are represented in the bottom panel of the Figure 6.

This information has been added to the Methods section and referenced in the Results for transparency and reproducibility.

In the pharmacological studies, the authors conclude that the phenotypes they observe (DNA damage and reduced pachytene spermatocytes) are due to loss of or persistence of cilia. This overinterprets the experiment. Chloral hydrate and MLN8237 certainly impact ciliation as claimed, but have additional cellular effects. Thus, it is possible that the observed phenotypes were not a direct result of cilia manipulation. Either additional controls must address this or the conclusions need to be more specific and toned down.

Response:

We thank the reviewer for this fair observation and have taken steps to strengthen and refine our interpretation. In the revised version, we now include data from 1-hour and 24-hour cultures for both control and chloral hydrate (CH)-treated samples (n = 3 biological replicates). The triple immunolabelling with γH2AX, SYCP3, and H1T allows accurate staging of zygotene (H1T⁻), early pachytene (H1T⁻), and late pachytene (H1T⁺) spermatocytes.

The revised Figure 7 now provides a more complete and statistically supported analysis of DNA damage dynamics, confirming that CH-induced deciliation leads to persistent γH2AX signal at 24 hours, indicative of delayed or defective DNA repair progression. We have also toned down our interpretation in the Discussion, acknowledging that CH could affect other cellular pathways.

As mentioned before, the conditional genetic model that we are currently generating will allow us to evaluate the role of cilia in meiotic DNA repair in a more direct and specific way.

Assuming the conclusions of the pharmacological studies hold true with the proper controls, the authors still conflate their findings with meiotic defects. Meiosis is not directly assayed, which makes this conclusion an overstatement of the data. The conclusions need to be rephrased to accurately reflect the data.

Response:

We agree that this aspect required clarification. As noted above, we have refined both the Results and Discussion sections to make clear that our assays specifically targeted meiotic spermatocytes.

We now present data for meiotic stages at zygotene, early pachytene and late pachytene. This is demonstrated with the labelling for SYCP3 and H1T, both specific marker for meiosis that are not detectable in non meiotic cells. We believe that this is indeed a way to assay the meiotic cells, however, we have specified now in the text that we are analysing potential defects in meiosis progression. We are sorry if this was not properly explained in the original manuscript: it is now rephrased in the new version both in the results and discussion section.

It is not clear why the authors chose not to use widely accepted assays of Hedgehog signaling. Traditionally, pathway activation is measured by transcriptional output, not GLI protein expression because transcription factor expression does not necessarily reflect transcription levels of target genes.

Response:

We agree with the reviewer that measuring mRNA levels of Hedgehog pathway target genes, typically GLI1 and PTCH1, is the most common method for measuring pathway activation, and is widely accepted by researchers in the field. However, the methods we use in this manuscript (GLI1 and GLI3 immunoblots) are also quite common and widely accepted:

Regarding GLI1 immunoblot, many articles have used this method to monitor Hedgehog signaling, since GLI1 protein levels have repeatedly been shown to also go up upon pathway activation, and down upon pathway inhibition, mirroring the behavior of GLI1 mRNA. Here are a few publications that exemplify this point:

• Banday et al. 2025 Nat Commun. DOI: 10.1038/s41467-025-56632-0 (PMID: 39894896)
• Shi et al 2022 JCI Insight DOI: 10.1172/jci.insight.149626 (PMID: 35041619)
• Deng et al. 2019 eLife, DOI: 10.7554/eLife.50208 (PMID: 31482846)
• Zhu et al. 2019 Nat Commun, DOI: 10.1038/s41467-019-10739-3 (PMID: 31253779)
• Caparros-Martin et al 2013 Hum Mol Genet, DOI: 10.1093/hmg/dds409 (PMID: 23026747) *note: due to manuscript-length limitations, not all cited references can be included in the text; they are listed here to substantiate our response.

As for GLI3 immunoblot, Hedgehog pathway activation is well known to inhibit GLI3 proteolytic processing from its full length form (GLI3-FL) to its transcriptional repressor (GLI3-R), and such processing is also commonly used to monitor Hedgehog signal transduction, of which the following are but a few examples:

• Pedraza et al 2025 eLife, DOI: 10.7554/eLife.100328 (PMID: 40956303)
• Somatilaka et al 2020 Dev Cell, DOI: 10.1016/j.devcel.2020.06.034 (PMID: 32702291)
• Infante et al 2018, Nat Commun, DOI: 10.1038/s41467-018-03339-0 (PMID: 29515120)
• Wang et al 2017 Dev Biol DOI: 10.1016/j.ydbio.2017.08.003 (PMID: 28800946)
• Singh et al 2015 J Biol Chem DOI: 10.1074/jbc.M115.665810 (PMID: 26451044) *note: due to manuscript-length limitations, not all cited references can be included in the text; they are listed here to substantiate our response.

In summary, we think that we have used two well established markers to look at Hedgehog signaling (three, if we include the immunofluorescence analysis of SMO, which we could not detect in meiotic cilia).

These Hh pathway analyses did not provide any convincing evidence that the prepubertal cilia we describe here are actively involved in this pathway, even though Hh signaling is cilia-dependent and is known to be active in the male germline (Sahin et al 2014 Andrology PMID: 24574096; Mäkelä et al 2011 Reproduction PMID: 21893610; Bitgood et al 1996 Curr Biol. PMID: 8805249).

That said, we fully agree that our current analyses do not allow us to draw definitive conclusions regarding Hedgehog pathway activity in meiotic cilia, and we now state this explicitly in the revised Discussion.

Also in the Hedgehog pathway experiment, it is confusing that the authors report no detection of SMO yet detect little to no expression of GLIR in their western blot. Undetectable SMO indicates Hedgehog signaling is inactive, which results in high levels of GLIR. The impact of this is that it is not clear what is going on with Hh signaling in this system.

Response:

It is true that, when Hh signaling is inactive (and hence SMO not ciliary), the GLI3FL/GLI3R ratio tends to be low.

Although our data in prepuberal mouse testes show a strong reduction in total GLI3 protein levels (GLI3FL+GLI3R) as these mice grow older, this downregulation of total GLI3 occurs without any major changes in the GLI3FL/GLI3R ratio, which is only modestly affected (suppl. Figure 6).

Hence, since it is the ratio that correlates with Hh signaling rather than total levels, we do not think that the GLI3R reduction we see is incompatible with our non-detection of SMO in cilia: it seems more likely that overall GLI3 expression is being downregulated in developing testes via a Hh-independent mechanism.

Also potentially relevant here is the fact that some cell types depend more on GLI2 than on GLI3 for Hh signaling. For instance, in mouse embryos, Hh-mediated neural tube patterning relies more heavily on GLI2 processing into a transcriptional activator than on the inhibition of GLI3 processing into a repressor. In contrast, the opposite is true during Hh-mediated limb bud patterning (Nieuwenhuis and Hui 2005 Clin Genet. PMID: 15691355). We have not looked at GLI2, but it is conceivable that it could play a bigger role than GLI3 in our model.

Moreover, several forms of GLI-independent non-canonical Hh signaling have been described, and they could potentially play a role in our model, too (Robbins et al 2012 Sci Signal. PMID: 23074268).

We have revised the discussion to clarify some of these points.

All in all, we agree that our findings regarding Hh signaling are not conclusive, but we still think they add important pieces to the puzzle that will help guide future studies.

There are multiple instances where it is not clear whether the authors performed statistical analysis on their data, specifically when comparing the percent composition of a population. The authors need to include appropriate statistical tests to make claims regarding this data. While the authors state some impressive sample sizes, once evaluated in individual categories (eg specific cell type and age) the sample sizes of evaluated cilia are as low as 15, which is likely underpowered. The authors need to state the n for each analysis in the figures or legends.

We thank the reviewer for highlighting this important issue. We have now included the sample size (n) for every analysis directly in the figure legends. Although this adds length, it improves transparency and reproducibility.

Regarding the doubts of Ref#3 about the different sample sizes, the number of spermatocytes quantified in each stage is in agreement with their distribution in meiosis (example, pachytene lasts for 10 days this stage is widely represented in the preparations, while its is much difficult to quantify metaphases I that are less present because the stage itself lasts for less than 24hours). Taking this into account, we ensured that all analyses remain statistically valid and representative, applying the appropriate statistical tests for each dataset. These details are now clearly indicated in the revised figures and legends.

Minor concerns:

1. The phrase "lactating male" is used throughout the paper and is not correct. We assume this term to mean male pups that have yet to be weaned from their lactating mother, but "lactating male" suggests a rare disorder requiring medical intervention. Perhaps "pre-weaning males" is what the authors meant.

Response:

We thank the reviewer for noticing this terminology error. The expression has been corrected to "pre-weaning males" throughout the manuscript.

The convention used to label the figures in this paper is confusing and difficult to read as there are multiple panels with the same letter in the same figure (albeit distinct sections). Labeling panels in the standard A-Z format is preferred. "Panel Z" is easier to identify than "panel III-E".

Response:

We thank the reviewer for this suggestion. All figures have been relabelled using the standard A-Z panel format, ensuring consistency and easier readability across the manuscript.

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Referee #3

Evidence, reproducibility and clarity

Summary:

In "The dynamics of ciliogenesis in prepubertal mouse meiosis reveals new clues about testicular development" Pérez-Moreno, et al. explore primary cilia in prepubertal mouse spermatocytes. Using a combination of microscopy, proteomics, and pharmacological perturbations, the authors carefully characterize prepubertal spermatocyte cilia, providing foundational work regarding meiotic cilia in the developing mammalian testis.

Major concerns:

1. The authors provide evidence consistent with cilia not being present in a larger percentage of spermatocytes or in other cells in the testis. The combination of electron microscopy and acetylated tubulin antibody staining establishes the presence of cilia; however, proving a negative is challenging. While acetylated tubulin is certainly a common marker of cilia, it is not in some cilia such as those in neurons. The authors should use at least one additional cilia marker to better support their claim of cilia being absent.

2. The conclusion that IFT88 localizes to centrosomes is premature as key controls for the IFT88 antibody staining are lacking. Centrosomes are notoriously "sticky", often sowing non-specific antibody staining. The authors must include controls to demonstrate the specificity of the staining they observe such as staining in a genetic mutant or an antigen competition assay.

3. There are many inconsistent statements throughout the paper regarding the timing of the first wave of spermatogenesis. For example, the authors state that round spermatids can be detected at 21dpp on line 161, but on line 180, say round spermatids can be detected a 19dpp. Not only does this lead to confusion, but such discrepancies undermine the validity of the rest of the paper. A summary graphic displaying key events and their timing in the first wave of spermatogenesis would be instrumental for reader comprehension and could be used by the authors to ensure consistent claims throughout the paper.

4. In the proteomics experiments, it is unclear why the authors assume that changes in protein expression are predominantly due to changes within the germ cells in the developing testis. The analysis is on whole testes including both the somatic and germ cells, which makes it possible that protein expression changes in somatic cells drive the results. The authors need to justify why and how the conclusions drawn from this analysis warrant such an assumption.

5. The authors should provide details on how proteins were categorized as being involved in ciliogenesis or flagellogenesis, specifically in the distinction criteria. It is not clear how the categorizations were determined or whether they are valid. Thus, no one can repeat this analysis or perform this analysis on other datasets they might want to compare.

6. In the pharmacological studies, the authors conclude that the phenotypes they observe (DNA damage and reduced pachytene spermatocytes) are due to loss of or persistence of cilia. This overinterprets the experiment. Chloral hydrate and MLN8237 certainly impact ciliation as claimed, but have additional cellular effects. Thus, it is possible that the observed phenotypes were not a direct result of cilia manipulation. Either additional controls must address this or the conclusions need to be more specific and toned down.

7. Assuming the conclusions of the pharmacological studies hold true with the proper controls, the authors still conflate their findings with meiotic defects. Meiosis is not directly assayed, which makes this conclusion an overstatement of the data. The conclusions need to be rephrased to accurately reflect the data.

8. It is not clear why the authors chose not to use widely accepted assays of Hedgehog signaling. Traditionally, pathway activation is measured by transcriptional output, not GLI protein expression because transcription factor expression does not necessarily reflect transcription levels of target genes.

9. Also in the Hedgehog pathway experiment, it is confusing that the authors report no detection of SMO yet detect little to no expression of GLIR in their western blot. Undetectable SMO indicates Hedgehog signaling is inactive, which results in high levels of GLIR. The impact of this is that it is not clear what is going on with Hh signaling in this system.

10. There are multiple instances where it is not clear whether the authors performed statistical analysis on their data, specifically when comparing the percent composition of a population. The authors need to include appropriate statistical tests to make claims regarding this data. While the authors state some impressive sample sizes, once evaluated in individual categories (eg specific cell type and age) the sample sizes of evaluated cilia are as low as 15, which is likely underpowered. The authors need to state the n for each analysis in the figures or legends.

Minor concerns:

1. The phrase "lactating male" is used throughout the paper and is not correct. We assume this term to mean male pups that have yet to be weaned from their lactating mother, but "lactating male" suggests a rare disorder requiring medical intervention. Perhaps "pre-weaning males" is what the authors meant.

2. The convention used to label the figures in this paper is confusing and difficult to read as there are multiple panels with the same letter in the same figure (albeit distinct sections). Labeling panels in the standard A-Z format is preferred. "Panel Z" is easier to identify than "panel III-E".

Significance

Overall, this is a well-done body of work that deserves recognition for the novel and implicative discoveries it presents. Assuming the conclusions hold true following appropriate statistical analysis and rephrasing, this paper would report the first documented evidence of meiotic cilia in the developing mammalian testis with sufficient rigor to become the foundational work on this topic.

This paper will be of interest to communities focused on germ cell development, cilia, and Hedgehog signaling. It may prompt a new perspective on Desert Hedgehog signaling as it pertains to spermatogenesis. Further, this work will be of interest to those studying male fertility, as it highlights the potential role of cilia in spermatogenesis.

Further, the proteomic analysis presented has the potential to invoke hypotheses and experimentation investigating the role of several proteins with previously uncharacterized roles in ciliogenesis, flagellogenesis, and/or spermatogenesis. The finding that the onset of ciliogenesis and flagellogenesis appear to be temporally linked has the potential to prompt research regarding shared molecular mechanisms dictating axonemal formation. We believe this paper has the potential to have an impact in its respective field, underscored by the exquisite microscopy and detailed characterization of meiotic cilia.

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Referee #1

Evidence, reproducibility and clarity

In this manuscript by Perez-Moreno et al., titled "The dynamics of ciliogenesis in prepubertal mouse meiosis reveal new clues about testicular maturation during puberty", the authors characterize the development of primary cilia during meiosis in juvenile male mice. The authors catalog a variety of testicular changes that occur as juvenile mice age, such as changes in testis weight and germ cell-type composition. They next show that meiotic prophase cells initially lack cilia, and ciliated meiotic prophase cells are detected after 20 days postpartum, coinciding with the time when post-meiotic spermatids within the developing testes acquire flagella. They describe that germ cells in juvenile mice harbor cilia at all substages of meiotic prophase, in contrast to adults where only zygotene stage meiotic cells harbor cilia. The authors also document that cilia in juvenile mice are longer than those in adults. They characterize cilia composition and structure by immunofluorescence and EM, highlighting that cilia polymerization may initially begin inside the cell, followed by extension beyond the cell membrane. Additionally, they demonstrate ciliated cells can be detected in adult human testes. The authors next perform proteomic analyses of whole testes from juvenile mice at multiple ages, which may not provide direct information about the extremely small numbers of ciliated meiotic cells in the testis, and is lacking follow up experiments, but does serve as a valuable resource for the community. Finally, the authors use a seminiferous tubule culturing system to show that chemical inhibition of Aurora kinase A likely inhibits cilia depolymerization upon meiotic prophase I exit and leads to an accumulation of metaphase-like cells harboring cilia. They also assess meiotic recombination progression using their culturing system, but this is less convincing.

Few suggestions/comments are listed below:

Major comments

1. There are a few issues with the experimental set up for assessing the effects of cilia depolymerization on DNA repair (Figure 7-II). First, how were mid pachytene cells identified and differentiated from early pachytene cells (which would have higher levels of gH2AX) in this experiment? I suggest either using H1t staining (to differentiate early/mid vs late pachytene) or the extent of sex chromosome synapsis. This would ensure that the authors are comparing similarly staged cells in control and treated samples. Second, what were the gH2AX levels at the starting point of this experiment? A more convincing set up would be if the authors measure gH2AX immediately after culturing in early and late cells (early would have higher gH2AX, late would have lower gH2AX), and then again after 24hrs in late cells (upon repair disruption the sampled late cells would have high gH2AX). This would allow them to compare the decline in gH2AX (i.e., repair progression) in control vs treated samples. Also, it would be informative to know the starting gH2AX levels in ciliated vs non-ciliated cells as they may vary.

2. The authors analyze meiotic progression in cells cultured with/without AURKA inhibition in Figure 8-III and conclude that the distribution of prophase I cells does not change upon treatment. Is Figure 8-III A and B the same data? The legend text is incorrect, so it's hard to follow. Figure 8-III A shows a depletion of EdU-labelled pachytene cells upon treatment. Moreover, the conclusion that a higher proportion of ciliated zygotene cells upon treatment (Figure 8-II C) suggests that AURKA inhibition delays cilia depolymerization (page 13 line 444) does not make sense to me.

3. How do the authors know that there is a monopolar spindle in Figure 8-IV treated samples? Perhaps the authors can use a different Tubulin antibody (that does not detect only acetylated Tubulin) to show that there is a monopolar spindle.

4. The authors state in the abstract that they provide evidence suggesting that centrosome migration and cilia depolymerization are mutually exclusive events during meiosis. This is not convincing with the data present in the current manuscript. I suggest amending this statement in the abstract.

Minor comments

1. The presence of cilia in all stages of meiotic prophase I in juvenile mice is intriguing. Why is the cellular distribution and length of cilia different in prepubertal mice compared to adults (where shorter cilia are present only in zygotene cells)? What is the relevance of these developmental differences? Do cilia serve prophase I functions in juvenile mice (in leptotene, pachytene etc.) that are perhaps absent in adults?

Related to the above point, what is the relevance of the absence of cilia during the first meiotic wave? If cilia serve a critical function during prophase I (for instance, facilitating DSB repair), does the lack of cilia during the first wave imply differing cilia (and repair) requirements during the first vs latter spermatogenesis waves?

In my opinion, these would be interesting points to discuss in the discussion section.

1. The authors state on page 9 lines 286-288 that the presence of cytoplasmic continuity via intercellular bridges (between developmentally synchronous spermatocytes) hints towards a mechanism that links cilia and flagella formation. Please clarify this statement. While the correlation between the timing of appearance of cilia and flagella in cells that are located within the same segment of the seminiferous tubule may be hinting towards some shared regulation, how would cytoplasmic continuity participate in this regulation? Especially since the cytoplasmic continuity is not between the developmentally distinct cells acquiring the cilia and flagella?

2. Individual germ cells in H&E-stained testis sections in Figure 1-II are difficult to see. I suggest adding zoomed-in images where spermatocytes/round spermatids/elongated spermatids are clearly distinguishable.

3. In Figure 2-II B, the authors document that most ciliated spermatocytes in juvenile mice are pachytene. Is this because most meiotic cells are pachytene? Please clarify. If the data are available (perhaps could be adapted from Figure 1-III), it would be informative to see a graph representing what proportions of each meiotic prophase substages have cilia.

4. I suggest annotating the EM images in Sup Figure 2 and 3 to make it easier to interpret.

5. The authors claim that the ratio between GLI3-FL and GLI3-R is stable across their analyzed developmental window in whole testis immunoblots shown in Sup Figure 5. Quantifying the bands and normalizing to the loading control would help strengthen this claim as it hard to interpret the immunoblot in its current form.

6. There are a few typos throughout the manuscript. Some examples: page 5 line 172, Figure 3-I legend text, Sup Figure 5-II callouts, Figure 8-III legend, page 15 line 508, page 17 line 580, page 18 line 611.

Significance

This work provides new information about an important but poorly understood cellular structure present in meiotic cells, the primary cilium. More generally, this work expands on our understanding of testis development in juvenile mice. The microscopy images presented here are beautiful. The work is mostly descriptive but lays the groundwork for future investigations. I believe that this study would of interest to the germ cell, meiosis, and spermatogenesis communities, and with a few modifications, is suitable for publication.

eLife Assessment

This manuscript uses adaptive-bandit simulations to describe the dynamics of the Pseudomonas-derived chephalosporinase PDC-3 β-lactamase and its mutants to better understand antibiotic resistance. The finding, that clinically observed mutations alter the flexibility of the Ω- and R2-loops, reshaping the cavity of the active site, is valuable to the field. The evidence is considered incomplete, however, with the need for analysis to demonstrate equilibrium weighting of adaptive trajectories and related measures of statistical significance.

Reviewer #2 (Public review):

Summary:

In the manuscript entitled "Ω-Loop mutations control dynamics 2 of the active site by modulating the 3 hydrogen-bonding network in PDC-3 4 β-lactamase", Chen and coworkers provide a computational investigation of the dynamics of the enzyme Pseudomonas-derived chephalosporinase 3 (PDC3) and some mutants associated with increased antibiotic resistance. After an initial analysis of the enzyme dynamics provided by RMSD/RMSF, the author conclude that the mutations alter the local dynamics within the omega loop and the R2 loop. The authors show that the network of hydrogen bonds in disrupted in the mutants. Constant pH calculations showed that the mutations also change the pKa of the catalytic lysine 67 and pocket volume calculations showed that the mutations expand the catalytic pocket. Finally, time-independent componente analysis (tiCA) showed different profiles for the mutant enzyme as compared to the wild type.

Strengths:

The scope of the manuscript is definitely relevant. Antibiotic resistance is an important problem and, in particular, Pseudomonas aeruginosa resistance is associated with an increasing number of deaths. The choice of the computational methods is also something to highlight here. Although I am not familiar with Adaptive Bandit Molecular Dynamics (ABMD), the description provided in the manuscript that this simulation strategy is well suited for the problem under evaluation.

Weaknesses:

In the revised version, the authors addressed my concerns regarding their use of the MSM, and in my view, their conclusions are now much more robust and well-supported by the data. While it would be very interesting to see a quantitative correlation between the effects of the mutations observed in the MD data and relevant experimental findings, I understand that this may be beyond the scope of the manuscript.

Reviewer #3 (Public review):

Summary:

This manuscript aims to explore how mutations in the PDC-3 3 β-lactamase alter its ability to bind and catalyse reactions of antibiotic compounds. The topic is interesting and the study uses MD simulations and to provide hypotheses about how the size of the binding site is altered by mutations that change the conformation and flexibility of two loops that line the binding pocket. Some greater consideration of the uncertainties and how the method choice affect the ability to compare equilibrium properties would strengthen the quantitative conclusions. While many results appear significant by eye, quantifying this and ensuring convergence would strengthen the conclusions.

