Author response:

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

Public Review

GENERAL QUESTIONS:

(1) For many enveloped viruses, the attachment factors - paradoxically - are also surface glycoproteins, often complexed with a distinct fusion protein. The authors note here that the glycoportiens do not inhibit the initial binding, but only limit the stability of the adhesive interface needed for subsequent membrane fusion and viral uptake. How these antagonistic tendencies might play out should be discussed.

When the surface density of receptor molecules for a virus with glycans increases, the density of free glycans not bound to the virus increases along with the amount of virus adsorbed. However, if the total amount of glycans is considered to be a function of the receptor density, the reaction may become more complicated. This complication may also be affected by the prolonged infection. If the receptor density on the cell surface is high, the infection inhibitory effect of glycans may not be obtained in a system in which a high concentration of virus is supplied from the outside world for a long time. This is because once viruses have entered the cell, they accumulate inside the cell, and viral infection is affected by the total accumulated amount, which is the integration of the number of viruses that have entered over time. This distinction indicates that the virus entry reaction and the total amount of infection in the cell must be considered separately. This is an important point, but it was not clearly mentioned in the original manuscript.

Our experiments were conducted under conditions that clearly allowed us to detect the virusinhibiting function of glycans without being affected by the above points. In order to clarify these points, we will revise this article as follows, referring to an experiment that is somewhat related to this discussion (the Adenovirus infection experiment into HEK293T cells shown in Figure S1F)..

(Page-3, Introduction)

While there are known examples of glycans that function as viral receptors (Thompson et al., 2019), these results demonstrate that a variety of glycoproteins negatively regulate viral infection in a wide range of systems. All of these results suggest that bulky membrane glycoproteins nonspecifically inhibit viral infection.

(Page 20, Discussion)

When the virus receptor is a glycoprotein or glycan itself, the inhibition of virus infection by glycans becomes more complex because the total amount of glycans is also a function of the receptor density. It is also important to note that the total amount of infection into a cell is the time integral of virus entry. Even if the probability of virus entry is significantly reduced by glycans, the cumulative number of virus entries may increase if high concentrations of virus continue to be supplied from outside the cell for a long period of time. In the case of Adenovirus, which continues to amplify in HEK293T cells after infection, we showed that MUC1 on the cell surface has an inhibitory effect on long-term cumulative infection (Supplementary Figure 1F). However, such an accumulation effect may be caseby-case depending on the virus cell system, and may be more pronounced when the cell surface density of virus receptor molecules is high. As a result, if the virus receptor molecule is a glycan or glycoprotein and infection continues for a long period of time, the infection inhibition effect may not be observed despite an apparent increase in the total amount of glycans in the cell. In any case, our results clarified the factor of virus entry inhibition dependent on the total amount of glycans because appropriate conditions were set.

(2) Unlike polymers tethered to solid surface undergoing mushroom-to-brush transition in densitydependent manner, the glycoproteins at the cell surface are of course mobile (presumably in a density-dependent manner). They can thus redistribute in spatial patterns, which serve to minimize the free energy. I suggest the authors explicitly address how these considerations influence the in vitro reconstitution assays seeking to assess the glycosylation-dependent protein packing.

We performed additional experiments using lipid bilayers that had lost fluidity, and found that there is no significant difference in protein binding between fluid and nonfluid bilayers. The redistribution of molecules due to molecular fluidity may play some roles but not in our experimental systems. It suggests that glycoproteins can generate intermolecular repulsion even in fluid conditions such as cell membranes, just as they do on the solid phase. This experiment was also very useful because it allowed us to compare our results in the fluid bilayer with solid-state measurements of saturation molecular density and the brush transition. This comparison gave us confidence that in the reconstituted membrane system, even at saturation density, the membrane proteins are not as stretched as they are in the condensed brush state. We have therefore added a new paragraph and a new figure (Supplementary Fig. 5B) to discuss this issue, as follows:

The molecular structural state of these proteins needs to be further discussed to estimate the contribution of f_el, which represents resistance to molecular elongation. Our results suggest that these densely packed nonglycosylated molecules are no longer in a free mushroom state. However, their saturation density was several times lower than previously reported brush transition densities, such as 65000 µm^-2 for 17 kDa polyacrylamide (R_F ~ 15 nm) on a solid surface (Wu et al., 2002). To compare our data on fluid bilayers with previously reported data on solid surfaces, we performed additional experiments with lipid bilayers that lost fluidity. No significant changes in protein binding between fluid and nonfluid bilayers were observed for both b-MUC1 and g-MUC1 molecules (Supplementary Figure 5B). This result suggests that membrane fluidity does not affect the average intermolecular distance or other relevant parameters that control molecular binding in the reconstituted system. Based on these, we speculate that the saturated protein density observed in our experiments is lower than or at most comparable to the actual brush transition density. Thus, although these crowded proteins may be restricted from free random motion, they are not significantly extended as in the condensed brush state, in which the contribution of resistance to molecular extension f_el is expected to be small relative to the overall free energy of the system.

(3) The discussion of the role of excluded volume in steric repulsion between glycoprotein needs clarification. As presented, it's unclear what the role of "excluded volume" effects is in driving steric repulsion? Do the authors imply depletion forces? Or the volume unavailable due to stochastic configurations of gaussian chains? How does the formalism apply to branched membrane glycoproteins is not immediately obvious.

Regarding the excluded volume due to steric repulsion between glycoproteins, we considered the volume that cannot be used by glycans as Gaussian chains branching from the main chain. We would like to expand on this point by adding several papers that make similar arguments. I'm glad you brought this up because we hadn't considered depletion forces - the excluded volume between glycoproteins should generate a depletion force, but in this case we believe this force will not have a significant effect on viruses that are larger than the glycoproteins. We also attempted to clarify the discussion in this section by focusing on intermolecular repulsion, and restructured paragraphs, which are also related to General Question 2 and Specific Question 2. The relevant part has been revised as follows. (page 15~page16)：

To compare the packing of proteins with different molecular weights and R_F, These were smaller than the coverage of molecules at hexagonal close packing that is ~90.7%. In contrast, the coverage of b-CD43 and b-MUC1 at saturated binding was estimated to be greater than 100% under this normalization standard, indicating that the mean projected sizes of these molecules in surface direction were smaller than those expected from their R_F Thus, it is clear that glycosylation reduces the saturation density of membrane proteins, regardless of molecular size.

Highly glycosylated proteins resisted densification, indicating that some intermolecular repulsion is occurring. In the framework of polymer brush theory, the intermolecular repulsion of densely packed highly glycosylated proteins is due to an increase in either f_el, f_int (d<R_F), or both (Hansen et al., 2003; Wu et al., 2002). The term of intermolecular interaction, f_int, is regulated by intermolecular steric repulsion, which occurs when neighboring molecules cannot approach the excluded volume created by the stochastic configuration of the polymer chain (Attili et al., 2012; Faivre et al., 2018; Kreussling and Ullman, 1954; Kuo et al., 2018; Paturej et al., 2016). The magnitude of this steric repulsion depends largely on R_F in dilute solutions, but the molecular structure may also affect it when molecules are densified on a surface. In other words, the glycans protruding between molecules can cause steric inhibition between neighboring proteins (Figure 5D). Such intermolecular repulsion due to branched side chains occurs only when the molecules are in close proximity and sterically interact on a twodimensional surface, but not in dilute solution, and does not occur in unbranched polymers such as underglycosylated proteins (Figure 5D). Based on the above, we propose the following model for membrane proteins: Only when the membrane proteins are glycosylated does strong steric repulsion occur between neighboring molecules during the densification process, suppressing densification.

The molecular structural state of these proteins needs to be further discussed to estimate the contribution of f_el, which represents resistance to molecular elongation. Our results suggest that these densely packed nonglycosylated molecules are no longer in a free mushroom state. However, their saturation density was several times lower than previously reported brush transition densities, such as 65000 µm^-2 for 17 kDa polyacrylamide (R_F ~ 15 nm) on a solid surface (Wu et al., 2002). To compare our data on fluid bilayers with previously reported data on solid surfaces, we performed additional experiments with lipid bilayers that lost fluidity. No significant changes in protein binding between fluid and nonfluid bilayers were observed for both b-MUC1 and g-MUC1 molecules (Supplementary Figure 5B). This result suggests that membrane fluidity does not affect the average intermolecular distance or other relevant parameters that control molecular binding in the reconstituted system. Based on these, we speculate that the saturated protein density observed in our experiments is lower than or at most comparable to the actual brush transition density. Thus, although these crowded proteins may be restricted from free random motion, they are not significantly extended as in the condensed brush state, in which the contribution of resistance to molecular extension f_el, is expected to be small relative to the overall free energy of the system.

Note that this does not mean that glycoproteins cannot form condensed brush structures: in fact, highly glycosylated molecules (e.g., MUC1) can form brush structures in cells when such proteins are expressed at very high densities. (Shurer et al., 2019). In these cells, ………. Such membrane deformation results in the increase of total surface area to reduce the density of glycoproteins, indicating that there is strong intermolecular repulsion between glycoproteins. In any case, the free energy of the system is determined by the balance between protein binding and insertion into the membrane, protein deformation, and repulsive forces between proteins, which determine the density of proteins depending on the configuration of the system. Thus, although strong intermolecular repulsions were prominently observed in our simplified system, this may not be the case in other systems. ……

(4) The authors showed that glycoprotein expression inversely correlated with viral infection and link viral entry inhibition to steric hindrance caused by the glycoprotein. Alternative explanations would be that the glycoprotein expression (a) reroutes endocytosed viral particles or (b) lowers cellular endocytic rates and via either mechanism reduce viral infection. The authors should provide evidence that these alternatives are not occurring in their system. They could for example experimentally test whether non-specific endocytosis is still operational at similar levels, measured with fluid-phase markers such as 10kDa dextrans.

The results of the experiment suggested by the reviewer are shown in the new Supplementary Figure 3B. (This results in generation of a new Supplementary Figure 3, and previous Supplementary Figures 4-5 are now renumbered as Supplementary Figures 5-6). Endocytosis of 10KDa dextran was attenuated by the expression of several large-sized molecules, but was not affected by the expression of many other glycoproteins that have the ability to inhibit infection. These results were clearly different from the results in which virus infection was inhibited more by the amount of glycan than by molecular weight. Therefore, it was found that many glycoproteins inhibit virus infection through processes other than endocytosis. Based on the above, we have added the following to the manuscript: （p9 New paragraph:）

We also investigated the effect of membrane glycoproteins on membrane trafficking, another process involved in viral infection. Expression of MUC1 with higher number of tandem repeats reduced the dextran transport in the fluid phase, while expression of multiple membrane glycoproteins that have infection inhibitory effects, including truncated MUC1 molecules, showed no effect on fluid phase endocytosis, indicating a molecular weight-dependent effect (Supplementary Figure 3B). The molecular weight-dependent inhibition of endocytosis may be due to factors such as steric inhibition of the approach of dextran molecules or a reduction in the transportable volume within the endosome. In any case, it is clear that many low molecular weight glycoproteins inhibit infection by disturbing processes other than endocytosis. Based on the above, we focus on the effect of glycoproteins on the formation of the interface between the virus and the cell membrane.

(5) The authors approach their system with the goal of generalizing the cell membrane (the cumulative effect of all cell membrane molecules on viral entry), but what about the inverse? How does the nature of the molecule seeking entry affect the interface? For example, a lipid nanoparticle vs a virus with a short virus-cell distance vs a virus with a large virus-cell distance?

Thank you for your interesting comment. If the molecular size of the ligand is large, it should affect virus adsorption and molecular exclusion from the interface. In lipid nanoparticle applications, controlling this parameter may contribute to efficiency. In addition, a related discussion is the influence of virus shell molecules that are not bound to the receptor. I will revise the text based on the above.

Discussion (as a new paragraph after the paragraph added in Q1):

In this study, we attempted to generalize the surface structure on the cell side, but the surface structure on the virus side may also have an effect. The efficiency of virus adsorption and the efficiency of cell membrane protein exclusion from the interface will change depending on the molecular length of the receptor-ligand, although receptor priming also has an effect. In addition, free ligands of the viral envelope or other coexisting glycoproteins may also have an effect as they are also required for exclusion from the virus-cell interface. In fact, there are reports that expression of CD43 and PSGL-1 on the virus surface reduces virus infection efficiency (Murakami et al., 2020). Such interface structure may be one of the factors that determine the infection efficiency that differs depending on the virus strain. More generally, modification of the surface structure may be effective for designing materials such as lipid nanoparticles that construct the interface with cell.

SPECIFIC QUESTIONS:

(1) The proposed mechanism indicates that glycosylation status does not produce an effect in the "trapping" of virus, but in later stages of the formation of the virus/membrane interface due to the high energetic costs of displacing highly glycosylated molecules at the vicinity of the virus/membrane interface. It is suggested to present a correlation between the levels of glycans in the Calu-3 cell monolayers and the number of viral particles bound to cell surface at different pulse times. Results may be quantified following the same method as shown in Figure 2 for the correlation between glycosylation levels and viral infection (in this case the resulting output could be number of viral particles bound as a function of glycan content).

The results of this experiment are now shown as Supplementary Figure 2F and 2G. We compared the amount of virus bound after incubation for 10 minutes or for 3 hours as in the infection experiment, but no negative correlation was found between the total amount of glycans on the surface of the Calu3 monolayer and the amount of virus bound. Interestingly, there was a sight positive correlation was detected, which may be due to concentrated virus receptor expressions in glycan-enriched cells. This result shows that glycoproteins do not strongly inhibit virus binding. We will amend the text as follows (see also Q6).

(Page 10)

Glycans could be one of the biochemical substances ……We found that a large number of SARS-CoV2-PP can still bind to cells even when cells expressed sufficient amounts of the glycoprotein that could account for the majority of glycans within these cells and inhibit viral infection (Figure 3A). Similarly, on the two-dimensional culture surface of Calu-3 cells, no negative correlation was observed between the number of viruses bound and the total amount of glycans on the cell surface (Supplementary Figure 2F-G). The slight positive correlation between bound virus and glycans may be due to higher expression levels of viral receptors in glycan-rich cells. ….

(2) The use of the purified glycosylated and non-glycosylated ectodomains of MUC1 and CD-43 to establish a relationship between glycosylation and protein density into lipid bilayers on silica beads is an elegant approach. An assessment of the impact of glycosylation in the structural conformation of both proteins, for instance determining the Flory radius of the glycosylated and non-glycosylated ectodomains by the FRET-FLIM approach used in Figure 4 would serve to further support the hypothesis of the article.

Unfortunately, the proposed experiment did not provide a strong enough FRET signal for analysis. This was due in part to the difficulty in constructing a bead-coated bilayer incorporating PlasMem Bright Red, which established a good FRET pair in cell experiments. We also tried other fluorescent molecules, but were unable to obtain a strong and stable FRET signal. Another reason may be that the curvature of the beads is larger than that of the cells, making it difficult to obtain a sufficient cumulative FRET effect from multiple membrane dyes. We plan to improve the experimental system in the future.

On the other hand, we also found that in this system, the signal changes were very subtle, making it difficult to detect molecular conformational changes using FRET. After reconsidering general questions (2) and (3), we speculated that the molecular density in the experiment, even at saturation binding, was below or at most equivalent to the brush transition point. In other words, proteins on the bead-coated bilayer may not be significantly extended in the vertical direction. Therefore, the conformational changes of these proteins may not be large enough to be detected by the FRET assay. We updated Figure 3C and Figure 5D (model description) to better reflect the above discussion and introduced the following discussion in the manuscript.

(page11)

We introduced the framework of conventional polymer brush theory to study the structure of viruscell interfaces containing proteins……. Numerous experimental measurements of the formation of polymer brushes have also been reported (Overney et al., 1996; Wu et al., 2002; Zhao and Brittain, 2000). In these measurements, the transition to a brush typically occurs at a density higher than that required to pack a surface with hemispherical polymers of diameter R_F. This is the point at which the energy loss due to repulsive forces between adjacent molecules (f_int) exceeds the energy required to stretch the polymer perpendicularly into a brush (f_el).

(page16)

The molecular structural state of these proteins needs to be further discussed to estimate the contribution of f_el, which represents resistance to molecular elongation. Our results suggest that these densely packed nonglycosylated molecules are no longer in a free mushroom state. However, their saturation density was several times lower than previously reported brush transition densities, such as 65000 µm^-2 for 17 kDa polyacrylamide (R_F ~ 15 nm) on a solid surface (Wu et al., 2002). To compare our data on fluid bilayers with previously reported data on solid surfaces, we performed additional experiments with lipid bilayers that lost fluidity. No significant changes in protein binding between fluid and nonfluid bilayers were observed for both b-MUC1 and g-MUC1 molecules (Supplementary Figure 5B). This result suggests that membrane fluidity does not affect the average intermolecular distance or other relevant parameters that control molecular binding in the reconstituted system. Based on these, we speculate that the saturated protein density observed in our experiments is lower than or at most comparable to the actual brush transition density. Thus, although these crowded proteins may be restricted from free random motion, they are not significantly extended as in the condensed brush state, in which the contribution of resistance to molecular extension f_el is expected to be small relative to the overall free energy of the system.

Note that this does not mean that glycoproteins cannot form condensed brush structures: in fact, highly glycosylated molecules (e.g., MUC1) can form brush structures in cells when such proteins are expressed at very high densities. (Shurer et al., 2019). In these cells, ………. Such membrane deformation results in the increase of total surface area to reduce the density of glycoproteins, indicating that there is strong intermolecular repulsion between glycoproteins. In any case, the free energy of the system is determined by the balance between protein binding and insertion into the membrane, protein deformation, and repulsive forces between proteins, which determine the density of proteins depending on the configuration of the system. Thus, although strong intermolecular repulsions were prominently observed in our simplified system, this may not be the case in other systems. ……

(3) The MUC1 glycoprotein is reported to have a dramatic effect in reducing viral infection shown in Fig 1F. On the contrary, in a different experiment shown in Fig2D and Fig2H MUC1 has almost no effect in reducing viral infection. It is not clear how these two findings can be compatible.

The immunostaining results show that the density of MUC1 molecules is very low in the experimental system in Figure 2 (Figure 2C), which is supported by the SC-RNASeq data (as shown in Supplementary Figure 2A, MUC1 is not listed as a top molecule). In other words, the MUC1 expression level in this experiment is too low to affect virus infection inhibition. On the other hand, the Pearson correlation function represents the strength of the linear relationship between two variables, so it is not the most appropriate indicator for seeing the correlation with the MUC1 expression level, which has little change (Figure 2D, 2F). In fact, even TOS analysis, which can see the correlation by focusing on the cells with the highest expression level, cannot detect the correlation (Figure 2H).Therefore, the MUC1 data in Figure 2DFH will be annotated and corrected in the figure legend.

Figure2 Legend:

MUC1 has a small mean expression level and variance, and is more affected by measurement noise than other molecules when calculating the Pearson correlation function (Figure 2C-2F). In addition, the number of cells in which expression can be detected is small, so no significant correlation was detected by TOS analysis (Figure 2H).

(4) Why is there a shift in the use of the glycan marker? How does this affect the conclusions? For the infection correlation relating protein expression with glycan content the PNA-lectin was used together with flow cytometry. For imaging the infection and correlating with glycan content the SSA-lectin is used.

For each cell line, we selected the lectin that could be measured over the widest dynamic range. This lectin is thought to recognize the predominant glycan species in the cell line (Fig. S1C, Fig. 2D). In our model, we believe that viral infection inhibition is not specific to the type of sugar, but is highly dependent on the total amount of glycans. If this hypothesis is correct, the reason we used different lectins in each experiment is simply to select the lectin that recognizes the most predominant glycan species that is most convenient for predicting the total amount of glycans in cells. This hypothesis is consistent with our observations, where the total amount of glycans estimated by different lectins could explain the infection inhibition in a similar way in the experiments in Figures 1 and 2, and the TOS analysis in Figure 2 showed that minor glycans also have an infection inhibitory effect. On the other hand, it is of course possible to predict the total amount of glycans more accurately by obtaining as much information on glycans as possible (related to Q5). Based on the above discussion, the manuscript will be revised as follows.

Page5

Using HEK293T cell lines exogenously expressing genes of these proteins tagged with fluorescent markers, their glycosylation was measured by binding of a lectin from Arachis hypogaea (PNA), and the number of these proteins in the cells was measured simultaneously. PNA was used for the measurement because it has a wider dynamic range than other lectins (Supplementary Figure 1C). This suggests that GalNAc recognized by PNA is predominantly present on glycans of HEK293T cells, especially on the termini of glycans that are amenable to lectin binding, compared to other saccharides.. …

page9  

Our findings suggest that membrane glycoproteins nonspecifically inhibit viral infection, and we hypothesize that their inhibitory function is also nonspecific depending on the type of glycan. Our hypothesis is consistent with the observations in the TOS analysis. Although minor saccharide species in the system (such as GlcNAc and GalNAc recognized by DSA, WGA, or PNA) showed anticolocalization with infection, their scores were much lower than those of major saccharide species. This suggests that all major and minor saccharide species have an infection inhibitory effect, but cells enriched with minor type glycans are only partially present in the system, and the contribution of these cells to virus inhibition is also partial. It is also consistent with the observation that the amount of GalNAc recognized by PNA determines the virus infection inhibition in HEK 293T cells (Figure 1). Therefore, we believe that our assay using a single type of predominantly expressed lectin is still useful for estimating the total glycan content. Nevertheless, the virus infection rate may show a better correlation with a more accurately estimated total glycan in each cell. For example, the use of multiple lectins with appropriate calibration to integrate multiple signals to simultaneously detect a wider range of saccharide species would allow for more accurate estimation. It should be noted that the amount of bound lectin does not necessarily measure the overall glycan composition but likely reflects the sugar population at the free end of the glycan chain to which the lectin binds most.

(5) The authors in several instances comment on the relevance and importance of the total glycan content. Nevertheless, these conclusions are often drawn when using only one glycan-binding lectin. In fact, the anti-correlation with viral infection is distinct for the various lectins (Fig 2D and Fig 2H). Would it make more sense to use a combination of lectins to get a full glycan spectrum?

As stated in the answer to Q4, we believe that we were able to detect the infection-suppressing effect of the total glycan amount by using the measurement value of the major component glycan as an approximation. However, as you pointed out, if we could accurately measure the minor glycan components and add up their values, we believe that we could measure the total glycan amount more accurately. In order to measure multiple glycans simultaneously and with high accuracy, some kind of biochemical calibration may be necessary to compare the measurements of lectin-glycan pairs with different binding constants. We believe that these are very useful techniques, and would like to consider them as a future challenge. The corrections listed in Q4 are shown below.

(Page 9)

Nevertheless, the virus infection rate may show a better correlation with a more accurately estimated total glycan in each cell. For example, the use of multiple lectins with appropriate calibration to integrate multiple signals to simultaneously detect a wider range of glycans would allow for more accurate estimation. …….

(6) Fig 3A shows virus binding to HEK cells upon MUC1 expression. Please provide the surface expression of the MUC1 so that the data can be compared to Fig 1F. Nevertheless, it is not clear why the authors used MUC expression as a parameter to assess virus binding. Alternatively, more conclusive data supporting the hypothesis would be the absence of a correlation between total glycan content and virus binding capacity.

The relationship between the expression level of MUC1 in each cell and the amount of virus binding is shown in Supplementary Figure 3A. There is no correlation between the two. In HEK293T cells, many glycans are modified with MUC1, so MUC1 was used as the indicator for analysis (Supplementary Figure 1C). As you pointed out, it is better to use the amount of glycan as an indicator, so we analyzed the relationship between the amount of bound virus and the amount of glycan on the surface on the Calu-3 monolayer (Supplementary Figure 2F, 2G, introduced in the answer to Specific (Q1)). In any case, no correlation was found between virus binding and surface glycans. I will correct the manuscript as follows.

(page 9)

Glycans could be one of the biochemical substances that link the intracellular molecular composition and macroscopic steric forces at the cell surface. To clarify this connection, we further investigated the mechanism by which membrane glycoproteins inhibit viral infection. First, we measured viral binding to cells to determine which step of infection is inhibited. We found that a large number of SARS-CoV2-PP can still bind to cells even when cells expressed sufficient amounts of the glycoprotein that could account for the majority of glycans within these cells and inhibit viral infection (Figure 3A). Similarly, on the two-dimensional culture surface of Calu-3 cells, no correlation was observed between the number of viruses bound and the total amount of glycans on the cell surface (Supplementary Figure 2F-G). These results indicate that glycoproteins do not inhibit virus binding to cells, but rather inhibit the steps required for subsequent virus internalization.

(7) While the use of the Flory model could provide a simplification for a (disordered) flexible structure such as MUC1, where the number of amino acids equals N in the Flory model, this generalisation will not hold for all the proteins. Because folding will dramatically change the effective polypeptide chain-length and reduce available positioning of the amino acids, something the authors clearly measured (Fig 4G), this generalisation is not correct. In fact, the generalisation does not seem to be required because the authors provide an estimation for the effective Flory radius using their FRET approach

Current theories generalizing the Flory model to proteins are incomplete, and it is certainly not possible to accurately estimate the size of individual molecules undergoing different folding. However, we found such a generalized model to be useful in understanding the overall properties of membrane proteins. In our experiments, we were indeed able to obtain the R_Fs of some individual molecules by FRET measurements. However, this modeling made it possible to estimate the distribution range of the RFs, including for larger proteins that cannot be measured by FRET. For example, from our results, we can estimate that the upper limit of the RFs of the longest membrane proteins is about 10.5 nm, assuming that the proteins follow the Flory model in all respects except for the shortening of the effective length due to folding. These analyses are useful for physical modeling of nonspecific phenomena, as in our case.

In order to discuss the balance between such theoretical validity and the convenience of practical handling, we revise the manuscript as follows.

(page 13) 

This shift in ν indicates that glycosylation increases the size of the protein at equilibrium, but the change in R_F is slight, e.g., a 1.3-fold increase for one of the longest ectodomains with N = 4000 when these values of ν are applied. This calculation also gives a rough estimate of the upper limit of the R_F of the extracellular domains of all membrane proteins in the human genome (approximately 10.5 nm). Physically, this change in ν by glycosylation may be caused by the increased intramolecular exclusion induced sterically between glycan chains. This estimated ν are much smaller than that of 0.6 for polymers in good solvents, possibly due to protein folding or anchoring effects on the membrane. In fact, the ν of an intrinsically disordered protein in solution has been reported to be close to 0.6 (Riback et al., 2019; Tesei et al., 2024). Overall, these analyses using the Flory model provide information on the size distribution of membrane proteins and the influence of glycans, although the model cannot predict the exact size of each protein due to its specific folding.

MINOR COMMENTS/EDITS:

(1) In Figures 2A and 2C, as well as Supplemental Figure 2C, the fluorescent images indicate that GFP expression differs among the various groups. Ideally, these should be at the same GFP expression level, as the glycan and antibody staining occurred post-viral infection. For instance, ACE2 is a well-known positive control and should enhance SARS-CoV-2 infection. Yet, based on the findings presented in Supplemental Figure 2C, ACE2 appears to correlate with the lowest infection rate. The relationship between the infection rate and key glycoproteins needs clearer quantification.

We measured the virus inhibition effect specific to each molecule using a cell line expressing low levels of viral receptors and glycoproteins (Fig. 1). On the other hand, the system in Fig. 2 contains diverse viral receptors and glycoproteins and has not been genetically manipulated. (We apologize that there was a typo in our description of experiment, which will be corrected, as shown below). The variation in infection rate between samples was caused by multiple factors but was not related to the molecule for which the correlation was measured. The receptor-based normalization used in the experiment in Fig. 1 cannot be applied in this system in Fig.2 due to the complexity of the gene expression profile. Therefore, instead of such parameter-based normalization, we applied Pearson correlation and TOS analysis. In the calculation of Pearson correlation, intensities are normalized. TOS analysis allows the analysis of colocalization between the groups with the highest fluorescence intensity. Therefore, in both cases of variation in overall infection rate and variation in the distribution of infected populations, samples with large variations can be reasonably compared by Pearson correlation and TOS analysis, respectively. We extend the discussion on statistics and revise the manuscript as follows.

(page 8-9)

To test this hypothesis, we infected a monolayer of epithelial cells endogenously expressing highly heterogeneous populations of glycoproteins with SARS-CoV-2-PP, and measured viral infection from cell to cell visually by microscope imaging. …

Pearson correlation is effective for comparing samples with varying scales of data because it normalizes the data values by the mean and variance. However, as observed in our experiments, this may not be the case when the distribution of data within a sample varies between samples. In addition, as has already been reported, the distribution of infected cells often deviates significantly from the normal distribution of data that is the premise of Pearson correlation (Russell et al., 2018) (Figure 2B). To further analyze data in such nonlinear situations, we applied the threshold overlap score (TOS) analysis (Figure 2G-H, Supplementary Figure 2E). This is one statistical method for analyzing nonlinear correlations, and is specialized for colocalization analysis in dual color images (Sheng et al., 2016). TOS analysis involves segmentation of the data based on signal intensity, as in other nonlinear statistics (Reshef et al., 2011). The computed TOS matrix indicates whether the number of objects classified in each region is higher or lower than expected for uniformly distributed data, which reflects co-localization or anti-localization in dual-color imaging data. For example, calculated TOS matrices show strong anti-localization for infection and glycosylation when both signals are high (Figure 2GH). This confirms that high infection is very unlikely to occur in cells that express high levels of glycans. The TOS analysis also yielded better anti-localization scores for some of the individual membrane proteins, especially those that are heterogeneously distributed across cells (Figure 2H). This suggests that TOS analysis can highlight the inhibitory function of molecules that are sparsely expressed among cells, reaffirming that high expression of a single type of glycoprotein can create an infection-protective surface in a single cell and that such infection inhibition is not protein-specific. In contrast, for more uniformly distributed proteins such as the viral receptor ACE2, TOS analysis and Pearson correlation showed similar trends, although the two are mathematically different (Figure 2D, 2H). Because glycoprotein expression levels and virus-derived GFP levels were treated symmetrically in these statistical calculations, the same logic can be applied when considering the heterogeneity of infection levels among cells. Therefore, it is expected that TOS analysis can reasonably compare samples with different virus infection level distributions by focusing on cells with high infection levels in all samples.

(2) For clarity, the authors should consider separating introductory and interpretive remarks from the presentation of results. These seem to get mixed up. The introduction section could be expanded to include more details about glycoproteins, their relevance to viral infection, and explanations of N- and O-glycosylation.

Following the suggestion, (1) we added an explanation of the relationship between glycoproteins and viral infection, and N-glycosylation and O-glycosylation to the Introduction section, and (2) moved the introductory parts in the Results section to the Introduction section, as follows.

（１; page3）

While there are known examples of glycans that function as viral receptors (Thompson et al., 2019), these results demonstrate that a variety of glycoproteins negatively regulate viral infection in a wide range of systems. These glycoprotein groups have no common amino acid sequences or domains. The glycans modified by these proteins include both the N-type, which binds to asparagine, and the O-type, which binds to serine and threonine. Furthermore, there have been no reports of infection-suppressing effects according to the specific monosaccharide type in the glycan. All of these results suggest that bulky membrane glycoproteins nonspecifically inhibit viral infection.

(2 : Page 4-5)

To confirm that glycans are a general chemical factor of steric repulsion, an extensive list of glycoproteins on the cell membrane surface would be useful. The wider the range of proteins to be measured, the better. Therefore, we collect information on glycoproteins on the genome and compile them into a list that is easy to use for various purposes. Then, by analyzing sample molecules selected from this list, it may be possible to infer the effect of the entire glycoprotein population on the steric inhibition of virus infection, despite the complexity and diversity of the Glycome (Dworkin et al., 2022; Huang et al., 2021; Moremen et al., 2012; Rademacher et al., 1988). Elucidation of the mechanism of how glycans regulate steric repulsion will also be useful to quantitatively discuss the relationship between steric repulsion and intracellular molecular composition. For this purpose, we apply the theories of polymer physics and interface chemistry.

Results

List of membrane glycoproteins in human genome and their inhibitory effect on virus infection

To test the hypothesis that glycans contribute to steric repulsion at the cell surface, we first generate a list of glycoproteins in the human genome and then measure the glycan content and inhibitory effect on viral infection of test proteins selected from the list (Figure 1A). To compile the list of glycoproteins, we ….

(3) In the sentence, "glycoproteins expressed lower than CD44 or other membrane proteins including ERBB2 did not exhibit any such correlation, although ERBB2 expressed ~4 folds higher amount than CD44 and shared ~7% among all membrane proteins," it is unclear which protein has a higher expression level: CD44 or ERBB2? Furthermore, the use of the word "although" needs clarification.

Corrected as follows:

(page 8)

……showed a weak inverse correlation with viral infection; even such a weak correlation was not observed with other proteins, including ERBB2, which is approximately four-fold more highly expressed than CD44

(4) In Supplementary Figure 5, please provide an explanation of the data in the figure legend, particularly what the green and red signals represent.

Corrected as follows:

STORM images of all analyzed cells, expressing designated proteins. The detected spots of SNAPsurface Alexa 647 bound to each membrane protein are shown in red, and the spots of CF568conjugated anti-mouse IgG secondary antibody that recognizes Spike on SARS-CoV2-PP are shown in green. For cells, a pair of two-color composite images and a CF658-only image are shown. Numbers on axes are coordinates in nanometer.

(5) It would be good to see a comprehensive demonstration of the exact method for estimation of membrane protein density (in the SI), since this is an integral part of many of the analyses in this paper. The method is detailed in the Methods section in text and is generally acceptable, but this methodology can vary quite widely and would be more convincing with calibration data provided.

We added flow cytometry and fluorometer data for calibration (Supplementary Figure 1L,M) and introduced a sentence explaining the procedure for obtaining the values used for calibration as follows:

(page 54)

…….Liposome standards containing fluorescent molecules (0.01– 0.75 mol% perylene (Sigma), 0.1– 1.25 mol% Bodipy FL (Thermo), and 0.005– 0.1% DiD) as well as DOPC (Avanti polar lipids) were measured in flow cytometry (Supplmentary Figure 1L). Meanwhile, by fluorimeter, fluorescence signals of these liposomes and known concentrations of recombinant mTagBFP2, AcGFP and TagRFP-657 proteins and SNAP-Surface 488 and Alexa 647 dyes (New England Biolabs) were measured in the same excitation and emission ranges as in flow cytometry assays (Supplementary Figure 1M). Ratios between the integral of fluorescent intensities in this range between two dyes of interest are used for converting the signals measured in flow cytometry. Additional information needed for calibration is the size difference between liposomes and cells. The average diameter of liposomes is measured to be 130 nm, and the diameter of HEK 293T cells is estimated to be 13 µm (Furlan et al., 2014; Kaizuka et al., 2021b; Ushiyama et al., 2015). From these data, the signal from cells acquired by flow cytometry can be calibrated to molecular surface density. For example, the Alexa 647 signal acquired by flow cytometry can be converted to the signal of the same concentration of DID dye using fluorometer data, but the density of the dye is unknown at this point. This converted DID signal can then be calibrated to the density on liposomes rather than cells using liposome flow cytometry data. Finally, adjusted for the size difference between liposomes and cells, the surface molecular density on cells is determined. By going through one cycle of these procedures, we could obtain calibration unit, such as 1 flow cytometry signal for a cell in the designated illumination and detection setting = 0.0272 mTagBFP2 µm^-2 on cell surface.

(Figure legend, Supporting Figure 1: )

… L. Flow cytometry measurements for liposomes containing serially diluted dye-conjugated lipids and fluorescent membrane incorporating molecules (Bodipy-FL, peryelene, and DID) with indicated mol%. Linear fitting shown was used for calibration.  M. Fluorescence emission spectrum for equimolar molecules (50µM for green and far-red channels, and 100µM for blue channel), excited at 405 nm, 488 nm, and 638 nm, respectively. Membrane dyes were measured as incorporated in liposomes. Purified recombinant mTagBFP2 was used.

(6) Fig 2A: The figure legend should describe the microscopy method for a quick and easy reference.