Strengths:

The significance of the problem is clearly described the relationship to prior literature is discussed extensively.

Comments on revised version:

I am concerned that the authors state in the response to reviews that it is not possible to get error bars on values due to the use of the AB-MD protocol that guides the simulations to unexplored basins. Yet the authors want to compare these values between the WT and mutants. This relates to RMSD, RMSF, % H-bond and volume calculations. I don't accept that you cannot calculate an uncertainty on a time averaged property calculated across the entire simulation. In these cases you can either run repeat simulations to get multiple values on which to do statistical analysis, or you can break the simulation into blocks and check both convergence and calculate uncertainties.

I note that the authors do provide error bars on the volumes, but the statistics given for these need closer scrutiny (I cant test this without the raw data). For example the authors have p<0.0001 for the following pair of volumes 1072 {plus minus} 158 and 1115 {plus minus} 242, or for SASA p<0.0001 is given for 2 identical numbers 155+/- 3.

I also remain concerned about comparisons between simulations run with the AB-MD scheme. While each simulation is an equilibrium simulation run without biasing forces, new simulations are seeded to expand the conformational sampling of the system. This means that by definition the ensemble of simulations does not represent and equilibrium ensemble. For example, the frequency at which conformations are sampled would not be the same as in a single much longer equilibrium simulation. While you may be able to see trends in the differences between conditions run in this way, I still don't understand how you can compare quantitative information without some method of reweighing the ensemble. It is not clear that such a rewieghting exists for this methods, in which case I advise some more caution in the wording of the comparisons made from this data.

At this stage I don't feel the revision has directly addressed the main comments I raised in the earlier review, although there is a stronger response to the comments of Reviewer #2.

Author response:

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

Reviewer #2 (Public review):

Summary:

In the manuscript entitled "Ω-Loop mutations control dynamics 2 of the active site by modulating the 3 hydrogen-bonding network in PDC-3 4 β-lactamase", Chen and coworkers provide a computational investigation of the dynamics of the enzyme Pseudomonas-derived chephalosporinase 3 (PDC3) and some mutants associated with increased antibiotic resistance. After an initial analysis of the enzyme dynamics provided by RMSD/RMSF, the author conclude that the mutations alter the local dynamics within the omega loop and the R2 loop. The authors show that the network of hydrogen bonds in disrupted in the mutants. Constant pH calculations showed that the mutations also change the pKa of the catalytic lysine 67 and pocket volume calculations showed that the mutations expand the catalytic pocket. Finally, time-independent componente analysis (tiCA) showed different profiles for the mutant enzyme as compared to the wild type.

Strengths:

The scope of the manuscript is definitely relevant. Antibiotic resistance is an important problem and, in particular, Pseudomonas aeruginosa resistance is associated with an increasing number of deaths. The choice of the computational methods is also something to highlight here. Although I am not familiar with Adaptive Bandit Molecular Dynamics (ABMD), the description provided in the manuscript that this simulation strategy is well suited for the problem under evaluation.

Weaknesses:

In the revised version, the authors addressed my concerns regarding their use of the MSM, and in my view, their conclusions are now much more robust and well-supported by the data. While it would be very interesting to see a quantitative correlation between the effects of the mutations observed in the MD data and relevant experimental findings, I understand that this may be beyond the scope of the manuscript.

Thank you for the careful evaluation and constructive comments. Regarding the suggestion of a more quantitative correlation with experimental observables, we agree that this would be valuable, and we have noted it as an important direction for future work.

Reviewer #3 (Public review):

Summary:

This manuscript aims to explore how mutations in the PDC-3 3 β-lactamase alter its ability to bind and catalyse reactions of antibiotic compounds. The topic is interesting and the study uses MD simulations and to provide hypotheses about how the size of the binding site is altered by mutations that change the conformation and flexibility of two loops that line the binding pocket. Some greater consideration of the uncertainties and how the method choice affect the ability to compare equilibrium properties would strengthen the quantitative conclusions. While many results appear significant by eye, quantifying this and ensuring convergence would strengthen the conclusions.

Strengths:

The significance of the problem is clearly described the relationship to prior literature is discussed extensively.

Comments on revised version:

I am concerned that the authors state in the response to reviews that it is not possible to get error bars on values due to the use of the AB-MD protocol that guides the simulations to unexplored basins. Yet the authors want to compare these values between the WT and mutants. This relates to RMSD, RMSF, % H-bond and volume calculations. I don't accept that you cannot calculate an uncertainty on a time averaged property calculated across the entire simulation. In these cases you can either run repeat simulations to get multiple values on which to do statistical analysis, or you can break the simulation into blocks and check both convergence and calculate uncertainties.

We thank the reviewer for raising this point. We would like to clarify that we did not intend to state that error bars are impossible to obtain under AB-MD. In fact, we reported error bars for several quantities derived from the AB-MD trajectories (we also broke the trajectories into blocks and calculated uncertainties for RMSF in our first-round response as you suggested). However, these data are closely related to your concern about comparing quantitative information without an appropriate reweighting of the ensemble. Therefore, in the revised manuscript, we removed quantitative analyses that were calculated directly from the raw AB-MD trajectories. Instead, the quantitative comparisons are now obtained from MSM analysis. We report pocket volumes and key interaction metrics for MSM metastable states, with corresponding error bars for these MSM-based quantities (Figure 6 and its supplementary figure).

I note that the authors do provide error bars on the volumes, but the statistics given for these need closer scrutiny (I cant test this without the raw data). For example the authors have p<0.0001 for the following pair of volumes 1072 {plus minus} 158 and 1115 {plus minus} 242, or for SASA p<0.0001 is given for 2 identical numbers 155+/- 3.

Thank you for this comment. As noted above, we have removed the table from the manuscript, and the pocket-volume results together with their error bars are now shown in Figure 6. To address the concern raised here and to avoid making the same mistake in future analyses, we re-examined how the statistics were computed. We believe the very small p-values were caused by treating per-frame MD values as independent observations in two-sample t-tests. Because consecutive MD frames are strongly time-correlated, they do not satisfy the independence assumption, which can greatly overestimate the effective sample size and lead to artificially small p-values. For the SASA, a p < 0.0001 is reported even though both values are shown as 155 ± 3. This is due to rounding, which can hide subtle underlying differences.

I also remain concerned about comparisons between simulations run with the AB-MD scheme. While each simulation is an equilibrium simulation run without biasing forces, new simulations are seeded to expand the conformational sampling of the system. This means that by definition the ensemble of simulations does not represent and equilibrium ensemble. For example, the frequency at which conformations are sampled would not be the same as in a single much longer equilibrium simulation. While you may be able to see trends in the differences between conditions run in this way, I still don't understand how you can compare quantitative information without some method of reweighing the ensemble. It is not clear that such a rewieghting exists for this methods, in which case I advise some more caution in the wording of the comparisons made from this data.

At this stage I don't feel the revision has directly addressed the main comments I raised in the earlier review, although there is a stronger response to the comments of Reviewer #2.

We thank the reviewer for reiterating this important point, and we agree with the underlying concern. Although AB-MD generates unbiased trajectories, the ensemble of simulations does not represent an equilibrium ensemble. As a result, statistics computed by simply concatenating all AB-MD trajectories should not be used for quantitative comparisons. In the original version, we acknowledge that we reported several quantitative descriptors directly from concatenated AB-MD frames, including (i) distributions of χ1 torsions, (ii) mean pocket volumes and SASA, and (iii) percentages of some key interactions. We agree that this was not appropriate given the adaptive sampling protocol. In the revised manuscript, we have removed these quantitative analyses.

We retained RMSD and RMSF analyses, but we have revised their wording and clarified their purpose. RMSD and RMSF are used only to summarize the structural variability and residue-level mobility observed across the collected trajectory segments and to motivate the selection of structural features for MSM construction. The manuscript now states: “Because AB-MD adaptively seeds new unbiased trajectories to expand conformational sampling, RMSD and RMSF are used here to summarize the structural variability and per-residue mobility observed across the collected trajectories.”

Regarding the reviewer’s question about reweighting, the Markov state model (MSM) provides a principled framework to obtain the stationary distribution π from the transition probability matrix T_τ. The resulting π<sub>i</sup> gives the equilibrium weight of each microstate i, and the corresponding discrete free energy can be written as Fⁱ=−k_BTln(π<sub>i</sup>). PCCA then coarse-grains the microstate space into a small number of metastable states. In the revised manuscript, quantitative comparisons are therefore derived from the MSM at the level of these metastable states, rather than from unweighted counts of concatenated AB-MD frames.

Accordingly, we have revised the sections “E219K and Y221A mutations facilitate proton transfer” and “Substitutions enlarge the active-site pocket to accommodate bulkier R1 and R2 groups of β-lactams”, and we have added new figures in Figure 6 and its figure supplement. The adjustments to the quantitative analyses do not affect our original conclusions.

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

Reviewer #1 (Public review):

Summary:

This manuscript uses adaptive sampling simulations to understand the impact of mutations on the specificity of the enzyme PDC-3 β-lactamase. The authors argue that mutations in the Ω-loop can expand the active site to accommodate larger substrates.

Strengths:

The authors simulate an array of variants and perform numerous analyses to support their conclusions. The use of constant pH simulations to connect structural differences with likely functional outcomes is a strength.

Weaknesses:

I would like to have seen more error bars on quantities reported (e.g., % populations reported in the text and Table 1).

We appreciate this point. Here, the population we analyze is intended to showcase conformational differences across variants rather than to estimate equilibrium occupancies. Although each system includes 100 trajectories, they were generated using an adaptive-bandit protocol. The protocol deliberately guides towards underexplored basins, therefore conformational heterogeneity betweentrajectories is expected by design. For example, in E219K the MSM decomposition shows that in states 1, 6, and 7 the K67(NZ)–S64(OG) distance is almost entirely > 6 Å, whereas in states 2 and 3 it is almost entirely < 3.5 Å (Figure 5—figure supplement 12). These distances suggest that the hydrogen bond fraction is approximately zero in states 1, 6, and 7, and close to one in states 2 and 3. In addition, the mean first passage time of the Markov state models suggests that the formation and disruption of this hydrogen bond occur on the microsecond timescale, which is far longer than the length of each individual trajectory (300 ns). Consequently, across the 100 replicas, some trajectories exhibit very low fractions, while others display the opposite trend. Under such bimodal, protocol-induced heterogeneity, computing an error bar across trajectories mainly visualizes the protocol’s dispersion and risks being misread as thermodynamic uncertainty, which is not central to our aim of comparing conformational differences between wild-type PDC-3 and variants. We therefore do not include the error bars. 

Reviewer #2 (Public review):

Summary:

In the manuscript entitled "Ω-Loop mutations control dynamics of the active site by modulating the 3 hydrogen-bonding network in PDC-3 4 β-lactamase", Chen and coworkers provide a computational investigation of the dynamics of the enzyme Pseudomonas-derived cephalosporinase 3 (PDC3) and some mutants associated with increased antibiotic resistance. After an initial analysis of the enzyme dynamics provided by RMSD/RMSF, the author concludes that the mutations alter the local dynamics within the omega loop and the R2 loop. The authors show that the network of hydrogen bonds is disrupted in the mutants. Constant pH calculations showed that the mutations also change the pKa of the catalytic lysine 67, and pocket volume calculations showed that the mutations expand the catalytic pocket. Finally, time-independent component analysis (tiCA) showed different profiles for the mutant enzyme as compared to the wild type.

Strengths:

The scope of the manuscript is definitely relevant. Antibiotic resistance is an important problem, and, in particular, Pseudomonas aeruginosa resistance is associated with an increasing number of deaths. The choice of the computational methods is also something to highlight here. Although I am not familiar with Adaptive Bandit Molecular Dynamics (ABMD), the description provided in the manuscript suggests that this simulation strategy is well-suited for the problem under evaluation.

Weaknesses:

In the description of many of their results, the authors do not provide enough information for a deep understanding of the biochemistry/biophysics involved. Without these issues addressed, the strength of the evidence is of concern.

We thank the reviewer for pointing out the need for deeper discussion of the biochemical and biophysical implications of our results. In our manuscript, we begin by examining basic structural metrics (e.g., RMSD and RMSF) which clearly indicate that the major conformational changes occur in the Ω-loop and the R2 loop. We have now added a paragraph to describe the importance of the Ωloop and highlighted it in the revised manuscript on lines 142-166 of page 6. This observation guided our subsequent focus on these regions, as well as on the catalytic site. Our analysis revealed notable alterations in the hydrogen bonding network—especially in interactions involving the K67-S64, K67N152, K67-G220, Y150-A292, and N287-N314 pairs. These observations led us to conclude that:

(1) Mutations E219K and Y221A facilitate the proton transfer of catalytic residues. This is consistent with prior experimental data showing that these substitutions produce the most pronounced increase in sensitivity to cephalosporin antibiotics (lines 210-212 in page 8 of the revised manuscript). 

(2) Substitutions enlarge the active-site pocket to accommodate bulkier R1 and R2 groups of β-lactams.This is in line with MIC measurements reported by Barnes et al. (2018), which showed that mutants with larger active-site pockets exhibit markedly greater sensitivity to cephalosporins with bulky side chains than others (lines 249-259 in pages 10).

Furthermore, we applied Markov state models (MSMs) to explore the timescales of the transitions between these different conformational states. We believe that these methodological steps support our conclusions.

Reviewer #3 (Public review):

Summary:

This manuscript aims to explore how mutations in the PDC-3 3 β-lactamase alter its ability to bind and catalyse reactions of antibiotic compounds. The topic is interesting, and the study uses MD simulations to provide hypotheses about how the size of the binding site is altered by mutations that change the conformation and flexibility of two loops that line the binding pocket. However, the study doesn't clearly describe the way the data is generated. While many results appear significant by eye, quantifying this and ensuring convergence would strengthen the conclusions.

Strengths:

The significance of the problem is clearly described, and the relationship to prior literature is discussed extensively.

Weaknesses:

The methods used to gain the results are not explained clearly, meaning it was hard to determine exactly how some data was obtained. The convergence and uncertainties in the data were not adequately quantified. The text is also a little long, which obscures the main findings.

We thank the reviewer for the suggestion. We respectfully ask the reviewer to specify which aspects of the data-generation methods are unclear so that we can include the necessary details in the next revision. Moreover, all statistics that are reported in the manuscript are obtained from extensive analyses of 300,000 simulation frames. The Markov state models have been validated by the ITS plots and Chapman-Kolmogorov (CK) test. The two-sample t-tests were also carried out for the volume and SASA.

Reviewer #2 (Recommendations for the authors):

(1) Figure 1D focus on the PDC3 catalytic site. However, the authors mentioned before that the enzyme has two domains, an alpha domain and an alpha/beta domain. The reader would benefit from a more detailed description of the enzyme, its active site, AND the location of the mutants under investigation in the figure.

We have updated Figure 1D and marked the positions of all mutations (V211A/G, G214A/R, E219A/G/K and Y221A/H), which have now been highlighted as spheres.

(2) Since in the journal format, the results come before the methods. It would be interesting to add a brief description of where the results came from. For example, in the first section of the results, the authors describe the flexibility of the omega loop and the R2 loop. However, the reader won't know what kind of simulation was used and for how long, for example. A sentence would add the required context for a deeper understanding here.

At the beginning of the Results and Discussion section we now state: “To investigate how the mutations in the Ω-loop affect PDC-3 dynamics, adaptive-bandit molecular dynamics (AB-MD) simulations were carried out for each system. 100 trajectories of 300 ns each (totaling 30 μs per system) were run.”

(3) Still in the same section, the authors don't define what change in RMSF is considered significant. For example, I can't see a relevant change in the RMSF for the omega loop between the et enzyme and the E219 mutants in Figure 2D. A more objective definition would be of benefit here.

Our analysis reveals that while the wild-type PDC-3 and the G214A, G214R, E214G, and Y221A variants exhibit an average per-residue RMSF of around 4 Å in the Ω-loop, the V211A and V211G variants show markedly lower values (around 1.5 Å), and the E219K and Y221H variants exhibit intermediate values between 2 and 2.5 Å. In addition, the fluctuations around the binding site should be seen collectively along with the fluctuations in the R2-loop. Importantly, we urge the reviewer to focus on the MDLovofit analysis in Figure 2C, where the dynamic differences between the core and the fluctuating loops is clearly evident.  

(4) In line 138, the authors state that "Therefore, the flexibility of these proteins is mainly caused by the fluctuations in the Ω-loops and R2-loop". This is quite a bold statement to be drawn at this point. First of all, there is no mention of it in the manuscript, but is there any domain movement? Figure 2C clearly shows that there is some mobility in omega and R2 loops. But there is no evidence shown in the manuscript that shows that "the flexibility of these proteins is mainly caused by the fluctuations in the" loops. Please consider rephrasing this sentence or adding more data, if available.

We have revised the wording to take the reviewer’s concern into account. The sentence now states: “Therefore, flexibility of PDC-3 is predominantly localized to the Ω- and R2-loops, whereas the remainder of the structure is comparatively rigid.” To further explain to the reviewer, the β lactamase enzymes are fairly rigid structures, where no large-scale domain motions occur. Instead, the enzyme communicates structurally via cross correlation of loop dynamics ( https://doi.org/10.7554/eLife.66567 ).  

(5) I guess, the most relevant question for the scope of the paper is not answered in this section. The authors show that the mobility of the omega- and R2-loops is altered by some mutations. Why is that? I wish I could see a figure showing where the mutations are and where the loops are. This question will come back in other sections.

We have updated Figure 1D to mark the positions of all mutations (V211A/G, G214A/R, E219A/G/K and Y221A/H) as spheres. The Ω- and R2-loops are also highlighted. All mutations map to the Ω-loop, indicating that these substitutions directly perturb this region. Notably, K67 forms a hydrogen bond with the backbone of G220 within the Ω-loop and another with the phenolic hydroxyl of Y150. Y150, in turn, hydrogen-bonds with A292 in the R2 loop. Together, the residue interaction network (G220– K67–Y150–A292) suggest a pathway by which Ω-loop mutations propagate their effects to the R2 loop.

(6) The authors then analyze the network of polar residues in the active site and the hydrogen bonds observed there. For the K67-N152 hydrogen bond, for example, there is a reduction in the occupancy from ~70% in the wild-type enzyme to ~30% and 40% in the mutants E219K and Y221, respectively. This finding is interesting. The question that remains is "why is that"? From the structural point of view, how does the replacement of E219 with a Lysine alter the hydrogen bond formation between K67 and N152? Is it due to direct competition? Solvent rearrangement? The reader is left without a clue in this section. Also, Figure 3B won't help the reader, since the mutated residues are not shown there. Please consider adding some information about why the authors believe that the mutations are disrupting the active site hydrogen bond network and showing it in Figure 3B.

We appreciate the comment and have updated Figures 1D and 3B to highlight the mutation sites. The change from ~70% in the wild type to ~30–40% in the E219K and Y221T variants reported in Table 1 refers to the S64–K67 hydrogen bond. In the wild type, K67 forms an additional hydrogen bond with G220 on the Ω-loop, which helps anchor the K67 side chain in a geometry that favors the S64–K67 interaction. In the variants, the mutations reshape the Ω-loop and frequently disrupt the K67–G220 contact. The loss of this local anchor increases the conformational dispersion of K67, which is consistent with the observed reduction of the S64–K67 occupancy. Furthermore, our observation that the mutations are disrupting the active-site hydrogen-bond network is a data-driven conclusion rather than a subjective inference. Across ten systems, our AB-MD simulations provided 30 µs of sampling per system. Saving one frame every nanosecond yielded 30,000 conformations per system and 300,000 in total. All hydrogen-bond and salt-bridge statistics were computed over this full ensemble. Thus, the conclusion that the mutations disrupt the active-site hydrogen-bond network follows directly from these ensemble statistics. 

(7) The pKa calculations and the pocket volume calculations show that the mutations expand the volume of the catalytic site and alter the microenvironment. Is there any change in the solvation associated with these changes? If the volume expands and the environment becomes more acidic, are there more water molecules in the mutants as compared to the wt enzyme? If so, can changes in solvation be associated with the changes in the hydrogen bond network? Would a simulation in the presence of a substrate be meaningful here? ( I guess it would!).

Regarding solvation, we observe a modest increase in transient water occupancy associated with the increase in volume of the pocket. The conserved deacylation water molecule is the most important and is always present throughout the simulation. Additional waters enter and leave the pocket but do not form persistent interactions that measurably perturb the hydrogen-bond network of the Ω- and R2-loops. We agree that simulations with a bound substrate would be informative. However, our study focuses on how Ω-loop mutations modulate the active site of apo PDC-3 and its variants. Within this scope, we find: (i) Amino acid substitutions change the flexibility of Ω-loops and R2-loops; (ii) E219K and Y221A mutations facilitate the proton transfer; (iii) Substitutions enlarge the active-site pocket to accommodate bulkier R1 and R2 groups of β-lactams.

(8) I have some concerns regarding the Markov State Modeling as shown here. After a time-independent component analysis, the authors show the projections on the components, which is different between wild wild-type enzyme and the mutants, and draw some conclusions from these changes. For example, the authors state that "From the metastable state results, we observe that E219K adopts a highly stable conformation in which all the tridentate hydrogen-bonding interactions (K67(NZ)-S64(OG), K67(NZ)N152(OD1) and K67(NZ)-G220(O) mentioned above are broken". This is conclusion is very difficult to draw from Figure 5 alone. Unless the macrostates observed in the MSM can be shown (their structures) and could confirm the broken interactions, I really don't believe that the reader can come to the same conclusion as drawn by the authors here. I would recommend the authors to map the macrostates back to the coordinates and show them (what structure corresponds to what macrostate). After showing that, it makes sense to discuss what macrostate is being favored by what mutation. Taking conclusions from tiCA projections only is not recommended. I very strongly suggest that the authors revisit this entire section, adding more context so that the reader can draw conclusions from the data that is shown.

We appreciate the reviewer’s concern. In the Markov state modeling section, our objective is to quantify the timescales (via mean first passage times) associated with the formation and disruption of the critical hydrogen bonds (K67(NZ)-S64(OG), K67(NZ)-N152(OD1), K67(NZ)-G220(O), Y150(N)A292(O), N287(ND2)-N314(OD1)) mentioned above. Representative structures illustrating these interactions are shown in Figures 3B and 4A. We agree that the main Figure 5 alone does not convey structural information. Accordingly, we provide Figure 5—figure supplements 12–16. Together, Figure 5B and Figure 5—figure supplements 12–16 map structures to metastable states, whereas Figures 3B and 4A supply atomistic detail of the interactions. Author response image 1 presents selected subplots from Figure 5— figure supplements 12–14. Together with the free-energy landscape in Figure 5A, these data indicate that E219K adopts a highly stable conformation in which all three K67-centered hydrogen bonds (K67(NZ)–S64(OG), K67(NZ)–N152(OD1), and K67(NZ)–G220(O)) are broken.

Author response image 1.

TICA plot illustrates the distribution of E219K with the colour indicating the K67(NZ)-S64(OG), K67(NZ)-N152(OD1) and K67(NZ)-G220(O) distance.

(9) As a very minor issue, there are a few typos in the manuscript text. The authors might want to take some time to revisit their entire text. Examples in lines 70, 197, etc.

Thank you for your comment. We have corrected these typos.

Reviewer #3 (Recommendations for the authors):

This manuscript aims to explore how mutations in the PDC-3 3 β-lactamase alter its ability to bind and catalyse reactions of antibiotic compounds. The topic is interesting, and the study uses MD simulations to provide hypotheses about how the size of the binding site is altered by mutations that change the conformation and flexibility of two loops that line the binding pocket.

However, the study doesn't clearly describe the way the data is generated and potentially lacks statistical rigour, which makes it uncertain if the key results are significant. As such, it is difficult to judge if the conclusions made are supported by data.

All necessary data-acquisition methods are described in the Methods section. The Markov state models have been validated by the ITS plot and the Chapman-Kolmogorov (CK) test (Figure 5—figure supplement 2–11) . The two-sample t-tests were also carried out for the volume and SASA (Table 2).

The results section jumps straight to reporting RMSD and RMSF values; however, it is not clear what simulations are used to generate this information. Indeed, the main text does not mention the simulations themselves at all. The methods section mentions that 10 independent MD simulations were set up for each system, but no information is given as to how long these were run or the equilibration protocol used. Then it says that AB-MD simulations were run, but it is not clear what starting coordinates were used for this or how the 10 replicates were fed into these simulations. Most importantly, are the RMSD and RMSF calculations and later distance distribution information derived from the equilibrium MD runs or from the AB-MD simulations?

Thank you for pointing this out. We have added “To investigate how the mutations in the Ω-loop affect PDC-3 dynamics, adaptive-bandit molecular dynamics (AB-MD) simulations were carried out for each system. 100 trajectories of 300 ns each (totaling 30 μs per system) were run.” to the Results and Discussion section. We didn’t run 10 independent MD simulations per system. We regret the typo in the Methods section that confused the reviewer. The sentence should have read – ‘All-atom MD simulations of wild-type PDC-3 and its variants were performed.’ Each system was equilibrated for 5 ns at 1 atmospheric pressure using Berendsen barostat. AB-MD simulations were initiated from these equilibrated structures. All analyses, apart from CpHMD, are based on the AB-MD trajectories.

If these are taken from the equilibrium simulations, then it is critical that the reproducibility and statistical significance of the simulations is established. This can be done by calculating the RMSD and RMSF values independently for each replicate and determining the error bars. From this, the significance of differences between WT and mutant simulations can be determined. Without this, I have no data to judge if the main conclusions are supported or not. If these are derived from the AB-MD simulations, then I want to know how the independent simulations were combined and reweighted to generate overall RMSD, RMSF, and distance distributions. Unless I misunderstand the approach, the individual simulations no longer sample all regions of conformational space the same relative amount you would see in a standard MD simulation - specific conformational regions are intentionally run more to enhance sampling, then the overall conformational distributions cannot be obtained from these simulations without some form of reweighting scheme. But no such scheme is described. In addition, convergence of the data is required to ensure that the RMSD, RMSF, and distances have reached stable values. It is possible that I am misunderstanding the approach here. But in that case, I hope the authors can clarify the method and provide a means of ensuring that the data presented is converged. Many of the differences are clear by eye, but it is important to know they are not random differences between simulations and rather reflect differences between them.

Thank you for raising this important point. In our AB-MD workflow, the adaptive bandit is used only for starting-structure selection (adaptive seeding). After each epoch, it chooses new starting snapshots from previously sampled conformations and launches the next runs. Each trajectory itself is standard, unbiased MD with no biasing potentials and no modification of the Hamiltonian. In other words, AB decides where we start, but does not alter the physics or sampling dynamics within an individual trajectory. In addition, our goal in this work is to compare variants under the same adaptive-bandit (AB) protocol, rather than to estimate equilibrium (Boltzmann) populations. Hence, we did not apply equilibrium reweighting to RMSD, RMSF, or distance distributions. However, MSM section provides reweighted reference results based on the MSM stationary distribution.

In the response to reviews, the authors state that the "RMSF is a statistical quantity derived from averaging the time series of atomic displacements, resulting in a fixed value without an inherent error bar." But normally we would run multiple replicates and get an error bar from the different values in each. To dismiss the request for uncertainties and error bars seems to miss the point. I strongly agree with the prior reviewer that comparisons between RMSF or other values should be accompanied by uncertainties and estimates of statistical significance.

Regarding the reviewers’ suggestion to present the data as a bar graph with error bars, we would like to note that RMSF is calculated as the time average of the fluctuations of each residue’s Cα atom over the entire simulation. As such, RMSF is a statistical quantity derived from averaging the time series of atomic displacements, resulting in a fixed value without an inherent error bar. We believe that our current presentation clearly and accurately reflects the local flexibility differences among the variants. Nearly all published studies report RMSF in this way, as indicated by the following examples:

Figure 3a in DOI: https://doi.org/10.1021/jacsau.2c00077

Figure 2 in DOI: https://doi.org/10.1021/acs.jcim.4c00089

Supplementary Fig. 1, 2, 5, 9, 12, 20, 22, 24, and 26 in DOI: https://doi.org/10.1038/s41467-022-293313

However, in response to the reviewers’ strong request, we present RMSF plots with error bars in our response letter. 

Author response image 2.

The root-mean-square fluctuation (RMSF) profiles of wild-type PDC-3 and its variants. Blue lines show the mean RMSF across 100 independent MD trajectories for each system; red translucent bands denote the standard deviation across trajectories. The Ω-loop (residues G183 to S226) is highlighted in yellow, and the R2-loop (residues L280 to Q310) is highlighted in blue.

It was good to see that convergence of the constant-pH simulations was shown. While it can be challenging to get absolute pH values from the implicit solvent-based simulations, the differences between the systems are large and the trends appear significant. I was not clear how the starting coordinates were chosen for these simulations. Is the end point of the classical simulations, or is a representative snapshot chosen somehow?

To ensure comparison, all systems used the X-ray crystal structure (PDB ID: 4HEF) with T79A substitution as the initial structure. The E219K and Y221A mutants were generated in silico using the ICM mutagenesis module. We have added the clarification in Methods section: “The starting structures were identical to those used for AB-MD.”

Significant figures: Throughout the text and tables, the authors present data with more figures than are significant. 1071.81+-157.55 should be reported as 1100 +/ 160 or 1070 =- 160 . See the eLife guidelines for advice on this.

Thank you for your suggestion. We have amended these now. 

The manuscript is very long for the results presented, and I feel that a clearer story would come across if the authors shortened the text so that the main conclusions and results were not lost.

We appreciate the suggestion. We examined the twenty most recent research articles published in eLife and found that they are either longer than or comparable in length to our manuscript.

Developmental psychologists divide the human lifespan into distinct age-related periods 1. Prenatal Development Begins at conception and ends with birth — rapid biological change.

1. Infancy and Toddlerhood Birth to about 2–3 years — major physical, motor, and sensory development.

2. Early Childhood (Preschool Years) About ages 2–6 — language expansion, social interaction, basic self-control skills.

3. Middle Childhood About ages 6–11 — school learning, friendships, logical thinking.

4. Adolescence Typically puberty to late teens — identity exploration, abstract reasoning.

5. Early Adulthood Approximately ages 18–40 — forming intimate relationships, career establishment.

6. Middle Adulthood Approximately 40–65 — sustaining careers, parenting, physical aging signs.

7. Late Adulthood 65 and older — reflection on life, adapting to physical changes, retirement.

Some models also include emerging adulthood (18–25) and discussions of death and dying

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

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Reply to the reviewers

Reviewer #1 (R1)

R1 General statement: Here, Escalera-Maurer and colleagues, present an up-to-date distribution of homologues of Hok toxic proteins belonging to the well-annotated, but otherwise functionally obscure, hok/Sok type I toxin-antitoxin system, across the RefSeq database. Although such computational analyses have been done in the past, the authors here find many more hok homologs than described before, and they categorise their distribution based on whether they are encoded on chromosomes, plasmids, or (pro)phages. These computational analyses are in general tricky with T1TAs, as their toxins are quite short (~50 amino acids, as is the case for Hok), which is why the authors here used three separate approaches to expand their search (nucleotide-level BLAST, protein-homology, or both combined with Infernal). The authors cluster the Hok homologues they find based on a 60% sequence identity cut-off (expanding the known clusters in the process), and proceeded to test 31 candidates belonging to 15 sequence-clusters for their toxicity in Salmonella Typhimurium LT2, showing that 30/31 were toxic upon induction. An interesting finding from their endeavours is that hok/Sok homologues are enriched within prophages and large plasmids, but are not enriched near bacterial anti-phage defense systems (in contrast to the SymE/SymR T1TA). The findings suggest that hok/Sok are indeed sometimes linked to phage and plasmid biology, although they might not be antiphage defenses per se (they have been clearly shown in the past to be addiction modules, and this is still clearly true).

Authors' answer to R1 General statement: __We do not state here that hok/Sok are not anti-phage defense systems, but we simply observe that they do not cluster with anti-phage defense systems. We have also observed (unpublished data) that known defense systems do not systematically cluster together with other defense systems. Therefore, strong association with other defense systems would have been a strong indication of their function in phage defense but the fact that we did not observe any association with defense systems does not exclude they are involved in phage defense. __

R1_C1: My expertise lies towards the experimental side of the authors' work, I thus cannot comment on the accuracy/robustness of the computational analyses performed here. The authors do a fine job in clearly stating their findings overall; I could follow most of the conclusions, and I deemed that most of them were supported by their work. Additionally, I find that this paper is a missed opportunity to uncover even more novel biology connected to the interesting hok/Sok T1TAs. The paper does not provide a new framework to think about what is the function of the chromosomal/prophage hok/Sok T1TA systems, although I realize that this is very difficult to accomplish, especially when considering that hok/Sok systems have been around in the literature for almost 40 years.

Authors' answer to R1_C1: We agree with the reviewer, as we indeed performed this analysis having in mind to clarify the role of hok/Sok systems. However, we still believe that our strong survey of Hok loci put in light their enrichment in various mobile genetic elements, such as prophage and large conjugative plasmids, which is indubitably linked to their function. In addition, our study will guide future experimental efforts in uncovering the function of these systems, for example by helping researchers to select relevant homologs to test for a specific function.__ __

R1_C2: My major comment is in regard to the Hok toxicity assays (Fig. 2). The authors state in the discussion that "Hok peptides originating from chromosomes are as toxic as those from plasmids", but I believe that the way that they tested their constructs might not have allowed them to see toxicity differences between the two groups. Specifically, using the multi-copy plasmid pAZ3 (pBR322 origin of replication; ~15-20 plasmid copies per chromosome) to induce the different Hok toxin homologues in Salmonella Typhimurium LT2 with arabinose might have masked toxicity differences that would otherwise be apparent on the chromosomal expression-level.

Some of the authors themselves have previously used the FASTBAC-Seq method to study the Hok homologue from plasmid R1, a useful technique during which a toxin is integrated in the chromosome, in order to study their toxicity under natural levels of expression. I believe that an ideal scenario would be to apply FASTBAC-seq to some of the 31 Hok homologues described here (e.g., a subset of plasmidic vs chromosomal Hok homologues) to shed light on potential toxicity differences between the Hok clusters. This would increase the value of the presented study.

Alternatively, the authors could employ an L-arabinose concentration gradient to titrate the expression levels of the Hok toxins in order to potentially see different toxicity levels from the different homologues. However, this is not going to work in the system as they are using it now for two reasons:

1. a) the S. Typhimurium LT2 (STm) used here has its arabinose utilization operon intact (araBAD), which means that Salmonella can catabolize arabinose to use it as a carbon source. This catabolization process interferes with the arabinose induction (i.e., Salmonella eats arabinose instead of using it as the Hok inducer). To ameliorate this, the authors could delete the araBAD operon in STm, rendering STm incapable of catabolizing arabinose, and repeat the experiments in that strain. Or use E. coli BW25113 as the expression host, which already has the araBAD operon deleted (it is not clear to me why the different Hok homologues would not be toxic in E. coli, as the different Hok homologues are widely diverse in sequence, as the authors found here).
2. b) Even with the araBAD operon deleted, the arabinose induction would be bimodally on or off in the population, due to the bimodal expression of the arabinose transporter (AraE; see Khlebnikov et al., 2002). This would again not allow for titratable arabinose-inducible expression from different concentrations of arabinose. The solution for this would be to co-express a separate plasmid with araE, which would render every cell the same in regards to arabinose permeability, and thus the system would be titratable (as explained in Khlebnikov et al., 2002). Therefore, if the authors would be interested to go towards this route, they would have to first delete the araBAD from STm, then transform STm with an araE plasmid, and redo the experiments. In addition, I would propose to the authors to use the drop plate method (agar plate-based), which is more sensitive compared to the liquid assays employed here.

Having said all that, I understand that all this experimental work would be strenuous and time-consuming, and although I would like to see it happen, this is not my paper. I would be content therefore if the authors toned down the claim that plasmidic vs chromosomal Hok homologues have the same toxicity, and discuss that chromosomal levels of toxicity are an important caveat that has not been explored here.

__Authors' answer to R1_C2: __ We thank the reviewer for the detailed suggestion on how to better assess toxicity differences by using an araBAD deletion mutant overexpressing araE. We repeated the arabinose induction assays using drop assays and strain BW25223 with plasmid pJAT13araE and our pAZ3 based plasmid carrying Hok CDS homologs. However, we obtained similar data, not being able to distinguish between the toxicity of chromosomal versus plasmidic CDS, even using different concentration of Arabinose. This is probably because low concentration of the Hok protein are sufficient for activity, but here we are bypassing all post-transcriptional silencing by the native Hok mRNAs by expressing directly the protein, and we are using a multicopy plasmid. We now included 0.01% arabinose induction drop assays in the manuscript as the data obtained with other arabinose concentration did not provide new information. In any case, we are still not accessing the native expression levels for the following reasons 1/ chromosomal level of toxicity were not explored here and 2/ only the toxicity of the coding sequence but not the full mRNA was tested. Indeed, we do not know the exact sequence of the hok homolog mRNAs and this is beyond the scope of the study. These remarks were clearly added in the discussion.

We agree that the sentence "Hok peptides originating from chromosomes are as toxic as those from plasmids" was too strong and we have added the caveats of our experimental design in the discussion. While we indeed did not compare the toxicity of the peptides, we still showed that chromosomal Hok can be toxic upon overexpression, which would not be the case if the sequences were degenerated.

The reviewer also suggests the use of the FASTBAC-Seq method, that we previously used to study Hok from the R1 plasmid, which is a method to study toxic type I toxins at the native expression level. While FASTBAC-Seq identifies loss-of-function mutants of the systems, it does not allow to determine a difference of toxicity between systems per se. In addition, FASTBAC-Seq was always done in the context of the full mRNA, not only the coding sequence, and these sequences are presently unknown for most homologs.

Other comments:

__R1_C3: __a) There is barely any discussion of the Sok component (RNA antitoxin) of the homologues; why is that? Could you please discuss Sok differences across the homologues, or at least explain why this is not discussed at all in the paper (e.g., in the discussion)?

Authors' answer to R1_C3: __It is not trivial to identify the Sok RNA sequence, this is why it was not done in this study, a paragraph was added in the discussion explaining this. __

__R1_C4: __b) In the results section, the Hok clusters are referred to as 62 in number ("Because Hok sequences were too short and variable to construct a meaningful phylogenetic tree, we clustered the Hok sequences with a 60% identity threshold and obtained 62 clusters"), but then in the discussion section, the cluster number becomes 74 ("We highlighted the high sequence variability within Hok peptides by obtaining a total of 74 clusters with 60% identity (Fig. S7)."). Which one is the right number, and why is there a discrepancy?

Authors' answer to R1_C4: We apologize for the discrepancy between the number. The first number corresponded to the Hok hits from the refSeq and we then added the Hok hits from the plasmid and virus databases (performed later in the manuscript). We clarified this information both in the result and discussion texts (61 clusters from RefSeq and 79 in total, 74 was a typo).__ __

__R1 Significance: __The most well-clarified aspect of the paper presented here is the distribution of Hok homologues, with the novel aspect of the location in which the hok/Sok T1TAs reside (i.e., chromosome, plasmid, or phage). There is room for the molecular genetics part to be developed further, as I discussed earlier, however this study is the most up-to-date characterization of the diversity of Hok homologues, and will be of interest to the T1TA and the general toxin-antitoxin field.

__Reviewer #2 (R2) __

R2 General statement: The authors examined how the Hok toxins are spread across bacterial genomes. The manuscript including its figures is hard to read and understand. I commented figure 1 in details, but similar comments apply to the other figures. Overall, the data lack clarity and precision. Finding information about sequences, clusters in the supplementary materials was not easy. The manuscript should be thoroughly revised. In addition, I believe that other aspects should be developed to expand the interest of the study, such as the co-occurrence of multiple systems in chromosomes, on plasmids and whether they are able to crosstalk. This might provide some evolutionary insights into the biology of these toxins.

__Authors' answer to R2 General statement: __We designed all figures according to established standards for scientific data visualization, although we recognize that different presentations may work better for different audiences. In our detailed response to Figure 1A, we explain how UpSet plots are constructed and interpreted, which we hope clarifies the visualization approach for the full dataset. We are open to discussing specific improvements if the reviewer has suggestions for enhanced clarity. To address concerns about accessibility, we want to clarify that all sequences are compiled in Table S1 with their clus100 identifiers, making them easy to locate. We are open to reorganizing supplementary materials if a different structure would be more user-friendly. Finally, we agree that an extensive analysis of co-occurrences and crosstalks would be valuable. However, predicting crosstalk bioinformatically for all genomes presents challenges, as it would require predicting RNA:RNA interactions between hok mRNA and Sok sequences, which are currently unknown. Given these limitations, this analysis was beyond the scope of the current study.

R2_C1: The introduction lacks information regarding the Hok protein (size, structure prediction, localization) as well as a bit of explanation about the reason of looking at these toxins. The description of the potential roles should be a bit expanded.

Authors' answer to R2_C1: Following the comment from the reviewer, we have provided additional information about Hok in the introduction.

__R2_C2: __When the authors talk about 'loci', they mean genes encoding Hok homologs if I understand correctly. They did not look for the Sok sequences (hok-sok loci).

__Author's answer to R2_C2: __Indeed, we did not look for the Sok sequences and we are only describing Hok homologs loci, that could either encode or lack a Sok homolog.

__R2_C3: __It is not clear what the authors did with the sequences for which they could not detect a start codon and a SD (although it is unusual to refer to SD in the context of protein sequence)

Authors' answer to R2_C3: The peptides were annotated by extending the initial hit until the first start codon. Therefore, all annotated peptides have a start codon. Shine-Dalgarno sequences were annotated when confidently predicted, to provide additional information. Sequences were not excluded based on the presence or absence of the SD.

__R2_C4: __Figure 1A is not clear. The total of the bars equal 32,532 which is the number of 'loci' detected by the combination of the different methods. However, it is not clear to me how many are redundant. For instance, I suppose that all the 8483 sequences that were retrieved using blastn and Infernal were retrieved using MMseqs2, blastn and Infernal. So, what is the actual number of sequences that were found? When the authors talk about 1264 distinct peptides, what do they mean? What are the numbers on the X axis (18209, 2260, 27728)?

Author's answer to R2_C4: Figure A1 is a very typical "UpSet" plot, as indicated in the legend (A. Lex, N. Gehlenborg, H. Strobelt, R. Vuillemot and H. Pfister, "UpSet: Visualization of Intersecting Sets," in IEEE Transactions on Visualization and Computer Graphics, vol. 20, no. 12, pp. 1983-1992, 31 Dec. 2014, doi: 10.1109/TVCG.2014.2346248). Those plots are a data visualization method for showing data with more than two intersecting sets. The Hok sequence hits were obtained by 3 different methods stated on the rows (MMseqs2, blastn and Infernal, therefore the number 18209 is the number of hits by the MMseqs2, 22680 the number of hits by blastn and 27728 the number of hits by Infernal). The columns show the intersections between these three sets. For example, the mentioned 8483 sequences (second column) were only found by blastn and Infernal but not by MMseqs2. The actual total number of sequences found is indeed 32 532. The 1264 distinct peptides are peptides with different sequences. After removing false positives, degenerated sequences and small peptides, we obtained 1264 unique Hok sequences that are found in the 32532 bacterial loci.

__R2_C5: __About Infernal: first the authors are stating that only 8% of the sequences are lost when not considering the mRNA structure - which they seem to consider as negligeable. Then in the next section, they state that Infernal is the best tool at identifying clusters that are not detected otherwise. Seems a bit contradictory.

__Authors' answer to R2_C5: __We appreciate the reviewer pointing out this apparent contradiction, we have clarified this part in the revised manuscript. Infernal uses both sequence and structure information simultaneously for homology detection. While only 8% of Infernal's hits are detected uniquely when structural information was considered, these sequences account for 9 additional clusters with notably high sequence diversity, which would otherwise have been undetected. Therefore, we believe that Infernal is the best tool to capture novel cluster diversity.

__R2_C6: __Cluster determination. The threshold was put at 60% identity. What is the rationale for the 60% identity? Given that the Hok sequences (like toxins and antitoxins from TA systems in general) are highly variable, this leads to a high number of clusters. I'm not sure of the relevance of these clusters. Are there any other criteria to define clusters?

Authors' answer to R2_C6: We selected 60% identity as a balance between capturing sequence diversity and generating interpretable results. We also tested 70, 80 and 90% and obtained 128, 221, 377 clusters, respectively, which would be too many for a meaningful visualization and interpretation. The best clustering method would be constructing a phylogenetic tree. However, as explained in the discussion, because the high sequence diversity prevented the construction of a reliable phylogenetic tree, clustering was used as an alternative strategy to identify and interpret patterns of sequence variability.

__R2_C7: __The authors claim that most of the Hok diversity is found on chromosomes. However, the number of chromosomal Hok is higher than that located on plasmids, which might be related to the different sizes of the different replicons ie, chromosomes being larger than plasmids. Is there a way to normalize by determining the density per size?

Authors' answer to R2_C7: We do not claim that chromosomes contain most of Hok diversity, as this would be indeed influenced by biases in the databases. We are just describing that we found most of the diversity in chromosomes, but we cannot conclude whether this is a true representation of the frequencies in nature.__ __

R2_C8: '46 of the 62 clusters contained 10 or less distinct sequences and might be in the process of degenerating'. The authors also linked this with SD detection. Please explain. From what was indicated earlier, I understand that sequences with premature stop codons or short sequences (Authors' answer to R2_C8: We did not remove sequences for which we could not predict the SD. Indeed, lacking SD is a sign that the hok mRNA might not be able to play its biological role and would be indicative that the sequences have degenerated. To evaluate this hypothesis, we experimentally tested 5 sequences without a predicted SD and two of those were not toxic (see Table S2). In order to assess if the low abundant clusters contained degenerated sequences we experimentally tested representatives from some of the clusters with only one Hok CDS and found most of them to be toxic.