Corrected as follows:

(Figure legend, Figure 2)

A. Maximum projection of Z-stack images at 1 µm intervals taken with a confocal microscope. SARSCoV2-pp-infected, air-liquid interface (ALI)-cultured Calu-3 cell monolayers were chemically fixed and imaged by binding of Alexa Fluor 647-labeled Neu5AC-specific lectin from Sambucus sieboldiana (SSA) and GFP expression from the infecting virus.

(7) Fig 2B: what is the color bar supposed to represent? Is it the pixel density per a particular value? Units and additional description are required. In addition, these are "arbitrary units" of fluorescence, but you should tell us if they've been normalized and, if so, how. They must have been normalized, since the values are between 0 and 1, but then why does the scale bar for SSA only go to 0.5?

The color bar shows the number of pixels for each dot, resulting in the scale for density scatter plot. The scale on the X-axis was incorrect. All these issues have been fixed in this revision, in the figure and in the legend as follows.

(Figure legend, Figure 2)

B. Density scatter plot of normalized fluorescence intensities in all pixels in Figure 2A in both GFP and SSA channels. Color indicates the pixel density.  

(8) Fig 3D has a typo: this should most likely be "grafted polymer."

(9) Fig 3E has a suspected typo: in the text, the author uses the word "exclusion" instead of "extrusion." The former makes more sense in this context.

(10) Fig 5A has a typo: "Suppoorted" instead of Supported Lipid Bilayer.

(11) Fig 7E-F has a suspected typo: Again, this should most likely be the word "exclusion" instead of "extrusion."

Thank you so much for pointing out these mistakes, I have corrected them all as suggested.

(12) Which other molecules are referred to, on page 6 (middle), that do not have an inhibitory effect? Please specify.

We specified the molecules that have inhibitory effects, and revised as follows: 

These proteins include those previously reported (MUC1, CD43) as well as those not yet reported (CD44, SDC1, CD164, F174B, CD24, PODXL) (Delaveris et al., 2020; Murakami et al., 2020). In contrast, other molecules (VCAM-1, EPHB1, TMEM123, etc.) showed little inhibitory effect on infection within the density range we used.

(13) Fig 2 B: the color LUT is not labelled nor explained.

Corrected as described in (7)

(14) Please provide the scale bars for figures Fig 2A, C, E and Suppl Fig 2C, D.

Corrected. 

(15) Please provide the name for the example of a 200 aa protein that is meant to inhibit viral infection but is not bigger than ACE2. Also providing the densities in Fig 3A would help to correlate the data to Fig 1F.

Corrected as follows: 

(page 10)

We found that a large number of SARS-CoV2-PP can still bind to cells even when cells expressed sufficient amounts of the glycoprotein (mean density ~50 µm^-2) that could account for the majority of glycans within these cells and inhibit viral infection (Figure 3A). …..

In our measurements, a protein with extracellular domain of ~200 amino acids (e.g. CD164 (138aa)) at a density of ~100 μm-2 showed significant inhibition in viral infection. This molecule is shorter than the receptor ACE2 (722 aa),

(16) In the experiments conducted in HeK cells expressing the different glycoproteins studies it is mentioned that results of infection were normalised by the amount ACE2 expression. Is the expression of receptor homogenous in the experiments conducted in Figure 2? Clarify in the methods if the expression of receptor has been quantified and somehow used to correct the intensity values of GFP used to determine infection.

As also explained for Q1, the system in Fig. 2 contains diverse viral receptors and glycoproteins, and the receptor-based normalization used in the experiment in Fig. 1 cannot be applied. Instead, we applied Pearson correlation and TOS analysis. In the calculation of Pearson correlation, intensities are normalized. TOS analysis allows the analysis of colocalization between the groups with the highest fluorescence intensity. Therefore, in both cases of variation in overall infection rate and variation in the distribution of infected populations, samples with large variations can be reasonably compared by Pearson correlation and TOS analysis, respectively. We extend the discussion on statistics and revise the manuscript as follows.

(page 8-9)

Pearson correlation is effective for comparing samples with varying scales of data because it normalizes the data values by the mean and variance. However, as observed in our experiments, this may not be the case when the distribution of data within a sample varies between samples. In addition, as has already been reported, the distribution of infected cells often deviates significantly from the normal distribution of data that is the premise of Pearson correlation (Russell et al., 2018) (Figure 2B). To further analyze data in such nonlinear situations, we applied the threshold overlap score (TOS) analysis (Figure 2G-H, Supplementary Figure 2E). This is one statistical method for analyzing nonlinear correlations, and is specialized for colocalization analysis in dual color images (Sheng et al., 2016). TOS analysis involves segmentation of the data based on signal intensity, as in other nonlinear statistics (Reshef et al., 2011). The computed TOS matrix indicates whether the number of objects classified in each region is higher or lower than expected for uniformly distributed data, which reflects co-localization or anti-localization in dual-color imaging data. For example, calculated TOS matrices show strong anti-localization for infection and glycosylation when both signals are high (Figure 2GH). This confirms that high infection is very unlikely to occur in cells that express high levels of glycans. The TOS analysis also yielded better anti-localization scores for some of the individual membrane proteins, especially those that are heterogeneously distributed across cells (Figure 2H). This suggests that TOS analysis can highlight the inhibitory function of molecules that are sparsely expressed among cells, reaffirming that high expression of a single type of glycoprotein can create an infection-protective surface in a single cell and that such infection inhibition is not protein-specific. In contrast, for more uniformly distributed proteins such as the viral receptor ACE2, TOS analysis and Pearson correlation showed similar trends, although the two are mathematically different (Figure 2D, 2H). Because glycoprotein expression levels and virus-derived GFP levels were treated symmetrically in these statistical calculations, the same logic can be applied when considering the heterogeneity of infection levels among cells. Therefore, it is expected that TOS analysis can reasonably compare samples with different virus infection level distributions by focusing on cells with high infection levels in all samples.

(17) Can you provide additional details about the method of thresholding to eliminate "background" localisations in STORM?

Method section was corrected as follows: 

(page 59)

…Viral protein spots not close to cell membranes were eliminated by thresholding with nearby spot density for cell protein. Specifically, the entire image was pixelated with a 0.5µm square box and all viral protein signals within the box that had no membrane protein signals were removed. Also, viral protein spots only sparsely located were eliminated by thresholding with nearby spot density for viral protein. This thresholding process removed any detected viral protein spot that did not have more than 100 other viral protein spots within 1µm.

(18) The article says "It was shown that the number of bound lectins correlated with the amount of glycans, not with number of proteins (Figure 1E)". Figure 1E correlates experimental PNA/mol with predicted glycosylation sites, not with the number of expressed proteins. Correct sentence with the right Figure reference.

As you pointed out, the meaning of this sentence was not clear. We have amended it as follows to clarify our intention:

(page 8)

Since a wide range of glycoproteins inhibit viral infection, it is possible that all types of glycoproteins have an additive effect for this function. ……. In this cell line, this inverse correlation was most pronounced when quantifying N-acetylneuraminic acid (Neu5AC, recognized by lectins SSA and MAL) compared to the various types of glycans, while some other glycans also showed weak correlations (Supplementary Figure 2C). These results showed that the amount of virus infection in cell anticorrelated with the amount of total glycans on the cell surface. As amount of glycans is determined by the total population of glycocalyx, infection inhibitory effect can be additive by glycoprotein populations as we hypothesized.

If the inhibitory effect is nonspecific and additive, the contribution of each protein is likely to be less significant. To confirm this, we also measured the correlation between the density of each glycoprotein and viral infection. CD44, which was shown to…….. Our results demonstrate that total glycan content is a superior indicator than individual glycoprotein expression for assessing infection inhibition effect generated by cell membrane glycocalyx. These results are consistent with our hypothesis regarding the additive nature of the nonspecific inhibitory effects of each glycoprotein.

Reviewer #1 (Public review):

Summary:

The manuscript analyses the effects of deleting the TgfbR1 and TgfbR2 receptors from endothelial cells at postnatal stages on vascular development and blood-retina barrier maturation in the retina. The authors find that deletion of these receptors affects vascular development in the retina but importantly it affects the infiltration of immune cells across the vessels in the retina. The findings demonstrate that Tgf-beta signaling through TgfbR1/R2 heterodimers regulates primarily the immune phenotypes of endothelial cells in addition to regulating vascular development, but has minor effects on the BRB maturation. The data provided by the authors provides a solid support for their conclusions.

Strengths:

(1) The manuscript uses a variety of elegant genetic studies in mice to analyze the role of TgfbR1 and TgfbR2 receptors in endothelial cells at postnatal stages of vascular development and blood-retina barrier maturation in the retina.

(2) The authors provide a nice comparison of the vascular phenotypes in endothelial-specific knockout of TgfbR1 and TgfbR2 in the retina (and to a lesser degree in the brain) with those from Npd KO mice (loss of Ndp/Fzd4 signaling) or loss of VEGF-A signaling to dissect the specific roles of Tgf-beta signaling for vascular development in the retina.

(3) The snRNAseq data of vessel segments from the brains of WT versus TgfbR1 -iECKO mice provides a nice analysis of pathways and transcripts that are regulated by Tgf-beta signaling in endothelial cells.

Weaknesses (Original Submission):

(1) The authors claim that choroidal neovascular tuft phenotypes are similar in TgfbrR1 KO and TgfbrR2 KO mice. However, the phenotypes look more severe in the TgfbrR1 KO rather than TgfbrR2 KO mice. Can the authors show a quantitative comparison of the number of choroidal neovascular tufts per whole eye cross-section in both genotypes?

(2) In the analysis of Sulfo-NHS-Biotin leakage in the retina to assess blood-retina barrier maturation, the authors claim that there is increased vascular leakage in the TgfbR1 KO mice. However, there does not seem like Sulfo-NHS-biotin is leaking outside the vessels. Therefore, it cannot be increased vascular permeability. Can the authors provide a detailed quantification of the leakage phenotype?

(3) The immune cell phenotyping by snRNAseq seems premature as the number of cells is very small. The authors should sort for CD45+ cells and perform single cell RNA sequencing.

(4) The analysis of BBB leakage phenotype in TgfbR1 KO mice needs to be more detailed and include some tracers in addition to serum IgG leakage.

(5) A previous study (Zarkada et al., 2021, Developmental Cell) showed that EC-deletion of Alk5 affects the D tip cells. The phenotypes of those mice look very similar to those shown for TgfbrR1 KO mice. Are D tip cells lost in these mutants by snRNAseq?

Comments on revisions:

The authors have addressed the major weaknesses that I raised with the original submission adequately in the revised manuscript.

Author response:

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

Reviewer #1 (Public review): 

Weaknesses: 

(1) The authors claim that choroidal neovascular tuft phenotypes are similar in TgfbrR1 KO and TgfbrR2 KO mice. However, the phenotypes look more severe in the TgfbrR1 KO rather than TgfbrR2 KO mice. Can the authors show a quantitative comparison of the number of choroidal neovascular tufts per whole eye cross-section in both genotypes? 

Thank you for asking about this.  Each VE-cad-CreER;TGFBR1 CKO/- and VE-cad-CreER;TGFBR2 CKO/- retina exhibits multiple zones of choroidal neovascularization.  The examples in Figures 1 and Figure 1 – Figure supplements 1 and 2 are mostly from retinas with loss of TGFBR1, but we could have chosen similar examples from retinas with loss of TGFBR2.  The quantification in the original version of Figure 1- Figure supplement 1 panel C had a labeling error.  It actually showed the quantification choroidal neovascularization (CNV) in the sum of both VE-cad-CreER;TGFBR1 CKO/- and VE-cad-CreER;TGFBR2 CKO/- retinas, not only in VE-cad-CreER;TGFBR1 CKO/- retinas as originally labeled.  The point that it made is that CNV is seen with loss of TGF-beta signaling but not in control retinas or retinas with loss of Norrin signaling.  We have now updated that plot by separating the data points for VE-cad-CreER;TGFBR1 CKO/- and VE-cad-CreER;TGFBR2 CKO/- retinas, so that they can be compared to each other.   The result shows ~2.5-fold more CNV in VE-cad-CreER;TGFBR2 CKO/- retinas compared to VE-cad-CreER;TGFBR1 CKO/-.  We think it likely that a more extensive sampling would show little or no difference between these two genotypes – but the data is what it is. This is now described in the Results section. 

We have also added a panel D to Figure 1- Figure supplement 1, which shows a retina flatmount analysis of CNV.  This is done by mounting the retina with the photoreceptor side up so that the outer retina can be optimally imaged. 

(2) In the analysis of Sulfo-NHS-Biotin leakage in the retina to assess blood-retina barrier maturation. The authors claim that there is increased vascular leakage in the TgfbR1 KO mice. However, it does not seem like Sulfo-NHS-biotin is leaking outside the vessels. Therefore, it cannot be increased vascular permeability. Can the authors provide a detailed quantification of the leakage phenotype? 

Thank you for raising this point.  Your comment prompted us to look at this question in greater depth with more experiments.  We have expanded Figure 2 to show and quantify a comparison between control (i.e. phenotypically WT), NdpKO, and TGFBR1 endothelial KO and we have expanded the associated part of the Results section (Figure 2C and D).  In a nutshell, control retinas show little Sulfo-NHS-biotin accumulation in or around the vasculature or in the parenchyma; NdpKO retinas show Sulfo-NHS-biotin accumulation in the vasculature and in the parenchyma (i.e., the area between the vessels); and VEcadCreER;Tgfbr1CKO/- retinas show Sulfo-NHS-biotin accumulation in the vascular tufts with minimal accumulation in the non-tuft vasculature and minimal leakage into the parenchyma.   The conclusion is that the bulk of the retinal vasculature in TGFBR1 endothelial KO mice is minimally or not at all leaky – very different from the situation with loss of Norrin/Frizzled4 signaling.

(3) The immune cell phenotyping by snRNAseq is premature, as the number of cells is very small. The authors should sort for CD45+ cells and perform single-cell RNA sequencing. 

Thank you for raising this point.  For the revised manuscript, we have performed additional snRNAseq analyses using the same tissue processing protocol as for our original snRNAseq data.  We have opted to homogenize the tissue and prepare nuclei (our original method) rather than dissociate the tissue and FACS sorting for CD45+ cells because the nuclear isolation approach is unbiased – we assume that nuclei from all cell types are present after tissue homogenization.  By contrast, we cannot be certain that CD45 FACS will capture the full range of immune cells since some cells may not express CD45, may express CD45 at low level, or may be tightly adherent to other cells, such as vascular endothelial cell.  Additionally, by following the original protocol, we can combine the original snRNAseq dataset and the new snRNAseq dataset.  In the revised manuscript we present the snRNAseq data from the combination of the original and the more recent snRNAseq datasets (revised Figure 4; N=628 immune cell nuclei).  The new analysis comes to the same conclusions as the original analysis: the immune cell infiltrate in the mutant retinas is composed of a wide variety of immune cells.

(4) The analysis of BBB leakage phenotype in TgfbR1 KO mice needs to be more detailed and include tracers as well as serum IgG leakage. 

As described in our response to query 2, we have conducted additional experiments to look at vascular leakage in control, VE-cad-CreER;TGFBR1 CKO/-, and NdpKO retinas.  We have also looked at Sulfo-NHS-biotin leakage in the VE-cadCreER;TGFBR1 CKO/- brain, and it is indistinguishable from WT controls.  Since Sulfo-NHS-biotin is a low MW tracer (<1,000 kDa), this implies that loss of TGF-beta signaling does not increase non-specific diffusion of either low or high MW molecules.  Therefore, the elevated levels of IgG in the brain parenchyma in young VE-cad-CreER;TGFBR1 CKO/- mice (Figure 8A) likely represents specific transport of IgG across the BBB.  Such transport is known to occur via Fc receptors expressed on vascular endothelial cells, although it is normally greater in the brain-to-blood direction than in the blood-to-brain direction.  For example, see Lafrance-Vanasse et al (2025) Leveraging neonatal Fc receptor (FcRn) to enhance antibody transport across the blood brain barrier.  Nat Commun. 16:4143.  This is now described in greater detail in the Results section.

(5) A previous study (Zarkada et al., 2021, Developmental Cell) showed that EC-deletion of Alk5 affects the D tip cells. The phenotypes of those mice look very similar to those shown for TgfbrR1 KO mice. Are D-tip cells lost in these mutants by snRNAseq? 

Please note: Alk5 is another name for TGFBR1.  This is noted in the second sentence of paragraph 4 of the Introduction.  The reviewer is correct: there are a lot of similarities because these are exactly the same KO mice.  Also, Zarkada and we used the same VEcadCreER to recombine the CKO allele.  The proposed snRNAseq analysis would serve as an independent check on the diving (D) tip vs stalk cell analyses published in Zarkada et al (2021) Specialized endothelial tip cells guide neuroretina vascularization and blood-retina-barrier formation. Dev Cell 56:2237-2251.  We have not gone in this direction because the question of tip vs. stalk cells and of subtypes of tip cells in WT vs. mutant retinas is beyond our focus on choroidal neovascularization and the role of immune cells and vascular inflammation.  The proposed snRNAseq analysis would also require a major effort since tip cells are rare and must be harvested from large numbers of early postnatal retinas followed by FACS enrichment for vascular endothelial cells.  Finally, we have no reason to doubt the results of Zarkada et al.

Reviewer #2 (Public review): 

Summary:

The authors meticulously characterized EC-specific Tgfbr1, Tgfbr2, or double knockout in the retina, demonstrating through convincing immunostaining data that loss of TGF-β signaling disrupts retinal angiogenesis and choroidal neovascularization. Compared to other genetic models (Fzd4 KO, Ndp KO, VEGF KO), the Tgfbr1/2 KO retina exhibits the most severe immune cell infiltration. The authors proposed that TGF-β signaling loss triggers vascular inflammation, attracting immune cells - a phenotype specific to CNS vasculature, as non-CNS organs remain unaffected. 

Strengths: 

The immunostaining results presented are clear and robust. The authors performed well-controlled analyses against relevant mouse models. snRNA-seq corroborates immune cell leakage in the retina and vascular inflammation in the brain. 

Weaknesses: 

The causal link between TGF-β loss, vascular inflammation, and immune infiltration remains unresolved. The authors' model posits that EC-specific TGF-β loss directly causes inflammation, which recruits immune cells. However, an alternative explanation is plausible: Tgfbr1/2 KO-induced developmental defects (e.g., leaky vessels) permit immune extravasation, subsequently triggering inflammation. The observations that vein-specific upregulation of ICAM1 staining and the lack of immune infiltration phenotypes in the non-CNS tissues support the alternative model. Late-stage induction of Tgfbr1/2 KO (avoiding developmental confounders) could clarify TGF-β's role in retinal angiogenesis versus anti-inflammation. 

Thank you for raising this point.  Your comment prompted us to look at this question in greater depth with more experiments.  We have expanded Figure 2 to show and quantify a comparison between control (i.e. phenotypically WT), NdpKO, and TGFBR1 endothelial KO and we have expanded the associated part of the Results section (Figure 2C and D).  In a nutshell, control retinas show little Sulfo-NHS-biotin accumulation in or around the vasculature or in the parenchyma; NdpKO retinas show Sulfo-NHS-biotin accumulation in the vasculature and in the parenchyma (i.e., the area between the vessels); and VEcadCreER;Tgfbr1CKO/- retinas show Sulfo-NHS-biotin accumulation in the vascular tufts with minimal accumulation in the non-tuft vasculature and minimal leakage into the parenchyma.   The conclusion is that the bulk of the retinal vasculature in TGFBR1 endothelial KO mice is minimally or not at all leaky – very different from the situation with loss of Norrin/Frizzled4 signaling.

In the revised manuscript, we have expanded the Discussion section to address the two alternative hypotheses raised by the reviewer.  Here are the relevant data in a nutshell: (1) vascular leakage into the parenchyma, as measured with sulfo-NHSbiotin, in TGFBR1 endothelial CKO retinas is far less than in NdpKO retinas, where nearly all ECs convert to a fenestration+ (PLVAP+) phenotype and there is leakage of sulfo-NHS-biotin, (2) ICAM1 in ECs in TGFBR1 endothelial CKO retinas increases several-fold more than in NdpKO or Frizzled4KO retinas, (3) TGFBR1 endothelial CKO retinas have more infiltrating immune cells than NdpKO or Frizzled4KO retinas, and (4) in TGFBR1 endothelial CKO retinas large numbers of immune cells are observed within and adjacent to blood vessels.  We think that the simplest explanation for these data is that loss of TGFbeta signaling in ECs causes an endothelial inflammatory state with enhanced immune cell extravasation.  That said, the case for this model is not water-tight, and there could be less direct mechanisms at play.  In particular, this model does not explain why the inflammatory phenotype is limited to CNS (and especially retinal) vasculature.

Regarding the last sentence of the reviewer’s comment (“Late stage induction…”), we have tried activating CreER recombination at different ages and we observe a large reduction in the inflammatory phenotype when recombination is initiated after vascular development is complete.   This observation suggests that the vascular developmental/anatomic defect – and perhaps the resulting retinal hypoxia response – is required for the inflammatory phenotype.  In the revised manuscript we have expanded the Results and Discussion sections to describe this observation.

Reviewer #1 (Recommendations for the authors): 

Suggestions for experiments: 

(1) The authors need to show a quantitative comparison of the number of choroidal neovascular tufts per whole eye crosssection in both genotypes (TgfbR1 and TgfbR2 KO mice). 

Thank you for raising this point.  The quantification in the original version of Figure 1- Figure supplement 1 panel C was mis-labeled.  It quantifies choroidal neovascularization (CNV) in both VE-cad-CreER;TGFBR1 CKO/- and VE-cadCreER;TGFBR2 CKO/- retinas, not VE-cad-CreER;TGFBR1 CKO/- retinas only as originally labeled.  The point it makes is that CNV is seen with loss of TGF-beta signaling but not in control retinas or retinas with loss of Norrin signaling.  We have now corrected that plot by separating the data points for VE-cad-CreER;TGFBR1 CKO/- and VE-cad-CreER;TGFBR2 CKO/- retinas, so that they can be compared to each other.   The result shows ~2.5-fold more CNV in VE-cad-CreER;TGFBR2 CKO/- retinas compared to VE-cad-CreER;TGFBR1 CKO/-.  This is now described in the Results section. 

(2) In the analysis of Sulfo-NHS-Biotin leakage in the retina to assess blood-retina barrier maturation. The authors should provide a detailed quantification of the leakage phenotype outside the vessels into the CNS parenchyma, both in the retina and brain, in TgfbR1 KO mice. 

Thank you for raising this point.  There is no detectable Sulfo-NHS-biotin leakage into the brain parenchyma in VE-cadCreER;TGFBR1 CKO/- mice.  We have expanded Figure 2 to show and quantify the data for retinal vascular leakage (Figure 2C and D).  The data show that in VE-cad-CreER;TGFBR1 CKO/- mice there is accumulation of Sulfo-NHS-biotin in the vascular tufts but minimal accumulation elsewhere in the retinal vasculature and minimal leakage of Sulfo-NHS-biotin into the retinal parenchyma.

(3) The immune cell phenotyping by snRNAseq is premature, as the number of cells is very small. The authors should sort for CD45+ cells and perform single-cell RNA sequencing to ascertain these preliminary data. 

Thank you for raising this point.  We have performed additional snRNAseq analyses using the same tissue processing protocol as for our original snRNAseq data to increase the numbers of cells.  We have opted to homogenize the tissue and prepare nuclei (our original method) rather than dissociating the cells and FACS sorting for CD45+ cells because the nuclear isolation approach is unbiased – we assume that nuclei from all cell types are present.  By contrast, we cannot be certain that CD45 FACS will capture the full range of immune cells, since some cells may not express CD45, may express CD45 at low level, or may be tightly adherent to other cells, such as vascular endothelial cell.  Additionally, by following the original protocol, we can combine the original snRNAseq dataset of and the new snRNAseq dataset.  In the revised manuscript we present the snRNAseq data from the combination of the original and the more recent snRNAseq datasets (revised Figure 4; N=628 immune cell nuclei).  The new analysis comes to the same conclusion as in the original submission, namely that the immune cell infiltrate in the mutant retinas is composed of a wide variety of immune cells.  The Results section has been expanded to describe this new data and analysis.    

(4) The analysis of BBB leakage phenotype in TgfbR1 KO mice needs to be more detailed and include tracers as well as serum IgG leakage. 

Sulfo-NHS biotin leakage in the VE-cad-CreER;TGFBR1 CKO/- brain is minimal, and it is indistinguishable from WT controls.  Since Sulfo-NHS biotin is a low MW tracer (<1,000 kDa), this implies that loss of TGF-beta signaling does not increase non-specific diffusion of either low or high MW molecules.  Therefore, the elevated levels of IgG in the brain parenchyma in young VE-cad-CreER;TGFBR1 CKO/- mice (Figure 8A) likely represents specific transport of IgG across the BBB.  Such transport is known to occur via Fc receptors expressed on vascular endothelial cells, although it is normally greater in the brain-to-blood direction than in the blood-to-brain direction.  For example, see Lafrance-Vanasse et al (2025) Leveraging neonatal Fc receptor (FcRn) to enhance antibody transport across the blood brain barrier.  Nat Commun. 16:4143.  This is now described in greater detail in the Results section.

(5) The authors should perform a more detailed RNAseq analysis of tip and stack (stalk) cells in TgfbrR1 KO mice to determine whether D tip cells are lost in these mutants by snRNAseq. 

The proposed snRNAseq analysis would serve as an independent check on the diving (D) tip vs stalk cell analyses published by Zarkada et al, who analyzed the same VE-cad-CreER;TGFBR1 CKO/- mutant mice, although they refer to the TGFBR1 gene by its alternate name ALK5 [Zarkada et al (2021) Specialized endothelial tip cells guide neuroretina vascularization and blood-retina-barrier formation. Dev Cell 56:2237-2251].  We have not gone in this direction because the question of tip vs. stalk cells and of subtypes of tip cells in WT vs. mutant retinas is beyond our focus on choroidal neovascularization and the role of immune cells and vascular inflammation.  The proposed snRNAseq analysis would also require a major effort since tip cells are rare and must be harvested from large numbers of early postnatal retinas followed by FACS enrichment for vascular endothelial cells.

Suggestions for improving the manuscript:  

(6) The statement that ECs acquire properties of immune cells (Page 2, Line 90) is incorrect. Endothelial cells may acquire characteristics of antigen presenting cells. 

Thank you for that correction.  Based on the review from Amersfoort et al (2022) (Amersfoort J, Eelen G, Carmeliet P. (2022) Immunomodulation by endothelial cells - partnering up with the immune system? Nat Rev Immunol 22:576-588) and the articles cited in it, we have changed the sentence to “Although vascular endothelial cells (ECs) are not generally considered to be part of the immune system, in some locations and under some conditions they acquire properties characteristic of immune cells, including secretion of cytokines, surface display of co-stimulatory or co-inhibitory receptors, and antigen presentation in association with MHC class II proteins (Pober and Sessa, 2014; Amersfoort et al., 2022).”  

(7) The statement in Page 3, Line 100-101 [In CNS ECs, quiescence is maintained in part by the actions of astrocyte-derived Sonic Hedgehog, with the result that few immune cells other than resident microglia are found within the CNS (Alvarez et al., 2011).] is incomplete. Wnt signaling also suppresses the expression of leukocyte adhesion molecules from endothelial cells and therefore helps with immune cell quiescence. 

Thank you for raising that point.  We have expanded that sentence to include Wnt signaling in CNS endothelial cells, as described in the following reference: Lengfeld JE, Lutz SE, Smith JR, Diaconu C, Scott C, Kofman SB, Choi C, Walsh CM, Raine CS, Agalliu I, Agalliu D. (2017) Endothelial Wnt/beta-catenin signaling reduces immune cell infiltration in multiple sclerosis. Proc Natl Acad Sci USA 114:E1168-E1177.

(8) It may be beneficial for the reader to separate the results of the vascular phenotypes related to choroidal neovascularization compared to retinal vascular development. 

Thank you for this suggestion.  The two topics are partly overlapping: choroidal neovascularization is described in Figure 1, and retinal development is described in Figures 1 and 2.  The challenge is that some of same images illustrate both phenotypes as in Figure 1, so the topics cannot be easily separated.

(9) In addition to comparing the phenotypes in Tgfb signaling mutant mice with Wnt signaling and VEGF-A signaling mutants, the authors should compare and contrast their data with those found in Alk5 KO mice, as there are a lot of similarities. 

The reviewer has alerted us to a nomenclature challenge which we will try to resolve in the introduction: Alk5 is just another name for TGFBR1.  The reviewer is correct: there are a lot of similarities between the present study and that of Zarkada et al (2021) because both use the same TGFBR1(=Alk5) CKO mice.

Reviewer #2 (Recommendations for the authors): 

Figure 2 

For 2B, the authors should clarify whether the two regions shown in the Tgfbr1 KO retina (P14) represent central vs. peripheral areas, as phenotype severity varies. 

For 2C, does the uneven biotin accumulation reflect developmental gradients (e.g., central-peripheral maturation timing)? 

Thank you for raising these points.  Regarding Figure 2B, these images are all from the mid-peripheral retina, where the phenotype is moderately severe.  This is now noted in the figure legend.

Regarding Figure 2C, the reviewer is correct that the pattern of Sulfo-NHS-biotin is uneven in VEcadCreER;Tgfbr1CKO/- retinas – it accumulates only in the tufts.  We have expanded Figure 2C to show a comparison between control (i.e.

phenotypically WT), NdpKO, and TGFBR1 endothelial KO retinas, and we have expanded the associated part of the Results section.  In a nutshell, control retinas show little Sulfo-NHS-biotin accumulation in the vasculature or in the parenchyma; NdpKO retinas show Sulfo-NHS-biotin accumulation in the vasculature and in the parenchyma (i.e., the area between the vessels); and VEcadCreER;Tgfbr1CKO/- retinas show Sulfo-NHS-biotin accumulation in the vascular tufts with minimal accumulation in the non-tuft vasculature and minimal leakage into the parenchyma.   The conclusion is that the bulk of the retinal vasculature in TGFBR1 endothelial KO mice is not leaky – very different from the situation with loss of Norrin/Frizzled4 signaling.

Figure 6 

The claim that PECAM1+ rings on veins reflect EC-immune cell binding is uncertain, as PECAM1 is also known to be expressed by immune cells. The complete correlation of PECAM1 and CD45 staining signals suggests that a subset of immune cells upregulates PECAM1. The VEcadCreER;Tgfbr1 flox/-; SUN1:GFP reporter would be helpful to delineate ECimmune cell proximity. Super-resolution imaging with Z-stacks could also resolve spatial relationships (luminal vs. abluminal immune cell adhesion). 

Thank you for this comment.  The reviewer is correct that, at the resolution of these images, we cannot determine whether the PECAM1 immunostaining signal is derived from ECs, from leukocytes, or from both.  This is now stated in the Results section.  The PECAM1-rich endothelial ring structure associated with leukocyte extravasation has been characterized in various publications, for example in (1) Carman CV, Springer TA. (2004) A transmigratory cup in leukocyte diapedesis both through individual vascular endothelial cells and between them. J Cell Biol 167:377-388 and (2) Mamdouh Z, Mikhailov A, Muller WA. (2009) Transcellular migration of leukocytes is mediated by the endothelial lateral border recycling compartment. J Exp Med 206:2795-2808.  The ring structures visualized in Figure 6D by PECAM1 immunostaining conform to the ring structures described in these and other papers.  In showing these structures, our point is simply that they likely represent sites of leukocyte extravasation.  This is now clarified in the text.  We have also added some additional references on leukocyte extravasation and the ring structures.

Figure 7 

A time-course analysis of ICAM1 would strengthen the mechanistic model. Does ICAM1 upregulation precede immune infiltration (supporting inflammation as the primary defect)? Given that immune cells appear by P14 (per snRNA-seq), is ICAM1 elevated earlier? 

This is an interesting idea, but based on what is known about leukocyte adhesion and extravasation we predict that there will not be a clean temporal separation between ICAM1 induction and leukocyte adhesion/infiltration.  That is, if the proinflammatory state causes an increase in the number of leukocytes, then as ICAM1 levels increase, leukocyte adhesion would also increase.  Similarly, if the presence of leukocytes increases the pro-inflammatory state, then as the number of leukocytes increases, the levels of ICAM1 would be predicted to increase.  Thus, we think that a time course analysis is unlikely to provide a definitive conclusion.

Figure 8-SF1 

In brain slices, a transient pan-IgG accumulation suggests a self-resolving defect in the BBB. However, this BBB impairment appears to be spatiotemporally distinct from ICAM1 upregulation. ICAM1 staining is restricted to the lesion site, aligning with immune cell-driven inflammation. 

Thank you for raising these points.  The reviewer is correct that these observations don’t fit together in a clear way.  There does not appear to be a general increase in brain vascular permeability in VE-cad-CreER;TGFBR1 CKO/- mice, as shown by sulfo-NHS-biotin.  However, there is a large and transient increase in IgG in the brain parenchyma, suggestive of a general vascular alteration, and – as the reviewer correctly notes – it is not accompanied by a generalized increase in ICAM1 vascular immunostaining.  At this point, we don’t have any real insight into the mechanistic basis of the transient IgG increase.

Thank you for handling this manuscript.

Reviewer #1 (Public review):

Summary:

Zhang et al. addressed the question of whether advantageous and disadvantageous inequality aversion can be vicariously learned and generalized. Using an adapted version of the ultimatum game (UG), in three phases, participants first gave their own preference (baseline phase), then interacted with a "teacher" to learn their preference (learning phase), and finally were tested again on their own (transfer phase). The key measure is whether participants exhibited similar choice preference (i.e., rejection rate and fairness rating) influenced by the learning phase, by contrasting their transfer phase and baseline phase. Through a series of statistical modeling and computational modeling, the authors reported that both advantageous and disadvantageous inequality aversion can indeed be learned (Study 1), and even be generalised (Study 2).

Strengths:

This study is very interesting, that directly adapted the lab's previous work on the observational learning effect on disadvantageous inequality aversion, to test both advantageous and disadvantageous inequality aversion in the current study. Social transmission of action, emotion, and attitude have started to be looked at recently, hence this research is timely. The use of computational modeling is mostly appropriate and motivated. Study 2 that examined the vicarious inequality aversion on conditions where feedback was never provided is interesting and important to strengthen the reported effects. Both studies have proper justifications to determine the sample size.

Weaknesses:

Despite the strengths, a few conceptual aspects and analytical decisions have to be explained, justified, or clarified.

INTRODUCTION/CONCEPTUALIZATION

(1) Two terms seem to be interchangeable, which should not, in this work: vicarious/observational learning vs preference learning. For vicarious learning, individuals observe others' actions (and optionally also the corresponding consequence resulted directly by their own actions), whereas, for preference learning, individuals predict, or act on behalf of, the others' actions, and then receive feedback if that prediction is correct or not. For the current work, it seems that the experiment is more about preference learning and prediction, and less so about vicarious learning. But the intro and set are heavily around vicarious learning, and late the use of vicarious learning and preference learning is rather mixed in the text. I think either tone down the focus on vicarious learning, or discuss how they are different. Some of the references here may be helpful: Charpentier et al., Neuron, 2020; Olsson et al., Nature Reviews Neuroscience, 2020; Zhang & Glascher, Science Advances, 2020

EXPERIMENTAL DESIGN

(2) For each offer type, the experiment "added a uniformly distributed noise in the range of (-10 ,10)". I wonder how this looks like? With only integers such as 25:75, or even with decimal points? More importantly, is it possible to have either 70:30 or 90:10 option, after adding the noise, to have generated an 80:20 split shown to the participants? If so, for the analyses later, when participants saw the 80:20 split, which condition did this trial belong to? 70:30 or 90:10? And is such noise added only to the learning phase, or also to the baseline/transfer phases? This requires some clarification.

(3) For the offer conditions (90:10, 70:30, 50:50, 30:70, 10:90) - are they randomized? If so, how is it done? Is it randomized within each participants, and/or also across participants (such that each participant experienced different trial sequences)? This is important, as the order especially for the leanring phase can largely impact on the preference learning of the participants.

STATISTICAL ANALYSIS & COMPUTATIONAL MODELING

(4) In Study 1 DI offer types (90:10, 70:30), the rejection rate for DI-AI averse looks consistently higher than that for DI averse (ie, blue line is above the yellow line). Is this significant? If so, how come? Since this is a between-subject design, I would not anticipate such a result (especially for the baseline). Also, for the LME results (eg, Table S3), only interactions were reported but not the main results.

(5) I do not particularly find this analysis appealing: "we examined whether participants' changes in rejection rates between Transfer and Baseline, could be explained by the degree to which they vicariously learned, defined as the change in punishment rates between the first and last 5 trials of the Learning phase." Naturally, participants' behavior in the first 5 trials in the learning phase will be similar to those in the baseline; and their behavior in the last 5 trials in the learning phase would echo those at the transfer phase. I think it would be stronger to link the preference learning results to the chance between baseline and transfer phase, eg, by looking at the difference between alpha (beta) at the end of the learning phase and the initial alpha (beta).

(6) I wonder if data from the baseline and transfer phases can also be modeled, using a simple Fehr-Schimdt model? This way, the change in alpha/beta can also be examined between the baseline and transfer phase.

(7) I quite liked Study 2 that tests the generalization effect, and I expected to see an adapted computational modeling to directly reflect this idea. Indeed, the authors wrote "[...] given that this model [...] assumes the sort of generalization of preferences between offer types [...]". But where exactly did the preference learning model assumed the generalization? In the methods, the modeling seems to be only about Study 1; did the authors advise their model to accommodate Study 2? The authors also ran simulation for the learning phase in Study 2 (Figure 6), and how did the preference updated (if at all) for offers (90:10 and 10:90) where feedback was not given? Extending/Unpacking the computational modeling results for Study2 will be very helpful for the paper.

Comments on revisions:

I kept my original public review, so that future readers can see the progress and development of the manuscript.

The authors have largely addressed my original questions/concerns, and I have two outstanding comments.

(a) Related to my original comment #6, where I suggested to apply the F-S model also to the baseline and transfer phase. The authors were inclined not to do it, but in fact later in comment #7 and in the manuscript they opted to use a more complex F-S-based model to their learning phase. I agree that the rejection rate is indeed a clear indication, but for completeness, it'd be more consistent and compelling if the paper follows a model-free (model-agnostic) and model-based approach in all phases of the experiment.

(b) Related to my original comment #4, I appreciate that the authors have provided more details of their LMM models. But I don't think it is accurate regardless. First, all offer levels (50:50, 30:70, 10:90), should not be coded as pure categorical levels. In fact, they have an ordinal meaning, a single ordinal predictor with three levels should be used. This also avoids the excessive number of interactions the authors have pointed out.

Second, running a model with only interactions without main effects is flawed. All textbooks on stats emphasize that without the presence of the main effects, the interpretation of interaction only is biased.

So these LMMs needs to be revised before the manuscript eventually gets to a version of record.

Reviewer #2 (Public review):

Summary:

This study investigates whether individuals can learn to adopt egalitarian norms that incur a personal monetary cost, such as rejecting offers that benefit them more than the giver (advantageous inequitable offers). While these behaviors are uncommon, two experiments aim to demonstrate that individuals can learn to reject such offers by observing a "teacher" who follows these norms. The authors use computational modelling to argue that learners adopt these norms through a sophisticated process, inferring the latent structure of the teacher's preferences, akin to theory of mind.

Strengths:

This paper is well-written and tackles an important topic relevant to social norms, morality, and justice. The findings are promising (though further control conditions are necessary to support the conclusions). The study is well-situated in the literature, with a clever experimental design and a computational approach that may offer insights into latent cognitive processes. In the revision, the authors clarified some questions related to the initial submission.

Weaknesses:

Despite these strengths, I remain unconvinced that the current evidence supports the paper's central claims. Below, I outline several issues that, in my view, limit the strength of the conclusions.

(1) Experimental Design and Missing Control Condition:

The authors set out to test whether observing a "teacher" who is averse to advantageous inequity (Adv-I) will affect observers' own rejection of Adv-I offers. However, I think the design of the task lacks an important control condition needed to address this question. At present, participants are assigned to one of two teachers: DIS or DIS+ADV. Behavioral differences between these groups can only reveal relative differences in influence; they cannot establish whether (and how) either teacher independently affects participants' own behavior. For example, a significant difference between conditions can emerge even if participants are only affected by the DIS teacher and are not affected at all by the DIS+ADV teacher. What is crucially missing here is a no-teacher control condition, which can then be compared with each teacher condition separately. This control condition would also control for pure temporal effects unrelated to teacher influence (e.g., increasing Adv-I rejections due to guilt build-up).

While this criticism applies to both experiments, it is especially apparent in Experiment 2. As shown in Figure 4, the interaction for 10:90 offers reflects a decrease in rejection rates following the DIS teacher, with no significant change following the DIS+ADV teacher. Ignoring temporal effects, this pattern suggests that participants may be learning NOT to reject from the DIS teacher, rather than learning to reject from the DIS+ADV teacher. On this basis, I do not see convincing evidence that participants' own choices were shaped by observing Adv-I rejections.

In the Discussion, the authors write that "We found that participants' own Adv-I-averse preferences shifted towards the preferences of the Teacher they just observed, and the strength of these contagion effects related to the degree of behavior change participants exhibited on behalf of the Teachers, suggesting that they internalized, at least somewhat, these inequity preferences." However, there is no evidence that directly links the degree of behaviour change (on the teacher's behalf) to contagion effects (own behavioural change). I think there was a relevant analysis in the original version, but it was removed from the current version.

(2) Modelling Efforts: The modelling approach is underdeveloped. The identification of the "best model" lacks transparency, as no model-recovery results are provided. Additionally, behavioural fits for the losing models are not shown, leaving readers in the dark about where these models fail. Readers would benefit from seeing qualitative/behavioural patterns that favour the winning model. Moreover, the reinforcement learning (RL) models used are overly simplistic, treating actions as independent when they are likely inversely related. For example, the feedback that the teacher would have rejected an offer provides evidence that rejection is "correct" but also that acceptance is "an error," and the latter is not incorporated into the modelling. In other words, offers are modelled as two-armed bandits (where separate values are learned for reject and accept actions), but the situation is effectively a one-armed bandit (if one action is correct, the other is mistaken). It is unclear to what extent this limitation affects the current RL formulations. Can the authors justify/explain their reasoning for including these specific variants? The manuscript only states Q-values for reject actions, but what are the Q-values for accept actions? This is unclear.

In Experiment 2, only the preferred model is capable of generalization, so it is perhaps unsurprising that this model "wins." However, this does not strongly support the proposed learning mechanism, lacking a comparison with simpler generalizing mechanisms (see following comments).

(3) Conceptual Leap in Modelling Interpretation: The distinction between simple RL models and preference-inference models seems to hinge on the ability to generalize learning from one offer to another. Whereas in the RL models, learning occurs independently for each offer (hence no cross-offer generalization), preference inference allows for generalization between different offers. However, the paper does not explore "model-free" RL models that allow generalization based on the similarity of features of the offers (e.g., payment for the receiver, payment for the offer-giver, who benefits more). Such models are more parsimonious and could explain the results without invoking a theory of mind or any modelling of the teacher. In such model versions, a learner acquires a functional form that allows prediction of the teacher's feedback based on offer features (e.g., linear or quadratic weighting). Because feedback for an offer modulates the parameters of this function (feature weights), generalization occurs without necessarily evoking any sophisticated model of the other person. This leaves open the possibility that RL models could perform just as well or even outperform the preference learning model, casting doubt on the authors' conclusions.

Of note: even the behaviourists knew that when Little Albert was taught to fear rats, this fear generalized to rabbits. This could occur simply because rabbits are somewhat similar to rats. But this doesn't mean Little Albert had a sophisticated model of animals that he used to infer how they behave.

In their rebuttal letter, the authors acknowledge these possibilities, but the manuscript still does not explore or address alternative mechanisms.

(4) Limitations of the Preference-Inference Model: The preference-inference model struggles to capture key aspects of the data, such as the increase in rejection rates for 70:30 DI offers during the learning phase (e.g., Fig. 3A, AI+DI blue group). This is puzzling. Thinking about this, I realized the model makes quite strong, unintuitive predictions which are not examined. For example, if a subject begins the learning phase rejecting the 70:30 offer more than 50% of the time (meaning the starting guilt parameter is higher than 1.5), then, over learning, the tendency to reject will decrease to below 50% (the guilt parameter will be pulled down below 1.5). This is despite the fact that the teacher rejects 75% of the offers. In other words, as learning continues, learners will diverge from the teacher. On the other hand, if a participant begins learning by tending to accept this offer (guilt < 1.5), then during learning, they can increase their rejection rate but never above 50%. Thus, one can never fully converge on the teacher. I think this relates to the model's failure in accounting for the pattern mentioned above. I wonder if individuals actually abide by these strict predictions. In any case, these issues raise questions about the validity of the model as a representation of how individuals learn to align with a teacher's preferences (given that the model doesn't really allow for such an alignment).

In their rebuttal letter, the authors acknowledged these anomalies and stated that they were able to build a better model (where anomalies are mitigated, though not fully eliminated). But they still report the current model and do not develop/discuss alternatives. A more principled model may be a Bayesian model where participants learn a belief distribution (rather than point estimates) regarding the teacher's parameters.

(5) Statistical Analysis: The authors state in their rebuttal letter that they used the most flexible random effect structure in mixed-effects models. But this seems not to be the case in the model reported in Table SI3 (the very same model was used for other analyses too). Indeed, here it seems only intercepts are random effects. This left me confused about which models were used.

Author response:

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

Reviewer #1 (Public Review): 

Most human traits and common diseases are polygenic, influenced by numerous genetic variants across the genome. These variants are typically non-coding and likely function through gene regulatory mechanisms. To identify their target genes, one strategy is to examine if these variants are also found among genetic variants with detectable effects on gene expression levels, known as eQTLs. Surprisingly, this strategy has had limited success, and most disease variants are not identified as eQTLs, a puzzling observation recently referred to as "missing regulation". 

In this work, Jeong and Bulyk aimed to better understand the reasons behind the gap between disease-associated variants and eQTLs. They focused on immune-related diseases and used lymphoblastoid cell lines (LCLs) as a surrogate for the cell types mediating the genetic effects. Their main hypothesis is that some variants without eQTL evidence might be identifiable by studying other molecular intermediates along the path from genotype to phenotype. They specifically focused on variants that affect chromatin accessibility, known as caQTLs, as a potential marker of regulatory activity. 

The authors present data analyses supporting this hypothesis: several disease-associated variants are explained by caQTLs but not eQTLs. They further show that although caQTLs and eQTLs likely have largely overlapping underlying genetic variants, some variants are discovered only through one of these mapping strategies. Notably, they demonstrate that eQTL mapping is underpowered for gene-distal variants with small effects on gene expression, whereas caQTL mapping is not dependent on the distance to genes. Additionally, for some disease variants with caQTLs but no corresponding eQTLs in LCLs, they identify eQTLs in other cell types. 

Altogether, Jeong and Bulyk convincingly demonstrate that for immune-related diseases, discovering the missing disease-eQTLs requires both larger eQTL studies and a broader range of cell types in expression assays. It remains to be seen what fractions of the missing diseaseeQTLs will be discovered with either strategy and whether these results can be extended to other diseases or traits. 

We thank the reviewer for their accurate summary of our study and positive review of our findings for immune-related diseases.

It should be noted that the problem of "missing regulation" has been investigated and discussed in several recent papers, notably Umans et al., Trends in Genetics 2021; Connally et al., eLife 2022; Mostafavi et al., Nat. Genet. 2023. The results reported by Jeong and Bulyk are not unexpected in light of this previous work (all of which they cite), but they add valuable empirical evidence that mostly aligns with the model and discussions presented in Mostafavi et al. 

We thank the reviewer for their positive review of our results and manuscript. As Reviewer #1 noted, whether our and others' observation extends to other diseases or traits is an open question. For instance, Figure 2b in Mostafavi et al., Nat. Genet. (2023) demonstrated that there was a spectrum of depletion of eQTLs and enrichment of GWAS signals in constrained genes across various tissues and traits, respectively. Therefore, gene expression constraint may play a larger or smaller role in different diseases or traits. That immune cell types and cell states are extremely diverse (Schmiedel et al., Cell (2018) and Calderon et al., Nat. Genet. (2019), just to name a few) likely adds to the complexity of gene regulation that contributes to immune-mediated disease.

Reviewer #2 (Public Review): 

Summary: 

eQTLs have emerged as a method for interpreting GWAS signals. However, some GWAS signals are difficult to explain with eQTLs. In this paper, the authors demonstrated that caQTLs can explain these signals. This suggests that for GWAS signals to actually lead to disease phenotypes, they must be accessible in the chromatin. This implies that for GWAS signals to translate into disease phenotypes, they need to be accessible within the chromatin. 

However, fundamentally, caQTLs, like GWAS, have the limitation of not being able to determine which genes mediate the influence on disease phenotypes. This limitation is consistent with the constraints observed in this study. 

We thank the reviewer for their accurate summary of our results.

(1) For reproducibility, details are necessary in the method section.

Details about adding YRI samples in ATAC-seq: For example, how many samples are there, and what is used among public data? There is LCL-derived iPSC and differentiated iPSC (cardiomyocytes) data, not LCL itself. How does this differ from LCL, and what is the rationale for including this data despite the differences?

Banovich et al., Genome Research (2018) (PMID: 29208628), who generated data using LCLderived iPSCs and differentiated iPSCs (cardiomyocytes), also generated ATAC-seq data from 20 YRI LCL samples. We analyzed those data to identify open chromatin regions (i.e., ATACseq peaks) in LCLs and merged the regions with open chromatin regions identified with 100 GBR LCL samples from two studies by Kumasaka et al. (Nature Genetics (2016)

PMID: 26656845 and Nature Genetics (2019) PMID: 30478436). However, we restricted the caQTL analysis to only the 100 GBR samples because of possible ancestry effects and batch effects. We attempted caQTL analysis with the 20 YRI samples as well, but the result was noisy, likely due to smaller sample size and lower read depth of the ATAC-seq data.

caQTL is described as having better power than eQTL despite having fewer samples. How does the number of ATAC peaks used in caQTL compare to the number of gene expressions used in eQTL?

The number of ATAC peaks used in caQTL (99,320) is ~6.7 times greater than the number of genes (14,872) used in the eQTL analysis. Therefore, there is a higher chance of detecting a significant caQTL signal and a significant colocalization signal than there is for eQTLs. However, we reasoned that since distal eQTLs are more easily detected as caQTLs and since increasing the sample size of eQTLs through meta-analysis uncovered additional eQTL colocalization at loci with caQTL colocalization only, colocalized caQTLs are likely capturing disease-relevant regulatory effects.

Details about RNA expression data: In the method section, it states that raw data (ERP001942) was accessed, and in data availability, processed data (E-GEUV-1) was used. These need to be consistent.

Thank you for pointing this out. We used the processed data from Expression Atlas (https://www.ebi.ac.uk/gxa/experiments/E-GEUV-1/Results), and that's what we meant by "We downloaded RNA expression level data of the LCL samples from the Expression Atlas." We have revised the “RNA expression data preparation” section in our manuscript to make the text clearer.

How many samples were used (the text states 373, but how was it reduced from the original 465, and the total genotype is said to be 493 samples while ATAC has n=100; what are the 20 others?), and it mentions European samples, but does this exclude YRI?

We thank the reviewer for pointing out these points of confusion. Our reported count of 493 samples included YRI samples with RNA-seq data or ATAC-seq data that we ultimately did not use for QTL analyses. There were 373 European samples with RNA-seq data that we used for eQTL analysis, and 100 GBR samples (including some that overlap with the 373 European samples) that we used for caQTL analysis. We have revised the text to clarify these points.

(2) Experimental results determining which TFs might bind to the representative signals of caQTL are required.

We agree that caQTL colocalization is just the start of elucidating the regulatory mechanism of a GWAS locus. Determining which TFs are bound and which TFs' binding is altered would be necessary to describe the causal regulatory mechanism. For this, we utilized the Cistrome database to search for TFs whose binding overlaps the colocalized caQTL peaks. We present the results of this analysis in Supplementary Table 3 and Supplementary Figure 4, both of which we have added in our revised manuscript. Overall, protein factors associated with active transcription, such as POL2RA, and several immune cell TFs, including RUNX3, SPI1, and RELA, were frequently detected in those peaks. Detecting these factors in most peaks supports the likelihood that the colocalized caQTL peaks are active cis-regulatory elements. These results are consistent with our observation of enriched caQTL-mediated heritability in regions with active histone marks (Figure 1).

(3) It is stated that caQTL is less tissue-specific compared to eQTL; would caQTL performed with ATAC-seq results from different cell types, yield similar results?

We thank the reviewer for the question. Calderon et al. (PMID: 31570894) observed that "most effects on allelic imbalance (of ATAC-seq) were shared regardless of lineage or condition". Yet, there were regions where a different cell type or state would show inaccessibility (Figure 4d in Calderon et al.). Thus, we expect that ATAC-seq results from different cell types (e.g., T cells, B cells, monocytes, etc.) would lead to additional caQTLs showing colocalization at cell-typespecific open chromatin. However, if a region is accessible in both cell types, caQTL may be detected in both. Moreover, Alasoo et al., Nature Genetics (2018) (PMID: 29379200) observed that “many disease-risk variants affect chromatin structure in a broad range of cellular states, but their effects on expression are highly context specific.” In both studies, the authors investigated immune cell types, and there could be different observations in non-immune cell types and other diseases and traits.

Reviewer #1 (Recommendations For The Authors): 

I think it would strengthen the paper to explore gene-level differences in the discovery of caQTLs and eQTLs. For example, complex disease-relevant genes, on average, have more/longer regulatory domains (as shown by Wang and Goldstein, AJHG 2020; Mostafavi et al., Nat. Genet. 2023). Therefore, it is plausible that for such genes, caQTLs are much more easily discoverable than eQTLs due to (i) a larger mutational target size for caQTLs, and (ii) dispersion of expression heritability across multiple domains, which hampers the discovery of eQTLs but not caQTLs, which are studied independently of other domains in the region. In other words, discovered caQTLs and eQTLs likely vary in terms of their distance to genes (as the authors report), as well as their target genes.

We thank the reviewer for the suggestion to explore gene-level differences. We expect that the effects of complex disease-relevant genes having more / longer regulatory domains, on average, to explain our observations. We agree on both of your points that there are many more regulatory elements that are captured as accessible regions than expressed genes and that genes often have multiple independent eQTLs leading to dispersion of heritability. The genelevel trend that we described was the distance of the regulatory element from the genes. Additional analyses would be a relevant future direction.

Also considering gene-level analysis, Mostafavi et al. show that the types of biases they report for eQTLs also apply to other molecular QTLs. It would be valuable to compare GWAS hits with versus without caQTL colocalization. Similarly, it would be insightful to compare GWAS hits with both colocalized caQTLs and eQTLs to GWAS hits with colocalized caQTLs but no eQTLs in any of the cell types. 

We thank the reviewer for the comment. Investigating for potential biases in the colocalized caQTL would be useful, but we considered it beyond the scope of this work. In terms of biological factors, we demonstrated through mediated heritability analyses that more accessible chromatin (based on ATAC-seq read coverage) and regions with active histone marks were enriched for autoimmune disease associations (Figure 1). Furthermore, as greater distance of the regulatory variant from the transcription start site significantly reduced the cis-heritability, we would expect that distance would play a major role, similar to Mostafavi et al.’s conclusions.

I don't think the argument for the role of natural selection contributing to the "missing regulation" is presented accurately. Specifically, large eQTLs acting on top trait-relevant genes are under stronger selection and thus, on average, segregate at lower frequencies. This makes them difficult to discover in eQTL assays. However, if not lost, they contribute as much, if not more, to trait heritability than weaker eQTLs at the same gene because their larger effects compensate for their lower frequency. At the most extreme, selection should have a "flattening" effect (e.g., see Simons et al., PLOS Biol 2018; O'Connor et al., AJHG 2019): weak and strong eQTLs at the same gene are expected to contribute equally to heritability. Therefore, the statement "Consequently, only weak eQTL variants, often in regions distal to the gene's promoter, may remain and affect traits" is not correct. If this turns out to be empirically true, other models, such as pleiotropic selection, need to explain it. 

We thank the reviewer for the correction. We agree with the comment and have revised the sentences in the introduction accordingly.

It is worth speculating why caQTLs may be more consistent across cell types than cis-eQTLs. Additionally, readers may infer from the paper that the focus should shift from eQTLs to caQTLs, which may not be the authors' intention. Perhaps these approaches are complementary: caQTLs can help with TSS-distal disease variants, while finding the target gene and regulatory context is more straightforward with eQTL colocalization. Addressing these points in the discussion will be helpful.

We appreciate the reviewer's suggestion to clarify the advantages of incorporating cis-eQTLs and caQTLs. Our argument is exactly as you put it, and we added a paragraph on this in the Discussion.

I believe the authors could do more to contextualize their findings within the existing literature on the subject, particularly Umans et al., Trends in Genetics 2021; Connally et al., eLife 2022; and Mostafavi et al., Nat. Genet. 2023. For instance, Umans et al. suggest that "if most standard eQTLs are generally benign, increasing sample size and adding more tissue types in an effort to identify even more standard eQTLs may not help us to explain many more disease risk mutations". Conversely, Mostafavi et al. argue for a multipronged approach, which appears more aligned with the authors' conclusions.

We followed the reviewer’s suggestion to place our work in the context of existing literature on this topic. Moreover, we clarified what our recommendations for future data generation are.

I thought Figures 1C-D were unclear. 

We added a sentence in the figure legend describing that stronger and more significant enrichment indicate that mediated heritability is concentrated in that subset.

Reviewer #2 (Recommendations For The Authors): 

Complete workflow figures for caQTL calling and eQTL calling are required. 

To improve clarity of the caQTL and eQTL calling workflow, we added Supplementary Figure 1.

Reviewer #2 (Public review):

Summary:

This manuscript reports high-resolution functional MRI data and MEG data revealing additional mechanistic information about an established paradigm studying how foveal regions of primary visual cortex (V1) are involved in processing peripheral visual stimuli. Because of the retinotopic organization of V1, peripheral stimuli should not evoke responses in the regions of V1 that represent stimuli in the center of the visual field (the fovea). However, functional MRI responses in foveal regions do reflect the characteristics of peripheral visual stimuli - this is a surprising finding first reported in 2008. The present study uses fMRI data with sub-millimeter resolution to study the how responses at different depths in the foveal gray matter do or don't reflect peripheral object characteristics during 2 different tasks: one in which observers needed to make detailed judgments about object identity, and one in which observers needed to make more coarse judgments about object orientation. FMRI results reveal interesting and informative patterns in these two conditions. A follow-on MEG study yields information about the timing of these responses. Put together, the findings settle some questions in the field and add new information about the nature of visual feedback to V1.

Strengths:

(1) Rigorous and appropriate use of "laminar fMRI" techniques.

(2) The introduction does an excellent job of contextualizing the work.

(3) Control experiments and analyses are designed and implemented well

Weaknesses:

(1) The use of the term "low order" to describe object orientation is potentially confusing. During review, the authors considered this issue and responded that they would continue with the use of the term low-order to describe object orientation because a low-pass spatial frequency filter would provide object orientation information. This is certainly a reasonable perspective; nonetheless, this reviewer thinks spatial frequencies that low are not readily represented by neurons in early visual cortex and it is common to use "low-order" to refer to features extracted in early visual areas, so I think this causes confusion.

(2) The methods contain a nice description of the methods for "correcting the vascular-related signals". I'm guessing this is the method that removed, e.g., 22% of foveal voxels (previous paragraph), but it's not entirely clear whether the voxel selection methods described in the "correcting the vascular-related signals" are describing the same processing step referred to in the previous paragraph as "a portion of voxels was removed based on large vein distribution".

(3) It is quite difficult to perform laminar analyses across multiple visual areas because distortion compensation is not perfect and registration of functional to anatomical data will always be a bit better in some places and a bit worse in others. An ideal manuscript would include some images showing registration quality in V1, LOC, and IPS regions for a few different participants, or include some kind of quality metric indicating the confidence in depth assignments in different regions.

(4) For the decoding analysis, it would be helpful to have more information about how samples were defined for each condition -- were the beta values for entire blocks used as samples for each condition, or were separate timepoints during a block used in the SVM as repeated samples for each condition?

Author response:

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

Reviewer #1 (Public review):

(1.1) The authors argue that low-level features in a feedback format could be decoded only from deep layers of V1 (and not superficial layers) during a perceptual categorization task. However, previous studies (Bergman et al., 2024; Iamshchinina et al., 2021) demonstrated that low-level features in the form of feedback can be decoded from both superficial and deep layers. While this result could be due to perceptual task or highly predictable orientation feature (orientation was kept the same throughout the experimental block), an alternative explanation is a weaker representation of orientation in the feedback (even before splitting by layers there is only a trend towards significance; also granger causality for orientation information in MEG part is lower than that for category in peripheral categorization task), because it is orthogonal to the task demand. It would be helpful if the authors added a statistical comparison of the strength of category and orientation representations in each layer and across the layers.

We agree that the strength of feedback information is related to task demand. Specifically, we would like to highlight the relationship between task demand and feedback information in the superficial layer. Previous studies have shown that foveal feedback information is observed only when the task requires the identity information of the peripheral objects (Williams et al., 2008; Fan et al., 2016; Yu and Shim, 2016). In this study, we found that the deep layer represented both orientation and categorical feedback information, while the superficial layer only represented categorical information. This suggests that feedback information in the superficial layer may be related to (or enhanced by) the task demands. In other words, if the experimental design required participants to discriminate orientation rather than object identity, we would expect stronger orientation information in foveal V1 and significant decoding performance of orientation feedback information in the superficial layer of foveal V1. This assumption is consistent with the anatomical connections of the superficial layer, which not only receives feedback connections but also sends outputs to higher-level regions for further processing. This is also consistent with Iamshchinina et al.’s observation that, when orientation information had to be mentally rotated and reported (i.e., task-relevant), it was observed in both the superficial and deep layers of V1. Bergmann et al. observed illusory color information in the superficial layer of V1, which may reflect a combination of lateral propagation and feedback mechanisms in the superficial layer that support visual filling-in phenomena. We have revised the discussion in the manuscript: In other words, if the experimental design required participants to discriminate orientation rather than object identity, we would expect stronger orientation information in foveal V1 and significant decoding performance of orientation feedback information in the superficial layer of foveal V1. Recent studies (Iamshchinina et al., 2021; Bergman et al., 2024) have also highlighted the relationship between feedback information and neural representations in V1 superficial layer.

To further demonstrate the laminar profiles of low- and high-order information, we have re-analyzed the data and added more fine-scale laminar profiles with statistical comparisons in the revised manuscript. The results again showed significant neural decoding performances in the deep layer of both category and orientation information, and only significant decoding performances of category information in the superficial layer.

(1.2) The authors argue that category feedback is not driven by low-level confounding features embedded in the stimuli. They demonstrate the ability to decode orientations, particularly well represented by V1, in the absence of category discrimination. However, the orientation is not a category-discriminating feature in this task. It could be that the category-discriminating features cannot be as well decoded from V1 activity patterns as orientations. Also, there are a number of these category discriminating features and it is unclear if it is a variation in their representational strength or merely the absence of the task-driven enhancement that preempts category decoding in V1 during the foveal task. In other words, I am not sure whether, if orientation was a category-specific feature (sharpies are always horizontal and smoothies are vertical), there would still be no category decoding.

The low-order features mentioned in the manuscript refer to visual information encoded intrinsically in V1, independent of task demands. In the foveal experiment, the task is to discriminate the color of fixation, which is unrelated to the category or orientation of the object stimuli. The results showed that only orientation information could be decoded from foveal V1. This indicates that low-order information, such as orientation, is strongly and automatically encoded in V1, even when it is irrelevant to the task. Meanwhile, category information could not be decoded, indicating that category information relies on feedback signals driven by attention or the task to the objects, both of which are absent in the fixation task. Other evidence indicates that category feedback is not driven by low-level features intrinsically encoded in V1. First, the laminar profiles of these two types of feedback information differ considerably (see response to 1.1). Second, only category feedback information was correlated with behavioral performance (MEG experiment). These findings demonstrate that category feedback information is task-driven and differs from the automatically encoded low-order information in foveal V1. The reviewer expressed some uncertainty that, whether “if orientation was a category-specific feature (sharpies are always horizontal and smoothies are vertical), there would still be no category decoding”. Our data showed that orientation could be automatically decoded in V1, regardless of task demand. Thus, if orientation was a category-specific feature in the foveal task (i.e., sharpies are always horizontal and smoothies are always vertical), category decoding would be successful in V1. However, in this scenario, the orientation and other shape features are not independent, thus preventing us to find out whether non-orientation shape features could be decoded in V1.  

Reviewer #2 (Public review):

(2.1) While not necessarily a weakness, I do not fully agree with the description of the 2 kinds of feedback information as "low-order" and "high-order". I understand the motivation to do this - orientation is typically considered a low-level visual feature. But when it's the orientation of an entire object, not a single edge, orientation can only be defined after the elements of the object are grouped. Also, the discrimination between spikies and smoothies requires detecting the orientations of particular edges that form the identifying features. To my mind, it would make more sense to refer to discrimination of object orientation as "coarse" feature discrimination, and orientation of object identity as "fine" feature discrimination. Thus, the sentence on line 83, for example, would read "Interestingly, feedback with fine and coarse feature information exhibits different laminar profiles.".

We agree that the object orientation (invariant to object category or identity) is defined on a larger spatial scale than the local orientation features such as local edges, however, in this sense, the object orientation is a coarse feature. In contrast, the category-defining information is mainly contributed by the local shape information (i.e., little cubes vs. bumps), which is more fine-scale information. One way to look at this difference is that the object orientation information is mainly carried by low-spatial frequency information and will survive low-pass filtering, hence “coarse”; while the object category information would largely be lost if the objects underwent low-pass spatial filtering.

We believe the labeling words “low-order” and “high-order” are consistent with the typical use of these terms in the literature, referring to features intrinsically encoded in early visual cortex vs. in high level object sensitive cortical regions. The more important aspects of our results are in their differential engagement in feedforward vs. feedback processing, with low-order features automatically represented in the early visual cortex during feedforward processing while high-order features represented due to feedback processing. Results from the foveal fMRI experiment (Exp. 2) strongly support this assumption that, when objects were presented at the fovea and the task was a fixation color task irrelevant to object information, foveal V1 could only represent orientation information, not category information. Notably, there was a dramatic difference in decoding performance in foveal V1 between Exp.1 and Exp.2, which ruled out the argument that both orientation and category information were driven by local edge information represented in V1.

(2.2) Figure 2 and text on lines 185, and 186: it is difficult to interpret/understand the findings in foveal ROIs for the foveal control task without knowing how big the ROI was. Foveal regions of V1 are grossly expanded by cortical magnification, such that the central half-degree can occupy several centimeters across the cortical surface. Without information on the spatial extent of the foveal ROI compared to the object size, we can't know whether the ROI included voxels whose population receptive fields were expected to include the edges of the objects.

The ROI of foveal V1 was defined using data from independent localizer runs. In each localizer run, flashing checkerboards of the same size as the objects in the task runs were presented at the fovea or in the periphery. The ROI of foveal V1 was identified as the voxels responsive to the foveal checkerboards. In other words, The ROI of foveal V1 included the voxels whose population receptive fields covered the entire object in the foveal visual field.

We included a figure in the revised manuscript comparing the activation maps induced by the foveal object stimulus in the task runs with the ROI coverage defined by the localizer runs. 

(2.3) Line 143 and ROI section of the methods: in order for the reader to understand how robust the responses and analyses are, voxel counts should be provided for the ROIs that were defined, as well as for the number (fraction) of voxels excluded due to either high beta weights or low signal intensity (lines 505-511).

In the revised manuscript, we have included the number of voxels in each ROI and the criteria for voxel selection:

For each ROI, the number of voxels depended on the size of the activated region, as estimated from the localizer data. The numbers are as follows: foveal V1, 2185 ± 389; peripheral V1, 1294± 215; LOC, 3451 ± 863; and pIPS, 5154 ± 1517. To avoid the signals of large vessels, a portion of voxels was removed based on the distribution of large vessels: V1 foveal, 22.5% ± 6.6%; V1 peripheral, 6.8% ± 3.9%; LOC, 16.1% ± 8.1% ; and pIPS, 5.1% ± 3.2%. For the decoding analysis, the top 500 responsive voxels in each ROI were selected to balance the voxel numbers across different ROIs for training and testing the decoder.

(2.4) I wasn't able to find mention of how multiple-comparisons corrections were performed for either the MEG or fMRI data (except for one Holm-Bonferonni correction in Figure S1), so it's unclear whether the reported p-values are corrected.

For the fMRI results, there is strong evidence showing that feedback information is sent to the foveal V1 during a peripheral object task (Williams et al., 2008; Fan et al., 2016; Yu and Shim, 2016). In addition, anatomical and functional evidence shows that the superficial and deep layers of V1 receive feedback information during visual processing. Therefore, in the current study, we specifically examined two types of feedback information in the superficial and deep layers of foveal V1, and did not apply multiple-comparison correction to the decoding results.

Regarding the MEG results, since we did not have a strong prior about when feedback information would arrive in the foveal V1, a cluster-based permutation method was used to correct for multiple comparisons in each time course. Specifically, for each time point, the sign of the effect for each participant was randomly flipped 50000 times to obtain the null hypothesis distribution for each time point. Clusters were defined as continuous significant time points in the real and flipped time series, and the effects in each cluster were summed to create a cluster-based effect. The most significant cluster-based effect in each flipped time series was then used to generate the corrected null hypothesis distribution.

We included these clarifications in Significance testing part of the revised manuscript.

Reviewer #1 (Recommendations for the authors):

It would be helpful if the authors could elaborate more on the fMRI decoding results in higher-order visual areas in the Discussion (there are recent studies also investigating higher-order visual areas (Carricarte et al., 2024) and associative areas (Degutis et al., 2024)) and relate it to the MEG information transmission results between the areas overlapping with the regions recorded in the fMRI part of the study.

We have discussed the fMRI decoding results in the LOC and IPS in the revised manuscript: 

In the current study, fMRI signals from early visual cortex and two high-level brain regions (LOC and pIPS) were recorded. Neural dynamics of these regions were extracted from MEG signals. Decoding analyses based on fMRI and MEG signals consistently showed that object category information could be decoded from both regions. These findings raise an important question:  Further Granger causality analysis indicates that the feedback information in foveal V1 was mainly driven by signals from the LOC. Layer-specific analysis showed that category information could be decoded in the middle and superficial layers of the LOC. A reasonable interpretation of this result is that feedforward information from the early visual cortex was received by the LOC’s middle layer, then the category information was generated and fed back to foveal V1 through the LOC’s superficial layer. A recent study (Carricarte et al., 2024) found that, in object selective regions in temporal cortex, the deep layer showed the strongest fMRI responses during an imagery task. Together, the results suggest that the deep and superficial layers correspond to different feedback mechanisms. It is worth noting that other cortical regions may also generate feedback signals to the early visual cortex. The current study did not have simultaneously recorded fMRI signals from the prefrontal cortex, but it has been shown that feedback signals can be traced back to the prefrontal cortex during complex cognitive tasks, such as working memory (Finn et al., 2019; Degutis et al., 2024). Further fMRI studies with submillimeter resolution and whole-brain coverage are needed to test other potential feedback pathways during object processing.

The behavioral performance seems quite low (67%), could authors explain the reasons for it?

We designed the object stimuli to be difficult to distinguish on purpose. Some of our pilot data showed that the more involved the participants were in the peripheral object task, the easier the foveal feedback information was to decoded. It is reasonable to assume that if the peripheral objects were easily distinguishable, the feedback mechanism may not be fully recruited during object processing. Furthermore, since we were decoding category and orientation information rather than identity information, the difficulty of distinguishing two objects from the same category and with the same orientation would not affect the decoding of category and orientation information in the neural signals.

Reviewer #2 (Recommendations for the authors):

(1) Line 52: the meaning of the sentence starting with "However, ..." is not entirely clear. Maybe the word "while" is missing after the first comma?

(2) Line 224. If I'm understanding the rationale for the MEG analysis correctly, it was not possible to localize foveal regions, but the cross-location decoding analysis was used to approximate the strength and timing of feedback information. If this is the case, "neural representations in the foveal region" were not extracted.