R2_C9: 'Only 7.3% of the unique sequences were found on both plasmids and chromosomes'. From this observation, the authors conclude that 'there is little stable transfer from chromosomes to plasmids or vice-versa'. I don't understand what this means. Do they mean identical sequences? The fact that sequences differ from chromosomes to plasmids does not rule out 'stable transfer'. What do they actually mean by stable transfer? Once the gene is horizontally transferred, it is fixed and vertically transmitted? Same comments apply to the inter-genera horizontal transfer by plasmids.

__Authors' answer to R2_C9: __Due to the impossibility of constructing a reliable phylogenetic tree, we used identity of sequences across different localizations or genera as our marker for recent, stable transfer events. We define stable transfer as the persistence of sequences in an unchanged form following horizontal transfer; long enough to be detected in current databases. Our approach likely underestimates total transfer events, as sequences accumulating mutations after transfer would not be captured. We would expect to observe numerous identical sequences across plasmids and chromosomes if frequent exchange were occurring, unless rapid mutation after the transfer prevented their detection as identical sequences. We have added a sentence to clarify this in the manuscript and removed the term stable transfer.

__R2_C10: __I don't understand the next section about 'family'. What do the authors mean about 'family'? Genera? The same apply to the next section about the Y to C recoding. Did the authors do point mutations in the conserved amino acids/codons to test whether they are important for toxicity? Some Hok variants lacks some of the conserved amino acids and are toxic (under overexpression conditions in Salmonella). What about T18, C31 and E42?

Authors' answer to R2_C10: Families (Enterobacteriaceae, Vibrionaceae etc... ) and genera (Escherichia, Salmonella etc...) refer to the taxonomic categories. Following the reviewer comment, we experimentally assessed the toxicity of Hok from R1 plasmid after mutating the conserved amino acids to alanine residues. All the mutants were found to be toxic under our expression conditions.

__R2_C11: __The prevalence of Hok in chromosomes or on plasmids might depend on various confounding parameters, such as the size, number of sequences available among others. The authors should find methods to correct for all that.

Authors' answer to R2_C11: Normalization would indeed be needed if we were comparing the prevalence on chromosomes vs the prevalence on plasmids. Here, we do not claim that Hok homologs are more prevalent in plasmid or chromosomes and only describe where we found them.

__R2_C12: __Link with defense systems. The threshold was set at 20 kb. Why this threshold?

Authors' answer to R2_C12: The size of defense islands in a previous report was approximately 40 kb, by setting up a 20 kb threshold we searched for defense systems in a region of 40 kb adjacent to each of the homologs (https://doi.org/10.1126/science.aar4120). If the specific homolog was part of a defense island we would expect that it is less than 20 kb apart from any defense system.

__R2 Significance: __The paper in its current state appears to serve the role of a data repository rather than a thorough and original analysis. It requires extensive revisions before it can be of interest to experts in the toxin-antitoxin field.

__ ____Reviewer #3 (R3): __

R3 General statement: In the manuscript, "The Hok bacterial toxin: diversity, toxicity, distribution and genomic localization," by Escalera-Maurer et al., investigate the distribution of Hok type I toxin proteins across bacterial species. The Hok-Sok type I toxin-antitoxin system was first described on plasmids where it serves to maintain the plasmid in a population of bacterial cells: translation of the hok mRNA is prevented via the small antitoxin RNA Sok. Upon plasmid loss, with no new transcription of sok, the highly stable hok mRNA is translated into a small protein, killing the plasmid-less cell. Homologues to the system were identified in the chromosome of E. coli in the 1990s, and subsequent analyses have identified identical systems in other bacterial chromosomes, though they are close relatives to E. coli. Given the increased number of bacterial genomes sequenced, the group examined how widespread Hok may be across bacteria. They used a combination of BLASTn, MMseqs2 (protein) and Infernal (RNA) to identify, as best possible, all possible homologs. They then used sequence identity cut-offs to form Hok "clusters," and identified key features of the cluster as well as tested toxicity of overproduction of 31 homologs in a strain of Salmonella. Overall, though a variety of bioinformatic predictions and analyses, the manuscript identifies an expanded number of Hok members not previously identified and broaden the species it is found in, supported that Hok is not associate with defense systems, and provides additional support that horizontal transfer of hok genes is likely via plasmids (where hok is presumed to have originated).

Major comments: There are some areas of the text that are a bit too definitive (these can be fixed or better explained in the text) and a few questions raised about the analyses and interpretations.

Authors' answer to R3 Major Comment: As suggested by the reviewer, we rephrased parts of the manuscript.

__These are the specific comments: __

Introduction R3_C1: First paragraph: "Toxin production leads to the death of the cell encoding it" For many chromosomally encoded systems, toxicity has only been observed via artificial overexpression. This is an important point, as for many systems, a true biological function remains unknown. Further, add caveats regarding toxin function (for systems with validated function, they are involved in...). Again, there are still many questions for many t-at systems, in particular the Type I systems.

__Authors' answer to R3_C1: __Indeed, the function of type 1 TA, in particular chromosomal ones, is still a matter of debate. While for hok/Sok R1, we previously showed death by expression at the chromosomal level, this was not shown for all TA (Le Rhun et al., NAR, 2023). We added that it could lead to the death or growth arrest of the cell instead and added the reviewer changes to for the function part.

__R3_C2: __Introduction: type I's are more narrow in distribution, but much of this is due to their size and lack of biochemical domains. Again, please clarify more here.

__Authors' answer to R3_C2: __We added the reviewer suggestion to the text.

__R3_C3: __Introduction: while Hok's have been found on chromosomes, in E. coli strains, there is clear evidence that many are inactive. This comes up in the discussion, but it is worth including briefly in the introduction.

Authors' answer to R3_C3: We have now added in the introduction that in the K12 laboratory strain, most chromosomal hok/Sok were found to be inactive.

__R3_C4: __For the predicted transmembrane domain: it would be worth to include a box/indication as to where that is within the peptide (with the understanding it may not be exact). Is there more/less variation here? I'm assuming all clusters/family have a predicted TM domain?

__Authors' answer to R3_C4: __When predicting the TM domain using DeepTMHMM - 1.0 prediction (https://services.healthtech.dtu.dk/services/DeepTMHMM-1.0/), 227 out of the 1264 unique Hok sequence are predicted to have a TM (transmembrane), 7 a SP (signal peptide) and a TM and 1025 have a SP. When predicting the TM of the consensus sequence (most abundant amino-acid) shown in Fig. 1D, region A8 to L25 is predicted to be inserted in the membrane, with the Nterm inside and Cterm outside.

__R3_C5: __What is the cutoff for being a Hok? Did they take the "last hit" and use that in additional searches to see if more appeared? If that was done, and the search was exhaustive, this really important to add for the reader.

Authors' answer to R3_C5: The MMseqs2 search was performed using 5 iterations as indicated in the M&M, meaning that the hits of the one search were used to search the database again five time in a raw. Importantly, an attempt to increase the number of iterations to 10 did not significantly increase the number of hits. Therefore, at least for the MMseqs2 search in the RefSeq database, we are close to being exhaustive.

__R3_C6: __Figure S4: the authors state that there was no difference in the degree of toxicity between the clusters. There do appear to be some peptides tested that at the arabinose concentration used did not repress growth as immediately as others. If higher arabinose concentration is used, does that eliminate these differences? OR are many of these suppressors-if diluted back again, do they grow as if they are non-toxic in arabinose?

Authors' answer to R3_C6: As suggested by Reviewer 1 (R1_C2), we performed titration of arabinose in a system overexpressing araE in a ΔaraBAD but were not able to find difference of toxicity in our conditions, see also our answer to R1_C2.

__R3_C7: __Discussion: "because non-functional homologs are expected to quickly accumulate mutations..." is a bit problematic. Hok is highly regulated-as are some of the other well-described type I toxins. In MG1655, while the coding sequence may be intact, there are other mutations and/or insertion elements that prevent expression (and be extension, function. Given the lack of consensus data for type Is, it is best to provide more context for this. If the authors wish to argue that they should quickly accumulate mutations, it would be good to provide additional rates/evidence (even for other loci) from the Enterobacteriaceae.

__Authors' answer to R3_C7: __We agree this statement might need to be supported further. We have removed this sentence to address this concern.

__Minor comments: __

__R3_C8: __For the sequences used in the search: please provide the sequence used in addition to the reference to the T1TAdb. Was the full-length hok mRNA, including mok, used? Please provide the nucleic acid sequence (and include description of whether full-length, etc.) in Materials and Methods or in Supplemental.

__Authors' answer to R3_C8: __Sequences and code were deposited on https://gitub.u-bordeaux.fr/alerhun/Escalera-Maurer_2025. This files named curated_Hok.fasta and hok.fa, corresponding to Hok protein and mRNA sequences respectively are available in the file "T1TAdb input".

__R3_C9: __60% identity was used for clustering. Did this become a problem-meaning separation of same property amino acid?

__Authors' answer to R3_C9: __We checked amino acid signatures for each cluster (Fig S2), but could not find anything relevant.

__R3_C10: __Fig. S2: for the clusters shown, please add in HokB, HokE, etc., to better correspond to Figure 1 in the main text.

__Authors' answer to R3_C10: __The clusters were annotated according to the suggestion.

__R3_C11: __Fig S1: this figure is challenging to orient-what are the numbers (8_10_85)?

Authors' answer to R3_C11: The figure was generated using the CLANS tool, with each unique sequence retrieved by our analysis shown as a dot. Hok homologous sequences are in red and cluster together, the outlier clusters are annotated with the numbers corresponding to their 60% identity cluster. We understand that separating the number using an underscore could lead to confusion, therefore we have now separated the numbers using a coma.

__R3_C12: __Please make a separate table or sheet for the experimentally tested peptides. Table S1 is quite large and a separate table/sheet would make this easier to find. If possible, please give the files names a more descriptive title (Table S1 in the name for example). This may be an issue with Review Commons but the individual file names were non-descript and the descriptions on the webpage did not indicate what the file contained.

__Authors' answer to R3_C12: __We named the files Table S1 and File_S1 to S7. We added a table S2 with the experimentally tested peptides. Note that identical peptides can be sometime found in several bacterial loci.

__R3_C13: __Figure S9: the black arrow for Hok is hard to see-it appears that the long grey bar going through multiple loci is indicative of Hok. Perhaps label this differently to make it easier on the reader (the line initially seemed to be a formatting issue and not indicative of the position of Hok.

__Authors' answer to R3_C13: __We have now added a new label to indicate where is Hok, and clarified it in the figure legend.

__R3_C14: __While the authors focused on Hok for this approach, which is fine and appropriate, can they comment at all about where mok is there in these new clusters/sub-families? Sok potential?

__Authors' answer to R3_C14: __We added a paragraph about Mok in the discussion.

__R3 Significance: __Overall the paper is a sound bioinformatic exercise and is improved with the testing of numerous "new" Hok proteins. Most of the comments can be done with some clarifications and maybe some additional analyses and/or verification which should take minimal time. The authors are over-emphatic at points as indicated and need to be more careful and precise with their language.

In terms of advancement, it advances the distribution of these systems and adds to the depth of sub-classes. The audience will be more specialized to those who study these systems.

Expertise: I have been studying type I toxin-antitoxin systems since the mid-2000s. We published a study examining (and mentioned well by this article!) the distribution in chromosomes of type I toxin-antitoxin systems, identified brand-new systems (that were chromosomally-limited at the time). My lab has continued to study regulation of type I toxins and distribution of chromosomally-only-encoded systems (so not Hok).

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

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Referee #3

Evidence, reproducibility and clarity

In the manuscript, "The Hok bacterial toxin: diversity, toxicity, distribution and genomic localization," by Escalera-Maurer et al., investigate the distribution of Hok type I toxin proteins across bacterial species. The Hok-Sok type I toxin-antitoxin system was first described on plasmids where it serves to maintain the plasmid in a population of bacterial cells: translation of the hok mRNA is prevented via the small antitoxin RNA Sok. Upon plasmid loss, with no new transcription of sok, the highly stable hok mRNA is translated into a small protein, killing the plasmid-less cell. Homologues to the system were identified in the chromosome of E. coli in the 1990s, and subsequent analyses have identified identical systems in other bacterial chromosomes, though they are close relatives to E. coli. Given the increased number of bacterial genomes sequenced, the group examined how widespread Hok may be across bacteria. They used a combination of BLASTn, MMseqs2 (protein) and Infernal (RNA) to identify, as best possible, all possible homologs. They then used sequence identity cut-offs to form Hok "clusters," and identified key features of the cluster as well as tested toxicity of overproduction of 31 homologs in a strain of Salmonella. Overall, though a variety of bioinformatic predictions and analyses, the manuscript identifies an expanded number of Hok members not previously identified and broaden the species it is found in, supported that Hok is not associate with defense systems, and provides additional support that horizontal transfer of hok genes is likely via plasmids (where hok is presumed to have originated).

Major comments: There are some areas of the text that are a bit too definitive (these can be fixed or better explained in the text) and a few questions raised about the analyses and interpretations. These are the specific comments:

Introduction

First paragraph: "Toxin production leads to the death of the cell encoding it" For many chromosomally encoded systems, toxicity has only been observed via artificial overexpression. This is an important point, as for many systems, a true biological function remains unknown. Further, add caveats regarding toxin function (for systems with validated function, they are involved in...). Again, there are still many questions for many t-at systems, in particular the Type I systems. Introduction: type I's are more narrow in distribution, but much of this is due to their size and lack of biochemical domains. Again, please clarify more here.

Introduction: while Hok's have been found on chromosomes, in E. coli strains, there is clear evidence that many are inactive. This comes up in the discussion, but it is worth including briefly in the introduction.

For the predicted transmembrane domain: it would be worth to include a box/indication as to where that is within the peptide (with the understanding it may not be exact). Is there more/less variation here? I'm assuming all clusters/family have a predicted TM domain?

What is the cutoff for being a Hok? Did they take the "last hit" and use that in additional searches to see if more appeared? If that was done, and the search was exhaustive, this really important to add for the reader.

Figure S4: the authors state that there was no difference in the degree of toxicity between the clusters. There do appear to be some peptides tested that at the arabinose concentration used did not repress growth as immediately as others. If higher arabinose concentration is used, does that eliminate these differences? OR are many of these suppressors-if diluted back again, do they grow as if they are non-toxic in arabinose?

Discussion: "because non-functional homologs are expected to quickly accumulate mutations..." is a bit problematic. Hok is highly regulated-as are some of the other well-described type I toxins. In MG1655, while the coding sequence may be intact, there are other mutations and/or insertion elements that prevent expression (and be extension, function. Given the lack of consensus data for type Is, it is best to provide more context for this. If the authors wish to argue that they should quickly accumulate mutations, it would be good to provide additional rates/evidence (even for other loci) from the Enterobacteriaceae.

Minor comments:

For the sequences used in the search: please provide the sequence used in addition to the reference to the T1TAdb. Was the full-length hok mRNA, including mok, used? Please provide the nucleic acid sequence (and include description of whether full-length, etc.) in Materials and Methods or in Supplemental.

60% identity was used for clustering. Did this become a problem-meaning separation of same property amino acid? Fig. S2: for the clusters shown, please add in HokB, HokE, etc., to better correspond to Figure 1 in the main text.

Fig S1: this figure is challenging to orient-what are the numbers (8_10_85)?

Please make a separate table or sheet for the experimentally tested peptides. Table S1 is quite large and a separate table/sheet would make this easier to find. If possible, please give the files names a more descriptive title (Table S1 in the name for example). This may be an issue with Review Commons but the individual file names were non-descript and the descriptions on the webpage did not indicate what the file contained.

Figure S9: the black arrow for Hok is hard to see-it appears that the long grey bar going through multiple loci is indicative of Hok. Perhaps label this differently to make it easier on the reader (the line initially seemed to be a formatting issue and not indicative of the position of Hok.

While the authors focused on Hok for this approach, which is fine and appropriate, can they comment at all about where mok is there in these new clusters/sub-families? Sok potential?

Significance

Overall the paper is a sound bioinformatic exercise and is improved with the testing of numerous "new" Hok proteins. Most of the comments can be done with some clarifications and maybe some additional analyses and/or verification which should take minimal time. The authors are over-emphatic at points as indicated and need to be more careful and precise with their language.

In terms of advancement, it advances the distribution of these systems and adds to the depth of sub-classes.

The audience will be more specialized to those who study these systems.

Expertise: I have been studying type I toxin-antitoxin systems since the mid-2000s. We published a study examining (and mentioned well by this article!) the distribution in chromosomes of type I toxin-antitoxin systems, identified brand-new systems (that were chromosomally-limited at the time). My lab has continued to study regulation of type I toxins and distribution of chromosomally-only-encoded systems (so not Hok).

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Version 3 of this preprint has been peer-reviewed and recommended by Peer Community in Genomics.<br /> See the peer reviews and the recommendation.

Version 3 of this preprint has been peer-reviewed and recommended by Peer Community in Genomics.<br /> See the peer reviews and the recommendation.

Reviewer #3 (Public review):

Summary:

This study investigates the role of BICC1 in the regulation of PKD1 and PKD2 and its impact on cytogenesis in ADPKD. By utilizing co-IP and functional assays, the authors demonstrate physical, functional, and regulatory interactions between these three proteins.

Strengths:

(1) The scientific principles and methodology adopted in this study are excellent, logical, and reveal important insights into the molecular basis of cystogenesis.

(2) The functional studies in animal models provide tantalizing data that may lead to a further understanding and may consequently lead to the ultimate goal of finding a molecular therapy for this incurable condition.

(3) In describing the patients from the Arab cohort, the authors have provided excellent human data for further investigation in large ADPKD cohorts. Even though there was no patient material available, such as HUREC, the authors have studied the effects of BICC1 mutations and demonstrated its functional importance in a Xenopus model.

Weaknesses:

This is a well-conducted study and could have been even more impactful if primary patient material was available to the authors. A further study in HUREC cells investigating the critical regulatory role of BICC1 and potential interaction with mir-17 may yet lead to a modifiable therapeutic target.

Conclusion:<br /> The authors achieve their aims. The results reliably demonstrate the physical and functional interaction between BICC1 and PKD1/PKD2 genes and their products.

The impact is hopefully going to be manifold:

(1) Progressing the understanding of the regulation of the expression of PKD1/PKD2 genes.

Comments on revision:

My comments have been addressed and sorted.

Author response:

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

Reviewer #1 (Public review):

(1) The authors devote significant effort to characterizing the physical interaction between Bicc1 and Pkd2. However, the study does not examine or discuss how this interaction relates to Bicc1's well-established role in posttranscriptional regulation of Pkd2 mRNA stability and translation efficiency.

The reviewer is correct that the present study has not addressed the downstream consequences of uthis interaction considering that Bicc1 is a posttranscriptional regulator of Pkd2 (and potentially Pkd1). We think that the complex of Bicc1/Pkd1/Pkd2 retains Bicc1 in the cytoplasm and thus restrict its activity in participating in posttranscriptional regulation (see Author response image 1). We, however, do not yet have data to support this and thus have not included this model in the manuscript. Yet, we have updated the discussion of the manuscript to further elaborate on the potential mechanism of the Bicc1/Pkd1/Pkd2 complex.

We have updated the discussion to include a discussion on the potential consequences on posttranscriptional regulation by Bicc1.

Author response image 1.

Model of BICC1, PC1 and PC2 self-regulation. In this model Bicc1 acts as a positive regulator of PKD gene expression. In the presence of ‘sufficient’ amounts of PC1/PC2 complex, it is tethered to the complex and remains biologically inactive (Fig. 1A). However, once the levels of the PC1/PC2 complex are reduced, Bicc1 is now present in the cytoplasm to promote expression of the PKD proteins, thereby raising their levels (Fig. 4B), which then in turn will ‘shutdown’ Bicc1 activity by again tethering it to the plasma membrane.

(2) Bicc1 inactivation appears to downregulate Pkd1 expression, yet it remains unclear whether Bicc1 regulates Pkd1 through direct interaction or by antagonizing miR-17, as observed in Pkd2 regulation. This should be further examined or discussed.

This is a very interesting comment. Vishal Patel published that PKD1 is regulated by a mir-17 binding site in its 3’UTR (PMID: 35965273). We, however, have not evaluated whether BICC1 participates in this regulation. A definitive answer would require utilization of the mice described in above reference, which is beyond the scope of this manuscript. We, however, have revised the discussion to elaborate on this potential mechanism. 

We have updated the discussion to include a statement on the potential direct regulation of Pkd1 mRNA by Bicc1.

(3) The evidence supporting Bicc1 and ADPKD gene cooperativity, particularly with Pkd1, in mouse models is not entirely convincing, likely due to substantial variability and the aggressive nature of Bpk/Bpk mice. Increasing the number of animals or using a milder Bicc1 strain, such as jcpk heterozygotes, could help substantiate the genetic interaction.

We have initially performed the analysis using our Bicc1 complete knockout, we previously reported on (PMID 20215348) focusing on compound heterozygotes. Yet, similar to the Pkd1/Pkd2 compound heterozygotes (PMID 12140187) no cyst development was observed when we sacrificed the mice as late as P21. Our strain is similar to the above mentioned jcpk, which is characterized by a short, abnormal transcript thought to result in a null allele (PMID: 12682776). We thank the reviewer for pointing us to the reference showing the heterozygous mice exhibit glomerular cysts in the adults (PMID: 7723240). This suggestion is an interesting idea we will investigate. In general, we agree with the reviewer that a better understanding of the contribution of Bicc1 to the adult PKD phenotype will be critical. To this end, we are currently generating a floxed allele of Bicc1 that will allow us to address the cooperativity in the adult kidney, when e.g. crossed to the Pkd1^RC/RC mice. Yet, these experiments are beyond the timeframe for this revision. 

No changes were made in the revised manuscript. 

Reviewer #2 (Public review):

(1) These results are potentially interesting, despite the limitation, also recognized by the authors, that BICC1 mutations seem exceedingly rare in PKD patients and may not "significantly contribute to the mutational load in ADPKD or ARPKD". The manuscript has several intrinsic limitations that must be addressed. 

As mentioned above, the study was designed to explore whether there is an interaction between BICC1 and the PKD1/PKD2 and whether this interaction is functionally important. How this translates into the clinical relevance will require additional studies (and we have addressed this in the discussion of the manuscript).

(2) The manuscript contains factual errors, imprecisions, and language ambiguities. This has the effect of making this reviewer wonder how thorough the research reported and analyses have been. 

We respectfully disagree with the reviewer on the latter interpretation. The study was performed with rigor. We have carefully assessed the critiques raised by the reviewer. As presented below, most of the criticisms raised by the reviewer have been easily addressed in the revised version of the manuscript. Yet, none of the critiques seems to directly impact the overall interpretation of the data. 

Reviewer #1 (Recommendations for the authors):

(1) The manuscript requires further editing. For example, figure panels and legends are mismatched in Figure 1

We have corrected the labeling of Figure 1. 

(2) Y-axis units and values are inconsistent in Figures 4b-4g, Supplementary Figures S2e and S2f are not referenced in the text, genotypes are missing in Supplementary Figure S3f, and numerous typographical errors are present.

In respect to the y-axis in Figure 4b-g, the scale is different for each of them, but that is intentional as one would lose the differences if they were all scaled identically. But we have now mentioned this in the figure legend to make the reader aware of it. In respect to the Supplemental Figure S2e,f, we included the panels in the description of the mutant BICC1 lines, but unfortunately forgot to reference them. This has now been done.

We have updated the labeling of the Y-axis for the cystic indices adding “[%]” as the unit and updated the figure legend of Figure 4. We have included the genotypes in Supplementary Figure S3f. The Supplementary Figure S2e,f is now mentioned in the supplemental material (page 9, 2^nd paragraph). 

Reviewer #2 (Recommendations for the authors):

(1) Previous data from mouse, Xenopus, and zebrafish suggest a crucial role for the RNAbinding protein Bicc1 in the pathogenesis of PKD, although BICC1 mutations in human PKD have not been previously reported." The cited sources (and others that were not cited) link Bicc1 mutations to renal cysts, similar to a report by Kraus (PMID: 21922595) that the authors cite later. However, a more direct link to PKD was reported by Lian and colleagues using whole Pkd1 mice (PMID: 20219263) and by Gamberi and colleagues using Pkd1 kidneys and human microarrays (PMID: 28406902). Although relevant, neither is cited here, and only the former is cited later in the manuscript.

Thanks for pointing this out. We have added these three citations.

We have added these three citations (PMID: 21922595, PMID: 20219263 and PMID: 28406902) in the indicated sentence.

(2) In Figure 1B, the lanes do not seem to correspond among panels, particularly evident in the panel with myc-mBicc1. Hence, it is difficult to agree with the presented conclusions.

We have corrected the labeling of the lanes in Figure 1b.

(3) In the Figure 1 legend: "(g) Western blot analysis following co-IP experiments, using an anti-mouse Bicc1 or anti-goat PC2 antibody as bait, identified protein interactions between endogenous PC2 and BICC1 in UCL93 cells. Non-immune goat and mouse IgG were included as a negative control." There is no mention of panel H, although this reviewer can imagine what the authors meant. The capitalization differs in the figure and legend. More troublingly, in panel G, a non-defined star indicates a strong band present in both immune and non-immune control.

We have corrected the figure legend of Figure 1 and clarified the non-specific band in the figure legend.

(4) In Figure 4, the authors do not show the matched control for the Bicc1 Pkd1 interaction in panel d, nor do they show a scale bar in either a) or d). Thus, the phenotypic severity cannot be properly assessed.

Thanks for pointing out the missing scale bars, which have now been added. In respect to the two kidneys shown in Figure 4d, the two kidneys shown are from littermates to illustrate the kidney size in agreement with the cumulative data shown in Figure 4e. Unfortunately, this litter did not have a wildtype control. As the data analysis in Figure 4e is based on littermates, mixing and matching kidneys of different litters does not seem appropriate. Thus, we have omitted showing a wildtype control in this panel. However, the size of the wildtype kidney can be seen in Figure 4a.

We have added the scale bar to both panels and have updated the figure legend to emphasize that the kidneys shown are from littermates and that no wildtype littermate was present in this litter.

(5) "Surprisingly, an 8-fold stronger interaction was observed between full-length PC1 and myc-mBicc1-ΔKH compared to mycmBicc1 or myc-mBicc1-ΔSAM." Assuming all the controls for protein folding and expression levels have been carried out and not shown/mentioned, this sentence seems to contradict the previous statement that Bicc1deltaSAM reduced the interaction with PC1 by 55%. Because the full length and SAM deletion have different interaction strengths, the latter sentence makes no sense.

The reduction in the levels of myc-mBicc1-ΔSAM compared to wildtype mycmBicc1 in respect to PC1 binding was not significant. We have clarified this in the text.

We have corrected the sentence and modified the Figure accordingly. 

(6) Imprecise statements make a reader wonder how to interpret the data: "More than three independent experiments were analyzed." Stating the sample size or including it in the figure would save space and improve confidence in the data presented.

We have stated the exact number of animals per conditions above each of the bars.

(7) "Next, we performed a similar mouse study for Pkd1 by reducing the gene dose of Pkd1 postnatally in the collecting ducts using a Pkhd1-Cre as previously described40" What did the authors mean?

The reference was included to cite the mouse strain, but realized that it can be mis-interpreted that the exact experiments has been performed previously. We have clarified this in the text.

We have reworded the sentence to avoid misinterpretation. 

(8) The authors examined the additive effects of knocking down Bicc1, Pkd1, and Pkd2 with morpholinos in Xenopus and, genetically, in mice. While the Bicc1[+/-] Pkd1 or 2[+/-] double heterozygote mice did not show phenotypes, the authors report that the Bicc1[-/-] Pkd1 or 2 [+/-] did instead show enlarged kidneys. What is the phenotype of a Bicc1[+/-] Pkd1 or 2 [-/-]? What we learn from the author's findings among the PKD population suggests that the latter situation would be potentially translationally relevant.

The mouse experiments were designed to address a cooperativity between Bicc1 and either Pkd1 or Pkd2 and whether removal of one copy of Pkd1 or Pkd2 would further worsen the Bicc1 cystic kidney phenotype. Thus, the parental crosses were chosen to maximize the number of animals obtained for these genotypes. Unfortunately, these crosses did not yield the genotypes requested by the reviewer. To address the contribution of Bicc1 towards the PKD population, we will need to perform a different cross, where we eliminate Pkd1 or Pkd2 in a floxed background of Bicc1 postnatally in adult mice. While we are gearing up to perform such an experiment, this is timewise beyond the scope of the manuscript. In addition, please note that we have addressed the question about the translation towards the PKD population already in the discussion of the original submission (page 13/14, last/first paragraph).

No changes have been made to the revised version of the manuscript.

(9) How do the authors interpret the milder effects of the Bicc1[-/-] Pkd1[+/-] compared to Bicc1[-/-] Pkd2[+/-] relative to the respective protein-protein interactions?

The milder effects are due to the nature of the crosses. While the Pkd2 mutant is a germline mutation, the Pkd1 mutant is a conditional allele eliminating Pkd1 only in the collecting ducts of the kidney. As such, we spare other nephron segments such as the proximal tubules, which also significantly contribute to the cyst load. As such these mouse data support the interaction between Pkd1 and Pkd2 with Bicc1, but do not allow us to directly compare the outcomes. While this was mentioned in the previous version of the manuscript, we have expanded on this in the revised version of the manuscript.

We have expanded the results section in the revised version of the manuscript highlighting that the two different approaches cannot be directly compared.

(10) How do the authors interpret that the strong Bicc1[Bpk] Pkd1 or Pkd2 double heterozygote mice did not have defects and "kidneys from Bicc1+/-:Pkd2+/- did not exhibit cysts (data not shown)", when the VEO PKD patients and - although not a genetic reduction - also the morpholino-treated Xenopus did?

VEO PKD patients are characterized by a loss of function of PKD1 or PKD2 and – as we propose in this manuscript - that BICC1 further aggravates the phenotype. Yet, we do not address either in the mouse or Xenopus experiments whether BICC1 is a genetic modifier. We are simply addressing whether the two genes show a genetic interaction. In the mouse studies, we eliminate one copy of Pkd1 or Pkd2 in the background of a hypomorphic allele of Bicc1. Similarly, in the Xenopus experiments, we employ suboptimal doses of the morpholino oligomers, i.e., concentrations that did not yield a phenotypic change and then asked whether removing both together show cooperativity. It is important to state that this is based on a biological readout and not defined based on the amount of protein. While we have described this already in the original manuscript (page 7, first paragraph), we have amended our description of the Xenopus experiment to make this even clearer. 

Finally, we agree with the reviewer that if we were to address whether Bicc1 is a modifier of the PKD phenotype in mouse, we would need to reduce Bicc1 function in a Pkd1 or Pkd2 mutants. Yet, we have recognized this already in the initial version of the manuscript in the discussion (page 14, first paragraph).

We have expanded the results section when discussing the suboptimal amounts of the morpholino oligos (Page 6, 1^st paragraph).

(11) Unclear: "While variants in BICC1 are very rare, we could identify two patients with BICC1 variants harboring an additional PKD2 or PKD1 variant in trans, respectively." Shortly after, the authors state in apparent contradiction that "the patients had no other variants in any of other PKD genes or genes which phenocopy PKD including PKD1, PKD2, PKHD1, HNF1s, GANAB, IFT140, DZIP1L, CYS1, DNAJB11, ALG5, ALG8, ALG9, LRP5, NEK8, OFD1, or PMM2."

The reviewer is correct. This should have been phrased differently. We have now added “Besides the variants reported below” to clarify this more adequately.

The sentence was changed to start with “Besides the variants reported below, […].”

(12) "The demonstrated interaction of BICC1, PC1, and PC2 now provides a molecular mechanism that can explain some of the phenotypic variability in these families." How do the authors reconcile this statement with their reported ultra-rare occurrence of the BICC1 mutations?

As mentioned in the manuscript and also in response to the other two reviewers, Bicc1 has been shown to regulate Pkd2 gene expression in mice and frogs via an interaction with the miR-17 family of microRNAs. Moreover, the miR-17 family has been demonstrated to be critical in PKD (PMID: 30760828, PMID: 35965273, PMID: 31515477, PMID: 30760828). In fact, both other reviewers have pointed out that we should stress this more since Bicc1 is part of this regulatory pathway. Future experiments are needed to address whether Bicc1 contributes to the variability in ADPKD onset/severity. Yet, this is beyond the scope of this study. 

Based on the comments of the two other reviewers we have further addressed the Bicc1/miR-17 interaction.

(13) The manuscript should use correct genetic conventions of italicization and capitalization. This is an issue affecting the entire manuscript. Some exemplary instances are listed below.

(a) "We also demonstrate that Pkd1 and Pkd2 modifies the cystic phenotype in Bicc1 mice in a dose-dependent manner and that Bicc1 functionally interacts with Pkd1, Pkd2 and Pkhd1 in the pronephros of Xenopus embryos." Genes? Proteins?

The data presented in this section show that a hypomorphic allele of Bicc1 in mouse and a knockdown in Xenopus yields this. As both affect the proteins, the spelling should reflect the proteins.

No changes have been made in the revised manuscript.

(b) The sentence seems to use both the human and mouse genetic capitalization, although it refers to experiments in the mouse system “to define the Bicc1 interacting domains for PC2 (Fig. 2d,e). Full-length PC2 (PC2-HA) interacted with full-length myc-mBICC1.”

We agree with the review that stating the species of the molecules used is critical, we have adapted a spelling of Bicc1, where BICC1 is the human homologue, mBicc1 is the mouse homologue and xBicc1 the Xenopus one.

We have highlighted the species spelling in the methods section and labeled the species accordingly throughout the manuscript and figures. 

(14) “Together these data supported our biochemical interaction data and demonstrated that BICC1 cooperated with PKD1 and PKD2.” Are the authors implying that these results in mice will translate to the human protein?

We agree that we have not formally shown that the same applies to the human proteins. Thus, we have changed the spelling accordingly.

We have revised the capitalization of the proteins. 

(15) The text is often unclear, terse, or inconsistent.

(a) “These results suggested that the interaction between PC1 and Bicc1 involves the SAM but not the KH/KHL domains (or the first 132 amino acids of Bicc1). It also suggests that the N-terminus could have an inhibitory effect on PC1-BICC1 association.” How do the authors define the N-terminus? The first 132 aa? KH/KHL domains?

This was illustrated in the original Figure 2A. The DKH constructs lack the first 351 amino acids. 

To make this more evident, we have specified this in the text as well.

(b) Similarly, the authors state below, "Unlike PC1, PC2 interacted with mycmBICC1ΔSAM, but not myc-mBICC1-ΔKH suggesting that PC2 binding is dependent on the N-terminal domains but not the SAM domain." It is unclear if the authors refer to the KH/KHL domains or others. Whatever the reference to the N-terminal region, it should also be consistent with the section above.

This is now specified in the text.

(c) Unclear: "We have previously demonstrated that Pkd2 levels are reduced in a complete Bicc1 null mice,22 performing qRT-PCR of P4 kidneys (i.e. before the onset of a strong cystic phenotype), revealed that Bicc1, Pkd1 and Pkd2 were statistically significantly down9 regulated (Fig. 4h-j)".

We have changed the text to clarify this. 

(d) “Utilizing recombinant GST domains of PC1 and PC2, we demonstrated that BICC1 binds to both proteins in GST-pulldown assays (Fig. 1a, b)." GST-tagged domains? Fusions?

We have changed the text to clarify this. 

(e) "To study the interaction between BICC1, PKD1 and PKD2 we combined biochemical approaches, knockout studies in mice and Xenopus, genetic engineered human kidney cells" > genetically engineered.

We have changed the text to clarify this.

(f) Capitalization (e.g., see Figure S3, ref. the Bpk allele) and annotation (e.g., Gly821Glu and G821E) are inconsistent.

We have homogenized the labeling of the capitalization and annotations throughout the manuscript. 

(g) What do the authors mean by "homozygous evolutionarily well-conserved missense variant"?

We have changed this is the revised version of the manuscript. 

Reviewer #3 (Public review/Recommendations to the authors):

(1) A further study in HUREC cells investigating the critical regulatory role of BICC1 and potential interaction with mir-17 may yet lead to a modifiable therapeutic target.

(2) This study should ideally include experiments in HUREC material obtained from patients/families with BICC1 mutations and studying its effects on the PKD1/2 complex in primary cell lines.

This is an excellent suggestion. We agree with the reviewer that it would have been interesting to analyze HUREC material from the affected patients. Unfortunately, besides DNA and the phenotypic analysis described in the manuscript neither human tissue nor primary patient-derived cells collected once the two patients with the BICC1 p.Ser240Pro variant passed away.

No changes to the revised manuscript have been made to address this point.

(3) Please remove repeated words in the following sentence in paragraph 2 of the introduction: "BICC1 encodes an evolutionarily conserved protein that is characterized by 3 K-homology (KH) and 2 KH-like (KHL) RNA-binding domains at the N-terminus and a SAM domain at the C-terminus, which are separated by a by a disordered intervening sequence (IVS).23-28".

This has been changed.

L'Intelligence Artificielle en Éducation : Défis Pédagogiques et Enjeux Démocratiques

Synthèse de la Direction

L'émergence de l'intelligence artificielle générative (IAG) en éducation représente bien plus qu'une simple innovation technique ; elle constitue une rupture anthropologique majeure.

Si l'IA promet une efficacité accrue par l'individualisation radicale des apprentissages via le learning analytics, elle menace paradoxalement les fondements de l'école républicaine : la construction du commun, l'exercice du jugement critique et le désir d'apprendre.

Le défi actuel n'est pas d'interdire l'outil, déjà omniprésent, mais de développer une pédagogie de la vigilance. Celle-ci repose sur le principe de réversibilité — n'utiliser l'IA que pour ce que l'on sait déjà faire — et sur la réaffirmation du rôle irremplaçable de l'enseignant comme passeur de valeurs et médiateur du débat démocratique.

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1. Nature et Fonctionnement de l'Intelligence Artificielle Générative

L'IA générative, popularisée par des outils comme ChatGPT ou Mistral, repose sur des mécanismes statistiques précis qui définissent ses capacités et ses limites.

Mécanismes techniques

• Base de données : Une accumulation massive de données (750 000 fois la Bible pour ChatGPT), qui reste néanmoins limitée par rapport à l'ensemble de la production humaine.

• Calculateur d'occurrences statistiques : L'IA ne « pense » pas ; elle calcule le mot qui a statistiquement le plus de probabilités de suivre le précédent.

• Le "Transformer" : Un outil récent permettant de prendre en compte le contexte pour affiner la pertinence statistique.

• Température et fluctuation : Réglée généralement à 0,8, la « température » permet d'introduire une part de fluctuation pour rendre les textes moins rigides et plus proches d'une opinion moyenne (opinion modale).

• Lissage linguistique : Un traitement systématique qui produit des textes à la syntaxe et à l'orthographe parfaites, souvent corrigés manuellement en amont par des opérateurs humains.

Une externalisation de la mémoire

L'IA s'inscrit dans la lignée historique de l'externalisation de la mémoire humaine (écriture, imprimerie, moteurs de recherche).

Ce phénomène soulève un débat ancien, déjà identifié par Platon dans le Phèdre : l'outil apporte-t-il la science ou seulement sa « semblance » ?

Le risque souligné est celui d'une remémoration venant « du dehors » plutôt que « du dedans », affaiblissant l'exercice même de la pensée.

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2. La Rupture du Learning Analytics et la Fin de la Forme Scolaire

L'IA introduit une rupture radicale à travers le learning analytics, une technique d'analyse de données visant à modéliser les stratégies d'apprentissage individuelles.

| Concept | Description et Conséquences | | --- | --- | | Individualisation Totale | Analyse des comportements sur tablette pour créer un logiciel strictement adapté au rythme, aux handicaps et aux préférences de l'élève. | | Séparation Instruction/Socialisation | Proposition de certains théoriciens (ex: Paul Jorion) de dissocier la transmission (confiée aux machines le matin) de la socialisation (activités sportives/artistiques l'après-midi). | | Obsolescence de la Classe | La classe traditionnelle, jugée inefficace pour gérer l'hétérogénéité, est remplacée par un tutorat machine disponible 24h/24. | | Risque d'Enfermement | L'adaptation permanente à l'utilisateur empêche la découverte de l'altérité et le dépassement de ses propres limites. |

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3. Ambitions vs Réalités : Une Analyse Critique

Le document identifie un décalage structurel entre les prétentions de l'IA et la réalité de sa production.

• L'accès à la connaissance : Si l'IA offre une rapidité d'investigation fabuleuse, elle est tributaire de sa base de données (biais idéologiques, absence d'événements censurés, prédominance masculine des concepteurs).

• La synthèse rigoureuse : L'IA privilégie l'académisme à la rigueur.

Elle procède par énumérations (souvent en base 3 ou 10) et agrège des concepts qu'il conviendrait de distinguer (ex: confondre besoin, niveau et intérêt).

• L'interdisciplinarité : Elle offre une illusion de complexité, mais réduit souvent le réel à des lieux communs et au "déjà-dit".

• La décision "pertinente" : En médecine ou en droit, l'IA réduit la situation (complexe et humaine) au seul problème (technique et algorithmique).

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4. Impacts et Défis pour l'Éducation

L'intégration de l'IA dans le milieu éducatif impose une refonte des pratiques d'évaluation et de transmission.

La mutation de l'évaluation

Face à l'industrialisation de la fraude, l'école doit :

• Passer du paradigme de la conformité (une seule bonne réponse) à celui de l'originalité de pensée.

• Réévaluer l'importance de l'oralité et du débat en face à face.

• Valoriser la démarche d'enquête (comment l'élève a cherché) plutôt que le seul résultat final.

Le principe de réversibilité

L'éducation doit enseigner que l'IA ne peut être utilisée que pour accélérer des tâches que l'individu sait déjà accomplir manuellement.

Utiliser l'IA pour ce que l'on ne maîtrise pas (ex: résumer un texte sans en comprendre la structure) conduit à une « bêtise artificielle » et à une perte de jugement.

Du savoir au désir d'apprendre

L'IA « comble le désir de savoir mais tue le désir d'apprendre ».

En fournissant des réponses immédiates, elle tarit la curiosité.

Le rôle de l'enseignant devient alors d'être un promoteur d'interrogations plutôt qu'un simple distributeur d'informations.

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5. IA, Réseaux Sociaux et Menaces sur la Démocratie

Le document souligne le lien entre l'IA et les mécanismes addictifs des réseaux sociaux, structurés pour enfermer l'utilisateur.

• Le tournant de 2009 : L'introduction des algorithmes de profilage (Facebook, puis TikTok) a remplacé l'ordre chronologique par le ciblage publicitaire.

• L'effet "Tunnel" : Contrairement à l'éducateur qui « ouvre des fenêtres », les algorithmes enferment l'individu dans ce qu'il aime déjà, empêchant toute sérendipité (découverte fortuite).

• L'anthropomorphisme (Effet Elisa) : L'IA se fait passer pour une personne pour gagner la confiance de l'utilisateur.

Il est impératif d'utiliser l'infinitif (ex: "faire", "chercher") plutôt que l'impératif pour marquer la distance avec la machine.

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6. Conclusions et Impératifs Éthiques

L'IA ne peut délibérer ni porter de valeurs. Elle ignore la temporalité humaine et la dimension incarnée du savoir.

Recommandations pour l'avenir :

1. Réhabiliter la conversation argumentée : Seul l'humain peut sortir d'un désaccord par le haut, en prenant en compte les divergences sans humilier l'autre.

2. Dénoncer le "solutionnisme technologique" : Tout problème humain n'est pas réductible à une solution technique. L'éthique doit primer sur l'efficacité.

3. Résister à la "machinisation" : Citant Adorno, le document rappelle que la barbarie commence par l'obéissance mécanique aux règles.

L'éducation doit donner la force de douter et de dire « non » aux évidences suggérées par les algorithmes.

En somme, l'IA doit rester un outil supervisé. L'enjeu civilisationnel est de préserver ce que seul l'humain peut faire : habiter sa parole, éprouver de la curiosité et construire un destin commun à travers le débat.

Author response:

Reviewer #1 (Public review):

The authors analysed large-scale brain-state dynamics while humans watched a short video. They sought to identify the role of thalamocortical interactions.

Major concerns

(1) Rationale for using the naturalistic stimulus

In terms of brain state dynamics, previous studies have already reported large-scale neural dynamics by applying some data-driven analyses, like energy landscape analysis and Hidden Markov Model, to human fMRI/EEG data recorded during resting/task states. Considering such prior work, it'd be critical to provide sufficient biological rationales to perform a conceptually similar study in a naturalistic condition, i.e., not just "because no previous work has been done". The authors would have to clarify what type of neural mechanisms could be missed in conventional resting-state studies using, say, energy landscape analysis, but could be revealed in the naturalistic condition.