(3) Figure 4. The key information is too small to see. The lines indicating where decoding performance was significant are quite thin but very important, and the text next to them indicating onset times of significant decoding is in such a small font size I needed to zoom in to 300% to read it (yes, my eyes are getting old and tired). Increasing the font size used to represent key information would be nice.

(4) Figure 4 caption. Line 270 describes the line color in the plots as yellow, but that color is decidedly orange to my eye.

(5) Line 340/341: Papers that define and describe feedback-receptive fields seem important to cite here:

Keller, A. J., Roth, M. M., & Scanziani, M. (2020). Feedback generates a second receptive field in neurons of the visual cortex. Nature, 582(7813), 545-549.

Kirchberger, L., Mukherjee, S., Self, M. W., & Roelfsema, P. R. (2023). Contextual drive of neuronal responses in mouse V1 in the absence of feedforward input. Science advances, 9(3), eadd2498.

(6) Lines 346-350: this sentence seems to have some missing or misused words, because the syntax isn't intact.

(7) Line 367: supports should be support.

We thank the reviewers for the comments and have corrected them in the manuscript.

Author response:

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

Reviewer #1 (Public review): 

Wang, Junxiu et al. investigated the underlying molecular mechanisms of the insecticidal activity of betulin against the peach aphid, Myzus persicae. There are two important findings described in this manuscript: (a) betulin inhibits the gene expression of GABA receptor in the aphid, and (b) betulin binds to the GABA receptor protein, acting as an inhibitor. The first finding is supported by RNA-Seq and RNAi, and the second one is convinced with MST and electrophysiological assays. Further investigations on the betulin binding site on the receptor protein provided a fundamental discovery that T228 is the key amino acid residue for its affinity, thereby acting as an inhibitor, backed up by site-directed mutagenesis of the heterologously-expressed receptor in E. coli and by CRISPR-genome editing in Drosophila.

Although the manuscript does have strengths in principle, the weaknesses do exist: the manuscript would benefit from more comprehensive analyses to fully support its key claims in the manuscript. In particular:

(1) The Western blotting results in Figure 5A & B appear to support the claim that betulin inhibits GABR gene expression (L26), as a decrease in target protein levels is often indicative of suppressed gene expression. The result description for Figure 5A & B is found in L312-L316, within Section 3.6 ("Responses of MpGABR to betulin"), where MST and voltage-clamp assays are also presented. It seems the observed decrease in MpGABR protein content is due to gene downregulation, rather than a direct receptor protein-betulin interaction. However, this interpretation lacks discussion or analysis in either the corresponding results section or the Discussion. In contrast, Figures 5C-F are specifically designed to illustrate protein-betulin interactions. Presenting Figure 5A & B alongside these panels might lead to confusion, as they support distinct claims (gene expression vs. protein binding/inhibition). Therefore, I recommend moving Figure 5A & B either to the end of Figure 3 or to a separate figure altogether to improve clarity and logical flow. A minor point in the Western blotting experiment is that although GAPDH was used as a reference protein, there is no explanation in the corresponding M&M section.

We thank the reviewer for the concise and accurate summary and appreciate the constructive feedback on the article’s strengths and weaknesses.

(A) According to your suggestion, the original Figure 5A and B have been inserted into Figure 3, following Figure 3D. The original Figure 3E-I has been saved as a new figure, to illustrate the RNAi assay.

(b) “GAPDH was used as a reference protein” has been supplied in the M&M section, see

Line 209.

(2) The description of the electrophysiological recording experiment is unclear regarding the use of GABA. I didn't realize that GABA, the true ligand of the GABA receptor, was used in this inhibition experiment until I reached the Results section (L321), which states, "In the presence of only GABA, a fast inward current was generated." Crucially, no details are provided on the experiment itself, including how GABA was applied (e.g., concentration, duration, whether GABA was treated, followed by betulin, or vice versa). This information is essential for reproducibility. Please ensure these details are thoroughly described in the corresponding M&M section.

We thank the reviewer for the valuable comments.

(a) Detailed information on how to apply GABA has been added to the corresponding M&M section (Lines 260-263): After 3 days of incubation, the oocytes were used for electrophysiological recording. GABA was dissolved in 1 × Ringer's solution to prepare 100 µM GABA solution. Subsequently, the 100 µM GABA solutions containing different concentrations of betulin (0, 5, 10, 20, 40, 80, 160, 320 µM) were used to perfuse the oocytes.

(b) Additionally, we also checked other contents of M&M section to ensure that sufficient detail has been supplied.

(3) The phylogenetic analysis, particularly concerning Figures 4 and 6B, needs significant attention for clarity and representativeness. First, your claim that MpGABR is only closely related to CAI6365831.1 (L305-L310) is inconsistent with the provided phylogenetic tree, which shows MpGABR as equally close to Metopolophium dirhodum (XP_060864885.1) and Acyrthosiphon pisum (XP_008183008.2). Therefore, singling out only Macrosiphum euphorbiae (CAI6365831.1) is not supported by the data. Second, the representation of various insect orders is insufficient. All 11 sequences in the Hemiptera category (in both Figure 4 and Figure 6B) are exclusively from the Aphididae family. This small subset cannot represent the highly diverse Order Hemiptera. Consequently, statements like "only THR228 was conserved in Hemiptera" (L338), "The results of the sequence alignment revealed that only THR228 was conserved in Hemiptera" (L430), or "THR228... is highly conserved in Hemiptera" (L486) are not adequately supported. Third, similar concerns apply to the Diptera order, which includes 10 Drosophila and 2 mosquito samples (not diverse or representative enough), and likely to other orders as well. Thereby, the Figure 6B alignment should be revised accordingly to reflect a more accurate representation or to clarify the scope of the analysis. Fourth, there's a discrepancy in the phylogenetic method used: the M&M section (L156) states that MEGA7, ClustalW, and the neighbor-joining method were used, while the Figure 4 caption mentions that MEGA X, MUSCLE, and the Maximum likelihood method were employed. This inconsistency needs to be clarified and made consistent throughout the manuscript. Fifth, I have significant concerns about the phylogenetic tree itself (Figure 4). A small glitch was observed at the Danaus plexippus node, which raises suspicion regarding potential manipulation after tree construction. More critically, the tree, especially within Coleoptera, does not appear to be clearly resolved. I am highly concerned about whether all included sequences are true GABR orthologs or if the dataset includes partial or related sequences that could distort the phylogeny. Finally, for Figure 6B, both protein (XP_) and nucleotide (XM_) sequences were mix used. I recommend using the protein sequences instead of nucleotide sequences in this figure panel, as protein sequences are more directly informative.

We thank the reviewer for the careful reading and valuable comments.

(a) Firstly, according to your comments, phylogenetic analysis has been re-performed with more represent species from each Order (Fig. 5 and Fig. 7B). The results revealed that only THR228 was conserved across 11 species in the Aphididae family of Hemiptera. Therefore, the expressions like "only THR228 was conserved in Hemiptera" have been revised to “among the four residues, only THR228 was conserved across 11 species in the Aphididae family of Hemiptera” (Line 106, Line 369, Line 477, and Lines 563-564).

(b) We have modified the description of Fig. 5 (the original Fig. 4): MpGABR  (XP_022173711.1) was found to be genetically closely related to CAI6365831.1 from Macrosiphum euphorbiae, XP 008183008.2 from Acyrthosiphon pisum, and XP 060864885.1 from Metopolophium dirhodum (Fig. 5 and Table S6). See Lines 342-346.

(c) Phylogenetic analysis was performed using MEGA7 with multiple amino acid sequence alignment (ClustalW) and the neighbor-joining method. We have revised the Fig. 5 (the original Fig. 4) caption to make it accurate and consistent throughout the manuscript.

(d) We are sorry about the small glitch at the Danaus plexippus node. Actually, after the phylogenetic tree was constructed, it was imported in Adobe Illustration for coloring and classification annotation. There may have been operational errors during the process of resizing the image, resulting in the occurrence of the small glitch. Besides, the unclear clustering of Coleoptera may be due to improper regulation of distance (pixels) of branch from nodes. Again, thanks for your careful reading. We have rebuilt the phylogenetic tree.

(e) Based on your suggestion, the sequence IDs have been unified as the protein sequence IDs (Fig. 5, Fig. 7B and Table S6)

(4) The Discussion section requires significant revision to provide a more insightful and interpretative analysis of the results. Currently, much of the section primarily restates findings rather than offering deeper discussion. For instance, L409-L419 restate the results, followed by the short sentence "Collectively, these results suggest that betulin may have insecticidal effects on aphids by inhibiting MpGABR expression". It could be further expanded to make it beneficial to elaborate on proposed mechanisms by which gene expression might be suppressed, including any potential transcription factors involved. In contrast, while L422-L442 also initially summarize results, the subsequent paragraph (L445-L472) effectively discusses the potential mechanisms of inhibitory action and how mortality is triggered, which is a good model for other parts of the section. However, all the discussion ends up with a short statement, "implying that betulin acts as a CA of MpGABR" (L472), which appears to be a leap. The inference that betulin acts as a competitive antagonist (CA) is solely based on the location of its extracellular binding site, which does not exactly overlap with the GABA binding site. It needs stronger justification or actually requires further experimental validation. The authors should consider rephrasing this statement to acknowledge the need for additional studies to definitively confirm this mechanism of action.

We appreciate the reviewer's careful reading and valuable feedback, which will certainly enhance the quality of our manuscript.

(a) Possible reasons for the effect of betulin on MpGABR expression have been discussed in our manuscript (Lines 455-466): The regulation of gene expression is sophisticated and delicate (Pope and Medzhitov 2018). The regulatory network controlling GABR expression remains unclear. In adult rats, epileptic seizures has been reported to increase the levels of brain-derived neurotrophic factor (BDNF), which in turn prompted the transcription factors CREB and ICER to reduce the gene expression of the GABR α1 subunit (Lund et al. 2008). In Drosophila, it has been demonstrated that WIDE AWAKE, which regulated the onset of sleep, interacted with the GABR and upregulated its expression level (Liu et al. 2014). In Drosophila brain, circular RNA circ_sxc was found to inhibit the expression of miR-87-3p in the brain through sponge adsorption, thereby regulating the expression of neurotransmitter receptor ligand proteins, including GABR, and ensuring the normal function of synaptic signal transmission in brain neurons (Li et al. 2024). However, it remains unclear how betulin reduces the expression of MpGABR, and further research is needed.

(b) In the Discussion section, we acknowledged the need for further research to ultimately confirm the mechanism by which betulin competes with GABA for binding to MpGABR (Lines 532-535): Although the mechanism by which betulin competes with GABA for binding to MpGABR requires further experimental validation, our work may have provided a novel target for developing insecticides.

(c) Besides, we have added the discussion of the sensitivity of GABA receptor to betulin in Discussion section (Lines 491-501): Studies on key amino acids that are crucial for GABR function has primarily focused on transmembrane regions. For instance, based on the mutational research and Drosophila GABR modeling approach, multiple key amino acids were identified as insecticide targets in the transmembrane domain (Nakao and Banba 2021). Guo et al. proposed that amino acid substitutions in the transmembrane domain 2 contribute to terpenoid insensitivity during plant-insect coevolution (Guo et al. 2023). However, these studies have neglected the extracellular domain. Our study signified that betulin targets the THR228 site in the extracellular domain of MpGABR, which is conserved only in the Aphididae family. Therefore, betulin is speculated to be a specific insecticidal substance evolved by plants in response to aphid infestation. Besides, further verification is needed to determine whether betulin is toxic to other insect species.

(d) Furthermore, the discussion of potential ecological risks of deploying betulin as a bioinsecticide has been elaborated in our manuscript (Lines 538-553): The development of bioinsecticides should not only focus on the toxic effects of active substance on target organisms, but also on their influence on the ecosystem (Haddi et al. 2020). Although our results indicate that betulin has specific toxicity to aphids, previous studies have reported that betulin and its derivatives had effects on Plutella xylostella L. (Huang et al. 2025), Aedes aegypti (de Almeida Teles et al. 2024), and Drosophila melanogaster (Lee and Min 2024). Therefore, further research is needed to determine whether there are other insecticidal mechanisms or off target effects of betulin. Additionally, betulin exhibits a wide range of pharmacological activities (Amiri et al. 2020), which have been used to treat various diseases, such as cancer (Lv 2023), glioblastoma (Li et al. 2022), inflammation (Szlasa et al. 2023) and hyperlipidemia (Tang et al. 2011). Before applying betulin in the field, it is necessary to fully verify and consider whether betulin has any impact on farmers' health. Furthermore, will betulin cause residue or diffusion in the process of field application? Will long-term application promote the evolution of resistance to aphids or other insects? These issues also need further experimental verification. In summary, before any field application, further research is needed on the environmental behavior, degradation process, and safety of betulin.

Reviewer #2 (Public review):

Summary:

This important study shows that betulin from wild peach trees disrupts neural signaling in aphids by targeting a conserved site in the insect GABA receptor. The authors present a nicely integrated set of molecular, physiological, and genetic experiments to establish the compound's species-specific mode of action. While the mechanistic evidence is solid, the manuscript would benefit from a broader discussion of evolutionary conservation and

potential off-target ecological effects.

Strengths:

The main strengths of the study lie in its mechanistic clarity and experimental rigor. The identification of a betulin-binding single threonine residue was supported by (1) site-directed mutagenesis and (2) functional assays. These experiments strongly support the specificity of action. Furthermore, the use of comparative analyses between aphids and fruit flies demonstrates an important effort to explore species specificity, and the integration of quantitative data further enhances the robustness of the conclusions.

Weaknesses:

There are several important limitations that need to be addressed. The manuscript does not explore whether the observed sensitivity to betulin reflects a broadly conserved feature of GABA receptors across animal lineages or a more lineage-specific adaptation. This evolutionary context is crucial for understanding the broader significance of the findings.

In addition, while the compound's aphicidal effect is well established, the potential for off-target effects in non-target organisms - especially vertebrates - remains unaddressed, despite prior evidence that betulin interacts with mammalian GABAa receptors. There is little discussion on the ecological or environmental safety of exogenous betulin application, such as persistence, degradation, or exposure risks.

We sincerely thank the reviewer for the time and effort dedicated to our manuscript's detailed review and assessment. The revision suggestions were constructive, and we have provided a point-by-point response to address them.

(a) Briefly introduce the evolutionary conservation of GABA receptors has been added in the Introduction (Lines 90-98): Previous study has proposed that vertebrate and human GABR genes maintain a broad and conservative gene clustering pattern, while in invertebrates, this pattern is missing, indicating that these gene clusters formed early in vertebrate evolution and were established after diverging from invertebrates. Notably, invertebrates each possess a unique GABR gene pair, which are homologous with human GABR α and β subunits, suggesting that the existing GABR gene cluster evolved from an ancestral α - β subunit gene pair (Tsang et al. 2006). During the coevolution of plants and insects, the duplications and amino acid substitutions in GABR may be beneficial for the adaptation to insecticides and terpenoid compounds (Guo et al. 2023).

(b) We have added the discussion of the sensitivity of GABA receptor to betulin in Discussion section (Lines 491-501): Studies on key amino acids that are crucial for GABR function has primarily focused on transmembrane regions. For instance, based on the mutational research and Drosophila GABR modeling approach, multiple key amino acids were identified as insecticide targets in the transmembrane domain (Nakao and Banba 2021). Guo et al. proposed that amino acid substitutions in the transmembrane domain 2 contribute to terpenoid insensitivity during plant-insect coevolution (Guo et al. 2023). However, these studies have neglected the extracellular domain. Our study signified that betulin targets the THR228 site in the extracellular domain of MpGABR, which is conserved only in the Aphididae family. Therefore, betulin is speculated to be a specific insecticidal substance evolved by plants in response to aphid infestation. Besides, further verification is needed to determine whether betulin is toxic to other insect species.

(c) The discussion of potential ecological risks of deploying betulin as a bioinsecticide has been elaborated in our manuscript (Lines 538-553): The development of bioinsecticides should not only focus on the toxic effects of active substance on target organisms, but also on their influence on the ecosystem (Haddi et al. 2020). Although our results indicate that betulin has specific toxicity to aphids, previous studies have reported that betulin and its derivatives had effects on Plutella xylostella L. (Huang et al. 2025), Aedes aegypti (de Almeida Teles et al. 2024), and Drosophila melanogaster (Lee and Min 2024). Therefore, further research is needed to determine whether there are other insecticidal mechanisms or off target effects of betulin. Additionally, betulin exhibits a wide range of pharmacological activities (Amiri et al. 2020), which have been used to treat various diseases, such as cancer (Lv 2023), glioblastoma (Li et al. 2022), inflammation (Szlasa et al. 2023) and hyperlipidemia (Tang et al. 2011). Before applying betulin in the field, it is necessary to fully verify and consider whether betulin has any impact on farmers' health. Furthermore, will betulin cause residue or diffusion in the process of field application? Will long-term application promote the evolution of resistance to aphids or other insects? These issues also need further experimental verification. In summary, before any field application, further research is needed on the environmental behavior, degradation process, and safety of betulin.

Reviewer #1 (Recommendations for the authors):

(1) L28 Provide the full name of MST.

Thanks for your suggestion. The full name of MST, microscale thermophoresis, has been supplied.

(2) L87 in the Order Hemiptera.

Thanks for your suggestion. Corrected.

(3) L99 "Leaf bioassay" would be better to differentiate the greenhouse and field bioassays.

Thanks for your suggestion. Corrected.

(4) L104 It should be 7 doses, including the "0 mg/mL" control.

Thanks for your suggestion. Corrected.

(5) L104 Since the LC50 of pymetrozine is 1.0612 mg/mL, a wider range of doses should have been tested compared to the dose range of betulin.

Thanks for your comment.

(a) Firstly, seven doses (0, 0.0625, 0.125, 0.25, 0.5, 1, and 2 mgmL^-1) were set to calculate the LC50 of betulin and pymetrozine. Since the LC50 values of betulin and pymetrozine are 0.1641 and 1.0612 mgmL^–1, respectively, which are within the set range, indicating that the set dose range is reasonable and the LC50 values of betulin and pymetrozine are reliable.

(b) To compare the control effects of betulin and pymetrozine against M. persicae, LC50 of betulin (0.1641 mgmL^-1) and pymetrozine (1.0612 mgmL^-1) were used to treat M. persicae.

(6) L109 Greenhouse and field bioassays.

Thanks for your suggestion. Corrected.

(7) L112 Tween-80 and acetone in L103. Keep the order consistent throughout the manuscript.

Thanks for your suggestion. Corrected.

(8) L122 Mortality was recorded at 1, 5, 9, and 14 days after treatment. Revise the other similar mistakes throughout the manuscript (e.g. L250, L254, L255, L256, L259, etc.).

Thanks for your suggestion. Corrected.

(9) L126 apterous instead of wingless (keep a consistent expression).

Thanks for your suggestion. Corrected.

(10) L138 Primer Premier?

Thanks for your comment. Corrected.

(11) L141 Add RPS18 primers in Table S2.

Thanks for your comment. Corrected.

(12) L155 MEGA7 vs. MEGAX (as described in the Figure 4 caption).

Thanks for your comment. Corrected.

(13) L156 NJ method vs. ML method (as described in the Figure 4 caption).

Thanks for your comment. Corrected.

(14) L157 2.7. RNAi assay (Remove "In vitro" and re-number the following M&M sections accordingly).

Thanks for your comment. Corrected.

(15) L163 Add dsGFP primers in Table S2.

Thanks for your comment. Corrected.

(16) L166 apterous instead of wingless (keep a consistent expression).

Thanks for your comment. Corrected.

(17) L172 Add the source of pET-B2M vector.

pET-B2M vector was obtained from BGI (Shenzhen, China), which has been added in our manuscript (Line 194).

(18) L195 coding sequence instead of cDNA.

Thanks for your comment. Corrected.

(19) L198 the mutations of R224A ...

Thanks for your comment. Corrected.

(20) L199 TYR), or T228R ...

Thanks for your comment. Corrected.

(21) L211 and 90 ng.

Thanks for your comment. Corrected.

(22) L213 genomic DNA instead of gDNA, because gDNA may be confused in the context of sgRNA.

Thanks for your suggestion. Corrected.

(23) L253 (Fig. 1A-B).

Thanks for your comment. Corrected.

(24) L268 Explain why these 15 DEGs were selected for qRT-PCR.

Thanks for your comment. These 15 DEGs were randomly selected and act as representative DEGs with different expression levels. The reason for selection of these 15 DEGs were added in the manuscript (Lines 295-296).

(25) L287 What about GABRB? It has a TM domain.

GABRB refers to “gamma-aminobutyric acid receptor subunit beta-like” annotated on NCBI. Theoretically, it should contain four transmembrane structural domains, while it has only one, indicating that it is incomplete.

(26) L297 Add dsGFP as another control group.

Thanks for your comment. Corrected.

(27) L299 increased by 30.44% (Remove a comma).

Thanks for your comment. Corrected.

(28) L308 XM_022318019.1 (or protein accession number with XP_).

Thanks for your comment. Corrected.

(29) L338 that THR228 was conserved only in Hemiptera.

Thanks for your comment. Since our original intention was to emphasize that THR228 is the only conserved among the four key amino acid residues, after careful consideration, we retained the expression "only THR228".

(30) L342 or T228R.

Thanks for your comment. Corrected.

(31) L382 Is pyrhidone a general name for pymetrozine?

Thanks for your comment. Corrected.

(32) L450 Remove "and so on".

Thanks for your comment. Corrected.

(33) Figure 1D: Remove "Environment friendly". Replace the plant pot image on the right side with the one sprayed with pymetrozine, like the one in Figure 1F.

Thanks for your comment. 

(a) "Environment friendly" in Figure 1D has been removed.

(b) We have attempted to modify the Figure 1D according to your suggestion. However, the modified Figure 1D is similar to Figure 1F and appears monotonous. Therefore, we have retained the original framework of Figure 1D.

(34) Figure 2E 111036117 and 111041856 are in different IDs (XM_). I suggest keeping GeneID in Figure 2E and Table S2, as shown in Table S4.

Thanks for your comment. Corrected.

(35) Figure 2H: Add unit of the heatmap values. Or just add the title (e.g., expression level) on top of the bar.

Thanks for your comment. Corrected.

(36) Figure 3A: Add "aa" next to 700.

Thanks for your comment. Corrected.

(37) Figure 3E-G: Revise the tick marks on Y-axis: 0.0, 0.5, 1.0, and 1.5.

Thanks for your comment. Corrected.

(38) Figure 5C: Remove "1" and move "WT" up to the position where "1" was.

Thanks for your comment. Corrected.

(39) Figure 5D: Revise the tick marks on the Y-axis: 0.0, 0.5, 1.0, and 1.5.

Thanks for your comment. Corrected.

(40) Figure 5E: Remove the decimal. (e.g. 5 uM, 10 uM, 20 uM, etc.).

Thanks for your comment. Corrected.

(41) Figure 6B: What are the numbers next to the amino acid sequences? Provide the information in the figure caption.

Thanks for your comment. The numbers next to the amino acid indicates the site of the last residue of the key amino acids, which was supplied in the figure caption.  

(42) Figure 6D: Revise the tick marks on the Y-axis: 0.0, 0.5, 1.0, and 1.5. The X-axis title should be betulin (see Figure 5D). In the figure caption at the 5th row from the top, R244A should be R224A.

Thanks for your comment. Corrected.

(43) Figure 7E: R122T (not R1272T).

Thanks for your comment. Corrected.

(44) Supplementary Figure 1: It should be Figure S1. Add dsGFP in the figure caption.

Thanks for your comment. Corrected.

(45) Figure S2: What are the two pink bars and the other bars in brown or blue? Add an appropriate explanation in the figure caption.

Thanks for your comment. Corrected.

(46) Table S1: r square?

Thanks for your comment. It is “r square” and corrected.

(47) Table S2: (a) Add horizontal lines to separate qPCR, RNAi, cloning, and heterologous expression from each other (b) Replace XM_022318017.1 and XM_022318019.1 with their corresponding GeneIDs, as shown in Table S4. (c) AK340444.1 is a sequence from another aphid (Acyrthosiphon pisum)-Revise it. (d) In the cloning primers, place MpGABR first, followed by MpGABRAP and MpGABRB, as shown in the manuscript and Table S5. (e) Also, in the cloning primers, MpGABRB and MpGABRAP use reverse primers without stop codon, while MpGABR uses stop codon (TCA = TGA in reverse)-Revise it accordingly. Otherwise, provide the reason.

Thanks for your comment. Corrected.

(48) Table S3: (a) Add "Drosophila melanogaster" and the target sequence ID in the table caption. Is it KF881792.1, as shown in Table S6? (b) Align the sequences to the left side. 

Thanks for your comment. 

(a) The GenBank number of target sequence is KF881792.1 (Drosophila melanogaster). We have added this information in the Table S3 note.

(b) It has been adjusted according to your suggestion.

(49) Table S5: (a) Replace the accession numbers with GeneID, as shown in Table S4. K340444.1 is a sequence from another aphid (Acyrthosiphon pisum), (b) Coding sequences with stop codon are 2082, 357, and 753, respectively, while the sequences without stop codon are 2079, 354, and 750, respectively. The lengths of the deduced amino acids are 693, 118, and 250. Revise accordingly.

Thanks for your comment. Corrected.

(50) Table S6: (a) Use GenBank No for protein sequences. There is no Gene ID in this table. (b) Order (instead of Class). (c) See my comment on the phylogenetic analysis above.

Thanks for your comment. Corrected.

(51) Table S7 (a) Add unit under "Binding Energy". (b) There are two ALA226 [Alkyl] with two different distances. (c) PHE227 at the bottom should be THR228?

Thanks for your comment.

(a) The unit of "Binding Energy" was kcalmol^–1, and it was added in the table caption.

(b) Refer to Figure 6A, there were two Alkyl interaction between ALA226 and betulin. Therefore, there were two ALA226 [Alkyl] with two different distances.

(c) Similarly, there were two Pi-Alkyl interactions between PHE227 and betulin. Thus, there were two rows of PHE227 in the table.

(52) Table S9 (a) R117T should be R122T. (b) r square?

Thanks for your comment. a and b Corrected.

Reviewer #2 (Recommendations for the authors):

(1) Introduction

(a) It lacks a deeper biological and evolutionary framing of the GABA receptor system. As GABA receptors are highly conserved across animal taxa, the observed interaction between betulin and the aphid GABA receptor could have broader implications. This possibility is not addressed in the current version, which limits the reader's appreciation of the relevance of this mode of action.

(b) Previous reports of betulin activity in mammalian systems are not mentioned in the introduction, even though they are directly relevant to concerns about off-target toxicity. Therefore, the introduction should be revised to (i) briefly introduce the evolutionary conservation of GABA receptors, and (ii) acknowledge that betulin may affect a broader range of organisms, which sets up the need for caution in its application.

Thanks for your important suggestions.

(a) Briefly introduce the evolutionary conservation of GABA receptors has been added in the Introduction (Lines 90-98): Previous study has proposed that vertebrate and human GABR genes maintain a broad and conservative gene clustering pattern, while in invertebrates, this pattern is missing, indicating that these gene clusters formed early in vertebrate evolution and were established after diverging from invertebrates. Notably, invertebrates each possess a unique GABR gene pair, which are homologous with human GABR α and β subunits, suggesting that the existing GABR gene cluster evolved from an ancestral α - β subunit gene pair (Tsang et al. 2006). During the coevolution of plants and insects, the duplications and amino acid substitutions in GABR may be beneficial for the adaptation to insecticides and terpenoid compounds (Guo et al. 2023).

(b) The possible effects of betulin on a broader range of organisms have been acknowledged in the Introduction section (Lines 68-77): An immune stimulant, Ir-Bet, was prepared using iridium complex and betulin, which evoked ferritinophagy-enhanced ferroptosis, thereby activating anti-tumor immunity (Lv 2023). The anti-inflammatory effect of betulin has been reported in macrophages at lymphoma site in mice (Szlasa et al. 2023). Betulin has been found to improve hyperlipidemia and insulin resistance and decrease atherosclerotic plaques by inhibiting the maturation of sterol regulatory element-binding protein (Tang et al. 2011). Besides, betulin and its derivatives have been found to exhibit insecticidal activity against Plutella xylostella L. (Huang et al. 2025), Aedes aegypti (de Almeida Teles et al. 2024), and Drosophila melanogaster (Lee and Min 2024).

(c) At the end of the introduction, we remind that betulin should be used with caution (Lines 111-112): However, given that betulin may affect a wider range of organisms, it should be used with caution.

(2) Method

Number of biological replicates in all assays and justification of thresholds used for significance in RNAi and survival experiments are not addressed in the manuscript.

Thanks for your careful reading. We have checked Materials and Methods section and added corresponding number of biological replicates in all assays. Besides, the p-values for the corresponding significance analyses of RNAi and survival experiments have been added to our Manuscript.

(2)  Discussion

(a) Consistent with the comments on the Introduction, the absence of discussion on (i) the evolutionary conservation of GABA receptor sensitivity to betulin, (ii) potential off-target effects in non-target insects and vertebrates (if so, this cannot be use for "eco-friendly pesticide" as the authors stated in the manuscript), and (iii) ecological risks associated with the exogenous application of betulin limits both the interpretive depth and applied relevance of the study.

(b) To strengthen the Discussion, the authors should consider addressing: (i) whether the observed sensitivity reflects a conserved pharmacological vulnerability across animal taxa or a lineage-specific adaptation; (ii) the potential ecological risks of deploying betulin as a bioinsecticide, and (iii) the need for future research into the environmental fate, degradation, and safety profile of betulin prior to any field-level application.

Thank you for your valuable comments.

(a) We have added the discussion of the sensitivity of GABA receptor to betulin in Discussion section (Lines 491-501): Studies on key amino acids that are crucial for GABR function has primarily focused on transmembrane regions. For instance, based on the mutational research and Drosophila GABR modeling approach, multiple key amino acids were identified as insecticide targets in the transmembrane domain (Nakao and Banba 2021). Guo et al. proposed that amino acid substitutions in the transmembrane domain 2 contribute to terpenoid insensitivity during plant-insect coevolution (Guo et al. 2023). However, these studies have neglected the extracellular domain. Our study signified that betulin targets the THR228 site in the extracellular domain of MpGABR, which is conserved only in the Aphididae family. Therefore, betulin is speculated to be a specific insecticidal substance evolved by plants in response to aphid infestation. Besides, further verification is needed to determine whether betulin is toxic to other insect species.

(b) The discussion of potential ecological risks of deploying betulin as a bioinsecticide has been elaborated in our manuscript (Lines 538-551): The development of bioinsecticides should not only focus on the toxic effects of active substance on target organisms, but also on their influence on the ecosystem (Haddi et al. 2020). Although our results indicate that betulin had specific toxicity to aphids, previous studies have reported that betulin and its derivatives had effects on Plutella xylostella L. (Huang et al. 2025), Aedes aegypti (de Almeida Teles et al. 2024), and Drosophila melanogaster (Lee and Min 2024). Therefore, further research is needed to determine whether there are other insecticidal mechanisms or off target effects of betulin. Additionally, betulin exhibits a wide range of pharmacological activities (Amiri et al. 2020), which have been used to treat various diseases, such as cancer (Lv 2023), glioblastoma (Li et al. 2022), inflammation (Szlasa et al. 2023) and hyperlipidemia (Tang et al. 2011). Before applying betulin in the field, it is necessary to fully verify and consider whether betulin has any impact on farmers' health. Furthermore, will betulin cause residue or diffusion in the process of field application? Will long-term application promote the evolution of resistance to aphids or other insects? These issues also need further experimental verification. 

(c) Additionally, at the end of the Discussion, we remind that more research is needed before any field application of betulin (Lines 551-553): In summary, before any field application, further research on the environmental behavior, degradation process, and safety of betulin is needed.

Reference

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de Almeida Teles AC, dos Santos BO, Santana EC, Durço AO, Conceição LSR, Roman Campos D, de Holanda Cavalcanti SC, de Souza Araujo AA, dos Santos MRV. 2024.

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Li Y, Wang Y, Gao L, Tan Y, Cai J, Ye Z, Chen A, Xu Y, Zhao L, Tong S, Sun Q, Liu B, Zhang S, Tian D, Deng G, Zhou J, Chen Q. 2022. Betulinic acid self-assembled nanoparticles for effective treatment of glioblastoma. Journal of Nanobiotechnology 20: 39.

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Lund IV, Hu Y, Raol YH, Benham RS, Faris R, Russek SJ, Brooks Kayal AR. 2008. BDNF selectively regulates GABAA receptor transcription by activation of the JAK/STAT pathway. Science Signaling 1: ra9.

Lv M, Zheng Y, Wu J, Shen Z, Guo B, Hu G, Huang Y, Zhao J, Qian Y, Su Z, Wu C, Xue X, Liu H, Mao Z. 2023. Evoking ferroptosis by synergistic enhancement of a cyclopentadienyl iridium-betulin immune agonist. Angewandte Chemie International Edition 62: e202312897.

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Szlasa W, Ślusarczyk S, Nawrot Hadzik I, Abel R, Zalesińska A, Szewczyk A, Sauer N, Preissner R, Saczko J, Drąg M, Poręba M, Daczewska M, Kulbacka J, Drąg Zalesińska M. 2023. Betulin and its derivatives reduce inflammation and COX-2 cctivity in macrophages. Inflammation 46: 573-583.

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Author response:

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

Reviewer #1 (Public review):

Summary:

The authors study the steady-state solutions of ODE models for molecular signaling involving ligand binding coupled to multi-site phosphorylation at saturating ligand concentrations. Although the results are in principle general, the work highlights the receptor tyrosine kinases (RTK) as model systems. After presenting previous ODE model solutions, the authors present their own "kinetic sorting" model, which is distinguished by ligand-induced phosphorylationdependent receptor degradation and the property that every phosphorylation state is signaling competent. The authors show that this model recovers the two types of non-monotonicity experimentally reported for RTKs: maximum activity for intermediate ligand affinity and maximum activity for intermediate kinase activity.

The main contribution of the work is in demonstrating that their model can capture both types of non-monotonicity, whereas previous models could at most capture non-monotonicity of ligand binding.

Strengths:

The question of how energy-dissipating, and thus non-equilibrium, molecular systems can achieve steady-state solutions not accessible to equilibrium systems is of fundamental importance in biomolecular information processing and self-organization. Although the authors do not address the energy requirements of their non-equilibrium model, their comparative analysis of different alternative non-equilibrium models provides insight into the design choices necessary to achieve non-monotonic control, a property that is inaccessible at equilibrium.

The paper is succinctly written and easy to follow, and the authors achieve their aims by providing convincing numerical solutions demonstrating non-monotonicity over the range of parameter values encompassing the biologically relevant regime.

Weaknesses:

(1) A key motivating framework for this work is the argument that the ability to tune to recognize intermediate ligand affinities provides a control knob for signal selection that is available to nonequilibrium systems. As such, this seems like a compelling type of ligand selectivity, which is a question of broad interest. However, as the authors note in the results, the previously published "limited signaling model" already achieves such non-monotonicity in ligand binding affinity. The introduction and abstract do not clearly delineate the new contributions of the model.

We thank the reviewer for this comment. We apologize for any unclear language on our part. The purpose of our work was not to identify the unique reaction scheme to obtain nonmonotonic dependence of network activity on ligand affinity and kinase activity. Rather, we were interested in exploring how such a dependence could arise from the interplay between two ubiquitous network motifs (multisite phosphorylation and active receptor degradation). Notably, as the reviewer later points out, previous models that incorporate only multisite phosphorylation only capture the non-monotonic dependence of network activity on ligand affinity and not kinase/phosphatase activity. We have now clarified this in the abstract (lines 14-16) and the introduction (lines 55-59). 

The novel benefit of the model introduced by the authors is that it also achieves a nonmonotonic response to kinase activity. Because such non-monotonicity is observed for RTK, this would make the authors' model a better fit for capturing RTK behavior. However, the broad significance of achieving non-monotonicity to kinase activity is not motivated or supported by empirical evidence in the paper. As such, the conceptual significance of the modified model presented by the authors is not clear.