We appreciate your insightful comments regarding the need for a biological rationale in our study. As you mentioned, there are similar studies, just like Meer et al. utilized Hidden Markov Models to identify various activation modes of brain networks that included subcortical regions[1], Song et al. linked brain states to narrative understandings and attentional dynamics[2, 3]. These studies could answer why we use naturalistic stimuli datasets. Moreover, there is evidence suggesting that the thalamus plays a crucial role in processing information in a more naturalistic context while pointing out the vital role in thalamocortical communications[4, 5]. So, we tended to bridge thalamic activity and cortical state transition using the energy landscape description.

To address these gaps in conventional resting-state studies, we explored an alternative method—maximum entropy modeling based on the energy landscape. This allowed us to validate how the thalamus responds to cortical state transitions. To enhance clarity, we will update our introduction to emphasize the motivations behind our research and the significance of examining these neural mechanisms in a naturalistic setting.

(2) Effects of the uniqueness of the visual stimulus and reproducibility

One of the main drawbacks of the naturalistic condition is the unexpected effects of the stimuli. That is, this study looked into the data recorded from participants who were watching Sherlock, but what would happen to the results if we analyzed the brain activity data obtained from individuals who were watching different movies? To ensure the generalizability of the current findings, it would be necessary to demonstrate qualitative reproducibility of the current observations by analysing different datasets that employed different movie stimuli. In fact, it'd be possible to find such open datasets, like www.nature.com/articles/s41597-023-02458-8.

We appreciate your concern regarding the reproducibility of our findings. The dataset from the "Sherlock" study is of high quality and has shown good generalizability in various research contexts. We acknowledge the importance of validating our results with different datasets to enhance the robustness of our conclusions. While we are open to exploring additional datasets, we intend to pursue this validation once we identify a suitable alternative. Currently, we are considering a comparison with the dataset from "Forrest Gump" as part of our initial plan.

(3) Spatial accuracy of the "Thalamic circuit" definition

One of the main claims of this study heavily relies on the accuracy of the localization of two different thalamic architectures: matrix and core. Given the conventional or relatively low spatial resolution of the fMRI data acquisition (3x3x3 mm^3), it appears to be critically essential to demonstrate that the current analysis accurately distinguished fMRI signals between the matrix and core parts of the thalamus for each individual.

We acknowledge the importance of accurately localizing the different thalamic architectures, specifically the matrix and core regions. To address this, we downsampled the atlas of matrix and core cell populations from the previous study from a resolution of 2x2x2 mm³ to 3x3x3 mm³, which aligns with our fMRI data acquisition. We would report the atlas as Supplementary Figures in our revision.

(4) More detailed analysis of the thalamic circuits

In addition, if such thalamic localisation is accurate enough, it would be greatly appreciated if the authors perform similar comparisons not only between the matrix and core architectures but also between different nuclei. For example, anterior, medial, and lateral groups (e.g., pulvinar group). Such an investigation would meet the expectations of readers who presume some microscopic circuit-level findings.

We appreciate your suggestion regarding a more detailed analysis of thalamic circuits. We have touched upon this in the discussion section as a forward-looking consideration. However, we believe that performing nuclei segmentation with 3T fMRI may not be ideal due to well-documented concerns regarding signal-to-noise ratio and spatial resolution. That said, we are interested in exploring these nuclei-pathway connections to cortical areas in future studies with a proper 7T fMRI naturalistic dataset.

(5) Rationale for different time window lengths

The authors adopted two different time window lengths to examine the neural dynamics. First, they used a 21-TR window for signal normalisation. Then, they narrowed down the window length to 13-TR periods for the following statistical evaluation. Such a seemingly arbitrary choice of the shorter time window might be misunderstood as a measure to relax the threshold for the correction of multiple comparisons. Therefore, it'd be appreciated if the authors stuck to the original 21-TR time window and performed statistical evaluations based on the setting.

Thank you for your valuable feedback regarding the choice of time window lengths. We aimed to maintain consistency in window lengths across our analyses. In light of your comments and suggestions from other reviewers, we plan to test our results using different time window lengths and report findings that generalize across these variations. Should the results differ significantly, we will discuss the implications of this variability in our revised manuscript.

(6) Temporal resolution

After identifying brain states with energy landscape analysis, this study investigated the brain state transitions by directly looking into the fMRI signal changes. This manner seems to implicitly assume that no significant state changes happen in one TR (=1.5sec), which needs sufficient validation. Otherwise, like previous studies, it'd be highly recommended to conduct different analyses (e.g., random-walk simulation) to address and circumvent this problem.

Thank you for raising this important point regarding temporal resolution. Many fMRI studies, such as those examining event boundaries during movie watching, operate under similar assumptions concerning state changes within one TR. For example, Barnett et al. processed the dynamic functional connectivity (dFC) with a window of 20 TRs (24.4s). So, we do not think it is a limitation but is a common question related to fMRI scanning parameters. To strengthen our analysis of state transitions and ensure they are not merely coincidental, we plan to conduct random-walk simulations, as suggested, to validate our findings in accordance with methodologies used in previous research.

Reviewer #2 (Public review):

Summary:

In this study, Liu et al. investigated cortical network dynamics during movie watching using an energy landscape analysis based on a maximum entropy model. They identified perception- and attention-oriented states as the dominant cortical states during movie watching and found that transitions between these states were associated with inter-subject synchronization of regional brain activity. They also showed that distinct thalamic compartments modulated distinct state transitions. They concluded that cortico-thalamo-cortical circuits are key regulators of cortical network dynamics.

Strengths:

A mechanistic understanding of cortical network dynamics is an important topic in both experimental and computational neuroscience, and this study represents a step forward in this direction by identifying key cortico-thalamo-cortical circuits. The analytical strategy employed in this study, particularly the LASSO-based analysis, is interesting and would be applicable to other data types, such as task- and resting-state fMRI.

We thanks for this comment and encouragement.

Weaknesses:

Due to issues related to data preprocessing, support for the conclusions remains incomplete. I also believe that a more careful interpretation of the "energy" derived from the maximum entropy model would greatly clarify what the analysis actually revealed.

Thank you for your valuable suggestions, and we apologize for any misunderstandings regarding the interpretation of the energy landscape in our study. To address this issue, we will include a dedicated paragraph in both the methods and results sections to clarify our use of the term "energy" derived from the maximum entropy model. This addition aims to eliminate any ambiguity and provide a clearer understanding of what our analysis reveals.

(1) I think the method used for binarization of BOLD activity is problematic in multiple ways.

a) Although the authors appear to avoid using global signal regression (page 4, lines 114-118), the proposed method effectively removes the global signal. According to the description on page 4, lines 117-122, the authors binarized network-wise ROI signals by comparing them with the cross-network BOLD signal (i.e., the global signal): at each time point, network-wise ROI signals above the cross-network signal were set to 1, and the rest were set to −1. If I understand the binarization procedure correctly, this approach forces the cross-network signal to be zero (up to some noise introduced by the binarization of network-wise signals), which is essentially equivalent to removing the global signal. Please clarify what the authors meant by stating that "this approach maintained a diverse range of binarized cortical states in data where the global signal was preserved" (page 4, lines 121-122).

Thank you for highlighting the potential issue with our binarization method. We appreciate your insights regarding the comparison of network-wise ROI signals with the cross-network BOLD signal, as this may inadvertently remove the global signal. To address this, we will conduct a comparative analysis of results obtained from both our current approach and the original pipeline. If we decide to retain our current method, we will carefully reconsider the rationale and rephrase our descriptions to ensure clarity regarding the preservation of the global signal and the diversity of binarized cortical states.

b) The authors might argue that they maintained a diverse range of cortical states by performing the binarization at each time point (rather than within each network). However, I believe this introduces another problem, because binarizing network-wise signals at each time point distorts the distribution of cortical states. For example, because the cross-network signal is effectively set to zero, the network cannot take certain states, such as all +1 or all −1. Similarly, this binarization biases the system toward states with similar numbers of +1s and −1s, rather than toward unbalanced states such as (+1, −1, −1, −1, −1, −1). These constraints and biases are not biological in origin but are simply artifacts of the binarization procedure. Importantly, the energy landscape and its derivatives (e.g., hard/easy transitions) are likely to be affected by these artifacts. I suggest that the authors try a more conventional binarization procedure (i.e., binarization within each network), which is more robust to such artifacts.

Related to this point, I have a question regarding Figure S1, in which the authors plotted predicted versus empirical state probabilities. As argued above, some empirical state probabilities should be zero because of the binarization procedure. However, in Figure S1, I do not see data points corresponding to these states (i.e., there should be points on the y-axis). Did the authors plot only a subset of states in Figure S1? I believe that all states should be included. The correlation coefficient between empirical and predicted probabilities (and the accuracy) should also be calculated using all states.

Thank you for your thoughtful examination of our data processing pipeline. We agree that a comparison between the conventional binarization method and our current approach is warranted, and we appreciate your suggestion. Upon reviewing Figure S1, we discovered that there was indeed an error related to the plotting style set to "log10." As you correctly pointed out, the data should reflect that the probabilities for states where all networks are either activated or deactivated are zero. We are very interested in exploring the state distributions obtained from both the original and current approaches, as your comments highlight important considerations. We sincerely appreciate your insightful feedback and will make sure to address these points thoroughly in our first revision.

c) The current binarization procedure likely inflates non-neuronal noise and obscures the relationship between the true BOLD signal and its binarized representation. For example, consider two ROIs (A and B): both (+2%, +1%) and (+0.01%, −0.01%) in BOLD signal changes would be mapped to (+1, −1) after binarization. This suggests that qualitatively different signal magnitudes are treated identically. I believe that this issue could be alleviated if the authors were to binarize the signal within each network, rather than at each time point.

Thank you for your important observation regarding the potential inflation of non-neuronal noise in our current binarization procedure. We recognize that this process could lead to qualitatively different signal magnitudes being treated similarly after binarization, as you illustrated with your example. While we acknowledge your point, we believe that conventional binarization pipelines may also encounter this issue, albeit by comparing signals to a network's temporal mean activity. To address this concern and maintain consistency with previous studies, we will discuss this limitation in our revised manuscript. Additionally, if deemed necessary, we will explore implementing a percentile-based threshold above the baseline to further refine our binarization approach. Your suggestion provides a valuable perspective, and we appreciate your insights.

(2) As the authors state (page 5, lines 145-148), the "energy" described in the energy landscape is not biological energy but rather a statistical transformation of probability distributions derived from the Boltzmann distribution. If this is the case, I believe that Figure 2A is potentially misleading and should be removed. This type of schematic may give the false impression that cortical state dynamics are governed by the energy landscape derived from the maximum entropy model (which is not validated).

Thank you for your valuable feedback regarding Figure 2A. We apologize for any confusion it may have created. While we recognize that similar figures are commonly used in literature involving energy landscapes (maximum entropy model), we agree that Figure 2A may mislead readers into thinking that cortical state dynamics are directly governed by the energy landscape derived from the maximum entropy model, which has not been validated. In light of your comments, we will remove Figure 2A and instead emphasize the analytical strategy presented in Figure 2B. Additionally, we will provide a simplified line graph as an illustrative example to clarify the concepts without the potential for misinterpretation.

Reviewer #3 (Public review):

Summary:

In this study, Liu et al. analyze fMRI data collected during movie watching, applied an energy landscape method with pairwise maximum entropy models. They identify a set of brain states defined at the level of canonical functional networks and quantify how the brain transitions between these states. Transitions are classified as "easy" or "hard" based on changes in the inferred energy landscape, and the authors relate transition probabilities to inter-subject correlation. A major emphasis of the work is the role of the thalamus, which shows transition-linked activity changes and dynamic connectivity patterns, including differential involvement of parvalbumin- and calbindin-associated thalamic subdivisions.

Strengths:

The study is methodologically complex and technically sophisticated. It integrates advanced analytical methods into high-dimensional fMRI data. The application of energy landscape analysis to movie-watching data appears to be novel as well. The finding on the thalamus involved energy state transition and provides a strong linkage to several theories on thalamic control functions, which is a notable strength.

Thanks for your comments on the novelty of our study.

Weaknesses:

The main weakness is the conceptual clarity and advances that this otherwise sophisticated set of analyses affords. A central conceptual ambiguity concerns the energy landscape framework itself. The authors note that the "energy" in this model is not biological energy but a statistical quantity derived from the Boltzmann distribution. After multiple reads, I still have major trouble mapping this measure onto any biological and cognitive operations. BOLD signal is a measure of oxygenation as a proxy of neural activity, and correlated BOLD (functional connectivity) is thought to measure the architecture of information communication of brain systems. The energy framework described in the current format is very difficult for most readers to map onto any neural or cognitive knowledge base on the structure and function of brain systems. Readers unfamiliar with maximum entropy models may easily misinterpret energy changes as reflecting metabolic cost, neural effort, or physiological variables, and it is just very unclear what that measure is supposed to reflect. The manuscript does not clearly articulate what conceptual and mechanistic advances the energy formalism provides beyond a mathematical and statistical report. In other words, beyond mathematical description, it is very hard for most readers to understand the process and function of what this framework is supposed to tell us in regards to functional connectivity, brain systems, and cognition. The brain is not a mathematical object; it is a biological organ with cognitive functions. The impact of this paper is severely limited until connections can be made.

Thank you for your insightful and constructive comments regarding the conceptual clarity of our energy landscape framework. We appreciate your perspective on the challenges of mapping the statistical measure of "energy" derived from the Boltzmann distribution onto biological and cognitive operations. To address these concerns, we will revise our manuscript to clarify our expressions surrounding "energy" and emphasize its probabilistic nature. Additionally, we will incorporate a series of analyses that explicitly relate the features of the energy landscape to cognitive processes and key parameters, such as brain integration and functional connectivity. We believe these changes will help bridge the gap between our mathematical framework and its relevance to understanding brain systems and cognitive functions.

Relatedly, the use of metaphors such as "valleys," "hills," and "routes" in multidimensional measures lacks grounding. Valleys and hills of what is not intuitive to understand. Based on my reading, these features correspond to local minima and barriers in a probability distribution over binarized network activation patterns, but similar to the first point, the manuscript does not clearly explain what it means conceptually, neurobiologically, or computationally for the brain to "move" through such a landscape. The brain is not computing these probabilities; they are measurement tools of "something". What is it? To advance beyond mathematical description, these measurements must be mapped onto neurobiological and cognitive information.

Thank you for your valuable feedback. In our revisions, we would aim to link the concept of rapid transition routes in the energy landscape to cognitive processes, such as narrative understanding and related features. By exploring these connections, we hope to provide a clearer context for how our framework can enhance understanding of cognitive functions and their neural correlates.

This conceptual ambiguity goes back to the Introduction. At the level of motivation, the purpose and deliverables of the study are not defined in the Introduction. The stated goal is "Transitions between distinct cortical brain states modulate the degree of shared neural processing under naturalistic conditions". I do not know if readers will have a clear answer to this question at the end. Is the claim that state transitions cause changes in inter-subject correlation, that they index moments of narrative alignment, or that they reflect changes in attentional or cognitive mode? This level of explanation is largely dissociated from the methods in their current form.

Thank you for highlighting this important point regarding the conceptual clarity in our Introduction. We appreciate your feedback about the motivation and objectives of the study. To clarify the stated goal of investigating how transitions between distinct cortical brain states modulate shared neural processing under naturalistic conditions, we will revise the manuscript to explicitly define the specific claims we aim to address. We will ensure that these explanations are closely tied to the methods employed in our study, providing a clearer framework for our readers.

Several methodological choices can use clarification. The use of a 21-TR window centered on transition offsets is unusually long relative to the temporal scale of fMRI dynamics and to the hypothesized rapidity of state transitions. On a related note, what is the temporal scale of state transition? Is it faster than 21 TRs?

Thank you for your insightful questions regarding our methodological choices. Our focus on specific state transitions necessitated the use of a 21-TR window. While it’s true that other transitions may occur within this window, averaging across the same transitions at different times allows us to identify distinctive thalamic BOLD patterns that precede cortical state transitions. This methodology enables us to capture relevant dynamics while ensuring that we focus on the transitions of interest. We appreciate your feedback, and this clarification will be included in our revised manuscript. We would also add a figure that describe the dwell time of cortical states.

The choice of movie-watching data is a strength. But, many of the analyses performed here, energy landscape estimation, clustering of states, could in principle be applied to resting-state data. The manuscript does not clearly articulate what is gained, mechanistically or cognitively, by using movie stimuli beyond the availability of inter-subject correlation.

Thank you for your question, which closely aligns with a concern raised by Reviewer #1. Our core hypothesis posits that naturalistic stimuli yield a broader set of brain states compared to those observed during resting-state conditions. To support this assertion, we will clearly articulate the findings from previous studies that relate to this hypothesis. Additionally, if appropriate, we will provide a comparative analysis between our data and resting-state data to highlight the differences and emphasize the uniqueness of the brain states elicited by naturalistic stimuli.

Because of the above issues, a broader concern throughout the results is the largely descriptive nature of the findings. For example, the LASSO analysis shows that certain state transitions predict ISC in a subset of regions, with respectable R² values. While statistically robust, the manuscript provides little beyond why these particular transitions should matter, what computations they might reflect, or how they relate to known cognitive operations during movie watching. Similar issues arise in the clustering analyses. Clustering high-dimensional fMRI-derived features will almost inevitably produce structure, whether during rest, task, or naturalistic viewing. What is missing is an explanation of why these specific clusters are meaningful in functional or mechanistic terms.

Thank you for your questions. In our revisions, we will perform additional analyses aimed at linking state transitions to cognitive processes more explicitly. Regarding clustering, we will provide a thorough discussion in the revised manuscript.

Finally, the treatment of the thalamus, while very exciting, could use a bit more anatomical and circuit-level specificity. The manuscript largely treats the thalamus as a unitary structure, despite decades of work demonstrating big functional and connectivity differences across thalamic nuclei. A whole-thalamus analysis without more detailed resolution is increasingly difficult to justify. The subsequent subdivision into PVALB- and CALB-associated regions partially addresses this, but these markers span multiple nuclei with overlapping projection patterns.

This suggestion aligns with the feedback from Reviewer #1. We believe that performing nuclei segmentation with 3T fMRI may not be ideal due to well-documented concerns regarding signal-to-noise ratio and spatial resolution. Therefore, investigating core and matrix cell projections across different thalamic nuclei using 7T fMRI presents a promising avenue for further study.

(1) Van Der Meer J N, Breakspear M, Chang L J, et al. Movie viewing elicits rich and reliable brain state dynamics [J]. Nature Communications, 2020, 11(1): 5004.

(2) Song H, Park B Y, Park H, et al. Cognitive and Neural State Dynamics of Narrative Comprehension [J]. Journal of Neuroscience, 2021, 41(43): 8972-8990.

(3) Song H, Shim W M, Rosenberg M D. Large-scale neural dynamics in a shared low-dimensional state space reflect cognitive and attentional dynamics [J]. Elife, 2023, 12.

(4) Shine J M, Lewis L D, Garrett D D, et al. The impact of the human thalamus on brain-wide information processing [J]. Nature Reviews Neuroscience, 2023, 24(7): 416-430.

(5) Yang M Y, Keller D, Dobolyi A, et al. The lateral thalamus: a bridge between multisensory processing and naturalistic behaviors [J]. Trends in Neurosciences, 2025, 48(1): 33-46.

Reviewer #3 (Public review):

Summary:

In this study, Liu et al. analyze fMRI data collected during movie watching, applied an energy landscape method with pairwise maximum entropy models. They identify a set of brain states defined at the level of canonical functional networks and quantify how the brain transitions between these states. Transitions are classified as "easy" or "hard" based on changes in the inferred energy landscape, and the authors relate transition probabilities to inter-subject correlation. A major emphasis of the work is the role of the thalamus, which shows transition-linked activity changes and dynamic connectivity patterns, including differential involvement of parvalbumin- and calbindin-associated thalamic subdivisions.

Strengths:

The study is methodologically complex and technically sophisticated. It integrates advanced analytical methods into high-dimensional fMRI data. The application of energy landscape analysis to movie-watching data appears to be novel as well. The finding on the thalamus involved energy state transition and provides a strong linkage to several theories on thalamic control functions, which is a notable strength.

Weaknesses:

The main weakness is the conceptual clarity and advances that this otherwise sophisticated set of analyses affords. A central conceptual ambiguity concerns the energy landscape framework itself. The authors note that the "energy" in this model is not biological energy but a statistical quantity derived from the Boltzmann distribution. After multiple reads, I still have major trouble mapping this measure onto any biological and cognitive operations. BOLD signal is a measure of oxygenation as a proxy of neural activity, and correlated BOLD (functional connectivity) is thought to measure the architecture of information communication of brain systems. The energy framework described in the current format is very difficult for most readers to map onto any neural or cognitive knowledge base on the structure and function of brain systems. Readers unfamiliar with maximum entropy models may easily misinterpret energy changes as reflecting metabolic cost, neural effort, or physiological variables, and it is just very unclear what that measure is supposed to reflect. The manuscript does not clearly articulate what conceptual and mechanistic advances the energy formalism provides beyond a mathematical and statistical report. In other words, beyond mathematical description, it is very hard for most readers to understand the process and function of what this framework is supposed to tell us in regards to functional connectivity, brain systems, and cognition. The brain is not a mathematical object; it is a biological organ with cognitive functions. The impact of this paper is severely limited until connections can be made.

Relatedly, the use of metaphors such as "valleys," "hills," and "routes" in multidimensional measures lacks grounding. Valleys and hills of what is not intuitive to understand. Based on my reading, these features correspond to local minima and barriers in a probability distribution over binarized network activation patterns, but similar to the first point, the manuscript does not clearly explain what it means conceptually, neurobiologically, or computationally for the brain to "move" through such a landscape. The brain is not computing these probabilities; they are measurement tools of "something". What is it? To advance beyond mathematical description, these measurements must be mapped onto neurobiological and cognitive information.

This conceptual ambiguity goes back to the Introduction. At the level of motivation, the purpose and deliverables of the study are not defined in the Introduction. The stated goal is "Transitions between distinct cortical brain states modulate the degree of shared neural processing under naturalistic conditions". I do not know if readers will have a clear answer to this question at the end. Is the claim that state transitions cause changes in inter-subject correlation, that they index moments of narrative alignment, or that they reflect changes in attentional or cognitive mode? This level of explanation is largely dissociated from the methods in their current form.

Several methodological choices can use clarification. The use of a 21-TR window centered on transition offsets is unusually long relative to the temporal scale of fMRI dynamics and to the hypothesized rapidity of state transitions. On a related note, what is the temporal scale of state transition? Is it faster than 21 TRs?

The choice of movie-watching data is a strength. But, many of the analyses performed here, energy landscape estimation, clustering of states, could in principle be applied to resting-state data. The manuscript does not clearly articulate what is gained, mechanistically or cognitively, by using movie stimuli beyond the availability of inter-subject correlation.