We thank the reviewer for this comment. We agree that the ability of our model to reproduce non-monotonic dependence on kinase/phosphatase activity was not sufficiently motivated in the original submission. We have now added a brief mention of the biological motivation for nonmonotonic kinase activity in the discussion (lines 229-247) to describe the potential biological significance of this behavior. In particular, non-monotonic kinase/phosphatase dependence may act as a safeguard, filtering out signaling cells with abnormally elevated kinase activity or suppressed phosphatase activity. In the presence of non-monotonic dependence on network activity, downstream signaling would remain contingent on extracellular cues, and cells with extreme kinase/phosphatase imbalances would fail to signal. This could prevent persistent, cueindependent activation, an especially important protective mechanism in pathways regulating metabolically taxing functions such as growth, proliferation, or mounting immune responses. Although direct experimental evidence for the widespread use of this mechanism is currently scarce, our motivation is supported both by the presence of similar regulatory behaviors of phosphatases which arise through distinct mechanisms (such as CD45 in T-cell receptor signaling, (Weiss, 2019)), but highlight the potential biological use of this strategy and by theoretical work on phosphorylation-dephosphorylation cycles, which demonstrates a similar effect in more general settings (Swain, 2013).

(2) Whereas previous models used in the literature are schematized in Figure 1, the model proposed by the authors is missing (see line 97 of page 3). Without the schematic, the text description of the model is incomplete.

We thank the reviewer for identifying this oversight, it has been corrected. See Figure 3 in the new text. 

(3) The authors use the activity of the first phosphorylation site as the default measure of activity. This choice needs to be justified. Why not use the sum of the activities at all sites?

We thank the reviewer for this comment. We in fact study all sites (Figure 5A in the resubmitted manuscript). Notably, as suggested by the reviewer, the concentration of the first site is indeed represented by the sum of concentrations of all phosphorylated species. The concentration of the 2^nd site is represented by the sum of concentrations of all species except for the first one and so on (lines 153-155). 

Reviewer #2 (Public review):

Summary:

In classical models of signaling networks, the signaling activity increases monotonically with the ligand affinity. However, certain receptors prefer ligands of intermediate affinity. In the paper, the authors present a new minimal model to derive generic conditions for ligand specificity. In brief, this requires multi-site phosphorylation and that high-anity complexes be more prone to degrade. This particular type of kinetic discrimination allows for overcoming equilibrium constraints.

Strengths:

The model is simple, and it adds only a few parameters to classical generic models. Moreover, the authors vary these additional parameters in ranges based on experimental observations. They explain how the introduction of these new parameters is essential to ligand specificity. Their model quantitatively reproduces the ligand specificity of a certain receptor. Finally, they provide a testable prediction.

Weaknesses:

The naming of certain variables may be confusing to readers.

We apologize for the confusion due to unclear presentation. We have clarified our definitions throughout the manuscript. 

Reviewer #1 (Recommendations for the authors):

(1) The abstract and introduction present the problem as if this model is solving the fundamental problem of non-monotonic dependence on ligand affinity. However, as the authors noted in their results, this problem has already been solved by a previous phosphorylation model with N-state degradation. What the authors' new model achieves is the additional experimentally observed non-monotonicity of kinase activity dependence. The abstract and introduction should be changed to reflect the actual novel contributions and also to motivate the biological significance of non-montonic kinase activity dependence.

We thank the reviewer for this comment. We apologize for any unclear language on our part. The purpose of our work was not to identify the unique reaction scheme to obtain nonmonotonic dependence of network activity on ligand affinity and kinase activity. Rather, we were interested in exploring how such a dependence could arise from two ubiquitous network motifs (multisite phosphorylation and active receptor degradation). Notably, as the reviewer later points out, previous models that incorporate only multisite phosphorylation only capture the nonmonotonic dependence of network activity on ligand affinity and not kinase/phosphatase activity. We have now clarified this in the abstract (lines 14-16) and the introduction (lines 55-59). We have also provided biological motivation behind nonmonotonic kinase activity dependance (lines 229-247). 

(2) It is important to show (in the supplemental materials if needed) that the closest equilibrium analog to the model (for example, reversible rate constants from each of the activated states to an inactive state) does not achieve non-monotonicity with ligand affinity.

We have added a model in the supplementary materials that represents a detailed balance Markov chain. In the model, we imagine that ligand bound receptors undergo a series of equilibrium transitions, all characterized by the same activation and inactivation rate. We show that at saturating ligand levels, the signaling output only depends on the ratio of the activation to the inactivation rate (i.e., the thermodynamic stability of the active site) (lines 466-488).

(3) Schematics for earlier models are described in Figure 1. However, no schematic for the actual model proposed by the authors is shown. This should be added as a subpanel to Figure 1.

We thank the reviewer for identifying our omission of our model schematic. We have included our model schematic as its own figure (Figure 3).

(4) Minor: Figure 1 is referred to as Figure?? In line 97 of page 3.

We thank the reviewer for identifying this error, it has been corrected. 

Reviewer #2 (Recommendations for the authors):

(1) There is an inconsistency between Figure 2(a) and Equation (1), it suggests that p_N is \omega^N/(\omega+\delta)^N. This makes more sense with the model defined in the supplementary material.

We thank the reviewer for identifying this error. Equation (1) has been updated to reflect the correct relationship.

(2) The figure presenting the model of the authors appears to be missing.

We thank the reviewer for identifying this error, it has been corrected (Figure 3 in the new manuscript). 

(3) The authors describe phosphorylation as irreversible in the intro, but then consider reversible phosphorylation in their model, which may be confusing to readers.

We thank the reviewer for identifying this source of possible confusion. We have clarified that dephosphorylation is taken to be a distinct irreversible reaction, see lines 105 - 112.

(4) The authors reuse similar names, e.g., network activity, kinase activity, signaling activity, activity. This is confusing.

We apologize for the confusion. We note that, within the context of our model, there are important distinctions between signaling activity (the amount of signaling competent receptors) and kinase activity (value corresponding to the phosphorylation rate). We have attempted to use these different terms correctly and are happy to make clarifying corrections if there are any places where a term is misused.  

(5) Several parameters are defined only in the captions of the figures, such as \beta and \rho.

We thank the reviewer for identifying this omission, we have added the definitions of beta and rho to the main text (see line 129). 

(6) The sentence at line 137 lacks some words: "Below, we kinetic...".

We thank the reviewer for identifying this error, we have added the missing words (“Below, we show how kinetic…”).

(7) The sentence at line 183 lacks some words: "When kinase activity...".

We thank the reviewer for identifying this error. We have now corrected it. 

(8) Figure 5 is very small.

We will work with the production team to increase the size of this figure.

Author response:

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

Reviewer #1 (Public review):  

Summary:

The manuscript by Cupollilo et al describes the development, characterization, and application of a novel activity labeling system; fast labelling of engram neurons (FLEN). Several such systems already exist but this study adds additional capability by leveraging an activity marker that is destabilized (and thus temporally active) as well as being driven by the full-length promoter of cFos. The authors demonstrate the activity-dependent induction and time course of expression, first in cultured neurons and then in vivo in hippocampal CA3 neurons after one trial of contextual fear conditioning. In a series of ex vivo experiments, the authors perform patch clamp analysis of labeled neurons to determine if these putative engram neurons differ from non-labelled neurons using both the FLEN system as well as the previously characterized RAM system. Interestingly the early labelled neurons at 3 h post CFC (FLEN+) demonstrated no differences in excitability whereas the RAMlabelled neurons at 24h after CFC had increased excitability. Examination of synaptic properties demonstrated an increase in sEPCS and mEPSC frequencies as well as those for sIPSCs and mIPSCs which was not due to a change in the mossy fiber input to these neurons.

Strengths:

Overall the data is of high quality and the study introduces a new tool while also reassessing some principles of circuit plasticity in the CA3 that have been the focus of prior studies.

Weaknesses:

No major weaknesses were noted.

Reviewer #2 (Public review): 

Summary: 

Cupollilo et al. investigate the properties of hippocampal CA3 neurons that express the immediate early gene cFos in response to a single foot shock. They compare ex-vivo the electrophysiological properties of these "engram neurons" labeled with two different cFos promoter-driven green markers: Their new tool FLEN labels neurons 2-6 h after activity, while RAM contains additional enhancers and peaks considerably later (>24 h). Since the fraction of labeled CA3 cells is comparable with both constructs, it is assumed (but not tested) that they label the same population of activated neurons at different time points. Both FLEN+ and RAM+ neurons in CA3 receive more synaptic inputs compared to non-expressing control neurons, which could be a causal factor for cFos activation, or a very early consequence thereof. Frequency facilitation and E/I ratio of mossy fiber inputs were also tested, but are not different in both cFos+ groups of neurons. One day after foot shock, RAM+ neurons are more excitable than RAM- neurons, suggesting a slow increase in excitability as a major consequence of cFos activation.

Strengths: 

The study is conducted to high standards and contributes significantly to our understanding of memory formation and consolidation in the hippocampus. Modifications of intrinsic neuronal properties seem to be more salient than overall changes in the total number of (excitatory and inhibitory) inputs, although a switch in the source of the synaptic inputs would not have been detected by the methods employed in this study

Weaknesses: 

With regard to the new viral tool, a direct comparison between the new tool FLEN and existing cFos reporters is missing. 

Reviewer #1 (Recommendations for the authors):

I have only minor suggestions for the authors to consider. 

(1) In the in vitro characterization, the percentage of labelled neurons seems very low after a powerful and prolonged activation. It was somewhat surprising and raised the question of how accurately the FLEN construct reflects endogenous cFOS activity. Could the authors speak to this?

The reviewer is correct that the level of FLEN positive neurons, as compared to mCherry positive neurons, is low as compared to studies using viral infection with RAM vectors in neuronal cultures (Sorensen et al, 2016, Sun et al, 2020), which is around 70-80% following chemical stimulation. The authors do not provide evidence however for a comparison with endogenous c-Fos activity in cell cultures. The reason for a discrepancy in the effect of chemical stimulation of cultured neurons is not clear, but may depend on culture conditions which may vary between labs. 

FLEN was constructed using a mouse c-Fos promoter (-355 to +109) (Cen et al, 2003). To answer the reviewer’s question we performed an additional experiment in cultured neurons in which we found that 77.1 % of FLEN positive neurons were also c-fos positive neurons (using immunocytochemistry).

(2) The authors compare the two labelling strategies and interpret their data with the presumption that both label a similar set of active neurons. This is particularly relevant when they suggest there might be a progressive increase in the excitability of active neurons with time. This is certainly a possibility, but the authors should also consider other possibilities that the two markers might label different populations of neurons. For example, if they require different thresholds for activation, it is possible that one is more sensitive to activity than the other. As these are unknown variables the authors should temper the interpretation accordingly.

Indeed, the reviewer is correct that this limitation should be discussed. We have added this as a point of discussion in the text (line 355-358). In the article describing the RAM strategy (Sorensen et al, 2016) the authors use RAM to label DG neurons activated during an experience in a context A (Figure 4). Exploiting the fact that engram cells are re-activated when the animal is re-exposed to the same environment of training (memory recall), they performed c-Fos staining 90 minutes following either context A or context B re-exposure. The RAM-c-Fos overlap percentage was higher in A-A rather than A-B (A-A was a bit more than 20%). This means that RAM has captured a group of cells during training that, at least in part, were re-activated during recall. This could in part support the assumption that RAM and c-Fos share a certain overlap. Of course, this was done in DG, while we worked in CA3. In addition, both strategies label in their great majority c-Fos+ neurons (see above answer to point #1). This can not completely rule out the possibility that FLEN and RAM label partly distinct population of activated cells. 

(3) An increase in the frequency of synaptic events is observed in neurons labelled with both markers. The authors propose that this may be due to an increase in synaptic contacts based on prior studies. However, as this is the first functional assessment why not consider changes in release probability as a mechanism for this finding? 

We have added this as a possibility in the text (line 362-363).

(4) It would be useful to include plots of the average frequency of m/sEPSCs and m/sIPSCs in Figures 4 and 5. These figures could also be combined into a single figure.

We agree with the reviewer that figure 4 and 5 could be merged into a single figure. In the revised version, figure 5A becomes panel C in figure 4. Text and figure descriptions were adjusted accordingly.

Reviewer #2 (Recommendations for the authors): 

(1) Abstract, line 24: "In contrast, FLEN+ CA3 neurons show an increased number of excitatory inputs." RAM+ neurons also show an increased number of excitatory inputs, so this is not "in contrast". Also, not just excitatory, but also inhibitory synaptic inputs are more numerous in cFos+ neurons. Please improve the summary of your findings.

“In contrast” referred to the fact that FLEN+ neurons do not show differences in excitability as compared to FLEN- neurons, as mentioned in the previous sentence. We now provide a more explicit sentence to explain this point: “On the other hand, like RAM+ neurons, FLEN+ CA3 neurons show an increased number of excitatory inputs.”

(2) Novel tool: Destabilized cFos reporters were introduced 23 years ago and are also part of the TetTag mouse. I am not sure that changing the green fluorescent protein to a different version merits a new acronym (FLEN). To convince the readers that this is more than a branding exercise, the authors should compare the properties (brightness, folding time, stability) of FLEN to e.g. the d2EGFP reporter introduced by Bi et al. 2002 (J Biotechnol. 93(3):231) and show significant improvements.

We thank the reviewer for this comment which compelled us to evaluate the features of other tools used to label neurons activated following contextual fear conditioing. The key properties of FLEN as compared to other tools used to label engrams is that: (i) it is a viral tool, as opposed to transgenic mice, (ii) a c-fos promoter drives the expression of a brightly fluorescent protein allowing their identification ex vivo for functional analysis, (iii) the fluorescent protein is rapidly destabilized, providing the possibility to label neurons only a few hours after their activation by a behavioural task.

We did not find any viral tools providing the possibility to label c-fos activated neurons for functional assesment. We have not been able to find references for the use of the d2EGFP reporter introduced by Bi et al. 2002 in a behavioural context. One of the major difference and improvement is certainly the brightness of ZsGreen. In cell cultures, ZsGreen1 showed a 8.6-fold increase in fluorescence intensity as compared with EGFP (Bell et al, 2007).

Amongst tools with comparable properties, eSARE was developed based on a synthetic Arc promoter driving the expression of a destabilized GFP (dEGFP) (Kawashima et al 2013). We initially used ESARE–dGFP but unfortunately, in our experimental conditions we found that the signal to noise ratio was not satisfactory (number of cells label in the home cage vs. following contextual fear conditining).

We developed a viral tool to avoid the use of transgenic reporter lines which require laborious breeding and is experimentally less flexible. Nevertheless, many transgenic mice based on the expression of fluorescent proteins under the control of IEG promoters have been developed and used. Some of these mice show a time course of expression of the transgene which is comparable to FLEN. For instance, in organotypic slices from Tet-Tag mice, the time course of expression of EGFP slices follows with a small delay endogenous cFOS expression, and starts decaying after 4 hours (Lamothe-Molina et al, 2022). However, the fluorescence was too weak to visualize neurons in the slice (Christine Gee, personal communication), and imaging is perfomed after immunocytochemistry against GFP. 

Therefore, we feel that the name given to the FLEN strategy is legitimate. The features of the FLEN strategy were summarized in the discussion (Lines 318-322).

(3) Line 214: "...FLEN+ CA3 PNs do not show differences in [...] patterns of bursting activity as compared to control neurons." It looks quite different to me (Figure 3E). Just because low n precludes meaningful statistical analysis, I would not conclude there is no difference.

We agree with the reviewer that the data in Figure 3E are not conclusive due to small sample size, which limits the reliability of statistical comparison. Additionally, the classification of bursting neurons is highly dependent on the specific criteria used, which vary considerably across the literature. To avoid overinterpretation or misleading conclusions, we decided to remove the panel E of Figure 3 showing the fraction of bursting neurons. Nevertheless, we draw the attention to the more robust and interpretable results: RAM⁺ neurons exhibit an increase in firing frequency and a distinct action potential discharge pattern, data which we believe are informative of altered excitability.

(4) Line 304: Remove the time stamp.

This was done.

(5) Line 334: "...results may be explained by an overall increased activity of CA1 neurons..." I don't understand - isn't CA1 downstream of CA3? 

The reviewer is correct that the sentence was misleading. We removed the reference to CA1, as it was more of a general principle about neuronal activity.

(6) Line 381: "resolutive", better use "sensitive". 

This was changed.

(7) Figure S3: Fear-conditioned animals were 3 days off Dox, controls only 2 days. As RAM expression accumulates over time off Dox, this is not a fair comparison.

We thank the reviewer for pointing out the incorrect reporting of the experimental design in Figure S3 panel A (bottom), which could lead to misinterpretation of results. In fact, the two groups of mice (CFC vs. HC) underwent all experimental steps in parallel. Specifically, both groups were maintained on and off Doxycycline for the same duration and received viral injection on the same day. 48 hours after Dox withdrawal, the CFC group was trained for contextual conditioning, while the HC group remained in the home cage in the holding room. All animals were thus sacrificed 72 hours after Dox removal. We have corrected the figure to accurately reflect this timeline.

(8) Please provide sequence information for c-cFos-ZsGreen1-DR. Which regulatory elements of the cFos promoter are included, is the 5' NTR included? This information is very important.

The information is now provided in the Methods section.

(9) Please provide the temperature during pharmacological treatments (TTX etc.) before fixation.

The pharmacological treatment was performed in the incubator at 37°C, this is now indicated in the methods.

Author response:

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

Reviewer #1(Public Review):

Major comments:

(1) Interpretation of key results and relationship between different parts of the manuscript. The manuscript begins with an information-transmission ansatz which is described as ”independent of the computational goal” (e.g. p. 17). While information theory indeed is not concerned with what quantity is being encoded (e.g. whether it is sensory periphery or hippocampus), the goal of the studied system is to *transmit* the largest amount of bits about the input in the presence of noise. In my view, this does not make the proposed framework ”independent of the computational goal”. Furthermore, the derived theory is then applied to a DDC model which proposes a very specific solution to inference problems. The relationship between information transmission and inference is deep and nuanced. Because the writing is very dense, it is quite hard to understand how the information transmission framework developed in the first part applies to the inference problem. How does the neural coding diagram in Figure 3 map onto the inference diagram in Figure 10? How does the problem of information transmission under constraints from the first part of the manuscript become an inference problem with DDCs? I am certain that authors have good answers to these questions - but they should be explained much better.

We are very thankful to the reviewer for highlighting the potential confusion surrounding these issues, in particular the relationship between the two halves of the paper – which was previously exacerbated by the length of the paper. We have now added further explanations at different points within the manuscript to better disentangle these issues and clarify our key assumptions. We have also significantly cut the length of the paper by moving more technical discussions to the Methods or Appendices. We will summarise these changes here and also clarify the rationale for our approach and point out potential disagreements with the reviewer.

Key to our approach is that we indeed do not assume the entire goal of the studied neural system (whether part of the sensory system or not) is to transmit the largest amount of information about the stimulus input (in the presence of noise). In fact, general computations, including the inference of latent causes of inputs, often require filtering out or ignoring some information in the sensory input. It is thus not plausible that tuning curves in general (i.e. in an arbitrary part of the nervous system) are optimised solely with regards to the criterion of information transmission. Accordingly we do not assume they are entirely optimised for that purpose. However, we do make a key assumption or hypothesis (which like any hypothesis might turn out to be partly or entirely wrong): that (1) a minimal feature of the tuning curve (its scale or gain) is entirely free to be optimised for the aim of information transmission (or more precisely the goal of combating the detrimental effect of neural noise on coding fidelity), (2) other aspects of the population tuning curve structure (i.e. the shape of individual tuning curves and their arrangement across the population) are determined by (other) computational goals beyond efficient coding. (Conceptually, this is akin to the modularization between indispensible error correction and general computations in a digital computer, and the need for the former to be performed in a manner that is agnostic as to the computations performed.) We have added two paragraphs in the manuscript which present the above rationale and our key hypothesis or assumption. The first of these was added to the (second paragraph of the) Introduction section, and the second is a new paragraph following Eq. 1 (which is about the gain-shape decomposition of the tuning curves, and the optimisation of the former based on efficient coding) of Results.

Our paper can be divided into two parts. In the first part, we develop a general, computationally agnostic (in the above sense, just as in the digital computer example), efficient coding theory. In the second part, we apply that theory to a specific form of computation, namely the DDC framework for Bayesian inference. The latter theory now determines the tuning curve shapes. When combined with the results of the first part (which dictate the tuning curve scale or gain according to efficient coding theory), this “homeostatic DDC” model makes full predictions for the tuning curves (i.e., both scale and shape) and how they should adapt to stimulus statistics.

So to summarise, it is not the case that the problem of information transmission (or rather mitigating the effect noise on coding fidelity under metabolic constraints), dealt with in the first part, has become a problem of Bayesian inference. But rather, the dictates of efficient coding for optimal gains for coding fidelity (under constraints) have been applied to and combined with a computational theory of inference.

We have added new expository text before and after Eq. 17 in Sec. 2.7 (at the beginning of the second part of the paper on homeostatic DDCs) to again make the connection with the first part and the rationale for its combination with the original DDC framework more clear.

With the changes outlined above, we believe and hope the connection between the two parts (which we agree with the reviewer, was indeed rather obscure previously) has been adequately clarified.

(2) Clarity of writing for an interdisciplinary audience. I do not believe that in its current form, the manuscript is accessible to a broader, interdisciplinary audience such as eLife readers. The writing is very dense and technical, which I believe unnecessarily obscures the key results of this study.

We thank the reviewer for this comment. We have taken several steps to improve the accessibility of this work for an interdisciplinary audience. Firstly, several sections containing dense, mathematical writing have now been moved into appendices or the Methods section, out from the main text; in their place we have made efforts to convey the core of the results, and to providing intuitions, without going into unnecessary technical detail. Secondly, we have added additional figures to help illustrate key concepts or assumptions (see Fig. 1B clarifying the conceptual approach to efficient coding and homeostatic adaptation, and Fig. 8A describing the clustered population). Lastly, we have made sure to refer back to the names of symbols more often, so as to make the analysis easier to follow for a reader with an experimental background.

(3) Positioning within the context of the field and relationship to prior work. While the proposed theory is interesting and timely, the manuscript omits multiple closely related results which in my view should be discussed in relationship to the current work. In particular, a number of recent studies propose normative criteria for gain modulation in populations: • Duong, L., Simoncelli, E., Chklovskii, D. and Lipshutz, D., 2024. Adaptive whitening with fast gain modulation and slow synaptic plasticity. Advances in Neural Information Processing Systems

Tring, E., Dipoppa, M. and Ringach, D.L., 2023. A power law describes the magnitude of adaptation in neural populations of primary visual cortex. Nature Communications, 14(1), p.8366.

Ml ynarski, W. and Tkaˇcik, G., 2022. Efficient coding theory of dynamic attentional modulation. PLoS Biology

Haimerl, C., Ruff, D.A., Cohen, M.R., Savin, C. and Simoncelli, E.P., 2023. Targeted V1 co-modulation supports task-adaptive sensory decisions. Nature Communications • The Ganguli and Simoncelli framework has been extended to a multivariate case and analyzed for a generalized class of error measures:

Yerxa, T.E., Kee, E., DeWeese, M.R. and Cooper, E.A., 2020. Efficient sensory coding of multidimensional stimuli. PLoS Computational Biology

Wang, Z., Stocker, A.A. and Lee, D.D., 2016. Efficient neural codes that minimize LP reconstruction error. Neural Computation, 28(12),

We thank the reviewer again for bringing these works to our attention. For each, we explain whether we chose to include them in our Discussion section, and why.

(1) Duong et al. (2024): We decided not to discuss this manuscript, as our assessment is that it is very relevant to our work. That study starts with the assumption that the goal of the sensory system under study is to whiten the signal covariance matrix, which is not the assumption we start with. A mechanistic ingredient (but not the only one) in their approach is gain modulation. However, in their case it is the gains of computationally auxiliary inhibitory neurons that is modulated and not (as in our case) the gain the (excitatory) coding neurons (i.e. those which encode information about the stimulus and whose response covariance is whitened). These key distinction make the connection with our work quite loose and we did not discuss this work.

(2) Tring et al. (2023): We have added a discussion of the results of this paper and its relationship to the results of our work and that of Benucci et al. This appears in the 7th paragraph of the Discussion. This study is indeed highly relevant to our paper, as it essentially replicates the Benucci et al. experiment, this time in awake mice (rather than anesthetised cats). However, in contrast to the resul‘ts of Benucci et al., Tring et al. do not find firing rate homeostasis in mouse V1. A second, remarkable finding of Tring et al. is that adaptation mainly changes the scale of the population response vector, and only minimally affects its direction. While Tring et al. do not portray it as such, this behaviour amounts to pure stimulus-specific adaptation without the neuron-specific factor found in the Benucci et al. results (see Eq. 24 of our manuscript). As we discuss in our manuscript, when our homeostatic DDC model is based on an ideal-observer generative model, it also displays pure stimulus-specific adaptation with no neuronal factor. Our final model for Benucci’s data did contain a neural factor, because we used a non-ideal observer DDC (in particular, we assumed a smoother prior distribution over orientations compared to the distribution used in the experiment - which has a very sharp peak – as it is more natural given the inductive biases we expect in the brain). The resultant neural factor suppresses the tuning curves tuned to the adaptor stimulus. Interestingly, when gain adaptation is incomplete, and happens to a weaker degree compared to what is necessary for firing rate homeostasis, an additional neural factor emerges that is greater than one for neurons tuned to the adaptor stimulus. These two multiplicative neural factors can approximately cancel each other; such a theory would thus predict both deviation from homeostasis and approximately pure stimulus-specific adaptation. We plan to explore this possibility in future work.

(3) Ml ynarski and Tkaˇcik (2022): We are now citing and discussing this work in the Discussion (penultimate paragraph), in the context of a possible future direction, namely extending our framework to cover the dynamics of adaptation (via a dynamic efficient gain modulation and dynamic inference). We have noted there that Mlynarski have used such a framework (which while similar has key technical differences with our approach) based on a task-dependent efficient coding objective to model top-down attentional modulation. By contrast, we have studied bottom-up and task-independent adaptation, and it would be interesting to extend our framework and develop a model to make predictions for the temporal dynamics of such adaptation.

(4) Haimerl et al. (2023): We have elected not to include this work within our discussion either, as we do not believe it is sufficiently relevant to our work to warrant inclusion. Although this paper also considers gain modulation of neural activity, the setting and the aims of the theoretical work and the empirical phenomena it is applied to are very different from our case in various ways. Most importantly, this paper is not offering a normative account of gain modulation; rather, gain modulation is used as a mechanism for enabling fast adaptive readouts of task relevant information.

(5) Yerxa et al. (2020): We have now included a discussion of this paper in our Discussion section. Note that, even though this study generalises the Ganguli and Simoncelli framework to higher diemsnions, just like that paper it still places strict requirements (which are arguably even more stringent in higher dimensions) on the form of the tuning curves in the population, viz. that there exists a differentiable transform of the stimulus space which renders these unimodal curves completely homogeneous (i.e., of the same shape, and placed regularly and with uniform density).

(6) Wang et al. (2016): We have included this paper in our discussion as well. As above, this paper does not consider general tuning curves, and places the same constraint on their shape and arrangement as in Ganguli and Simoncelli paper.

More detailed comments and feedback:

(1) I believe that this work offers the possibility to address an important question about novelty responses in the cortex (e.g. Homann et al, 2021 PNAS). Are they encoding novelty per-se, or are they inefficient responses of a not-yet-adapted population? Perhaps it’s worth speculating about.

We are not sure why the relatively large responses to “novel” or odd-ball stimuli should be considered inefficient or unadapted: in the context in which those stimuli are infrequent odd-balls (and thus novel or surprising when occurring), efficient coding theory would indeed typically predict a large response compared to the (relatively suppressed) responses to frequently occurring stimuli. Of course, if the statistics change and the odd-ball stimulus now becomes frequent, adaptation should occur and would be expected to suppress responses to this stimulus. As to the question of whether (large) responses to infrequent stimuli can or should be characterised as novelty responses: this is partly an interpretational or semantic issue – unless it is grounded in knowledge of how downstream populations use this type of coding in V1, which could then provide a basis for solidly linking them to detection of novelty per se. In short, our theory, could be applied to Homann et al.’s data, but we consider that beyond the scope of the current paper.

(2) Clustering in populations - typically in efficient coding studies, tuning curve distributions are a consequence of input statistics, constraints, and optimality criteria. Here the authors introduce randomly perturbed curves for each cluster - how to interpret that in light of the efficient coding theory? This links to a more general aspect of this work - it does not specify how to find optimal tuning curves, just how to modulate them (already addressed in the discussion).

We begin by addressing the reviewer’s more general concern regarding the fact that our theory does not address the problem of finding optimal tuning curves, only that of modulating them optimally. As we expound within the updated version of the paper (see the newly expanded 3rd paragraph in Sec. 2.1 and the expanded 2nd paragraph in Introduction), it is not plausible that the sole function of sensory systems, and neural circuits more generally, is the transmission of information. There are many other computational tasks which must be performed by the system, such as the inference of the latent causes of sensory inputs. For many such tasks, it is not even desirable to have complete transmission of information about the external stimulus, since a substantial portion of that information is not important for the task at hand, and must be discarded. For example, such discarding of information is the basis of invariant representations that occur, e.g., in higher visual areas. So we recognise that tuning curve shapes are in general dictated and shaped by computational goals beyond transmission of information or error correction. As such, we have remained agnostic as to the computational goals of neural systems and therefore the shape of the tuning curve. We have made the assumption and adopted the postulate that those computational goals determine the shape of the tuning curves, leaving the gains to be adjuted freely for the purpose of mitigating the effect noise on coding fidelity (this is similar to how error correction is done in computers independendently of the computations performed). by assuming that those computational goals are captured adequately by the shape of tuning curves, this leaves us free to optimise the gains of those curves for purely information theoretic objectives. Finally, we note that the case where the tuning curve shapes are additionally optimised for information transmission is a special case of our more general approach. For further discussion, see the updated version of our introduction.

We now turn to our choice to model clusters using random perturbations. This is, of course, a toy model for clustering tuning curves within a population. With this toy model we are attempting to capture the important aspects of tuning curve clusters within the population while not over-complicating the simulations. Within any neural population, there will be tuning curves that are similar; however, such curves will inevitably be heterogeneous, as opposed to completely identical. Thus, when we cluster together similar curves there will be an “average” cluster tuning curve (found by, e.g., normalising all individual curves and taking the average), which all other tuning curves within the cluster are deviations from. The random perturbations we apply are our attempt to capture these deviations. However, note that the perturbations are not fully random, but instead have an “effective dimensionality” which we vary over. By giving the perturbations an effective dimensionality, we aim to capture the fact that deviations from the average cluster tuning curve may not be fully random, and may display some structure.

(3) Figure 8 - where do Hz come from as physical units? As I understand there are no physical units in simulations.

We have clarified this within the figure caption. The within-cluster optimisation problem requires maximising a quadratic program subject to a constraint on the total mean spike count of the cluster. The objective for the quadratic program is however mathematically homogeneous. So we can scale the variables and parameters in a consistent to be in units of Hz – i.e., turn them into mean firing rates, instead of mean spike counts, with an assumption on the length of the coding time interval. We fix this cluster firing rate to be k × 5 Hz, so that the average single-neuron firing rate is 5 Hz (based on empirical estimates – see our Sec. 2.5). This agrees with our choice of µ in our simulations (i.e., µ = 10) if we assume a coding interval of 0.1 seconds.

(4) Inference with DDCs in changing environments. To perform efficient inference in a dynamically changing environment (as considered here), an ideal observer needs some form of posterior-prior updating. Where does that enter here?

A shortcoming of our theory, in its current form, is that it applies only to the system in “steady-state”, without specifying the dynamics of how adaptation temporlly evolves (we assume the enrivonment has periods of relative stability that are of relatively long duration compared to the dynamical timescales of adaptation, and consider the properties of the well-adapted steady state population). Thus our efficient coding theory (which predicts homeostatic adaptation under the outlined conditions) is silent on the time-course over which homeostasis occurs. Likewise, the DDC theory (in its original formulation in Vertes & Sahani) is silent on dynamic updating of posteriors and considers only static inference with a fixed internal model. We have now discuss a new future directoin in the Discussion (where we cite the work of Mlynarski and Tkacik) to point out that our theory can in principle be extended (based on dynamic inference and efficient coding) to account for the dynamics of attention, but this is beyond the scope of the current work.

(5) Page 6 - ”We did this in such a way that, for all , the correlation matrices, (), were derived from covariance matrices with a 1/n power-law eigenspectrum (i.e., the ranked eigenvalues of the covariance matrix fall off inversely with their rank), in line with the findings of Stringer et al. (2019) in the primary visual cortex.” This is a very specific assumption, taken from a study of a specific brain region - how does it relate to the generality of the approach?

Our efficient coding framework has been formulated without relying on any specific assumptions about the form of the (signal or noise) correlation matrices in cortex. The homeostatic solution to this efficient coding problem, however, emerges under certain conditions. But, as we demonstrate in our discussion of the analytic solutions to our efficient coding objective and the conditions necessary for the validity of the homeostatic solution, we expect homeostasis to arise whenever the signal geometry is sufficiently high-dimensional (among other conditions). By this we mean that the fall-off of the eigenvalues of the signal correlation matrix must be sufficiently slow. Thus, a fall-off in the eigenvalue spectrum slower than 1/n would favor homeostasis even more than our results. If the fall off was faster, then whether or not (and to what degree) firing rate homeostasis becomes suboptimal depends on factors such as the fastness of the fall-off and also the size of the population. Thus (1) rate homeostasis does not require the specific 1/n spectrum, but that spectrum is consistent with the conditions for optimality of rate homeostasis, (2) in our simulations we had to make a specific choice, and relying on empirical observations in V1 was of course a well-justified choice (moreover, as far as we are aware, there have been no other studies that have characterised the spectrum of the signal covariance matrix in response to natural stimuli, based on large population recordings).

Reviewer #2 (Public Review):

Strengths:

The problem of efficient coding is a long-standing and important one. This manuscript contributes to that field by proposing a theory of efficient coding through gain adjustments, independent of the computational goals of the system. The main result is a normative explanation for firing rate homeostasis at the level of neural clusters (groups of neurons that perform a similar computation) with firing rate heterogeneity within each cluster. Both phenomena are widely observed, and reconciling them under one theory is important.

The mathematical derivations are thorough as far as I can tell. Although the model of neural activity is artificial, the authors make sure to include many aspects of cortical physiology, while also keeping the models quite general.

Section 2.5 derives the conditions in which homeostasis would be near-optimal in the cortex, which appear to be consistent with many empirical observations in V1. This indicates that homeostasis in V1 might be indeed close to the optimal solution to code efficiently in the face of noise.

The application to the data of Benucci et al 2013 is the first to offer a normative explanation of stimulus-specific and neuron-specific adaptation in V1.

We thank the reviewer for these assessments.

Weaknesses:

The novelty and significance of the work are not presented clearly. The relation to other theoretical work, particularly Ganguli and Simoncelli and other efficient coding theories, is explained in the Discussion but perhaps would be better placed in the Introduction, to motivate some of the many choices of the mathematical models used here.

We thank the reviewer for this comment; we have updated our introduction to make clearer the relationship between this work and previous works within efficient coding theory. Please see the expanded 2nd paragraph of Introduction which gives a short account of previous efficient coding theories and now situates our work and differentiates it more clearly from past work.

The manuscript is very hard to read as is, it almost feels like this could be two different papers. The first half seems like a standalone document, detailing the general theory with interesting results on homeostasis and optimal coding. The second half, from Section 2.7 on, presents a series of specific applications that appear somewhat disconnected, are not very clearly motivated nor pursued in-depth, and require ad-hoc assumptions.

We thank the reviewer for this suggestion. The reviewer is right to note that our paper contains both the exposition of a general efficient coding theory framework in addition to applications of that framework. Following your advice we have implemented the following changes. (1) significantly shortened or entirely moved some of the less central results in the second half of Results, to the Methods or appendices (this includes the entire former section 2.7 and significant shortening of the section on implementation of Bayes ratio coding by divisive normalisation). (2) We have added a new figure (Fig 1B) and two long pieces of text to the (2nd paragraph of) Introduction, after Eq. (1), and in Sec. 2.7 (introducing homeostatic DDCs) to more clearly explain and clarify the assumptions underlying our efficient coding theory, and its connection with the second half of the Results (i.e. application to DDC theory of Bayesian inference), and better motivate why we consider the homeostatic DDC.

For instance, it is unclear if the main significant finding is the role of homeostasis in the general theory or the demonstration that homeostatic DDC with Bayes Ratio coding captures V1 adaptation phenomena. It would be helpful to clarify if this is being proposed as a new/better computational model of V1 compared to other existing models.

We see the central contribution of our work as not just that homeostasis arises as a result of an efficient coding objective, but also that this homeostasis is sufficient to explain V1 adaptation phenomena - in particular, stimulus specific adaptation (SSA) - when paired with an existing theory of neural representation, the DDC (itself applied to orientation coding in V1). Homeostatic adaptation alone does not explain SSA; nor do DDCs. However, when the two are combined they provide an explanation for SSA. This finding is significant, as it unifies two forms of adaptation (SSA and homeostatic adaptation) whose relationship was not previously appreciated. Our field does not currently have a standard model of V1, and we do not claim to have provided one either; rather, different models have captured different phenomena in V1, and we have done so for homeostatic SSA in V1.

Early on in the manuscript (Section 2.1), the theory is presented as general in terms of the stimulus dimensionality and brain area, but then it is only demonstrated for orientation coding in V1.

The efficient coding theory developed in Section 2 is indeed general throughout, we make no assumptions regarding the shape of the tuning curves or the dimensionality of the stimulus. Further, our demonstrations of the efficient coding theory through numerical simulations - make assumptions only about the form of the signal and noise covariance matrices. When we later turn our attention away from the general case, our choice to focus on orientation coding in V1 was motivated by empirical results demonstrating a co-occurrence of neural homeostasis and stimulus specific adaptation in V1.

The manuscript relies on a specific response noise model, with arbitrary tuning curves. Using a population model with arbitrary tuning curves and noise covariance matrix, as the basis for a study of coding optimality, is problematic because not all combinations of tuning curves and covariances are achievable by neural circuits (e.g. https://pubmed.ncbi.nlm.nih.gov/27145916/ )

First, to clarify, our theory allows for complete generality of neural tuning curve shapes, and assumes a broad family of noise models (which, while not completely arbitrary, includes cases of biological relevance and/or models commonly used in the theoretical literature). Within this class of noise covariance models, we have shown numerical results for different values for different parameters of the noise covariance model, but more importantly, have analytically outlined the general properties and requirements on noise strength and structure (and its relationship to tuning curves and signal structure) under which homeostatic adaptation would be optimal. Regarding the point that not all combinations of tuning curves and noise covariances occur in biology or are achievable by neural circuits: (1) If we are guessing correctly the specific point of the reviewer’s reference to the review paper by Kohn et al. 2016, we have in fact prominently discussed the case of information limiting noise which corresponds to a specific relationship between signal structure (as determined by tuning curves) and noise structure (as specified by the noise covariance matrix). Our family of noise models include that biologically relevant case and we have indeed paid it particular attention in our simulations and discussions (see discussion of Fig. 7 in Sec. 2.3, and that of aligned noise in Sec. 2.5). (2) As for the more general or abstract point that not all combinations of noise covariance and tuning curve structures are achievable by neural circuits, we can make the following comments. First, in lieu of a full theoretical or empirical understanding of the achievable combinations (which does not exist), we have outlined conditions for homeostatic adaptations under a broad class of noise models and arbitrary tuning curves. If some combinations within this class are not realised in biology, that does not invalidate the theoretical results, as the latter have been derived under more general conditions, which nevertheless include combinations that do occur in biology and are achievable by neural circuits (which, as pointed out, include the important case of aligned noise and signal structure – as reviewed in Kohn et al.– to which we have paid particular attention).

The paper Benucci et al 2013 shows that homeostasis holds for some stimulus distributions, but not others i.e. when the ’adapter’ is present too often. This manuscript, like the Benucci paper, discards those datasets. But from a theoretical standpoint, it seems important to consider why that would be the case, and if it can be predicted by the theory proposed here.

The theory we provide predicts that, under certain (specified) conditions, we ought to see deviation from exact homeostatic results; indeed, we provide a first order approximation to the optimal gains in this case which quantifies such deviations when they are small. However, unfortunately the form of this deviation depends on a precise choice of stimulus statistics (e.g. the signal correlation matrix, the noise correlation matrix averaged over all stimulus space, and other stimulus statistics), in contrasts to the universality of the homeostatic solution, when it is a valid approximation. In our model of Benucci et al.’s experiment, we restrict to a simple one-dimensional stimulus space (corresponding to orientated gratings), without specifying neural responses to all stimuli; as such, we are not immediately able to make predictions about whether the homeostatic failure can be predicted using the specific form of deviation from homeostasis. However, we acknowledge that this is a weakness of our analysis, and that a more complete investigation would address this question. For reasons of space, we elected not to pursue this further. We have added a paragraph to our Discussion (8th paragraph) explaining this.

Reviewer#1 (Recommendations for the authors):

(1) To make the article more accessible I would suggest the following:

(a) Include a few more illustrations or diagrams that demonstrate key concepts: adaptationof an entire population, clustering within a population, different sources of noise, inference with homeostatic DDCs, etc.

We thank the reviewer for this suggestion - we have added an additional figure in (Figure 8, Panel A) to explain the concept of clustering within a population. We also added a new panel to Figure 1 (Figure 1B) which we hope will clarify the conceptual postulate underlying our efficient coding framework and its link to the second half of the paper.

(b) Within the text refer to names of quantities much more often, rather than relying onlyon mathematical symbols (e.g. w,r,Ω, etc).

We thank the reviewer for the suggestion; we have updated the text accordingly and believe this has improved the clarity of the exposition.

(2) It is hard to distill which components of the considered theory are crucial to reproducing the experimental observations in Figure 12. Is it the homeostatic modulation, efficient coding, DDCs, or any combination of those or all of them necessary to reproduce the experiment? I believe this could be explained much better, also with an audience of experimentalists in mind.

We have updated the text to provide additional clarity on this matter (see the pointers to these changes and additions in the revised manuscript, given above in response to your first comment). In particular, reproducing the experimental results requires combining DDCs with homeostatic modulation – with the latter a consequence of our efficient coding theory, and not an independent ingredient or assumption.

(3) It would be good to comment on how sensitive the results are to the assumptions made, parameter values, etc. For example: do conclusions depend on statistics of neural responses in simulated environments? Do they generalize for different values of the constraint µ? This could be addressed in the discussion / supplementary material.

This issue is already discussed extensively within the text - see Sec. 2.4, Analytical insight on the optimality of homeostasis, and Sec. 2.5, Conditions for the validity of the homeostatic solution to hold in cortex. In these sections, we outline that - provided a certain parameter combination is small - we expect the homeostatic result to hold. Accordingly, we anticipate that our numerical results will generalise to any settings in which that parameter combination remains small.

(4) How many neurons/units were used for simulations?

We apologies for omitting this detail; we used 10,000 units for our simulations. We have edited both the main text and the methods section to reflect this.

(5) Typos etc: a) Figure 5 caption - the order of panels B and C is switched. b) Figure 6A - I suggest adding a colorbar.

Thank you. We have relabelled the panels B and C in the appropriate figures so that the ordering in the figure caption is correct. We feel that a colourbar in figure 6A would be unnecessary, since we are only trying to convey the concept of uniform correlations, rather than any particular value for the correlations; as such we have elected not to add a colourbar. We have, however, added a more explicit explanation of this cartoon matrix in the figure caption, by referring to the colors of diagonal vs off-diagonal elements.

Reviewer#2 (Recommendations for the authors):

The text on page 10, with the perturbation analysis, could be moved to a supplement, leaving here only the intuition.

We thank the reviewer for this suggestion; we have moved much of the argument into the appendix so as to not distract the reader with unnecessary technical details.

Text before eq. 12 “...in cluster a maximize the objective...” should be ‘minimize’?

The cluster objective as written is indeed maximised, as stated in the text. Note that, in the revised manuscript, this argument has been moved to an appendix to reduce the density of mathematics in the main text.

Top of page 25 “S₀ and S₀” should be “S₀ and S₁”?

Thank you, we have corrected the manuscript accordingly.

Author response:

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

Reviewer #1 (Public review): 

In this manuscript, Chen et al. investigate the role of the membrane estrogen receptor GPR30 in spinal mechanisms of neuropathic pain. Using a wide variety of techniques, they first provide convincing evidence that GPR30 expression is restricted to neurons within the spinal cord, and that GPR30 neurons are well-positioned to receive descending input from the primary sensory cortex (S1). In addition, the authors put their findings in the context of the previous knowledge in the field, presenting evidence demonstrating that GRP30 is expressed in the majority of CCK-expressing spinal neurons. Overall, this manuscript furthers our understanding of neural circuity that underlies neuropathic pain and will be of broad interest to neuroscientists, especially those interested in somatosensation. Nevertheless, the manuscript would be strengthened by additional analyses and clarification of data that is currently presented. 

Strengths: 

The authors present convincing evidence for the expression of GPR30 in the spinal cord that is specific to spinal neurons. Similarly, complementary approaches including pharmacological inhibition and knockdown of GPR30 are used to demonstrate the role of the receptor in driving nerve injury-induced pain in rodent models. 

Weaknesses: 

Although steps were taken to put their data into the broader context of what is already known about the spinal circuitry of pain, more considerations and analyses would help the authors better achieve their goal. For instance, to determine whether GPR30 is expressed in excitatory or inhibitory neurons, more selective markers for these subtypes should be used over CamK2. Moreover, quantitative analysis of the extent of overlap between GPR30+ and CCK+ spinal neurons is needed to understand the potential heterogeneity of the GPR30 spinal neuron population, and to interpret experiments characterizing descending SI inputs onto GPR30 and CCK spinal neurons. Filling these gaps in knowledge would make their findings more solid. 

Thank you very much for your constructive feedback.

In response to your suggestion, we have used more specific markers to distinguish excitatory (VGLUT2) and inhibitory (VGAT) neurons via in situ hybridization. These analyses revealed that GPR30 is predominantly expressed in excitatory neurons of the superficial dorsal horn (SDH), as presented in the Results section (lines 117-120) and in Figure 2A-B.

Additionally, we performed a quantitative analysis to determine the extent of co-localization between GPR30+ and CCK+ neurons. The data were included in the Results (lines 131–132) and Figure 2G.

Reviewer #2 (Public review):

Using a variety of experimental manipulations, the authors show that the membrane estrogen receptor G protein-coupled estrogen receptor (GPER/GPR30) expressed in CCK+ excitatory spinal interneurons plays a major role in the pain symptoms observed in the chronic constriction injury (CCI) model of neuropathic pain. Intrathecal application of selective GPR30 agonist G-1 induced mechanical allodynia and thermal hyperalgesia in male and female mice. Downregulation of GPR30 in CCK+ interneurons prevented the development of mechanical and thermal hypersensitivity during CCI. They also show the up modulation of AMPA receptor expression by GPR30. 

Generally, the conclusions are supported by the experimental results. I also would like to see significant improvements in the writing and the description of results. 

Methodological details for some of the techniques are rather sparse. For example, when examining the co-localization of various markers, the authors do not indicate the number of animals/sections examined. Similarly, when examining the effect of shGper1, it is unclear how many cells/sections/animals were counted and analyzed. 

In other sections, there is no description of the concentration of drugs used (for example, Figure 4H). In Figures 4C-E, there is no indication of the duration of the recordings, the ionic conditions, the effect of glutamate receptor blockers, etc 

Some results appear anecdotal in the way they are described. For example, in Figure 5, it is unclear how many times this experiment was repeated. 

We sincerely appreciate your valuable feedback and thoughtful recommendations.

To address your concerns regarding methodological transparency, we have added the following details to the revised manuscript:

The number of animals and sections analyzed in co-localization studies.

The number of cells/sections/animals used in each quantification following shGper1 treatment.

The concentrations of drugs administered (e.g., in Figure 4H).

Detailed recording conditions, including duration, ionic composition, and pharmacological conditions (Figures 4C-E).

In addition, we have thoroughly revised the writing throughout the manuscript to enhance clarity and precision in the description of our findings.

Reviewer #3 (Public review): 

Summary: 

The authors convincingly demonstrate that a population of CCK+ spinal neurons in the deep dorsal horn express the G protein-coupled estrogen receptor GPR30 to modulate pain sensitivity in the chronic constriction injury (CCI) model of neuropathic pain in mice. Using complementary pharmacological and genetic knockdown experiments they convincingly show that GPR30 inhibition or knockdown reverses mechanical, tactile, and thermal hypersensitivity, conditioned place aversion, and c-fos staining in the spinal dorsal horn after CCI. They propose that GPR30 mediates an increase in postsynaptic AMPA receptors after CCI using slice electrophysiology which may underlie the increased behavioral sensitivity. They then use anterograde tracing approaches to show that CCK and GPR30 positive neurons in the deep dorsal horn may receive direct connections from the primary somatosensory cortex. Chemogenetic activation of these dorsal horn neurons proposed to be connected to S1 increased nociceptive sensitivity in a GPR30-dependent manner. Overall, the data are very convincing and the experiments are well conducted and adequately controlled. However, the proposed model of descending corticospinal facilitation of nociceptive sensitivity through GPR30 in a population of CCK+ neurons in the dorsal horn is not fully supported. 

Strengths: 

The experiments are very well executed and adequately controlled throughout the manuscript. The data are nicely presented and supportive of a role for GPR30 signaling in the spinal dorsal horn influencing nociceptive sensitivity following CCI. The authors also did an excellent job of using complementary approaches to rigorously test their hypothesis. 

Weaknesses: 

The primary weakness in this manuscript involves overextending the interpretations of the data to propose a direct link between corticospinal projections signaling through GPR30 on this CCK+ population of spinal dorsal horn neurons. For example, even in the cropped images presented, GPR30 is present in many other CCK-negative neurons. Only about a quarter of the cells labeled by the anterograde viral tracing experiment from S1 are CCK+. Since no direct evidence is provided for S1 signaling through GPR30, this conclusion should be revised. 

Thank you for your encouraging comments and critical insights.

We fully acknowledge the concern regarding the proposed direct involvement of corticospinal projections in modulating nociceptive behavior via GPR30 in CCK+ neurons. While our anterograde tracing experiments suggest anatomical overlap, we agree that definitive evidence of functional connectivity is lacking.

Accordingly, we have revised the Abstract, Discussion, and Graphical Abstract to present our findings more cautiously. We now describe our observations as indicating that S1 projections potentially interact with GPR30⁺ spinal neurons, rather than asserting a definitive functional link.

To support this revised interpretation, we performed additional quantitative analyses examining the co-localization among S1 projections, CCK+, and GPR30+ neurons. Furthermore, we clarified that the chemogenetic activation studies targeted a mixed neuronal population and did not exclusively manipulate CCK+ neurons.

These changes aim to better align our conclusions with the presented data and provide a more nuanced framework for future investigations.

Reviewer #1 (Recommendations for the authors): 

Major corrections 

(1) Figure 2: The authors conclude that GPR30 is mainly expressed in excitatory spinal neurons because they are labeled by a virus with a Camk2 promoter. While there is evidence that Camk2 is specific to excitatory neurons in the brain, based on RNAseq datasets (e.g. Linnarsson Lab, http://mousebrain.org/adolescent/genesearch.html ) this is less clear cut within the spinal cord. A more direct way to assess the relative expression of GPR30 in excitatory versus inhibitory neurons would be to perform immunohistochemistry or FISH with GPR30/Vglut2/Vgat. 

Alternatively, if this observation is not crucial for the overall arch of the story, I recommend the authors eliminate these data, as they do not support the idea that GPR30 is mainly in excitatory neurons. 

We thank the reviewer for highlighting this important limitation. To strengthen our conclusion regarding the neuronal identity of GPR30-expressing cells, we performed fluorescent in situ hybridization (FISH) using vGluT2 (marker for excitatory neurons) and VGAT (marker for inhibitory neurons). The results confirmed that GPR30 is predominantly expressed in vGluT2-positive excitatory neurons within the spinal cord. These new data are presented in the revised manuscript (lines 117-120) and shown in Figure 2A-B.

(2) (2a) Figure 2: The authors also report that GPR30 is expressed in most CCK+ spinal neurons. A more rigorous way to present the data would be to perform quantification and report the % of CCK neurons that are GPR30. 

(2b) More importantly, it is unclear what % of GPR30 neurons are CCK+. These types of quantifications would provide useful insights into the heterogeneity of CCK and GPR30 neuron populations, and help align findings of experiments using the behavioral pharmacology using GRP antagonists to the knockdown of Gper1 in CCK spinal neurons - for instance, does a population of GRP30+/CCK- neurons exist? If so, it would be worth discussing what role (if any) that population might play in nerve injury-induced mechanical allodynia. 

Understanding the breakdown of GPR30 populations becomes even more relevant when the authors characterize which cell types are targeted by descending projections from S1. It is clear that the vast majority of CCK+ neurons that receive descending input from S1 neurons are GPR30+, but there are many other GPR30+ neurons that do not receive input from SI neurons presented in 5M. Is this simply because only a small fraction of CCK+/GPR30+ neurons are targeted by descending S1 projections, or could they represent a distinct population of GPR30 neurons? 

(2a) We appreciate the suggestion. Quantification showed that approximately 90% of CCK⁺ neurons express GPR30, and about 50% of GPR30⁺ neurons co-express CCK. These data are now provided in the revised Results (lines 131-132) and in Figure 2F-G.

(2b) Indeed, our data reveal that a substantial portion of GPR30⁺ neurons do not co-express CCK. While this study focuses on GPR30 function in CCK⁺ neurons, we recognize the potential relevance of GPR30⁺/CCK⁻ populations. We have addressed this point in the Discussion (lines 303-306):

“However, it should be noted that half of GPR30⁺ neurons are not co-localized with CCK⁺ neurons, and further studies are needed to explore the function of these GPR30⁺/CCK⁻ neurons in neuropathic pain.”

Regarding descending input, our data in Figure 5 show that S1 projections selectively innervate a subset (~30%) of CCK⁺ neurons, most of which co-express GPR30. This suggests that S1-targeted CCK⁺/GPR30⁺ neurons may represent a functionally distinct population. We have added clarification to the revised manuscript, while acknowledging that further studies are needed to elucidate the roles of non-targeted GPR30⁺ neurons.

(3) Throughout the manuscript both male and female mice were used in experiments. Rather than referring to male and female mice as different genders, it would be more appropriate to describe them as different sexes. 

As suggested, we have replaced all instances of “gender” with “sex” throughout the revised manuscript.

(4) Figure 5: To increase the ease of interpreting the figure, in panels 5J and 5N, it would be helpful to indicate directly on the figure panel which another marker was assessed in double-labeling analyses.

We have revised Figures 5J and 5N to include clear labels identifying the markers used in double-labeling analyses, to improve interpretability.

Minor corrections: 

(1) Line 36, I believe the authors mean to say "GPER/GPR30 in spinal neurons", rather than just "spinal". 

Corrected as suggested. The sentence now reads (line 34):

“Here we showed that the membrane estrogen receptor G-protein coupled estrogen receptor (GPER/GPR30) in spinal neurons was significantly upregulated in chronic constriction injury (CCI) mice…”

(2) There are minor grammatical errors throughout the manuscript that interfere with comprehension. Proofreading/editing of the English language use may be beneficial. 

We have thoroughly revised the manuscript for clarity and corrected grammatical and syntactic errors to improve readability.

(3) Line 169-170, reads "Known that EPSCs are mediated by glutamatergic receptors like AMPA receptors and several studies have been reported the relationship between GPR30 and AMPA receptor25,29". Rewriting the sentence such that it better describes what the known relationship is between GPR30 and AMPA would be helpful in setting up the rationale of the experiment in Figure 4. 

We have rewritten this section to better clarify the rationale behind the electrophysiological experiments (lines 161-164):

“Given that EPSCs are primarily mediated through glutamatergic receptors such as AMPA receptors, and emerging evidence suggesting that GPR30 enhances excitatory transmission by promoting clustering of glutamatergic receptor subunits, we examined whether GPR30 modulates EPSCs via AMPA receptor-dependent mechanisms.”

(4) Line 198-199 "Then we explored the possible connections among GPR30, S1-SDH projections and CCK+ neuron." In the context of spinal circuitry, "connections" may raise the expectation that synaptic connectivity will be evaluated. What I think best describes what the authors investigated in Figure 5 is the "relationship" between GPR30, S1-SDH projections, and CCK+ neurons. 

We have revised the sentence accordingly (lines 184-186):

“Building on previous findings suggesting a functional interaction between S1-SDH projections and spinal CCK⁺ neurons, our current study aimed to further elucidate the structural relationship among GPR30, S1-SDH projections, and CCK⁺ neurons.”

(5) Figure 5: To increase the ease of interpreting the figure, in panels 5J and FN, it would be helpful to indicate directly on the figure panel which other marker was assessed in double-labeling analyses. 

We have added direct labels to figure panels to clarify double-labeled analyses in the revised Figure 5J and 5N.

Reviewer #2 (Recommendations for the authors): 

(1) Can the authors provide more detail about the distribution of CCK+ cells in the spinal cord and, in particular, the localization of double-stained (CCK/cfos) neurons? 

We thank the reviewer for this suggestion. To better characterize the distribution of CCK⁺ neurons within the spinal dorsal horn (SDH), we performed immunostaining in CCK-tdTomato mice using lamina-specific markers: CGRP (lamina I), IB4 (lamina II), and NF200 (lamina III–V). Our results demonstrate that CCK⁺ neurons are primarily localized in the deeper laminae of the SDH. These findings are now described in the revised Results (lines 126–129) and shown in Figure 2E.

In addition, we conducted c-Fos immunostaining in CCK-Ai14 mice and found increased activation of CCK⁺ neurons following CCI. This supports the involvement of CCK⁺ neurons in neuropathic pain. These data are included in the Results (lines 129–131) and Supplementary Figure S4.

(2) Figure 2A. There is no formal quantification of the percentage of TdTomato+ neurons that are also CCK+. The description of these results is insufficient. 

We appreciate this point and have revised the description of Figure 2A accordingly. To strengthen our analysis, we conducted additional FISH experiments with vGluT2 and VGAT probes. Quantification revealed that GPR30 is predominantly expressed in excitatory neurons (approximately 60%). These data are shown in the revised Results (lines 117-119) and Figures 2A-B and S3. This supports our conclusion that GPR30 is largely localized to excitatory spinal interneurons.

(3) Figure 4H. What is the evidence that these are AMPA-mediated currents? This is not explained in the text. 

Thank you for raising this point. We now provide detailed experimental procedures to clarify that the recorded EPSCs are AMPA receptor–mediated. Specifically, spinal slices from CCK-Cre mice were used, and excitatory postsynaptic currents were recorded in the presence of APV (100 μM, NMDA receptor blocker), bicuculline (20 μM, GABA_A receptor blocker), and strychnine (0.5 μM, glycine receptor blocker), ensuring that the observed currents were AMPA-dependent. These methodological details are now clearly described in the revised Results (lines 165–173) and supported by prior literature (Zhang et al., J Biol Chem 2012; Hughes et al., J Neurosci 2010).

(1) Yan Zhang, Xiao Xiao, Xiao-Meng Zhang, Zhi-Qi Zhao, Yu-Qiu Zhang (2012). Estrogen facilitates spinal cord synaptic transmission via membrane-bound estrogen receptors: implications for pain hypersensitivity. J Biol Chem. Sep 28;287(40):33268-81.

(2) Ethan G Hughes, Xiaoyu Peng, Amy J Gleichman, Meizan Lai, Lei Zhou, Ryan Tsou, Thomas D Parsons, David R Lynch, Josep Dalmau, Rita J Balice-Gordon (2010). Cellular and synaptic mechanisms of anti-NMDA receptor encephalitis. J Neurosci. 2010 Apr 28;30(17):5866-75.

(4) What is the signaling mechanism leading to a larger amplitude of currents after G-1 infusion? 

We thank the reviewer for this important question. G-1 is a selective agonist for GPR30. Based on previous studies by Luo et al. (2016), we speculate that activation of GPR30 may increase the clustering of glutamatergic receptor subunits at postsynaptic sites, thereby enhancing AMPA receptor-mediated currents. While our current study did not directly address the intracellular signaling cascade, we have incorporated this mechanistic speculation in the Discussion.

Jie Luo, X.H., Yali Li, Yang Li, Xueqin Xu, Yan Gao, Ruoshi Shi, Wanjun Yao, Juying Liu, Changbin Ke (2016). GPR30 disrupts the balance of GABAergic and glutamatergic transmission in the spinal cord driving to the development of bone cancer pain. Oncotarget 7, 73462-73472. 10.18632/oncotarget.11867.

(5) Figure 4I. Please include error bars. 

We have revised Figure 4I to include error bars, as requested.

(6) Line 198. What is the evidence that AAV2/1 EF1α FLP is an antegrade trans monosynaptic marker? 

We thank you for this request. AAV2/1 has been widely used for anterograde monosynaptic tracing based on its properties (Wang et al., Nat Neurosci 2024; Wu et al., Neurosci Bull 2021): (1) it infects neurons at the injection site and undergoes active anterograde transport; (2) newly assembled viral particles are released at synapses and infect postsynaptic partners; (3) in the absence of helper viruses, the spread halts at the first synapse, ensuring monosynaptic restriction. We have elaborated on this in the revised manuscript (line 198), citing Wang et al. (Nat Neurosci 2024) and Wu et al. (Neurosci Bull 2021).

(1) Hao Wang, Qin Wang, Liuzhe Cui, Xiaoyang Feng, Ping Dong, Liheng Tan, Lin Lin, Hong Lian, Shuxia Cao, Huiqian Huang, Peng Cao, Xiao-Ming Li (2024). A molecularly defined amygdalaindependent tetra-synaptic forebrain-tohindbrain pathway for odor-driven innate fear and anxiety. Nat Neurosci. 2024 Mar;27(3):514-526.

(2) Zi-Han Wu, Han-Yu Shao, Yuan-Yuan Fu, Xiao-Bo Wu, De-Li Cao, Sheng-Xiang Yan, Wei-Lin Sha, Yong-Jing Gao, Zhi-Jun Zhang (2021). Descending Modulation of Spinal Itch Transmission by Primary Somatosensory Cortex. Neurosci Bull. 2021 Sep;37(9):1345-1350.

(7) Figure 5G. I do not understand the logic of this experiment. A Cre AAV is injected in the S1 cortex. Why should this lead to the expression of tdTomato on a downstream (postsynaptic?) neuron? The authors should quote the literature that supports this anterograde transsynaptic transport.

We appreciate this question. As described in previous studies (e.g., Wu et al., Neurosci Bull 2021), AAV2/1-Cre injected into the S1 cortex leads to Cre expression in projection targets due to transsynaptic anterograde transport. Subsequent injection of a Cre-dependent AAV (AAV2/9-DIO-mCherry) into the spinal cord enables specific labeling of postsynaptic neurons that receive input from S1. We have clarified this mechanism in line 206 and provided the appropriate citation.

Zi-Han Wu, Han-Yu Shao, Yuan-Yuan Fu, Xiao-Bo Wu, De-Li Cao, Sheng-Xiang Yan, Wei-Lin Sha, Yong-Jing Gao, Zhi-Jun Zhang (2021). Descending Modulation of Spinal Itch Transmission by Primary Somatosensory Cortex. Neurosci Bull. 2021 Sep;37(9):1345-1350.

(8) The same question arises when interpreting the results obtained in Figure 6.

We thank the reviewer for the question, and we have addressed it in point (7).

(9) Line 257. How do the authors envision that estrogen would change its modulation of GPR30 under basal and neuropathic conditions? Is there any evidence for this speculation? 

We thank the reviewer for raising this thoughtful question. In the current study, we focused on pharmacologically manipulating GPR30 activity via its selective agonist and antagonist. We did not directly investigate how endogenous estrogen regulates GPR30 under physiological and neuropathic states. We have recognized this limitation and highlighted the need for future research to investigate this regulatory mechanism.

(10-20) In my opinion, the entire manuscript needs a careful revision of the English language. While one can follow the text, it contains numerous grammatical and syntactic errors that make the reading far from enjoyable. I am highlighting just a few of the many errors. 

We appreciate the reviewer’s honest assessment. The manuscript has undergone thorough language editing by a native English speaker to correct grammatical errors, improve clarity, and enhance overall readability. We also restructured several sections, particularly the Discussion, to improve logical flow.

(21) The discussion of results is a bit disorganized, with disconnected sentences and statements, and somewhat repetitive. For example, lines 303 to 306 lack adequate flow. It is also quite long and includes general statements that add little to the discussion of the new findings (lines 326-333). 

We agree and have revised the Discussion extensively. Disconnected or repetitive sentences (e.g., lines 303-306, 326-333) have been removed or rewritten. For instance, we added a new transitional paragraph (lines 307-311) to improve flow:

“Abnormal activation of neurons in the SDH is a key contributor to hyperalgesia, and enhanced excitatory synaptic transmission is a major mechanism driving increased neuronal excitability. Therefore, we evaluated excitatory postsynaptic currents (EPSCs) and observed increased amplitudes in CCK⁺ neurons following CCI, suggesting elevated excitability in these neurons.”

We also removed redundant generalizations to maintain a focused discussion of our novel findings.

Reviewer #3 (Recommendations for the authors): 

(1) What is the distribution of GPR30 throughout the spinal cord and DRG? The authors demonstrate that this can overlap with a CCK+ population, but there are many GPR30+ and CCK negative neurons, even in the cropped images presented. It would be helpful to quantify the colocalization with CCK. 

We thank the reviewer for this important point. As shown in the revised manuscript, GPR30 is expressed in both the spinal cord and dorsal root ganglia (DRG). However, our updated data (Figure 1B) demonstrate that Gper1 mRNA levels in the DRG are not significantly altered after CCI, suggesting a limited involvement of DRG GPR30 in neuropathic pain. These results are described in the revised Results (line 94).

Regarding spinal co-expression, we performed a detailed quantification. Approximately 90% of CCK⁺ neurons express GPR30, while about 50% of GPR30⁺ neurons are CCK⁺. These co-localization results are now included in the revised Results and presented in Figure 2G.

(2) It is clear that CCI and GPR30 influence excitatory synaptic transmission in CCK+ neurons. However, these experiments do not fully support the authors' claims of a postsynaptic upregulation of AMPARs. Comparing amplitudes and frequencies of spontaneous EPSCs cannot necessarily distinguish a pre- vs postsynaptic change since some of these EPSCs can arise from spontaneous action potential firing. I suggest revising this conclusion. 

We appreciate these insightful comments. We fully agree that our data from spontaneous EPSC recordings (sEPSCs) in CCK⁺ neurons are not sufficient to distinguish between pre- and postsynaptic mechanisms, as sEPSCs may include spontaneous presynaptic activity. Therefore, we have revised the text throughout the manuscript to avoid overstating conclusions related to postsynaptic AMPA receptor upregulation.

(3) What is the rationale for the evoked EPSC experiments from electrical stimulation in "the deep laminae of SDH?" I do not think that this experiment can rule out a presynaptic contribution of GPR30 to the evoked responses, particularly if these are Gs-coupled at presynaptic terminals. Paired-pulse stimulations could help answer this question, otherwise, alternative interpretations, also related to the point above, should be provided. 

We thank the reviewer for this thoughtful critique. Indeed, electrical stimulation of the deep SDH laminae does not exclude presynaptic involvement, especially considering that GPR30 is a G protein–coupled receptor (GPCR) and could act presynaptically. We agree that paired-pulse ratio (PPR) analysis would be more informative in distinguishing pre- from postsynaptic effects, but this was not performed due to technical limitations in our current experimental setup.

Accordingly, we have revised our interpretations in both the Results and Discussion to acknowledge that our data do not rule out presynaptic contributions. We now state that GPR30 activation enhances EPSCs in CCK⁺ neurons, while further studies are needed to dissect the precise site of action.

(4) I appreciate the challenging nature of the trans-synaptic viral labeling approaches, but the chemogenetic and Gper knockdown experiments do not selectively target this CCK+ population of deep dorsal horn neurons. The data are clear that each of these components (descending corticospinal projections, CCK neurons, and GPR30) can modulate nociceptive hypersensitivity, but I do not agree with the overall conclusion that each of are directly linked as the authors propose. I recommend revising the overall conclusion and title to reflect the convincing data presented. 

We thank the reviewer for this critical observation. We agree that while our data show functional roles for descending cortical input, CCK⁺ neurons, and GPR30 in modulating pain hypersensitivity, the evidence does not establish a definitive direct circuit integrating all three components.

In response, we have revised our conclusions to reflect this limitation. Specifically, we avoided claiming a direct functional link among S1 projections, CCK⁺ neurons, and GPR30. Instead, we now propose that GPR30 modulates neuropathic pain primarily through its action in CCK⁺ spinal neurons, with potential involvement of descending facilitation from the somatosensory cortex.

Additionally, we have revised the manuscript title to better reflect our mechanistic focus:<br /> “GPR30 in spinal CCK-positive neurons modulates neuropathic pain.”

Minor Corrections

(1) The authors should refer to mice by sex, not gender. 

Corrected throughout the manuscript.

(2) Page 9, line 195: "significantly" is used to refer to co-localization of 28.1%. What is this significant to? 

We have revised the sentence to accurately describe the observed percentage, without implying statistical significance:

“Our co-staining results revealed that a high proportion of CCK⁺ S1-SDH postsynaptic neurons expressed GPR30” (line 198-199).

(3) I recommend modifying some of the transition phrases like "by the way," "what's more," and "besides". 

All informal expressions have been replaced with academic alternatives including “Furthermore,” “Additionally,” and “Moreover.”

(4) Additional guides to mark specific laminae in the dorsal horn would be useful. 

We added immunostaining with laminar markers (CGRP for lamina I and NF200 for lamina III–V), and these data are now shown in Figure 2E and described in the Results (lines 126-129).

(5) Page 5, line 115: immunochemistry should be immunohistochemistry. 

Corrected as suggested.

(6) Page 6, line 136: "Confirming the structural connnections" was not demonstrated here. Perhaps co-localization between GPR30 and CCK+. 

The text was revised to “To functionally interrogate GPR30 and CCK⁺ neurons in neuropathic pain...” (line 133).

(7) Page 8, line 166: unsure what "took and important role" means. 

This phrasing was corrected for clarity and replaced with an accurate scientific description.

(8) Page 8, line 168: "IPSCs of spinal CCK+ neurons" implies that they are sending inhibitory inputs. 

We revised the term to “EPSCs” to correctly reflect excitatory synaptic currents in CCK⁺ neurons.

(9) Page 8, line 169: "Known that EPSCs" is missing an introductory phrase. 

The sentence was rewritten to include an appropriate introductory clause (lines 161–164):

“Given that EPSCs are primarily mediated through glutamatergic receptors such as AMPA receptors...”

(10) Page 10, line 227 and 228: "adequately" and "sufficiently" should be adequate and sufficient. 

We corrected these terms to the proper adjective forms: “adequate” and “sufficient” (lines 224-225).

Reviewer #2 (Public review):

Summary:

This is a very interesting study from Vandendoren and colleagues examining the role of PVN oxytocin neurons during thermoregulatory behaviors, in particular during thermoregulatory huddling. The findings are important and compelling, and have implications for the thermoregulation field as well as the social/naturalistic behavior field.

Strengths:

The study is very creative and tackles a challenging task to examine how natural and social behavior influences neural circuits for a homeostatic system such as thermoregulation. The authors use a combination of state-of-the-art tools (photometry, optogenetics, automated behavior tracking, thermal imaging, and core body temperature measurement), often in combination with each other, to produce a rigorous and high-dimensional dataset. Carrying out tightly temperature-controlled experiments and examining natural behavior, neural activity, and body physiology simultaneously is quite a feat. I applaud the authors for taking this on in a rigorous and detailed manner. This paper will be valuable for both the thermoregulation field as well as for researchers interested in naturalistic social behaviors. The conclusions are supported by the data.

Weaknesses:

I have a number of questions and suggestions for clarification that would help improve the interpretation of the findings.

(1) Figure 1D-F: It would be helpful to include representative images of cFos expression in the PVN, LS, and DMH during both quiescent and solo huddling conditions, to better illustrate the reported differences.