Because of the above issues, a broader concern throughout the results is the largely descriptive nature of the findings. For example, the LASSO analysis shows that certain state transitions predict ISC in a subset of regions, with respectable R² values. While statistically robust, the manuscript provides little beyond why these particular transitions should matter, what computations they might reflect, or how they relate to known cognitive operations during movie watching. Similar issues arise in the clustering analyses. Clustering high-dimensional fMRI-derived features will almost inevitably produce structure, whether during rest, task, or naturalistic viewing. What is missing is an explanation of why these specific clusters are meaningful in functional or mechanistic terms.

Finally, the treatment of the thalamus, while very exciting, could use a bit more anatomical and circuit-level specificity. The manuscript largely treats the thalamus as a unitary structure, despite decades of work demonstrating big functional and connectivity differences across thalamic nuclei. A whole-thalamus analysis without more detailed resolution is increasingly difficult to justify. The subsequent subdivision into PVALB- and CALB-associated regions partially addresses this, but these markers span multiple nuclei with overlapping projection patterns.

Reviewer #1 (Public review):

The authors analysed large-scale brain-state dynamics while humans watched a short video. They sought to identify the role of thalamocortical interactions.

Major concerns

(1) Rationale for using the naturalistic stimulus

In terms of brain state dynamics, previous studies have already reported large-scale neural dynamics by applying some data-driven analyses, like energy landscape analysis and Hidden Markov Model, to human fMRI/EEG data recorded during resting/task states. Considering such prior work, it'd be critical to provide sufficient biological rationales to perform a conceptually similar study in a naturalistic condition, i.e., not just "because no previous work has been done". The authors would have to clarify what type of neural mechanisms could be missed in conventional resting-state studies using, say, energy landscape analysis, but could be revealed in the naturalistic condition.

(2) Effects of the uniqueness of the visual stimulus and reproducibility

One of the main drawbacks of the naturalistic condition is the unexpected effects of the stimuli. That is, this study looked into the data recorded from participants who were watching Sherlock, but what would happen to the results if we analyzed the brain activity data obtained from individuals who were watching different movies? To ensure the generalizability of the current findings, it would be necessary to demonstrate qualitative reproducibility of the current observations by analysing different datasets that employed different movie stimuli. In fact, it'd be possible to find such open datasets, like www.nature.com/articles/s41597-023-02458-8.

(3) Spatial accuracy of the "Thalamic circuit" definition

One of the main claims of this study heavily relies on the accuracy of the localization of two different thalamic architectures: matrix and core. Given the conventional or relatively low spatial resolution of the fMRI data acquisition (3x3x3 mm^3), it appears to be critically essential to demonstrate that the current analysis accurately distinguished fMRI signals between the matrix and core parts of the thalamus for each individual.

(4) More detailed analysis of the thalamic circuits

In addition, if such thalamic localisation is accurate enough, it would be greatly appreciated if the authors perform similar comparisons not only between the matrix and core architectures but also between different nuclei. For example, anterior, medial, and lateral groups (e.g., pulvinar group). Such an investigation would meet the expectations of readers who presume some microscopic circuit-level findings.

(5) Rationale for different time window lengths

The authors adopted two different time window lengths to examine the neural dynamics. First, they used a 21-TR window for signal normalisation. Then, they narrowed down the window length to 13-TR periods for the following statistical evaluation. Such a seemingly arbitrary choice of the shorter time window might be misunderstood as a measure to relax the threshold for the correction of multiple comparisons. Therefore, it'd be appreciated if the authors stuck to the original 21-TR time window and performed statistical evaluations based on the setting.

(6) Temporal resolution

After identifying brain states with energy landscape analysis, this study investigated the brain state transitions by directly looking into the fMRI signal changes. This manner seems to implicitly assume that no significant state changes happen in one TR (=1.5sec), which needs sufficient validation. Otherwise, like previous studies, it'd be highly recommended to conduct different analyses (e.g., random-walk simulation) to address and circumvent this problem.

Reviewer #1 (Public review):

In this study, Acosta-Bayona et al. investigate whether heavy metal (HM) stress can induce phenotypic and molecular responses in teosinte parviglumis that resemble traits associated with domestication, and whether genes within a domestication-linked region show patterns consistent with reduced genetic diversity and signatures of selection. The authors exposed both maize and teosinte parviglumis to a fixed dose of copper and cadmium, representing an essential and a non-essential element, respectively. They assessed shoot and root phenotypic traits at a defined developmental stage in plants exposed to HM stress versus control. They then integrated these phenotypic results with expanded analyses of genetic diversity across a broader chromosome 5 interval, which was previously associated with domestication-related traits. Overall, the revisions improve the clarity and the robustness of the analyses, as well as make the conclusions better aligned with the evidence.

The revised manuscript is strengthened by several additions.

(1) The authors broaden the genetic analysis beyond a small set of loci and evaluate nucleotide variability across several genes within the linked chromosome 5 interval, which improves the interpretation of diversity patterns and reduces concerns about a too narrow locus selection or regional linkage effects driving the conclusions.

(2) The expression analyses are now presented with clearer methodological separation and stronger quantitative support. Now, tissue/developmental RT-PCR profiles are distinguished from real-time qPCR assays used to test HM-induced expression changes, with appropriate replication and statistical reporting.

(3) The authors include a transcriptome-scale element by analyzing multiple published and publicly available HM-stress transcriptome datasets and reporting shared differentially expressed genes across studies, which supports the interpretation that the observed expression changes align with broader HM-responsive transcriptional programs.

However, it remains challenging to distinguish which aspects of the HM responses observed here represent novel insight versus patterns already reported in maize HM-stress studies. In addition, the link between HM exposure and domestication history remains indirect: reduced diversity patterns and stress-responsive expression do not, on their own, demonstrate human-driven selection or a specific paleoenvironmental scenario, and alternative explanations related to general stress responses or regional evolutionary processes cannot be fully excluded.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1(Public review):

In this study, Acosta-Bayona et al. aim to better understand how environmental conditions could have influenced specific gene functions that may have been selected for during the domestication of teosinte parviglumis into domesticated maize. The authors are particularly interested in identifying the initial phenotypic changes that led to the original divergence of these two subspecies. They selected heavy metal (HM) stress as the condition to investigate. While the justification for this choice remains speculative, paleoenvironmental data would add value; the authors hypothesize that volcanic activity near the region of origin could have played a role.

The justification of choice to investigate the effects of heavy metal stress is not speculative. As mentioned now in the Abstract, the elucidation of the genome from the Palomero toluqueño maize landrace revealed heavy metal effects during domestication (Vielle-Calzada et al., Science 2009). Our aim was to test the hypothesis that heavy metal (HM) stress influenced the evolutionary transition of teosinte parviglumis to maize.

(1) Although the paper presents some interesting findings, it is difficult to distinguish which observations are novel versus already known in the literature regarding maize HM stress responses. The rationale behind focusing on specific loci is often lacking. For example, a statistically significant region identified via LOD score on chromosome 5 contains over 50 genes, yet the authors focus on three known HM-related genes without discussing others in the region. It is unclear why ZmHMA1 was selected for mutagenesis over ZmHMA7 or ZmSKUs5.

We appreciated the depth and value of this comment.

Maize phenotypic responses to sublethal concentrations to heavy metals – copper (Cu) and cadmium (Cd) in particular - are well characterized and published, and in agreement with our results. In the first section of the Results (pgs 7 and 8), we added pertinent references to clearly show which observations are already known. By contrast, teosinte parviglumis responses are in all cases novel. To our knowledge this is the first study that analyzed in detail the phenotypic response of teosinte to sublethal concentrations of heavy metals, specifically Cu and Cd. We have now emphasized the novelty of these observations (pg 8).

To address the fact that we only focused on three known HM-related genes without discussing others in the statistically significant region identified via LOD score on chr.5, we have added a full section that reads as follows (pgs. 11 to 13 of the new version):

“Large-scale genomic and transcriptomic comparisons indicate that many HM response genes were positively selected across the maize genome.

To expand the results well beyond the analysis of the three genes previously described, we performed a detailed analysis of genetic diversity across the 11.47 Mb genomic region comprised between Z_mSKUs5_ and ZmHMA1. This additional analysis reveals general tendencies in the quantity and nature of loci that were affected by positive selection during the teosinte parviglumis to maize transition in a region identified via LOD score on chr.5. We compared nucleotide variability by using 100 bp bins covering loci composed of two 30 Kb segments up and downstream of coding sequences, respectively, and the coding sequence itself, for 173 genes present within the genomic region comprised between ZmSKUs5 and ZmHMA (Figure S1 and Supplementary File 6). Two types of statistical tests (ANOVA and Wilcoxon) were applied to nucleotide variability comparisons using the entirety of each locus. The Benjamini-Hochber procedure allowed an estimation of the false discovery rate (FDR<0.05) to avoid type I errors (false positives). Although some individual loci appear as differently classified depending on the statistical test applied (22 out of 173 loci), the general differences in nucleotide variability are consistently maintained within the subregions described below. We found that 166 out of 173 loci show signatures of positive selection and are roughly organized in five independent subregions of variable length. The first six loci are consecutively ordered in a 402 Kb subregion that includes ZmSKUs5. A second group of 13 consecutive loci expands over a 1.44 Mb subregion that contains NRAMP ALUMINUM TRANSPORTER1, also involved in HM response through uptake of divalent ions. A third group of 17 consecutive loci expands over 1.28 Mb; eleven contain genes encoding for uncharacterized proteins. The fourth group is composed of 57 consecutive loci expanding over 3.22 Mb and contains genes encoding for DEFECTIVE KERNEL55, AUXIN RESPONSE FACTOR16, and peroxydases involved in responses to oxydative stress. The fifth group contains 12 consecutive loci expanding over 713 Kb and contains ZmHMA1. An additional segment of approximately 1.17 Mb and containing 25 consecutive loci that were positively selected expands away from the ZmSKUs5-ZmHMA1 segment; it also contains several genes encoding for peroxydases. Although multiple loci include genes that could be involved in abiotic stress and oxidative responses, these results suggest that multiple factors other than HM stress could have played a role in the evolutionary mechanisms that affected the genetic diversity of chr.5 during the teosinte parviglumis to maize transition.

To further analyze the possibility that HM response could have played a role in maize emergence and subsequent domestication, we analyzed large scale transcriptomic data corresponding to independent experiments aiming at understanding the response of maize roots to HM stress. Six available transcriptomes were selected for in-depth analysis because they presented a fold change strictly higher than 1, and their results were supported by false discovery rates (FDR<0.05). These six transcriptomes (Table S5) included HM response datasets corresponding to growth conditions that not only incorporated Cu, but also lead (Pb) and chromium (Cr) that were not included in the substrate of our experiments. Transcriptional profiles were obtained from roots of plants at different stages: maize seedlings (Shen et al., 2012; Gao et al., 2015; Zhang et al., 2024a), three week old plantlets (Yang et al., 2023), and plants at V2 stage (Zhang et al., 2024b; Fengxia et al., 2025). A total of 120 genes shared by all six transcriptomes were found to be differentially expressed under HM stress conditions (66 upegulated and 54 downregulated; Figure S3), including ZmSKUs5, ZmHMA1 and ZmHMA7; 52 of them (43.3%) are located in maize loci showing less than 70% of the nucleotide variability found in teosinte parviglumis, suggesting that they were affected by positive selection (Yamasaki et al., 2005; Supplementary File 7). Of 18 mapping in chr.5, twelve are within the 82 cM that fractionates into multiple QTLs under selection during the parviglumis to maize transition. Interestingly, five additional loci containing HM response genes completely lack SNPs within their total length in both parviglumis and maize, and 19 additional loci lack SNPs in at least one 30 Kb segment or their coding region (Supplementary File 7), suggesting the frequent presence of ultraconserved genomic regions in many loci containing HM response genes. When this same analysis was conducted in a set of loci comprising 63 genes previously identified as differentially expressed in response to abiotic stress not directly related to HM responses (hypoxia; nutritional deficiency; soil alkalinity; drought; soil salinity), 18 loci (28.6%) showed less than 70% of the nucleotide variability found in teosinte parviglumis. Only one of them maps in chr.5 and none contained segments or coding regions lacking SNPs in parviglumis or maize. These results suggest that in contrast to other types of abiotic stress response genes, loci comprising a large set of genes that unambiguously respond to HM stress caused by chemical elements of diverse nature were affected by positive selection during the parviglumis to maize transition, irrespectively of their position in the genome.”

The detailed analysis of genetic diversity across 11.47 Mb of chr.5 in the genomic region comprised between ZmSKUs5 and ZmHMA1 in presented as Supplementary File 6.

The analysis of genetic diversity in loci encompassing heavy metal response genes shared by six transcriptomes and abiotic stress controls are described in Supplementary File 7.

In the Discussion (pgs. 21 and 22), we added a paragraph section that reads as follows:

“Although loss of genetic diversity is usually the result of human selection during domestication, it can also represent a consequence of natural selective pressures favoring fitness of specific teosinte parviglumis allelic variants better adapted to environmental changes and subsequently affected by human selection during the domestication process. This possibility is reflected by widely spread selective sweeps affecting a large portion of chr.5 that contains hundreds of genes showing signatures of positive selection. The analysis of 11.47 Mb covering the ZmHMA1ZmSKUs5 segment confirms the presence of large but discrete genomic subregions that were positively selected during the teosinte parviglumis to maize transition. Although several contain genes involved in HM response and oxidative stress, the diversity of gene functions does not necessarily favor abiotic stress over other factors that could be at the origin of selective forces affecting these regions. By contrast, a large scale transcriptomic survey indicates that genes consistently responding to HMs (Cu, Cd, Pb and Cr ) show signatures of positive selection at unusual high frequencies (43.3%) as compared to loci containing genes responding to other types of abiotic stress (28.6%). Our identification of HM response genes affected by positive selection is far from being exhaustive. Nevertheless, it agrees with the expected effects of a widespread selective sweep caused by environmental changes that influenced the parviglumis to maize transition at the genetic level. Of intriguing interest are 24 loci that partially or completely lack SNPs in both teosinte parviglumis and maize, suggesting possible genetic bottlenecks occurred before the teosinte to maize transition. Examples of other edaphological factors driving genetic divergence either in the teosintes or maize include local adaptation to phosphorus concentration in mexicana and parviglumis (Aguirre-Liguori et al. 2019), and fast maize adaptation to changing iron availability through the action of genes involved in its mobilization, uptake, and transport (Benke and Stich 2011). Our results reveal a teosinte parviglumis environmental plasticity that could be related to the function of HM response genes positively selected during the teosinte parviglumis to maize transition. Previous studies have demonstrated that transposable elements (TEs) contribute to activation of maize genes in response to abiotic stress, affecting up to 20% of the genes upregulated in response to abiotic stress, and as many as 33% of genes that are only expressed in response to stress (Makarevitch et al., 2015). It is therefore possible that the HM response of some specific genes that influenced maize emergence or domestication could be mediated by TEs influencing or driving their transcriptional regulation.”

The mutagenic analysis of ZmHMA7 and ZmSKUs5 will be included in a different publication.

(2) The idea that HM stress impacted gene function and influenced human selection during domestication is of interest. However, the data presented do not convincingly link environmental factors with human-driven selection or the paleoenvironmental context of the transition. While lower nucleotide diversity values in maize could suggest selective pressure, it is not sufficient to infer human selection and could be due to other evolutionary processes. It is also unclear whether the statistical analysis was robust enough to rule out bias from a narrow locus selection. Furthermore, the addition of paleoclimate records (Paleoenvironmental Data Sources as a starting point) or conducting ecological niche modeling or crop growth models incorporating climate and soil scenarios would strengthen the arguments.

We think that the detailed analysis of genetic diversity across 11.46 Mb covering the ZmSKUs5 to ZmHMA1 genomic segment – and its statistical validation - provides a precise understanding of the selective sweep dimensions in chr.5.

We do agree that lower nucleotide diversity values in maize are not sufficient to infer human selection. Because many HM response loci show unusually low nucleotide variability in teosinte parviglumis (see the results of the transcriptomic analysis presented above), we cannot discard the possibility that natural selection forces related to environmental changes could have affected native populations of teosinte parviglumis.

To further explore the link between environmental factors, natural or human-driven selection, and the paleoenvironmental context of the parviglumis to maize transition, we revised paleoenvironmental and geological records and added results in two sections that read as follows (pgs. 17 to 20):

“Paleoenvironmental studies reveal periods of climatic instability in the presumed region of maize emergence during the early Holocene.

It is well accepted that temperature fluctuations, volcanism and anthropogenic impact shaped the distribution and abundance of plant species in the Transmexican Volcanic Belt (TMVB) during the last 14,000 years (Torrescano-Valle et al. 2019). The TMVB has produced close to 8000 volcanic structures (Ferrari et al., 2011), transforming the relief multiple times, and causing hydrographic and soil changes that actively modified the distribution and composition of plant communities in Central Mexico. Detailed paleoenvironmental data for the Pleistocene and Holocene is available for several lacustrine zones located within the 50 to 100 km range of the region currently considered the cradle of maize domestication (Matzuoka et al. 2002; Figure 5a). In Lake Zirahuén (102°44′ W; 19°26′ N and approximately 2075 meters above sea level; index [i] in Figure 5a), pollen, microcharcoal and magnetic susceptibility analyses of two sedimentary sequences reveals three periods of major ecological change during the early and middle Holocene.

Between 9500 and 9000 calibrated years before present (cal yr BP), pine forests seem to have been associated with summer insolation increases. A second peak of forest change occurred at around 8200 cal yr BP, coinciding with cold oscillations documented in the North Atlantic. Finally, events occurred between 7500 and 7100 cal yr BP shows an abrupt change in the plant community related to humid Holocene climates and a presumed volcanic event (Lozano-García et al., 2013). The environmental history of the central Balsas watershed has also been documented by pollen, charcoal, and sedimentary analysis conducted in three lakes and a swamp of the Iguala valley (Piperno et al. 2007). Paleoecological records of lake Ixtacyola (8°20N, 99°35W and approximately 720 meters above sea level; index [ii] in Figure 5a) and lake Ixtapa (8°21N, 99°26W) indicate that an important increase in temperature and precipitation occurred between 13000 and 10000 cal yr BP. The pollen record of Ixtacyola showed that members of the genus Zea were already part of the vegetation coverage by 12900 to 13000 cal yr BP, suggesting that some teosintes – likely including parviglumis - were commonly found at elevation areas where they do not presently occur. Lake Almoloya (also named Chignahuapan; 19°05N, 99°20E and approximately 2575 meters above sea level; index [iii] in Figure 5a) in the upper Lerma basin is only 20 Km from the crater of the Nevado de Toluca that is responsible for creating the late Pleistocene Upper Toluca Pumice layer over which the Lerma basin is deposited. Pollen records indicate the presence of Zea species by 11080 to 10780 cal yr BP. As for other locations, an important period of climatic instability prevailed between 11500 and 8500 cal yr BP (Ludlow-Wiechers et al., 2005). Humidity fluctuations occurred until 8000 cal yr BP, with a stable temperate climate between 8500 and 5000 cal yr BP. Although pollen and diatom studies are often difficult to interpret at a regional scale, the overall results presented above suggest consistent periods of Zea plants present in periods of environmental and climatic instability that correlate with the history of volcanic activity during the early Holocene, as described in the next section.

Temporal and geographical convergence between volcanic eruptions and maize emergence during the Holocene.

Current evidence indicates that the emergence and domestication of maize initiated in Mesoamerica some time around 9,000 yr BP (Matsuoka et al. 2002). The current location of teosinte parviglumis populations that are phylogenetically most closely allied with maize are currently distributed in a region located between the Michoacan-Guanajuato Volcanic Field (MGVF) at their northwest, and the Nevado de Toluca and Popocatéptl volcanoes at their east and northeast (Figure 5a; Matsuoka et al. 2002). Precise records of field data indicate that ten accessions were collected in the Balsas river drainage near Teloloapan and Sierra de Huautla (Guerrero), at approximately 100 km south of the Nevado de Toluca crater. Three other accessions were collected near Tejupilco de Hidalgo and Zacazonapan (Estado de México), at approximately 50 to 60 km from the Nevado de Toluca crater (8762, JSG y LOS-161, and JSG-391). And four other accessions were located in Michoacan, at a location within the MGVF (accession 8763), or at mid-distance between the MGVF and the Nevado de Toluca crater (accessions JSG y LOS-130, 8761, and 8766).

The most important source of HMs in ancient soils of Mesoamerica is TMBV-dependent volcanic activity through short- and long-term effects related to lava deposits, ores, hydrothermal flow, and ash (Torrescano-Valle et al. 2019). The Nevado de Toluca volcano produced one of the most powerful eruptions from central Mesoamerica in the Holocene, giving rise to the Upper Toluca Pumice deposit at 12621 to 12025 cal yr BP (Arce et al., 2003; Figure 5b). The pumice fallout blanketed the Lerma and Mexico basins with 40 cm of coarse ash (Bloomfield and Valastro 1977; Arce et al. 2003). A second eruption dated by 36Cl exposure occurred at 9700 cal yr BP (Arce et al. 2003; Figure 5b), and the most recent eruption occurred at 3580 to 3831 cal yr BP (Macías et al. 1997). During the early and middle Holocene, the Popocatéptl volcano produced at least four eruptions dated 13037-12060, 10775–9564, 8328-7591, and 6262-5318 cal yr BP (Siebe et al. 1997); three other important eruptions occurred during the late Holocene, between 2713 and 733 cal yr BP (Siebe and Macías, 2006). In addition, the MGFV is a monogenetic volcanic field for which 23 independent eruptions have been documented during the Holocene, 21 of them located towards the southern part of the field, in close proximity to the region harboring some of the teosinte parviglumis populations most closely related to maize. Three of these eruptions occurred in the early Holocene (El Huanillo 1130 to 9688 cal yr BP; La Taza 10649 to 10300 cal yr BP; Cerro Grande 10173 to 9502 cal yr BP; Figure 5b), and three others during the initial period of the middle Holocene, between 8400 and 7696 cal yr BP (La Mina, Los Caballos, and Cerro Amarillo; Figure 5b). On average, a new volcano forms every ~435 years in the MGFV (Macías and Arce, 2019). No less than 16 other eruptions occurred between 7159 cal yr BP and the present time (Figure 5b). Soils of volcanic origin (andosols) are currently distributed in regions north-west from the Nevado de Toluca and Popocatéptl craters, in close proximity with teosinte parviglumis populations most closely related to maize (Figure S5). Although modern distribution of teosinte populations may differ from their distribution around 9000 yr BP, and unknown populations more closely related to maize may yet to be discovered, this data indicates that the date and region where maize emerged is convergent with the dates and locations of several volcanic eruptions occurred during the Holocene in that same region.”

(3) Despite the interest in examining HM stress in maize and the presence of a pleiotropic phenotype, the assessment of the impact of gene expression is limited. The authors rely on qPCR for two ZmHMA genes and the locus tb1, known to be associated with maize architecture. A transcriptomic analysis would be necessary to 1- strengthen the proposed connection and 2- identify other genes with linked QTLs, such as those in the short arm of chromosome 5.