(2) Figure 1C: The data suggest a general suppression of neural activity during sleep-associated quiescent huddling, which somewhat complicates the interpretation of what specifically the active huddling cells are responding to. A more informative control might have been a comparison between huddling and a more generic form of social engagement (e.g., dyadic sniffing) to assess whether huddling-responsive neurons are broadly tuned to social stimuli. While it may not be feasible to add this experimentally at this time, a brief discussion of this limitation in the main text would be valuable.

(3) Figure 2H-J vs. Figure 1: The fiber photometry data suggest increased PVN activity during quiescent huddling vs active huddling, which appears to contrast with the cFos results from Figure 1. It would be helpful for the authors to comment on possible reasons for this discrepancy-e.g., methodological differences, temporal resolution, or cell-type specificity.

(4) Figure 2O: A comparable linear regression for active huddling would be informative to assess whether the observed relationships extend across behavioral states.

(5) Temperature manipulation: The use of floor temperature changes presents a distinct physiological and sensory experience from, for example, manipulation of ambient temperature. A discussion of how this choice may affect neural circuit engagement or interpretation of thermoregulatory responses would be beneficial.

(6) Correlations with behavior: Across the manuscript, it would be informative to see correlations between huddle duration and neural activity (e.g., cFos expression, calcium signal magnitude). Similarly, do longer huddles produce greater thermogenic effects?

(7) Lactating vs. virgin mothers: The inclusion of maternal data is intriguing but feels somewhat disconnected from the central huddling-thermoregulation narrative. If these experiments are to remain, additional explanation of their rationale and how they fit into the broader story would help clarify their relevance.

(8) Optogenetic manipulation: Have the authors tested the effect of PVN OT neuron stimulation or inhibition during huddling? Even a negative result would be of interest to the field. If these data exist (main or supplementary), I apologize for missing them. If not, the authors might consider including them or commenting briefly on any attempts or challenges in carrying out these experiments.

Reviewer #3 (Public review):

Summary:

The authors aimed to elucidate the relationship between physiological state (i.e., behavioral status and thermogenic sympathetic activity) and the activity of hypothalamic paraventricular oxytocin (PVNOT) neurons in female mice. They studied this by combining automated classification of mouse behavior via video-based analysis with calcium imaging of PVNOT neuron activity. Sympathetic thermogenesis was inferred from surface temperature changes captured by infrared thermography, and the authors provided their custom analysis scripts in the manuscript. Notably, they found that a strong, pulsatile activation of PVNOT neurons was "occasionally" observed immediately before the animals transitioned from a resting to an active state. This pulsatile activity was observed in both pair-housed and individually housed animals. While PVNOT neurons are often associated with social behaviors, this finding suggests that the oxytocinergic system is also engaged during naturalistic behaviors, even in the absence of social interactions. If experiments were more convincingly performed and presented, the results would point to a broader physiological role of central oxytocin, including in the regulation of fundamental brain states and homeostatic processes, and offer a new perspective on the functional significance of central oxytocin signaling.

Strengths:

The oxytocinergic neural system is believed to subserve a wide range of physiological functions, and elucidating these roles requires monitoring PVNOT neuronal activity under various behavioral contexts, as well as manipulating this activity to establish causal links. In the present study, the authors show a technically sound experimental framework that integrates behavioral tracking in both individually and group-housed mice with the observation and manipulation of PVNOT neuron activity. This experimental setup represents a valuable methodological resource for researchers investigating the physiological functions of oxytocin.

Weaknesses:

While this study successfully established a new experimental setup for simultaneous analyses of behavior and PVNOT neuronal activity, there are several concerns regarding the interpretation of the results and the robustness of the conclusions, which should be more thoroughly addressed.

(1) The study relies on the assumption that calcium imaging and optogenetic manipulation were restricted only to PVNOT neurons. However, the specificity of AAV-mediated gene expression was not verified quantitatively. A fair number of cell bodies in the PVN expressed GCaMP8s, but not OT, indicating potential off-target expression (see Figure S2A, B). The lack of quantitative validation weakens confidence in the causal interpretation of the results.

(2) The study focuses on the transition from rest to active states following pulsatile activity of PVNOT neurons. However, the physiological significance of this pulsatile activity remains unclear. According to the authors, pulsatile activity occurred with an approximately 20% probability within 100 seconds prior to the end of the resting state. This implies that, in the remaining 80% of rest-to-active transitions, pulsatile PVNOT activity did not occur, suggesting that it is not essential for initiating the transition. A comparative analysis of behavioral and thermogenic changes between transitions with and without pulsatile PVNOT activity would help to further clarify the functional relevance of this phenomenon and strengthen the authors' interpretation of the findings.

(3) The study identifies a correlation between pulsatile activity of PVNOT neurons and rest-to-active transitions, and tests for a causal relationship using optogenetic stimulation. However, since PVNOT neurons are known to co-release other neurotransmitters such as glutamate, it remains unclear whether the observed effects are mediated specifically through oxytocin receptor signaling. To address this question, functional intervention experiments using oxytocin receptor antagonists or receptor knockout mice are necessary.

(4) The authors attempted to detect BAT thermogenesis and skin vasomotion using infrared thermography. This technique measures only skin hair temperatures (since the skin was not shaved), but does not measure "BAT temperature" or "vasomotor tone". As seen in Figure 5E, the temperatures of the body surface areas ("BAT", "Rump", and "Dorsal surface") mostly changed in parallel, indicating that these temperatures are strongly affected by body core temperature. Therefore, the thermographic measurements in this study did not provide convincing information on BAT thermogenesis or skin vasomotion. To avoid misleading reports, the authors need to use other techniques to directly measure temperatures, such as telemetry.

(5) Photostimulation of PVNOT neurons increased Tb after 400 sec (6.6 min) (Figure 5). This latency is too long to conclude that the neuronal stimulation elicited BAT thermogenesis. A more reasonable explanation is that the increase in Tb was caused by the induction of physical activity (Figure S4C), which slowly generates heat and contributes to the elevation of Tb. However, this view contradicts the authors' claim. To address this concern, the authors should directly measure BAT thermogenesis and compare it with the rate of Tb elevation. If BAT thermogenesis occurs, the rate at which the BAT temperature increases must exceed the rate at which Tb rises.

Reviewer #2 (Public review):

Summary:

This study investigates the role of the enzyme Alcohol Dehydrogenase 5 (ADH5) in brown adipose tissue (BAT) during aging. BAT is crucial for thermogenesis and energy balance, but its function and mass diminish with age, contributing to metabolic dysfunction and age-related diseases. ADH5, also known as S-nitrosoglutathione reductase, regulates nitric oxide (NO) signaling by damaging S-nitrosylation modifications from proteins. The authors show that aging in mice leads to increased protein S-nitrosylation but reduced ADH5 expression in BAT, resulting in impaired metabolic and cognitive functions. Deletion of ADH5 in BAT accelerates tissue senescence and systemic metabolic decline.

Mechanisticaremoving lly, aging suppresses ADH5 via downregulation of heat shock factor 1 (HSF1), a master regulator of protein homeostasis. Importantly, pharmacologically boosting HSF1 improves BAT function and mitigates both metabolic and cognitive declines in aged mice. The findings highlight a critical HSF1-ADH5 pathway in BAT that protects against aging-related dysfunction, suggesting that targeting this pathway may offer new therapeutic strategies for improving metabolic health and cognition during aging.

Strengths:

This research provides insight into the interplay between redox biology, proteostasis, and metabolic decline in aging. By identifying a specific enzyme that controls SNO status in BAT and further developing a therapy to target ADH5 in BAT to prevent age-related decline, the authors have identified a putative mechanism to combat age-related decline in BAT function.

Weaknesses:

(1) Sex needs to be considered as a biological variable, at a minimum in the reporting of the phenotypes observed in this manuscript, but also potentially by further experimentation. The only mention of sex I could find is that the authors reported the general protein SNO status in BAT is increased with age in male C57Bl/6J mice. Is this also true in female mice? For all of the ADH5 knockout mouse data, are these also male mice? Do female ADH5 knockout mice have a consistent phenotype, or are the sex differences?

(2) It would be helpful to know the extent of ADH5 loss in the adipose tissue of knockout mice, either by mRNA or by immunoblotting for ADH5. It could also be helpful to know if ADH5 is deleted from the inguinal adipose tissue of these mice, especially since they seem to accumulate fat mass as they age (Figure 2B).

(3) For Figure 4D, the ChiP, it would be better to show the IgG control pulldowns. Also, there's an unexpected thing where all the values for the Adh5 flox mice are exactly the same - how is this possible? Finally, it's not clear how these BAT samples were treated with HSF1A - was this done in vivo or ex vivo?

(4) I didn't understand what was on the y-axis in Figure 5A, nor how it was measured. I assume it's HSF1A, and maybe it's the part in the methods with the Metabolomic Analysis, but this wasn't clear. It would also help if release from the NC-Vehicle formulation could be included as a negative control.

(5) What happens to BAT protein S-nitrosylation in HSF1A-treated mice?

(6) Figure 1B: What is the age of the positive (ADH5BKO) and negative (Adh5 fl) mice?

(7) Figure 1F: Can you clarify what I'm looking at in the P16ink4a panels? The red staining? Is the blue staining DAPI? This is also a problem in Figures 3C, 3D and 5G, and 5I. Figure 4B looks great - maybe this could be used as an example?

(8) Figure 3B looks a bit odd since 7 of the 12 total mice seem to have an IL-beat level of exactly 5. I was a bit unclear about why arbitrary units were used for IL-1β levels since it says an ELISA was used to quantify IL-1β; however, in the methods the authors describe a Bio-Rad Laboratories Bio-plex Pro Mouse Cytokine 23-Plex approach, which I don't think is an ELISA. Can the approach to measuring IL-1β be clarified, and could the authors explain why they can't show units of mass for IL-1β levels?

(9) Figure 2C and 2D: I don't really understand why the Heat or VO2 need to be expressed as fold changes. Can't these just be expressed with absolute units? It's also confusing why the heat fold change is 1.0 in the light and the dark for the floxed animal. I bet this is because the knockout is normalized to the floxed animal for light and then normalized again for the dark period, but since both are on the same graph, readers could be confused into thinking there is no difference in the heat production or VO2 between light and dark, which would be surprising. This could all just be solved if absolute units were used.

Author response:

Reviewer #1 (Public review):

The topic is appealing given the rise in the aging population and the unclear role of BAT function in this process. Overall, the study uses several techniques, is easy to follow, and addresses several physiological and molecular manifestations of aging.  However, the study lacks an appropriate statistical analysis, which severely affects the conclusions of the work. Therefore, interpretation of the findings is limited and must be done with caution. 

We greatly appreciate the reviewer’s encouragement. Our team is fully committed to maintaining clarity and rigor in the design, execution, and reporting of this study. We are grateful to the reviewers for bringing these issues to our attention. We also acknowledge and are working on that several statistical analyses could be reperformed to better emphasize our focus on the genetic effect of ADH5 deletion in mice of the same age.

Reviewer #2 (Public review):

Strengths: 

This research provides insight into the interplay between redox biology, proteostasis, and metabolic decline in aging. By identifying a specific enzyme that controls SNO status in BAT and further developing a therapy to target ADH5 in BAT to prevent age-related decline, the authors have identified a putative mechanism to combat age-related decline in BAT function. 

We greatly appreciate the reviewer’s encouragement. 

Weaknesses: 

(1) Sex needs to be considered as a biological variable, at a minimum in the reporting of the phenotypes observed in this manuscript, but also potentially by further experimentation. 

We thank the reviewer for the insightful remark, and we agree with the reviewer that sex needs to be considered as a biological variable. We will assess ADH5 expression in aged female mice.

(2)  It would be helpful to know the extent of ADH5 loss in the adipose tissue of knockout mice, either by mRNA or by immunoblotting for ADH5. It could also be helpful to know if ADH5 is deleted from the inguinal adipose tissue of these mice, especially since they seem to accumulate fat mass as they age (Figure 2B). 

We thank the reviewer for the comment/suggestion. Indeed, we have measured the ADH5 expression in both brown adipose tissue (BAT) and inguinal adipose tissue (iWAT). We regret that we did not include our results in the first submission and will provide these results in the revised manuscript.

(3)  For Figure 4D, the ChiP, it would be better to show the IgG control pulldowns. Finally, it's not clear how these BAT samples were treated with HSF1A - was this done in vivo or ex vivo? 

We thank the reviewer for their thoughtful comment and will provide detailed information in the revised manuscript.

(4) I didn't understand what was on the y-axis in Figure 5A, nor how it was measured.

We apologize for not making these critical points clearer in the first submission. In the revised manuscript we will include, in detail, the logistics of the experiments in the materials and methods section, figure annotation and figure legends.  

(5) What happens to BAT protein S-nitrosylation in HSF1A-treated mice? 

We thank the reviewer for the insightful remark, and we will measure general protein Snitrosylation status in the BAT of HSF1A-treated mice. 

(6) Figure 1B: What is the age of the positive (ADH5BKO) and negative (Adh5 fl) mice? 

We regret that we did not describe our results clearly in the first submission and will provide detailed information in the revised manuscript.

(7) Figure 1F: Can you clarify what I'm looking at in the P16ink4a panels? The red staining? Is the blue staining DAPI? This is also a problem in Figures 3C, 3D and 5G, and 5I. Figure 4B looks great - maybe this could be used as an example?  

We regret that we did not present results clearly in the first submission and will provide detailed information in the revised manuscript.

(8) Figure 3B looks a bit odd. Can the approach to measuring IL-1β be clarified, and could the authors explain why they can't show units of mass for IL-1β levels? 

We will provide detailed information in the revised manuscript.

(9) Figure 2C and 2D: I don't really understand why the Heat or VO2 need to be expressed as fold changes. Can't these just be expressed with absolute units? 

We thank the reviewer for the insightful comment. We will present these results as suggested in the revised manuscript.

Author response:

(1) Stable annual dynamics vs. episodic outbreaks

We agree that RVF is classically described as producing periodic epidemics interspersed with long inter-epidemic periods, often linked to extreme rainfall events. Our model predicts more regular seasonal dynamics, which reflects the endemic transmission patterns we have observed in The Gambia through serological surveys. In the revision, we will:

Clarify that while epidemics occur in other parts of sub-Saharan Africa, our results may indicate a different epidemiological narrative in The Gambia, with sustained but low-level circulation (hyperendemicity).

Discuss how model assumptions (e.g. seasonality, homogenous mixing) may bias results toward stable dynamics.

Highlight the implications of this for interpretation and for public health decision-making.

(2) Use of network analysis

We acknowledge the reviewer’s concern. The network analysis was conducted descriptively to characterize cattle movement patterns and the structure of herd connections, but it was not formally incorporated into the model. In revisions we will:

Clarify this distinction in the manuscript to avoid overinterpretation.

Emphasize the need for future modelling work using finer-scale movement data, which could support more realistic herd metapopulation dynamics and better capture heterogeneity in transmission.

(3) RVFV reproductive impacts

While RVF outbreaks are known to cause abortions and neonatal deaths, these occur during relatively rare epidemics. In the Gambian context, where we’re not observing such large episodic outbreaks but rather low-level circulation, the annual impact of RVF infection on births is likely modest compared to baseline herd turnover. Moreover, cattle demography is partly managed, with replacement and movement buffering birth rates against short-term losses.

Our model includes birth as a constant demographic process, it’s reasonable to assume stable population since we are not explicitly modelling outbreak-scale reproductive losses. This is consistent with other RVF transmission models that adopt a similar simplifying assumption. However, we will acknowledge this simplification as a limitation in the revised manuscript.

(4) Missing ODEs for M herds in the dry season

We thank the reviewer for identifying this omission. The ODEs for M herds in the dry season were not included in the appendix due to an oversight, though demographic turnover was incorporated in the model code. We will add the missing equations to the appendix.

(5) Role of immunity loss and model structure (SIR vs. SIRS)

We acknowledge that the decline of detectable antibodies over time (seropositivity decay/seroreversion) is an important consideration in RVFV serology, but whether this reflects true loss of protective immunity after natural infection remains unknown. Biologically, it is plausible that infected cattle develop long-lasting protection, as suggested by studies in humans, but there is an absence of longitudinal field data. From a modelling perspective, our aim was to predict age-seroprevalence curve dependent on FOI estimates and assess its ability to reproduce observed cross-sectional seroprevalence patterns. We therefore adopted a parsimonious SIR framework, treating loss of seropositivity as a potential explanation for the observed age disparity rather than modelling it as loss of immunity. In revisions we will:

Clarify this rationale, emphasising that there is no direct evidence for waning immunity following natural RVFV infection in cattle, although evidence of seropositivity decay has been suggested in human.

Further discuss the seropositivity decay rates predicted in our survey and their possible relation to test sensitivity.

Highlight that while a SIRS structure could generate different long-term dynamics, evaluating this requires stronger evidence for true immunity loss; we consider this an important future modelling direction.

(6) RVFV induced mortality in serocatalytic model

We thank the reviewer for this comment. Disease-induced mortality was included in the serocatalytic model through the mortality parameter (γ), but we recognise that this might not have been sufficiently clear in the text. In revisions we will clarify in the Methods and Appendix.

(7) Clarifying previous vs. current study components

We will revise the Methods and Appendix to make clearer distinctions between our previous work (e.g. household survey data collection, seroprevalence estimates) and the analyses undertaken for this manuscript (e.g. model development and fitting).

(8) Limitations paragraph

We will expand the limitations section to specifically identify the assumptions contributing most to uncertainty. We will then outline how these may bias transmission dynamics and intervention estimates.

(9) Movement ban simulations & suitability of model for vaccination interventions

We appreciate the reviewer’s concerns regarding the movement ban simulation. On reassessment, we agree that our model structure might not be ideally suited to exploring them. In the revised manuscript, we will remove this analysis and emphasize how our modelling framework is more suited to exploring cattle vaccination scenarios, including targeting of specific herd types (e.g. T vs. M vs. L). We note that we are currently developing separate work focused on vaccination strategies in cattle, where this model structure might be more directly applicable, and will reserve a deeper investigation of vaccination interventions for that forthcoming publication.

Reviewer #2 (Public review):

Summary:

The intracellular pathogen Toxoplasma gondii scavenges metal ions such as iron and zinc to support its replication; however, mechanistic studies of iron and zinc uptake are limited. This study investigates the function of a putative iron and zinc transporter, ZFT. In this paper, the authors provide evidence that ZFT mediates iron and zinc uptake by examining the regulation of ZFT expression by iron and zinc levels, the impact of altered ZFT expression on iron sensitivity, and the effects of ZFT depletion on intracellular iron and zinc levels in the parasite. The effects of ZFT depletion on parasite growth are also investigated, showing the importance of ZFT function for the parasite.

Strengths:

A key strength of the study is the use of multiple complementary approaches to demonstrate that ZFT is involved in iron and zinc uptake. Additionally, the authors build on their finding that loss of ZFT impairs parasite growth by showing that ZFT depletion induces stage conversion and leads to defects in both the apicoplast and mitochondrion.

Weaknesses:

(1) Excess zinc was shown not to alter ZFT expression, but a cation chelator (TPEN) did lead to decreased expression. While TPEN is often used to reduce zinc levels, does it have any effect on iron levels? Could the reduction in ZFT after TPEN treatment be due to a reduction in the level of iron or another cation?

(2) ZFT expression was found to be dynamic depending on the size of the vacuole, based on mean fluorescence intensity measurements. Looking at protein levels by Western blot at different times during infection would strengthen this finding.

(3) ZFT localization remained at the parasite periphery under low iron conditions. However, in the images shown in Figure S1c, larger vacuoles (containing 4-8 parasites) are shown for the untreated conditions, and single parasite-containing vacuoles are shown for the low iron condition. As ZFT localization is predominantly at the basal end of the parasite in larger PV and at the parasite periphery for smaller vacuoles, it would be better to compare vacuoles of similar size between the untreated and low-iron conditions.

redo

To get list of combos: ``` letters = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z'] numbers = [1, 2, 3, 4, 5,6 ,7, 8, 9, 0] combinations_letters = itertools.combinations(letters, 3) combinations_numbers = itertools.combinations(numbers, 3) combined_results = itertools.product(combinations_letters, combinations_numbers) # 6 character license

combinations_numbers_2 = itertools.combinations(numbers, 4) combined_results_2 = itertools.product(combinations_letters, combinations_numbers_2) # 7 character license for combo in combined_results: print(combo)

for combo in combined_results_2: print(combo) ``` The number of estimated license plates with the format LLLNNNN is ((26)^3)((10)^4)), which is 175,760,000. For LLLNNN, it is ((26)^3)((10)^4)), which 17,576,000. The total number of combos is 193,336,000.

not complete: letters = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z'] number = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']

final = []

for word in map("".join, product("K", "8", letters,

letters = ['A', 'B', 'C'] num = [1, 2, 3, 4] for i in letters: for j in num: if i != j: print(i + j) Doesn't work

This line is extracted from Goldsmith's "The Vicar of Wakefield", referring to a young woman, Olivia, who was seduced by the wicked Squire Thornhill. Tricked into a fraudulent marriage by the notorious womanizer, she is left disgraced in the eyes of society, and the stigma of her seduction taints her family's reputation by extension. Olivia thus sings a ballad of her own lament: "When lovely woman stoops to folly, / And finds, too late, that men betray, / ... / The only art her guilt to cover, / To hide her shame from ev'ry eye / To give repentance to her lover, / And wring is bosom, is-- to die" (133-4). This line encapsulates the rigid moral standards to which women are held in society: purity and virtue are held above all, thus determining their absolute value as a person. However, although Olivia is framed as a victim of male seduction, she is simultaneously blamed for her own disgrace by "stooping to folly", with the entirety of punishment ultimately falling upon the woman. Furthermore, another important message is emphasized by Olivia: "the only art" left to woman who have lost their chastity is death. Dying emerges as the only feasible resolution to the issue; it acts not only as an expression of despair, but as a socially endorsed means of restoring dignity, banishing guilt, and even receiving remorse from others.

On the other hand, Eliot echoes a similar notion of female disgrace "The Fire Sermon". The typist, passive and disengaged during her forced sexual encounter with the clerk, offers no resistance but equally no desire to his intimate advancements. As a result, her moral reputability is irrevocably tarnished; while she was once "lovely", she is now reduced to a hollow emblem of sexual exploitation and impurity. Additionally, she condemns herself to emotional isolation, "pac[ing] about her room again, alone" (line 253). In this moment, Eliot not only depicts the moral disrepute of this singular woman in the modern wasteland, but gestures towards the broader commentary on the tainted condition of womanhood, stripped of its agency and dignity.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public review):

Shigella flexneri is a bacterial pathogen that is an important globally significant cause of diarrhea. Shigella pathogenesis remains poorly understood. In their manuscript, Saavedra-Sanchez et al report their discovery that a secreted E3 ligase effector of Shigella, called IpaH1.4, mediates the degradation of a host E3 ligase called RNF213. RNF213 was previously described to mediate ubiquitylation of intracellular bacteria, an initial step in their targeting of xenophagosomes. Thus, Shigella IpaH1.4 appears to be an important factor in permitting evasion of RNF213-mediated host defense.

Strengths:

The work is focused, convincing, well-performed, and important. The manuscript is well-written.

We would like to thank the reviewer for their time evaluating our manuscript and the positive assessment of the novelty and importance of our study. We provide a comprehensive response to each of the reviewer’s specific recommendations below and highlight any changes made to the manuscript in response to those recommendations.

Reviewer #1 (Recommendations for the authors):

(1) In the abstract (and similarly on p.10), the authors claim to have shown "IpaH1.4 protein as a direct inhibitor of mammalian RNF213". However, they do not show the interaction is direct. This, in my opinion, would require demonstrating an interaction between purified recombinant proteins. I presume that the authors are relying on their UBAIT data to support the direct interaction, but this is a fairly artificial scenario that might be prone to indirect substrates. I would therefore prefer that the 'direct' statement be modified (or better supported with additional data). Similarly, on p.7, the section heading states "S. flexneri virulence factors IpaH1.4 and IpaH2.5 are sufficient to induce RNF213 degradation". The corresponding experiment is to show sufficiency in a 293T cell, but this leaves open the participation of additional 293T-expressed factors. So I would remove "are sufficient to", or alternatively add "...in 293T cells".

We agree with the reviewer and made the recommended changes to the text in the abstract, in the results section on page 7, and in the Discussion on page 11. During the revision of our manuscript two additional studies were published that provide convincing biochemical evidence for the direct interaction between IpaH1.4 and RNF213 (PMID: 40205224; PMID: 40164614). These studies address the reviewer’s concern extensively and are now briefly discussed and cited in our revised MS.

(2) In the abstract the authors state "Linear (M1-) and lysine-linked ubiquitin is conjugated to bacteria by RNF213 independent of the linear ubiquitin chain assembly complex (LUBAC)." However, it is not shown that RNF213 is able to directly perform M1-ubiquitylation. It is shown that RNF213 is required for M1-linked ubiquitylation in IpaH1.4 or MxiE mutants, this is different than showing conjugation is done by RNF213 itself. This should be reworded.

We agree and edited the text accordingly

(3) Introduction: one of the main points of the paper is that RNF213 conjugates linear ubiquitin to the surface of bacteria in a manner independent of the previously characterized linear ubiquitin conjugation (LUBAC) complex. This is indeed an interesting result, but the introduction does not put this discovery in much context. I would suggest adding some discussion of what was known, if anything, about the type of Ub chain formed by RNF213, and specifically whether linear Ub had previously been observed or not.

We now provide context in the Introduction on page 3 and briefly discuss previous work that had implicated LUBAC in the ubiquitylation of cytosolic bacteria. We emphasize that LUBAC specifically generates linear (M1-linked) ubiquitin chains, while the types of ubiquitin linkages deposited on bacteria through RNF213-dependent pathways had remained unidentified.

(4) Figure 3C: is the difference in 7KR-Ub between WT and HOIP KO cells significant? If so, the authors may wish to acknowledge the possibility that HOIP partially contributes to M1-Ub of MxiE mutant Shigella

The frequencies at which bacteria are decorated with 7KR-Ub is not statistically different between WT and HOIP KO cells. We have included this information in the panel description of Figure 3.

(5) On page 11, the authors state that "...we observed that LUBAC is dispensable for M1-linked ubiquitylation of cytosolic S. flexneri ∆ipaH1.4. We found that lysine-less internally tagged ubiquitin or an M1-specific antibody bound to S. flexneri ∆ipaH1.4 in cells lacking LUBAC (HOIL-1KO or HOIPKO) but failed to bind bacteria in RNF213-deficient cells". In fact, what is shown is that M1-ubiquitylation in ∆ipaH1.4 infection is RNF213-dependent (5E), but the work with lysine mutants, HOIP or HOIL-1 KOs are all with ∆mxiE, not ∆ipaH1.4 (3B) in this version of the manuscript. Ideally, the data with ∆ipaH1.4 could be added, but alternatively, the conclusion could be re-worded.

We now include the data demonstrating that staining of ∆ipaH1.4 with an M1-specific antibody is unchanged from WT cells in HOIL-1 KO and HOIP KO cells. These data are shown in supplementary data (Fig. S3E) and referred to on page 9 of the revised manuscript.

(6) The UBAIT experiment should be explained in a bit more detail in the text. The approach is not necessarily familiar to all readers, and the rationale for using Salmonella-infected ceca/colons is not well explained (and seems odd). Some appropriate caution about interpreting these data might also be welcome. Did HOIP or HOIL show up in the UBAIT? This perhaps also deserves some discussion.

As expected, HOIP (listed under its official gene name Rnf31 in the table of Fig.S2B) was identified as a candidate IpaH1.4 interaction partner as the third most abundant hit from the UBAIT screen. Remarkably, Rnf213 was the hit with the highest abundance in the IpaH1.4 UBAIT screen. To address the reviewer’s comments, we now explain the UBAIT approach in more detail and provide the rational for using intestinal protein lysates from Salmonella infected mice. The text on page 8 reads as follows: “To investigate potential physical interactions between IpaH1.4 and IpaH2.5, we reanalyzed a previously generated dataset that employed a method known as ubiquitin-activated interaction traps (UBAITs) (32). As shown in Fig. S2A, the human ubiquitin gene was fused to the 3′ end of IpaH2.5, producing a C-terminal IpaH2.5-ubiquitin fusion protein. When incubated with ATP, ubiquitin-activating enzyme E1, and ubiquitin-conjugating enzyme E2, the IpaH2.5-ubiquitin "bait" protein is capable of binding to and ubiquitylating target substrates. This ubiquitylation creates an iso-peptide bond between the IpaH2.5 bait and its substrate, thereby enabling purification via a Strep affinity tag incorporated into the fusion construct (32). IpaH2.5-ubiquitin bait and IpaH3-ubiquitin control proteins were incubated with lysates from murine intestinal tissue. To detect interaction partners in a physiologically relevant setting, we used intestinal lysates derived from mice infected with Salmonella, which in contrast to Shigella causes pronounced inflammation in WT mice and therefore better simulates human Shigellosis in an animal model. Using UBAIT we identified HOIP (Rnf31) as a likely IpaH2.5 binding partner (Fig. S2B), thus confirming previous observations (28) and validating the effectiveness our approach. Strikingly, we identified mouse Rnf213 as the most abundant interaction partner of the IpaH2.5-ubiquitin bait protein (Fig. S2B). Collectively, our data and concurrent reports showing direct interactions between IpaH1.4 and human RNF213 (36, 37) indicate that the virulence factors IpaH1.4 and IpaH2.5 directly bind and degrade mouse as well as human RNF213.”

(7) It would be helpful if the authors discussed their results in the context of the prior work showing IpaH1.4/2.5 mediate the degradation of HOIP. Do the authors see HOIP degradation? If indeed HOIP and RNF213 are both degraded by IpaH1.4 and IpaH2.5, are there conserved domains between RNF213 and HOIP being targeted? Or is only one the direct target? A HOIP-RNF213 interaction has previously been shown (https://doi.org/10.1038/s41467-024-47289-2). Since they interact, is it possible one is degraded indirectly? To help clarify this, a simple experiment would be to test if RNF213 degraded in HOIP KO cells (or vice-versa)?

We appreciate the reviewer’s suggestions. We conducted the proposed experiments and found that WT S. flexneri infections result in RNF213 degradation in both WT and HOIP KO cells. Similarly, we found that HOIP degradation was independent of RNF213. We have included these data in Figs. 5A and S3B of our revised submission. A study published during revisions of our paper demonstrates that the LRR of IpaH1.4 binds to the RING domains of both RNF213 and LUBAC (PMID: 40205224). We refer to this work in our revised manuscript.

Reviewer #2 (Public review):

Summary:

The authors find that the bacterial pathogen Shigella flexneri uses the T3SS effector IpaH1.4 to induce degradation of the IFNg-induced protein RNF213. They show that in the absence of IpaH1.4, cytosolic Shigella is bound by RNF213. Furthermore, RNF213 conjugates linear and lysine-linked ubiquitin to Shigella independently of LUBAC. Intriguingly, they find that Shigella lacking ipaH1.4 or mxiE, which regulates the expression of some T3SS effectors, are not killed even when ubiquitylated by RNF213 and that these mutants are still able to replicate within the cytosol, suggesting that Shigella encodes additional effectors to escape from host defenses mediated by RNF213-driven ubiquitylation.

Strengths:

The authors take a variety of approaches, including host and bacterial genetics, gain-of-function and loss-of-function assays, cell biology, and biochemistry. Overall, the experiments are elegantly designed, rigorous, and convincing.

Weaknesses:

The authors find that ipaH1.4 mutant S. flexneri no longer degrades RNF213 and recruits RNF213 to the bacterial surface. The authors should perform genetic complementation of this mutant with WT ipaH1.4 and the catalytically inactive ipaH1.4 to confirm that ipaH1.4 catalytic activity is indeed responsible for the observed phenotype.

We would like to thank the reviewer for their time evaluating our manuscript and the positive assessment of our work, especially its scientific rigor. We conducted the experiment suggested by the reviewer and included the new data in the revised manuscript. As expected, complementation of the ∆ipaH1.4 with WT IpaH1.4 but not with the catalytically dead C338S mutant restored the ability of Shigella to efficiently escape from recognition by RNF213 (Figs. 5C-D).

Reviewer #2 (Recommendations for the authors):

The authors should perform genetic complementation of the ipaH1.4 mutant with WT ipaH1.4 and the catalytically inactive ipaH1.4 to confirm that ipaH1.4 catalytic activity is indeed responsible for the observed phenotype.

We performed the suggested experiment and show in Figs. 5C-D that complementation of the ∆ipaH1.4 mutant with WT IpaH1.4 but not with the catalytically dead C338S mutant restored the ability of Shigella to efficiently escape from recognition by RNF213. These data demonstrate that the catalytic activity of IpaH1.4 is required for evasion of RNF213 binding to the bacteria.

Reviewer #3 (Public review):

Summary:

In this study, the authors set out to investigate whether and how Shigella avoids cell-autonomous immunity initiated through M1-linked ubiquitin and the immune sensor and E3 ligase RNF213. The key findings are that the Shigella flexneri T3SS effector, IpaH1.4 induces degradation of RNF213. Without IpaH1.4, the bacteria are marked with RNF213 and ubiquitin following stimulation with IFNg. Interestingly, this is not sufficient to initiate the destruction of the bacteria, leading the authors to conclude that Shigella deploys additional virulence factors to avoid this host immune response. The second key finding of this paper is the suggestion that M1 chains decorate the mxiE/ipaH Shigella mutant independent of LUBAC, which is, by and large, considered the only enzyme capable of generating M1-linked ubiquitin chains.

Strengths:

The data is for the most part well controlled and clearly presented with appropriate methodology. The authors convincingly demonstrate that IpaH1.4 is the effector responsible for the degradation of RNF213 via the proteasome, although the site of modification is not identified.

Weaknesses:

(1)The work builds on prior work from the same laboratory that suggests that M1 ubiquitin chains can be formed independently of LUBAC (in the prior publication this related to Chlamydia inclusions). In this study, two pieces of evidence support this statement -fluorescence microscopy-based images and accompanying quantification in Hoip and Hoil knockout cells for association of M1-ub, using an antibody, to Shigella mutants and the use of an internally tagged Ub-K7R mutant, which is unable to be incorporated into ubiquitin chains via its lysine residues. Given that clones of the M1-specific antibody are not always specific for M1 chains, and because it remains formally possible that the Int-K7R Ub can be added to the end of the chain as a chain terminator or as mono-ub, the authors should strengthen these findings relating to the claim that another E3 ligase can generate M1 chains de novo.

(2) The main weakness relating to the infection work is that no bacterial protein loading control is assayed in the western blots of infected cells, leaving the reader unable to determine if changes in RNF213 protein levels are the result of the absent bacterial protein (e.g. IpaH1.4) or altered infection levels.

(3)The importance of IFNgamma priming for RNF213 association to the mxiE or ipaH1.4 strain could have been investigated further as it is unclear if RNF213 coating is enhanced due to increased protein expression of RNF213 or another factor. This is of interest as IFNgamma priming does not seem to be needed for RNF213 to detect and coat cytosolic Salmonella.<br /> Overall, the findings are important for the host-pathogen field, cell-autonomous/innate immune signaling fields, and microbial pathogenesis fields. If further evidence for LUBAC independent M1 ubiquitylation is achieved this would represent a significant finding.

We would like to thank the reviewer for their time evaluating our manuscript and the positive assessment of our work and its significance. We provide a comprehensive response to the main three critiques listed under ‘weaknesses’ and also have responded to each of the reviewer’s specific recommendations below. We highlight any changes made to the manuscript in response to those recommendations.