Real-time qPCR is an accurate and reliable approach to assess the expression of specific genes such as ZMHMA1 and Tb1, but we agree that our results do not allow to establish a direct regulatory link between the function of Tb1, the pleiotropic parviglumis phenotype under HM stress, and the function of ZmHMA1. We also concede that the large transcriptional analysis of HM response in maize (presented above) does not allow to elucidate a possible connection between these two genes. We have substantially downplayed our conclusion in this section by modifying the end of the section in pg. 17, that now reads:

“These results do not allow to directly link the regulation of ZmHMA1 expression to the function of Tb1; however, they open an opportunity to further investigate the possibility that under HM stress, the formation of secondary ramifications in teosinte parviglumis could be repressed by transcription factors of the TCP family, including Tb1.”

This is also emphasized in the Discussion (pg 21) as follows:

“Under HM stress, we also show that Tb1 is overexpressed in the apical meristem of teosinte parviglumis, suggesting that formation of secondary ramifications is repressed by Tb1 function under HM stress, as in extant maize. At this stage we cannot discard the possibility that Tb1 upregulation in parviglumis reflects a more generalized response to abiotic stress; however, the expression ZmHMA1 is downregulated in W22 wild-type maize meristems in the presence of HMs but upregulated in teosinte parviglumis meristems, suggesting that a specific regulatory shift relating HM responses and ZmHMA1 function occurred during the teosinte parviglumis to maize transition.”

On the other hand, the transcriptional analysis the identification of 52 additional HM response genes showing signatures of positive selection occurred during the parviglumis to maize transition; 12 of them map to chr.5 within the region having linked QTLs within the short arm of chr.5. So far, genes involved in HM response and oxidative stress represent the most prevalent class of genes identified within the genomic region showing pleiotropic effects on domestication and multiple linked QTLs in chr.5.

Reviewer #2 (Public review):

Summary:

This work explores the phenotypic developmental traits associated with Cu and Cd responses in teosinte parviglumis, a species evolutionary related to extant maize crops. Cu and Cd could serve as a proxy for heavy metals present in the soils. The manuscript explores potential genetic loci associated with heavy metal responses and domestication identified in previous studies. This includes heavy metal transporters, which are unregulated during stress. To study that, the authors compare the plant architecture of maize defective in ZmHMA1 and speculate on its association with domestication.

Strengths:

Very few studies covered the responses of teosintes to heavy metal stress. The physiological function of ZmHMA1 in maize also gives some novelty in this study. The idea and speculation section is interesting and well-implemented.

Weaknesses:

The authors explored Cu/Cd stress but not a more comprehensive panel of heavy metals, making the implications of this study quite narrow. Some techniques used, such as end-point RT-PCR and qPCR, are substandard for the field. The phenotypic changes explored are not clearly connected with the potential genetic mechanisms associated with them, with the exception of nodal roots. If teosintes in response to heavy metal have phenotypic similarity with modern landraces of maize, then heavy metal stress might have been a confounding factor in the selection of maize and not a potential driving factor. Similar to the positive selection of ZmHMA1 and its phenotypic traits. In that sense, there is no clear hypothesis of what the authors are looking for in this study, and it is hard to make conclusions based on the provided results to understand its importance. The authors do not provide any clear data on the potential influence of heavy metals in the field during the domestication of maize. The potential role of Tb-1 is not very clear either.

Thank you for these comments. We have now emphasized our hypothesis in the abstract and the last paragraph of the Introduction (pg. 6):

“To test the hypothesis that heavy metal (HM) stress influenced the evolutionary transition of teosinte to maize, we exposed both subspecies to sublethal concentrations of copper and cadmium etc…”

A comprehensive panel of heavy metals would not be more accurate in terms of simulating the composition of soils evolving across 9,000 years in the region where maize presumably emerged. Copper (Cu) and cadmium (Cu) correspond each to a different affinity group for proteins of the ZmHMA family. ZmHMA1 has preferential affinity for Cu and Ag (silver), whereas ZmHMA7 has preferential affinity to Cd, Zn (zinc), Co (cobalt), and Pb (lead). Since these P1b-ATPase transporters mediate the movement of divalent cations, their function remains consistent regardless of the specific metal tested, provided it belongs to the respective affinity group. By applying sublethal concentrations of Cd (16 mg/kg) and Cu (400 mg/kg), we caused a measurable physiological response while allowing plants to complete their life cycle, including the reproductive phase, facilitating a comprehensive analysis of metal stress adaptation. Whereas higher doses impair flowering or are lethal, lower Cu/Cd concentrations do not consistently show conventional phenotypic responses such as reduced plant growth (AbdElgawad et al. 2020; Atta et al., 2023)

Based on comments by both reviewers, we present now a large transcriptional analysis that incorporates HM responses to lead (Pb) and chromium (Cr), in addition to Cu. Results show that many genes responding to Pb and Cr were also positively selected across the maize genome, suggesting that HM stress led to a ubiquitous rather than a specific evolutionary response to heavy metals (please see our response to Reviewer#1 and sections in pgs. 11 to 13) .

Real-time qPCR is an accurate and reliable approach to assess the expression of specific genes such as ZMHMA1 and Tb1, but we agree that our results do not allow to establish a direct regulatory link between the function of Tb1, the pleiotropic parviglumis phenotype under HM stress, and the function of ZmHMA1. We also concede that the large transcriptional analysis of HM response in maize (presented above) does not allow to elucidate a possible connection between these two genes. Therefore, we have substantially downplayed our conclusion in this section by modifying the end of the section in pg. 17, that now reads:

“These results do not allow to directly link the regulation of ZmHMA1 expression to the function of Tb1; however, they open an opportunity to further investigate the possibility that under HM stress, the formation of secondary ramifications in teosinte parviglumis could be repressed by transcription factors of the TCP family, including Tb1.”

There are two phenotypic changes clearly connected with the genetic mechanisms involved in the parviglumis to maize transition: plant height and the number of seminal roots (not nodal roots). These changes have been now emphasized in the Abstract and the description of the results.

Regarding the possibility for HM stress to represent a confounding factor in the selection of maize and not a driving factor, we expanded the genomic analysis of genetic diversity well beyond the analysis of the three genes under initial study, to cover a segment of 11.47 Mb comprised between ZmSKUs5 and ZmHMA1. We compared nucleotide variability by using 100 bp bins covering loci composed of two 30 Kb segments up and downstream of coding sequences, respectively, and the coding sequence itself, for 173 genes present within the genomic region comprised between ZmSKUs5 and ZmHMA (Figure S1 and Supplementary File 6). The full analysis is presented in a new section pgs. 11 and 12. We found that 166 out of 173 loci show signatures of positive selection and are roughly organized in five independent subregions of variable length. Four out of five subregions contain more than one HM or oxidative stress response gene within loci showing signatures of positive selection. Although multiple factors other than HM stress could have played a role in the evolutionary mechanisms that affected the genetic diversity of chr.5, large scale transcriptomic data corresponding to independent experiments aiming at understanding the response of maize roots to HM stress allowed the identification of 49 additional HM response genes within loci showing positive selection across the genome, a proportion (43.3%) far greater than the proportion of loci containing response genes to other types of abiotic stress not related to HMs (28.6%). These results are described in detail in pgs. 12 and 13 (Figure S3 and Supplementary File 7). These results provide strong evidence in favor of HM stress and not another factor driving positive selection.

We now provide precise and pertinent paleoenvironmental data on the potential influence of heavy metals in the field. In sections pgs. 17 to 20 we review paleoenvironmental studies revealing periods of climatic instability in the presumed region of maize emergence during the early Holocene, and data indicating that the date and region where maize emerged is convergent with the dates and locations of several volcanic eruptions occurred during the early and middle Holocene in that same region. Please see responses to Reviewer#1 for details.

We agree that our results do not allow to establish a direct regulatory link between the function of Tb1, the pleiotropic parviglumis phenotype under HM stress, and the function of ZmHMA1. We also concede that the large transcriptional analysis of HM response in maize (presented above) does not allow to elucidate a possible connection between these two genes. Therefore, we have substantially downplayed our conclusion in this section by modifying the end of the section in pg. 17, that now reads:

“These results do not allow to directly link the regulation of ZmHMA1 expression to the function of Tb1; however, they open an opportunity to further investigate the possibility that under HM stress, the formation of secondary ramifications in teosinte parviglumis could be repressed by transcription factors of the TCP family, including Tb1.”

This is also emphasized in the Discussion (pg 21) as follows:

“Under HM stress, we also show that Tb1 is overexpressed in the apical meristem of teosinte parviglumis, suggesting that formation of secondary ramifications is repressed by Tb1 function under HM stress, as in extant maize. At this stage we cannot discard the possibility that Tb1 upregulation in parviglumis reflects a more generalized response to abiotic stress; however, the expression ZmHMA1 is downregulated in W22 wild-type maize meristems in the presence of HMs but upregulated in teosinte parviglumis meristems, suggesting that a specific regulatory shift relating HM responses and ZmHMA1 function occurred during the teosinte parviglumis to maize transition.”

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

While the dataset generated provides an interesting foundation for hypothesis testing on HM stress and domestication, the current data do not sufficiently support the conclusions of the manuscript.

(1) The description of maize and teosinte architecture under HM stress is well presented.

However, traits like shoot height, leaf size reduction, and biomass loss also occur under other environmental stresses such as drought and salinity. Additional evidence beyond shoot and root architecture would help validate the link between tb1 expression and specific ZmHMA genes under HM stress, or whether it reflects a more generalized stress response.

We have already addressed in detail this point in the public response to Reviewer#1.

(2) The nucleotide variability analysis is interesting, but I would have liked to see additional information to clarify the choice of the data selection and the strength of the conclusions with human selection.

We have already addressed in detail this point in the public response to Reviewer#1.

a) The choice of Tripsacum dactyloides as the outgroup to determine nucleotide variability seems to be distant, and I wonder whether other combinations with a closer outgroup or multiple outgroups were tried to provide a more accurate context.

Nucleotide variability in Tripsacum dactyloides is used to graphically illustrate an external reference and not as an outgroup in the extended analysis of genetic diversity at the locus and genomic level. We did not used Tripsacum dactyloides as an outgroup in our statisticalm analysis. We could have indeed a closer teosinte subspecies as an outgroup, but at this stage no data warrants that environmentally-related selective pressures could have affected genetic diversite in other teosintes. This possibility in currently being investigated.

b) Evolutionary differences not related to human influence could affect the results. The phrase "order of magnitude difference in π values" needs statistical validation (e.g., confidence intervals, p-values).

We agree and have eliminated the sentence, as it is no longer relevant at the light of the detailed genomic analysis of genetic diversity prsented in Supplementary File 6.

c) The comparison with ZmGLB1, a neutral control locus, suggests that domestication-related changes in nucleotide variability are specific to the three candidate genes. However, the concept of neutrality is complex, and while ZmGLB1 may be considered neutral in this case, the argument does not address the possibility of other factors, such as linked selection, that could influence variability in these genes. Referencing Hufford et al. is insufficient and would require a deeper argument.

We also agree with this comment. We think that the influence and consequences of linked selection are now well documented for 11.46 Mb analyzed in chr.5 (pgs 11 and 12) in the main text and Supplementary File 6).

(3) The statement: "Our evidence indicates that HM stress revealed a teosinte parviglumis environmental plasticity that is directly related to the function of specific HM response genes that were affected by domestication through human selection" is not supported by the presented data. The rationale for the specific Cd/Cu dosage used is unclear. A dose-response gradient would better demonstrate the nature and strength of the plastic response.

Previous reports support the rationale for the specific HM dosage in this study; Cu/Cd dosage response gradients have been conducted in maize (AbdElgawad et al. 2020; Atta et al., 202), but since no studies have been conducted in teosinte, we reasoned that it was important to apply the same treatment to both subspecies. We have now emphasized this rationale by adding the following in pg XX: “Whereas higher doses impair flowering or are lethal, lower Cu/Cd concentrations do not consistently show conventional phenotypic responses such as reduced plant growth (AbdElgawad et al. 2020; Atta et al., 2023)”.

We agree that the statement raised by the reviewer needed revision at the light of our results. We did revise the statement to accurately reflect our current evidence as follows: “Our results reveal a teosinte parviglumis environmental plasticity that is likely related to the function of HM response genes positively selected during the teosinte parviglumis to maize transition.”

(4) In maize, TEs are known to influence gene expression under abiotic stress, including for tb1 (PMID: 25569788). Since the author appears to make a causative conclusion between ZmHMA1, TB1, and HM stress, I would have liked to see a whole-transcriptome analysis and not a curation of two genes to determine whether other factors, such as TEs, can have that would lead to similar outcomes.

We agree that is definetely a possibility that we have not investigated at this stage. However, we added a pargraph to reflect this pertinent suggestion:

“Previous studies have demonstrated that transposable elements (TEs) contribute to activation of maize genes in response to abiotic stress, affecting up to 20% of the genes upregulated in response to abiotic stress, and as many as 33% of genes that are only expressed in response to stress (Makarevitch et al., 2015). It is therefore possible that the HM response of some specific genes that influenced maize emergence or domestication could be mediated by TEs influencing or driving their transcriptional regulation.”

(5) I would suggest that the authors carefully review the tables, figures, and the corresponding legends. For example :

a) Table 2 is called before Table 1, I would therefore suggest changing the numbering to reflect the paragraph order.

Thank you for your help, we did change the order of the Tables in the new version.

b) In Table 2, it is not clear whether the P value applies to the mean difference between WT and the mutant zmhma1, either in the presence or the absence of heavy metals. In addition, the authors need to use the P-value to estimate the differences between WT in the absence vs presence of HM, and WT in the absence of HM versus the mutant in the absence of HM (idem for presence).

We did address this issue in detail and added P-values and specific pairwise comparisons to that Table (now Table 1). Data are presented as mean ± standard deviation and were tested by a paired Student’s T-Test. When the effects were significant according to T-Test, the treatments were compared with the Welch two sample T-Test at P < 0.05.

c) Table 1 and Table 2: Indicate what type of statistical test was used and the number of plants used for each experiment (n). Also, I recommend the use of scientific notation for the P-values.

The statistical tests have now been indicated, scientific notation has been added to the P-values; the number of plants and biological replicates are indicated in the Methods section.

d) Lines 202 and 204: I assume Table 1 should be called instead of Table 2.

This error has been corrected.

e) General: In the text, when significance is highlighted along with measurements, the p-value needs to be added.

We have added the P-value along the measurement for all significant differences.

f) In the text, it is also mentioned that "the expression of ZMHMA1 was significantly increased in the presence of HMs (Figure 3c)". We are looking here at an RT-PCR, which is qualitative and without a robust quantitative comparison and statistics, I cannot conclude this assessment based on the presented evidence. No statistical measure is indicated here.

Panel 3c is not RT-PCR but a real-time qPCR, showing relative fold-change, normalized to actin, with a 3-technical triplicate per 3 biological replicates). We have added error bars (SD) and P-values represented by asterisks (calculated with Student's t statistic) to support significant differences (P<0.05 and P<0.01). ZmHMA1 expression was significantly increased in the presence of HMs only in teosinte; there was no significant difference in maize.

g) Figure 3 should at least have the gene name in the figure to quickly understand the figure panel. The key conserved domains should also be identified.

We agree and apologize for the omission. The gene names have been added adjacent to the structures.

h) Sentence at lines 459-460 lacks words and punctuation.

This unfortunate rror has also been corrected.

i) Figure S1, the reference Lemmon and Doebley, 2024 should be Lemmon and Doebley, 2014 to harmonize with the text.

The correct year is 2014. We have corrected this error.

Reviewer #2 (Recommendations for the authors):

(1) The narrative should be clearer, starting with a clearer hypothesis that is later sustained or not in the results, and then discussed in the idea and speculation section.

Thank you for the comment. We have clarified the hypothesis, it is included in the abstract and the last paragraph of the Introduction. We hope it is now clear that the evidence presented supports our hypothesis

(2) Focus more on traits that are relevant, for example, nodal and seminal roots.

We modified the text to emphasize three relevant traits. In the case of teosinte under HM stress, absence of tillering and increase in the number of female inflorescences. In the case of the zmha1 mutant under HM stress, differences in the number of nodal roots, and differences in height.

(3) RNA-seq in Cu/Cd stress could make the work much more useful and complete.

As previously mentioned, we have incorporated a large scale transcriptional analysis on the basis of six transcriptomes statistically validated (Table S5). Please see sections pgs. 11 to 13 for details.

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What is this?

I didn't know this but it seems really helpful and makes a lot of sense!

I didn't know that play citations were different from if a regular novel was being used as a source.

Pneuma Mechanics. The Hebrew Ruach and Greek Pneuma both translate to "Wind," "Breath," or "Spirit." In John 3:8, Jesus describes the Kingdom-led life as being like the wind—unpredictable in origin but undeniable in effect. Trying to "be the engine" is an attempt to operate by the Sarx (flesh/effort). The Sailboat mode is the biblical technology of being "led by the Spirit," where the power source is external and infinite.

2 or more atoms that share valence electrons forming a molecular compound

From top to bottom:

1-North Pole 90 degrees N 2-Arctic circle 66.5 degrees N 3-Tropic of cancer 23.5 degrees N 4-Equator 0 degree 5-Tropic of Capricorn 23.5 degrees S 6-Antarctic circle 66.5 degrees S 7-South pole 90 degrees S

A: Summer B: Daylength is longer than other seasons according to earth revolution in Figure 3.2 C:1.what season am I in 2. When the sun sets 3.how Tall my shadow looks like when I’m in front of the sun while it is setting. D: Sports that happens “mostly” in the summer such as kayaking, playing golf, swimming, etc.

Question 6.

The series of steps that you should take to write are: Pre-writing, Drafting, Revising, Proofreading.

A 70 year old woman arrives to the clinic for a yearly exam. She has been stressed lately with retirement, aging, and life changes. You know that persistent stress induced inflammation of the brain cells can cause: 1. Anxiety 2. Alzheimer disease 3. Dizziness 4. Encephalopathy

A single mother brings her 14 year old son into a clinic at the recommendation of his teacher. The son displays classic signs of add, adhd, and presents with difficulty self regulating; displaying early signs of depression and isolation. Which of the following exposures could have played a part in his brain development?

A.) His mother worked at Mc. Donalds for the term of her pregnancy and regularly ate food the restaurant had to decrease cost of living.

B.) His mother had an issue breast feeding and so the child was only formula fed as in infant.

C.) The mother had to live with her family in the first five years of his life, with 12 people in a two-bedroom house, in a neighborhood with high crime rates.

D.) His grandmother who he lived with was a heavy smoker and exposed him once a week to second hand smoke.

Le Sentiment d'Appartenance : Moteur de la Réussite Scolaire

Synthèse Exécutive

Le sentiment d'appartenance en milieu scolaire est un besoin psychologique fondamental, défini comme le sentiment d'être accepté, respecté, inclus et soutenu au sein de la communauté éducative.

Loin d'être un simple facteur de confort émotionnel, il constitue un levier puissant pour la motivation, la réussite des élèves et la prévention du décrochage.

Son contraire, le sentiment de rejet ou d'exclusion, engendre des émotions négatives telles que l'anxiété et la dépression.

La construction de ce sentiment ne se décrète pas ; elle se cultive à travers une approche systémique et intentionnelle.

Elle repose sur la satisfaction de trois besoins psychologiques de base : l'autonomie, la compétence et l'appartenance sociale.

Les stratégies efficaces incluent la co-construction de projets d'établissement impliquant l'ensemble des acteurs (élèves, enseignants, personnels), la création de rituels et de symboles fédérateurs, et la mise en place d'un climat de confiance et de respect mutuel.

Les initiatives de terrain, comme les systèmes de "maisons", peuvent dynamiser ce sentiment mais comportent des risques de conformité et de rivalité si elles ne sont pas soigneusement encadrées.

Le sentiment d'appartenance ne concerne pas uniquement les élèves.

Il est tout aussi crucial pour les personnels, dont l'engagement et le bien-être dépendent fortement de leur intégration dans une équipe soudée et d'un projet partagé.

En fin de compte, un fort sentiment d'appartenance enclenche un cercle vertueux, renforçant le sentiment d'efficacité personnelle et collective, et incitant les individus à s'engager dans des défis plus complexes, générant ainsi un épanouissement et un accomplissement accrus pour toute la communauté scolaire.

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1. Définition et Fondements Théoriques du Sentiment d'Appartenance

A. Un Besoin Humain Fondamental

Le sentiment d'appartenance est une motivation humaine si essentielle que son absence peut entraîner de graves conséquences psychologiques.

S'appuyant sur les travaux de référence de Roy Baumeister et Mark Leary (1995), le professeur Jean Eut le définit comme "le sentiment d'être accepté et compris par les gens qui nous entourent".

Ce besoin satisfait génère des émotions positives comme le bien-être et la joie.

Inversement, son contraire est défini comme "le sentiment d'être rejeté, exclu ou ignoré par les autres", menant à des émotions négatives telles que l'anxiété, la dépression, la solitude et la jalousie.

Les recherches montrent que, parmi toutes les variables objectives pouvant contribuer au bonheur (dans les sociétés où les besoins physiologiques sont satisfaits), la seule qui ressort objectivement est la présence d'un réseau social solide.

B. Spécificités en Contexte Scolaire

Appliqué à l'école, le sentiment d'appartenance est défini par Carole Good et Kathleen Grady (1993) comme "la mesure dans laquelle les élèves se sentent personnellement acceptés, respectés, inclus et soutenu par les autres dans l'environnement social scolaire".

Il s'agit d'une construction multidimensionnelle complexe, dont la terminologie dans la recherche est variée (lien scolaire, engagement, climat scolaire, etc.), ce qui a pu affaiblir la cohérence des travaux sur le sujet.

Néanmoins, trois facteurs semblent déterminants pour qu'un enfant se sente bien à l'école :

1. Se sentir compétent sur le plan académique.

2. Se sentir socialement lié et valorisé.

3. Se sentir relativement autonome.

Un outil de mesure, l'échelle du sentiment psychologique d'appartenance à l'école, a été validé en version française en 2024, offrant un moyen pratique pour la communauté éducative d'appréhender ce concept.

C. La Théorie de l'Autodétermination

Le sentiment d'appartenance est l'un des trois piliers de la théorie de l'autodétermination d'Edward Deci et Richard Ryan.

Pour qu'un individu soit en bonne santé mentale et psychique, trois besoins fondamentaux doivent être satisfaits :

• Le besoin d'autonomie : Le sentiment d'être à l'origine de ses propres actions.

• Le besoin de compétence : Le sentiment d'être efficace dans son environnement.

• Le besoin d'appartenance sociale : Le sentiment d'être connecté et accepté par les autres.

Ces trois besoins sont intrinsèquement liés et doivent être considérés de manière globale lors de la conception de tout dispositif visant à renforcer le climat scolaire.

2. La Construction du Sentiment d'Appartenance : Approches et Stratégies