(1) As the reviewer correctly pointed out, 7KR ubiquitin cannot only be used for linear ubiquitylation but can also function as a donor ubiquitin and can be attached as mono-ubiquitin to a substrate or to an existing ubiquitin chain as a chain terminator. To distinguish between 7KR INT-Ub signals originating from linear versus mono-ubiquitylation, we followed the reviewer’s advice and generated a N-terminally tagged 7KR INT-Ub variant. The N-terminal tag prevents linear ubiquitylation but still allows 7KR INT-Ub to be attached as a mono-ubiquitin. We found that the addition of this N-terminal tag significantly reduced but not completely abolished the number of Δ_mxiE_ bacteria decorated with 7KR INT-Ub. These data are shown in a new Fig. S1 and indicate that 7KR lacking the N-terminal tag is attached to bacteria both in the form of linear (M1-linked) ubiquitin and as donor ubiquitin, possibly as a chain terminator. While we cannot rule out that the anti-M1 antibodies used here cross-react with other ubiquitin linkages, we reason that the 7KR data strongly argues that linear ubiquitin is part of the ubiquitin coat encasing IpaH1.4-deficient cytosolic Shigella. Collectively, our data show that both linear and lysine-linked (especially K27 and K63) ubiquitin chains are part of the RNF213-dependent ubiquitin coat on the surface of IpaH1.4 mutants. And furthermore, our data strongly indicate that this ubiquitylation of IpaH1.4 mutants is independent of LUBAC.

(2) We used GFP-expressing strains of S. flexneri for our infection studies and were therefore able to use GFP expression as a loading control. We have incorporated these data into our revised figures. These new data (Figs. 4A, 5A, and S3B) show that bacterial infection levels were comparable between WT and mutant infections and that therefore the degradation of RNF213 (or HOIP – see new data in Fig. S3B) is not due to differences in infection efficiency.

(3) We agree with the reviewer that the mechanism by which RNF213 binds to bacteria is an important unanswered question. Similarly, whether other ISGs have auxiliary functions in this process or whether binding efficiencies vary between different bacterial species are important questions in the field. However, these questions go far beyond the scope of this study and were therefore not addressed in our revisions.

Reviewer #3 (Recommendations for the authors):

(1) An N-terminally tagged K7R-ub should be used as a control to test whether the signal found around the mutant shigella is being added via the N terminal Met into chains. As it is known that certain batches of the M1-specific antibodies are in fact not specific and able to detect other chain types, the authors should test the specificity of the antibody used in this study (eg against different di-Ub linkage types) and include this data in the manuscript.

We agree with the reviewer in principle. The anti-linear ubiquitin (anti-M1) monoclonal antibody, clone 1E3, prominently used in this study was tested by the manufacturer (Sigma) by Western blotting analysis and according to the manufacturer “this antibody detected ubiquitin in linear Ub, but not Ub K11, Ub K48, Ub K63.” However, this analysis did not include all possible Ub linkage types and thus the reviewer is correct that the anti-M1 antibody could theoretically also detect some other linkage types. To address this concern, we added new data during revisions demonstrating that 7KR INT-Ub targeting to S. flexneri is largely dependent on the N-terminus (M1) of ubiquitin. Our combined observations therefore overwhelmingly support the conclusion that linear (M1-linked) as well as K-linked ubiquitin is being attached to the surface of IpH1.4 S. flexneri bacteria in an RNF213-dependent and LUBAC-independent manner.

(2) The M1 signal detected on bacteria with the antibody is still present in either Hoip or Hoil KO’s but due to the potential non-specificity of the antibody, the authors should test whether K7R ub is detected on bacteria in the Hoil ko (in addition to Hoip KO). This would strengthen the authors’ data on LUBAC-independent M1 and is important because Hoil can catalyse non-canonical ubiquitylation.

The specific linear ubiquitin-ligating activity of LUBAC is enacted by HOIP. We show that linear ubiquitylation of susceptible S. flexneri mutants as assessed by anti-M1 ubiquitin staining or 7KR INT-Ub recruitment occurs in HOIPKO cells at WT levels (Figs. 3B, 3C, S3E [new data]). In our view , these data unequivocally show that the observed linear ubiquitylation of cytosolic S. flexneri ipaH1.4 and mxiE mutants is independent of LUBAC.

(3) For Figure 4A, do mxiE bacteria show similar invasion - authors should include a bacterial protein control to show levels of bacteria in WT and mxiE infected conditions. A similar control should be included in Figure 5A.

We used GFP-expressing strains of S. flexneri for our infection studies and were therefore able to use GFP expression as a loading control. We have incorporated these data into our revised figures. These new data (Figs. 4A, 5A, and S3B) show that bacterial infection levels were comparable between WT and mutant infections and that therefore the degradation of RNF213 (or HOIP – see new data in Fig. S3B) is not due to differences in infection efficiency.

(4) Can the authors speculate why IFNg priming is needed for the coating of Shigella mxiE mutant but not in the case of Salmonella or Burkholderia? Is this just amounts of RNF213 or something else?

In our studies we did not directly compare ubiquitylation rates of cytosolic Shigella, Burkholderia, and Salmonella bacteria with each other under the same experimental conditions. However, such a direct comparison is needed to determine whether IFNgamma priming is required for RNF213-dependent bacterial ubiquitylation of some but not other pathogens. Two papers published during the revisions of our manuscript (PMID: 40164614, PMID: 40205224) reports robust RNF213 targeting to IpaH1.4 Shigella mutants in unprimed cells HeLa cells (whereas we used A549 and HT29 cells). Therefore, differences in reagents, cell lines, and/or other experimental conditions may determine whether IFNgamma priming is necessary to observe substantial RNF213 translocation to cytosolic bacteria.

(5) Typos - there are several, but this is hard to annotate with line numbers so the authors should proofread again carefully.

We proofread the manuscript and corrected the small number of typos we identified

Author response:

The following is the authors’ response to the previous reviews

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Wang and Colleagues present a study aimed at demonstrating the feasibility of repeated ultrasound localization microscopy (ULM) recording sessions on mice chronically implanted with a cranial window transparent to US. They provided quantitative information on their protocol, such as the required number of Contrast enhancing microbubbles (MBs) to get a clear image of the vasculature of a brain coronal section. Also, they quantified the co-registration quality over time-distant sessions and the vasodilator effect of isoflurane.

Strengths:

The study showed a remarkable performance in recording precisely the same brain coronal section over repeated imaging sessions. In addition, it sheds light on the vasodilator effect of isoflurane (an anesthetic whose effects are not fully understood) on the different brain vasculature compartments, although, as the Authors stated, some insights in this aspect have already been published with other imaging techniques. The experimental setting and protocol are very well described.

Wang and co-authors submitted a revised version of their study, which shows improvements in the clarity of the data description.

However, the flaws and limitations of this study are substantially unchanged.

The main issues are:

Statistics are still inadequate. The TOST test proposed in this revised version is not equivalent to an ANOVA. Indeed, multivariate analyses should be the most appropriate, given that some quantifications were probably made on multiple vessels from different mice. The 3 reviewers mentioned the flaws in statistics as the primary concern.

Response 01: We thank the reviewer for raising this important point. We fully acknowledge the limitations of our current statistical analysis. We would like to clarify that the TOST procedure was applied exclusively to the measurements taken from the same vessel segment in the same animal across different time points, with the purpose of evaluating the consistency of vessel diameter measurements. We recognize that the statistical analysis in this study remains limited, which we have acknowledged as a key limitation in the manuscript. This constraint arises primarily from the limited number of animals, and our analysis should be interpreted as a representative case study rather than a generalized statistical conclusion. We have revised the manuscript to clarify these points and to more explicitly acknowledge the statistical limitations.

(Line 329) “Our current study primarily focused on demonstrating the feasibility of longitudinal ULM imaging in awake animals, instead of conducting a systematic investigation of how isoflurane anesthesia alters cerebral blood flow. Due to the limited number of animals used, the analyses presented in this work should be interpreted as example case studies. While the trends observed across animals were consistent, the small sample size restricts the scope of statistical inference. For future work, it would be valuable to design more rigorous control experiments with larger sample sizes to systematically compare the effects of isoflurane anesthesia, awake states, and other anesthetics that do not induce vasodilation on cerebral blood flow.”

No new data has been added, such as testing other anesthetics.

Response 02: We acknowledge that the current study does not include data involving other anesthetics, and we have also discussed this point in our initial response. In fact, we did attempt to use other anesthetics such as ketamine. However, we found it difficult to draw reliable conclusions due to experimental limitations such as variable anesthesia recovery profiles and injection timing, as elaborated in the following paragraphs. Therefore, we decided not to include these data in the current study to avoid potential misinterpretation.

One major limitation of our experimental setup is that imaging in the awake state is necessarily conducted after a brief period of isoflurane-anesthesia. This brief anesthesia allows for the intravenous injection of microbubbles via the tail vein. Isoflurane is particularly suited for this purpose due to its rapid onset and offset. Mice can recover quickly once the gas is withdrawn, which enables relatively consistent post-anesthesia imaging in the awake state.

In contrast, other anesthetic agents present challenges. Their recovery profiles are slower, more variable, and less controllable. Reversal drugs can be administered to awaken the animals, but they add another variability. These may lead to greater fluctuations in cerebral hemodynamics and factors introduce uncertainty in the timing of bolus microbubble injection. As such, our current setup is not ideal for systematically comparing different anesthetics and could yield misleading results.

A more appropriate strategy for comparing awake ULM imaging with different anesthetics would be performing awake imaging first, followed by imaging under anesthesia. This would ensure that the awake condition is free from residual anesthetic effects. However, this method raises higher requirement in bubble delivery, as no anesthesia can be used for the intravenous injection.

To address this, we are actively exploring another solution using indwelling jugular vein catheterization. By surgically implanting a catheter into the jugular vein prior to imaging, we can establish a stable and reproducible route for microbubble delivery in fully awake animals without any anesthesia induction. This method has the potential to enable direct and reliable comparisons across different physiological states. However, the implementation of this technique and the associated experimental findings go beyond the scope of the current study and will be presented in a future manuscript.

In the present work, we have emphasized the methodological limitations of our approach and clarified that our primary goal is to highlight the necessity and feasibility of awake-state ULM imaging. The focus is not to comprehensively characterize the effects of different anesthetic agents on microvascular brain flow. We appreciate your understanding and interest in this important future direction. 

Based the responses and previous revision, we have further refined the discussion of the relevant limitations:

(Line 324) “Although isoflurane is widely used in ultrasound imaging because it provides long-lasting and stable anesthetic effects, it is important to note that the vasodilation observed with isoflurane is not representative of all anesthetics. Some anesthesia protocols, such as ketamine combined with medetomidine, do not produce significant vasodilation and are therefore preferred in experiments where vascular stability is essential, such as functional ultrasound imaging. Our current study primarily focused on demonstrating the feasibility of longitudinal ULM imaging in awake animals, instead of conducting a systematic investigation of how isoflurane anesthesia alters cerebral blood flow. Due to the limited number of animals used, the analyses presented in this work should be interpreted as example case studies. While the trends observed across animals were consistent, the small sample size restricts the scope of statistical inference. For future work, it would be valuable to design more rigorous control experiments with larger sample sizes to systematically compare the effects of isoflurane anesthesia, awake states, and other anesthetics that do not induce vasodilation on cerebral blood flow.”

(Line 347) “Another limitation of this study is the potential residual vasodilatory effect of isoflurane anesthesia on awake imaging sessions and the short imaging window available after bolus injection. The awake imaging sessions were conducted shortly after the mice had emerged from isoflurane anesthesia, required for the MB bolus injections. The lasting vasodilatory effects of isoflurane may have influenced vascular responses, potentially contributing to an underestimation of differences in vascular dynamics between anesthetized and awake state. In addition, since microbubbles are rapidly cleared from circulation, the duration of effective imaging is limited to only a few minutes, which also overlaps with the anesthesia recovery period, constraining the usable awake-state imaging window. Future improvement on microbubble infusion using an indwelling jugular vein catheter presents a promising alternative to address these limitations. This method allows for stable microbubble infusion without the need for anesthesia induction, ensuring that the awake imaging condition is free from residual anesthetic effects. Moreover, it has the potential to extend the duration of imaging sessions, offering a longer and more stable time window for data acquisition. Furthermore, by performing ULM imaging in the awake state first, instead of starting with anesthetized imaging, researchers can achieve a more rigorous comparison of how various anesthetics influence cerebral microvascular dynamics relative to the awake baseline.”

The Authors still insist on using the term Vascularity which they define as: 'proportion of the pixel count occupied by blood vessels within each ROI, obtained by binarizing the ULM vessel density maps and calculating the percentage of the pixels with MB signal.'. Why not use apparent cerebral blood volume or just CBV? Introducing an unnecessary and redundant term is not scientifically acceptable. In this revised version, vascularity is also used to indicate a higher vascular density (Line 275), which does not make sense: blood vessels do not generate from the isoflurane to the awake condition in a few minutes. Rev2 also raised this point.

Response 03: Thank you for revisiting this important point. We acknowledge that the term vascularity is difficult to interpret for readers, and we also recognize that we did not sufficiently justify its use in the earlier version.

Based on your suggestion, we have now replaced all instances of “vascularity” with “fractional vessel area”. While the underlying definition remains the same, fractional vessel area offers a more intuitive description. The term “fractional” denotes that the vessel area is normalized to the total area of the selected ROI. This normalization is essential for fair comparisons across ROIs of different sizes, such as Figures 4i–k to evaluate various brain regions. We would also like to clarify that this was not introduced as an unnecessary or redundant term, but rather as a more suitable metric for longitudinal ULM analysis. We did consider using apparent cerebral blood volume (CBV), estimated from microbubble counts. However, we found that it was less robust and meaningful in the context of longitudinal ULM comparisons. Below we provide further justification for using the vessel area instead:

(1) Using the vessel area is more robust:

In longitudinal ULM comparisons, normalization across time points is essential to enable fair and meaningful comparisons. In our study, we normalized the data based on a cumulative 5 million microbubbles (e.g., Fig. 2). Other normalization strategies could also be adopted, as long as the resulting vascular maps reach a sufficiently saturated state. However, even with normalization, it remains important to use a quantitative metric that is minimally biased and invariant to experimental fluctuations across time points. Vessel area, derived from binarized vessel maps, is less sensitive to variations in acquisition time and microbubble concentration. This is because repeated microbubble trajectories through the same location are not counted multiple times. In contrast, apparent CBV, calculated from the microbubble counts, is more susceptible to different concentration conditions. Since repeated detections in the same location accumulate, the metric can be dependent on injection efficiency and imaging duration. While CBV may still be valid under well-controlled, steady-state conditions, we found the vessel area to be a more robust and reliable metric for longitudinal analysis under our current bolus-injection protocol.

(2) Using the vessel area is more meaningful:

Compared to CBV, the vessel area provides a more direct representation of structural characteristics such as vessel diameter. Anesthesia-induced vasodilation leads to an increase in vessel diameter. Although local diameter changes can be assessed by manually selecting vessel segments, this approach is labor-intensive and prone to selection bias. To enable a more comprehensive and objective assessment of such morphological changes, fractional vessel area provides a more informative alternative to CBV, as it captures diameter-related variations at a global or regional scale, and avoids potential biases associated with manually selecting specific vessels or regions.

In response to: vascularity is also used to indicate a higher vascular density (Line 275), which does not make sense: blood vessels do not generate from the isoflurane to the awake condition in a few minutes.

We agree that blood vessels cannot be generated in a few minutes. Vascularity (now fractional vessel area) should be interpreted as apparent vessel density, which reflects a probabilistic estimate of vessel density based on the detectable microbubble. 

Both apparent vessel density and apparent CBV are indirect, sampling-based approximations of vascular features, and both are fundamentally limited by microbubble detection sensitivity. Low microbubble concentrations lead to underestimation of both CBV and vessel area. A change from zero to non-zero in these metrics does not imply the physical appearance or disappearance of vessels, but rather reflects a change in the likelihood of detecting flow in each region.

In summary, while neither fractional vessel area (vascularity in previous versions) nor apparent CBV is a perfect metric due to the inherent limitations of ULM, we believe the vessel area provides a more robust and meaningful parameter for our longitudinal comparisons. We have revised the main text to include this explanation and acknowledge the limitations and interpretation of fractional vessel area more explicitly.

Revision in Results:

(Line 181) “To validate the broader applicability of our findings, we conducted ROI-based analyses using fractional vessel area and mean velocity as primary metrics. These metrics extended the analysis of vessel diameter and flow velocity to entire brain regions or selected ROIs, which provides a more objective assessment of cerebral blood flow changes at a global scale and reduces the bias associated with manually selecting vessel segments. For vessel area measurements, the term fractional denotes that the vessel area is normalized to the total area of the selected ROI. This normalization is essential for fair comparisons across ROIs of different sizes.”

Revision in Methods: definition of vascularity

(Line 571) “In ROI-based analysis, we focused on two primary parameters: fractional vessel area and mean velocity. Fractional vessel area was defined as the proportion of the pixel count occupied by blood vessels within each ROI, obtained by binarizing the ULM vessel density maps and calculating the percentage of the pixels with MB signal. Mean velocity was calculated by averaging all non-zero pixel of velocity estimates within the ROI. The velocity distribution within each ROI was also visualized using violin plots, as shown in Fig. 2, 4 and 6, to illustrate the range and density of flow velocity estimates across different acquisition. In this study, we focused on these two metrics because they represent the most straightforward extension of single-vessel analysis to brain-wide vascular changes.”

We put our ROI analysis code on GitHub and added a “Code availability” section. We hope it can serve as a foundation for users to explore different quantitative metrics in their own longitudinal ULM studies. We hope to provide an example to inspire further exploration.

(Line 578) “Code availability

To support quantitative longitudinal analysis of ULM data, we developed an open-source MATLAB application (https://github.com/ekerwang/ULMQuantitativeAnalysis). This tool is designed to facilitate ROI-based analysis of ULM images for longitudinal comparisons. It supports multiple quantification metrics, including but not limited to vessel area and mean velocity used in this study. Users can select and adapt different metrics based on their specific applications, as a wide range of ULM-based quantification metrics have been developed for different pathological and pharmacological studies.”

The long-term recordings mentioned by the Authors refer to the 3-week time frame analyzed in this study. However, within each acquisition, the time available from imaging is only a few minutes (< 10', referring to most of the plots showing time courses) after the animals' arousal from isoflurane and before bubbles disappear. This limitation should be acknowledged.

Response 04: Thank you for this comment. We agree that the current imaging sessions are constrained by the short time window available after the animal’s arousal from isoflurane and before bubbles disappear. This limitation indeed restricts the duration of usable awake-state imaging in our current bolus injection protocol. As discussed earlier, we are actively exploring the use of a jugular vein catheterization approach to address this limitation. This approach has the potential to extend the imaging session duration and provide a longer, more stable time window. We have now acknowledged this limitation more explicitly in the revised Discussion section.

(Line 347) “Another limitation of this study is the potential residual vasodilatory effect of isoflurane anesthesia on awake imaging sessions and the short imaging window available after bolus injection. The awake imaging sessions were conducted shortly after the mice had emerged from isoflurane anesthesia, required for the MB bolus injections. The lasting vasodilatory effects of isoflurane may have influenced vascular responses, potentially contributing to an underestimation of differences in vascular dynamics between anesthetized and awake state. In addition, since microbubbles are rapidly cleared from circulation, the duration of effective imaging is limited to only a few minutes, which also overlaps with the anesthesia recovery period, constraining the usable awake-state imaging window. Future improvement on microbubble infusion using an indwelling jugular vein catheter presents a promising alternative to address these limitations. This method allows for stable microbubble infusion without the need for anesthesia induction, ensuring that the awake imaging condition is free from residual anesthetic effects. Moreover, it has the potential to extend the duration of imaging sessions, offering a longer and more stable time window for data acquisition. Furthermore, by performing ULM imaging in the awake state first, instead of starting with anesthetized imaging, researchers can achieve a more rigorous comparison of how various anesthetics influence cerebral microvascular dynamics relative to the awake baseline.”

The more precise description of the number of mice and blood vessels analyzed in Figure 6 makes it apparent the limited number of independent samples used to support the findings of this work. A limitation that should be acknowledged. The newly provided information added as Supplementary Figure 1 should be moved to the main text, eventually in the figure legends. The limited data in support of the findings was also highlighted by Rev2 and, indirectly, by Rev3.

Response 05: We acknowledge the limited number of independent samples used in this study. In the revised manuscript, we have explicitly emphasized this limitation in the Discussion section. Specifically, we added the following statement:

(Line 329) “Our current study primarily focused on demonstrating the feasibility of longitudinal ULM imaging in awake animals, instead of conducting a systematic investigation of how isoflurane anesthesia alters cerebral blood flow. Due to the limited number of animals used, the analyses presented in this work should be interpreted as example case studies. While the trends observed across animals were consistent, the small sample size restricts the scope of statistical inference. For future work, it would be valuable to design more rigorous control experiments with larger sample sizes to systematically compare the effects of isoflurane anesthesia, awake states, and other anesthetics that do not induce vasodilation on cerebral blood flow.”

Following your suggestion, we have also moved the newly provided information (the table in Supplementary Figure 1) into figure captions. In addition, we have modified in the Methods section to ensure that this information is clear.

(Line 406) “Eight healthy female C57 mice (8-12 weeks) were used for this study, numbered as Mouse 1 to Mouse 8. Three mice (Mouse 1–3) were used to compare imaging results between awake and anesthetized states (Fig. 3 and 4). Three additional mice (Mouse 4–6) underwent longitudinal imaging over a three-week period (Fig. 5 and 6). Among them, Mouse 4 was also used as an example to demonstrate the overall system schematic and saturation conditions (Fig. 1 and 2). Several mice (Mouse 2, 6, 7, and 8) exhibited suboptimal cranial window quality or image artifacts and were included to illustrate common surgical or imaging issues (Supplementary Fig. 1). The specific usage of each animal is also annotated in the corresponding figure captions.”

Reviewer #2 (Public Review):

The authors present a very interesting collection of methods and results using brain ultrasound localization microscopy (ULM) in awake mice. They emphasize the effect of the level of anesthesia on the quantifiable elements assessable with this technique (i.e. vessel diameter, flow speed, in veins and arteries, area perfused, in capillaries) and demonstrate the possibility of achieving longitudinal cerebrovascular assessment in one animal during several weeks with their protocol.

The authors made a good rewriting of the article based on the reviewers' comments. One of the message of the first version of the manuscript was that variability in measurements (vessel diameter, flow velocity, vascularity) were much more pronounced under changes of anesthesia than when considering longitudinal imaging across several weeks. This message is now not quite mitigated, as longitudinal imaging seems to show a certain variability close to the order of magnitude observed under anesthesia. In that sense, the review process was useful in avoiding hasty conclusion and calls for further caution in ULM awake longitudinal imaging, in particular regarding precision of positioning and cancellation of tissue motion.

Strengths:

Even if the methods elements considered separately are not new (brain ULM in rodents, setup for longitudinal awake imaging similar to those used in fUS imaging, quantification of vessel diameters/bubble flow/vessel area), when masterfully combined as it is done in this paper, they answer two questions that have been longrunning in the community: what is the impact of anesthesia on the parameters measured by ULM (and indirectly in fUS and other techniques)? Is it possible to achieve ULM in awake rodents for longitudinal imaging? The manuscript is well constructed, well written, and graphics are appealing.

The manuscript has been much strengthened by the round of review, with more animals for the longitudinal imaging study.

Weaknesses:

Some weaknesses remain, not hindering the quality of the work, that the authors might want to answer or explain.

When considering fig 4e and fig 4j together: it seems that in fig 4e the vascularity reduction in the cortical ROI is around 30% for downward flow, and around 55% for upward flow; but when grouping both cortical flows in fig 4j, the reduction is much smaller (~5%), even at the individual level (only mouse 1 is used in fig 4e). Can you comment on that?

Response 06: Thank you for carefully pointing this out. This discrepancy arises primarily from differences in ROI selections.

The vascularity metric (now we changed the term into fractional vessel area, based on Reviewer 1’s comments) is calculated as the proportion of vessel-occupied pixels relative to the total ROI area. As such, it is best suited for longitudinal comparisons within the same ROI rather than across-ROI comparisons, particularly when the size and vessel composition of the ROIs differ.

In Fig. 4e, the cortical ROI includes mostly the penetrating vessels, which are selected due to their clear distinction between upward (venous) and downward (arterial) flow directions. Pial vessels were intentionally excluded because flow direction alone does not reliably distinguish arteries from veins in these surface vessels. Thus, the goal of this analysis was to indicate arteriovenous differences, rather than to represent the full cortical vascular changes.

In contrast, the ROIs used in Fig. 4j aim to provide a more comprehensive view of cortical vascular responses without distinguishing flow direction. That’s why both penetrating and pial vessels are included. Since pial vessels showed relatively smaller vascularity changes within the coronal cross-sections analyzed in our study, their inclusion in the cortical ROI likely contributed to the smaller overall reduction in vascularity observed in Figure 4j.

To address this potential confusion, we have added further clarification in the Results section of the revised manuscript.

(Line 209) “It is worth noting that prior analyses (Fig. 4d–h) aimed to illustrate arteriovenous differences. Since pial vessels are difficult to distinguish as arteries or veins based on flow direction in coronal plane imaging, they were excluded from the ROI selection in those analyses. In the current whole-brain comparisons (Fig. 4i-k), the cortical ROIs no longer exclude pial vessels, since distinguishing between arteries and veins is not required. This aims to provide a more comprehensive representation of cortical vasculature.”

When considering fig 4e, fig 4j, fig 6e and fig 6i altogether, it seems that vascularity can be highly variable, whether it be under anesthesia or vascular imaging, with changes between 5 to 40%. Is this vascularity quantification worth it (namely, reliable for example to quantify changes in a pathological model requiring longitudinal imaging)?

Response 07: Thank you for raising this important point. We found that imaging in the awake state is inherently more variable than under anesthesia. In contrast, anesthetized imaging offers a more controlled and stable physiological condition, as anesthesia suppresses many sources of variation. For pathological studies, if the vascular or hemodynamic changes induced by anesthesia do not interfere with the scientific question being addressed, imaging under anesthesia can still be a practical and effective approach, due to its experimental simplicity and better physiological consistency.

The higher variability observed in awake imaging arises from both physiological fluctuations in animals and unavoidable experimental inconsistencies, such as small misalignment on the imaging plane across sessions. If the research question aims to avoid the confounding effects of anesthesia, then instead of suppressing variation through anesthesia, it is important to acknowledge the natural baseline variation in the awake state. However, efforts should be made to minimize technical sources of variation. We have added a brief discussion of this issue at the end of the manuscript to reflect this consideration.

(Line 396) “However, it is also important to note that although longitudinal awake imaging presents promise to avoid the confounding effects of anesthetics, imaging under anesthesia remains more convenient and controllable in many cases. For applications where the physiological question of interest is not sensitive to anesthesia-induced vascular effects, anesthetized imaging still offers a simpler and more stable approach. Awake imaging inherently exhibits greater physiological variability. However, care must be taken at the experimental level to minimize confounding sources of variation, such as stress level of the animal or handling inconsistencies, to ensure that the measurements are physiologically meaningful.”

Regarding whether fractional vessel area (formerly referred to as vascularity) is a worthwhile metric for longitudinal quantification: based on our experience and comparisons, we found vessel area to be relatively robust and informative (see also Response 02 to Reviewer 1 for details). However, we acknowledge that other quantitative metrics—such as microbubble count, tortuosity, or flow directionality—may be more suitable depending on the specific pathological model or research question. How these metrics perform in awake imaging and longitudinal disease models is indeed an open and important question. We hope our work can serve as a foundation to inspire further investigation in this direction. To facilitate such exploration, we have developed and open-sourced a MATLAB-based analysis tool that supports multiple quantitative ULM metrics for longitudinal comparison. We encourage users to adapt and extend this framework to evaluate different quantitative metrics.

(Line 578) “Code availability

To support quantitative longitudinal analysis of ULM data, we developed an open-source MATLAB application (https://github.com/ekerwang/ULMQuantitativeAnalysis). This tool is designed to facilitate ROI-based analysis of ULM images for longitudinal comparisons. It supports multiple quantification metrics, including but not limited to vessel area and mean velocity used in this study. Users can select and adapt different metrics based on their specific applications, as a wide range of ULM-based quantification metrics have been developed for different pathological and pharmacological studies.”

Reviewer #2 (Recommendations For The Authors):

Images in figure 4 lack color bars.

Response 08: Thank you for pointing this out. The color bars for the images in Figure 4 are the same as those used in the corresponding images in Figure 3. We have now added the explanation of color bars to the revised version of Figure 4 caption.

Fig 4d: upward and downward are probably swapped.

Response 09: Thank you for pointing this out, and we apologize for the oversight. They were mistakenly swapped. We have corrected this error in the revised figure.

No quantitative conclusions are drawn regarding the changes in vessel diameter under anesthesia? Is it not significant? If it is not then why bring changes in diameter to our attention in fig 3 (white arrows) and figure 4b?

Response 10: Our intention in highlighting diameter changes in Figure 3 (white arrows) and Figure 4b was to provide an illustrative example of isoflurane-induced diameter changes at the single-vessel level. These examples are meant to serve as case studies, not as the basis for broad statistical conclusions.

In the initial version of the manuscript, we attempted to draw quantitative conclusions by measuring vessel diameters from ten manually selected vessel segments at each location. However, based on feedback from other reviewers, we decided to remove this analysis in the revised version. Manual selection of vessel segments is highly subjective and prone to bias, limiting its reliability for quantitative interpretation.

Instead, we focused on ROI-based analysis using fractional vessel area (formerly referred to as vascularity), which reflects widespread changes in vessel diameter across regions. It is a more generalizable and less biased metric for quantifying vascular diameter changes.

We further explained this in the Results section:

(Line 181) “To validate the broader applicability of our findings, we conducted ROI-based analyses using fractional vessel area and mean velocity as primary metrics. These metrics extended the analysis of vessel diameter and flow velocity to entire brain regions or selected ROIs, which provides a more objective assessment of cerebral blood flow changes at a global scale and reduces the bias associated with manually selecting vessel segments. For vessel area measurements, the term fractional denotes that the vessel area is normalized to the total area of the selected ROI. This normalization is essential for fair comparisons across ROIs of different sizes.”

Line 210 "In summary, statistical analysis revealed a decrease in individual vessel diameter" this does not seem to be supported by this version of the manuscript as no analysis is done on a representative group of vessels for the diameter.

Response 11: Thank you for pointing out this important issue. In line with our previous response (Response 10), we would like to clarify that the analysis of individual vessel diameter was intended to serve as an example study, rather than a statistically supported conclusion based on a group of vessels. To avoid confusion, we have removed the phrase “statistical analysis revealed a decrease in individual vessel diameter” from the manuscript. 

The meaning of the *** in fig 6b and 6c should be clarified as: -it is not explicitly stated - the equivalence test interpretation is less usual than other tests.

Response 12: We thank the reviewer for pointing out this important issue. We agree that the use of asterisks (***) in Fig. 6b and 6c may have led to confusion, as such markers are typically associated with statistical significance in difference testing. In our case, the analysis was based on the two one-sided test (TOST) procedure to assess statistical equivalence, which is indeed less commonly used and could be misinterpreted.

To address this, we have replaced the asterisks *** in the figure with the label “equiv.”, which more clearly reflects the intended interpretation. Additionally, we have revised the figure caption and the main text to explicitly state that these markers denote statistical equivalence (not difference) as determined by TOST, with the equivalence margin defined as three times the standard deviation of one week.

(Figure 6 Caption) “Statistical analysis was performed using the two one-sided test (TOST) to evaluate consistency of measurement. The label “equiv.” indicates statistically equivalent measurements (p < 0.001), defined as interweek differences smaller than three times the standard deviation of one week.”

(Line 240) “Statistical testing of equivalence was conducted using the two one-sided test (TOST) procedure, which evaluates whether the difference between two time points falls within a predefined equivalence margin. Specifically, equivalence is defined as the inter-week difference being smaller than three times the standard deviation of one week. A statistically significant result in TOST (p < 0.001) supports the interpretation that the measurements are statistically equivalent, which is denoted as “equiv.” in the figures.”

Line 237 and following: please consider rephrasing into "To further generalize these findings and examine longitudinal variation in ROI-based analysis, we used Mouse 4 as an example to show the consistency of blood flow density across different flow directions in the cortex (Fig. 6d) and extended the quantitative analysis to all three mice (Fig. 6e) (individual ULM upward and downward flow images for all three mice over the threeweek longitudinal study period can be found in Supplementary Fig. 4)." The paragraph will make much more sense.

Response 13: We appreciate your helpful rephrasing. We have fully adopted your proposed revision to enhance the clarity and coherence of the text. The sentence now reads exactly as you recommended:

(Line 250): “To further generalize these findings and examine longitudinal variation in ROI-based analysis, we used Mouse 4 as an example to show the consistency of blood flow density across different flow directions in the cortex (Fig. 6d) and extended the quantitative analysis to all three mice (Fig. 6e) (individual ULM upward and downward flow images for all three mice over the three-week longitudinal study period can be found in Supplementary Fig. 4).”

Line 248: "While arterial and venous flow velocity distributions exhibit clear distinctions, their variations over the three weeks remained acceptable" the meaning of acceptable remains elusive.

Response 14: Thank you for pointing out the ambiguity in the phrase “remained acceptable”. To improve clarity and precision, we have revised the sentence to provide a more informative description. The updated sentence now reads:

(Line 261) “While arterial and venous flow velocity distributions exhibit clear distinctions, the distribution shapes remained relatively consistent across the three weeks. Specifically, variation in median velocity were within 1 mm/s. In contrast, anesthesia-induced changes can lead to velocity shifts exceeding 1 mm/s.”

Line 253: consider rephrasing in "Despite subcortical regions showing the largest vascularity variability consecutive to anesthesia-induced changes, vascularity in those regions was relatively stable values in the longitudinal study" as otherwise the link between the 2 parts of the sentence feels odd.

Response 15: Thank you for your constructive suggestion regarding the logical flow of the sentence. We fully agree with your point and have revised the sentence exactly as you proposed.

(Line 268) “Despite subcortical regions showing the largest vascularity variability consecutive to anesthesia-induced changes, vascularity in those regions was relatively stable values in the longitudinal study.”

Reviewer #1 (Public review):

Summary:

In this manuscript, Weir et al. investigate why the 13-lined ground squirrel (13LGS) retina is unusually rich in cone photoreceptors, the cells responsible for color and daylight vision. Most mammals, including humans, have rod-dominant retinas, making the 13LGS retina both an intriguing evolutionary divergence and a valuable model for uncovering novel mechanisms of cone generation. The developmental programs underlying this adaptation were previously unknown.

Using an integrated approach that combines single-cell RNA sequencing (scRNAseq), scATACseq, and histology, the authors generate a comprehensive atlas of retinal neurogenesis in 13LGS. Notably, comparative analyses with mouse datasets reveal that in 13LGS, cones can arise from late-stage neurogenic progenitors, a striking contrast to mouse and primate retinas, where late progenitors typically generate rods and other late-born cell types but not cones. They further identify a shift in the timing (heterochrony) of expression of several transcription factors. Further, the authors show that these factors act through species-specific regulatory elements. And overall, functional experiments support a role for several of these candidates in cone production.

Strengths:

This study stands out for its rigorous and multi-layered methodology. The combination of transcriptomic, epigenomic, and histological data yields a detailed and coherent view of cone development in 13LGS. Cross-species comparisons are thoughtfully executed, lending strong evolutionary context to the findings. The conclusions are, in general, well supported by the evidence, and the datasets generated represent a substantial resource for the field. The work will be of high value to both evolutionary neurobiology and regenerative medicine, particularly in the design of strategies to replace lost cone photoreceptors in human disease.

Weaknesses:

(1) Overall, the conclusions are strongly supported by the data, but the paper would benefit from additional clarifications. In particular, some of the conclusions could be toned down slightly to reflect that the observed changes in candidate gene function, such as those for Zic3 by itself, are modest and may represent part of a more complex regulatory network.

(2) Additional explanations about the cell composition of the 13LGS retina are needed. The ratios between cone and rod are clearly detailed, but do those lead to changes in other cell types?

(3) Could the lack of a clear trajectory for rod differentiation be just an effect of low cell numbers for this population?

(4) The immunohistochemistry and RNA hybridization experiments shown in Figure S2 would benefit from supporting controls to strengthen their interpretability. While it has to be recognized that performing immunostainings on non-conventional species is not a simple task, negative controls are necessary to establish the baseline background levels, especially in cases where there seems to be labeling around the cells. The text indicates that these experiments are both immunostainings and ISH, but the figure legend only says "immunohistochemistry". Clarifying these points would improve readers' confidence in the data.

(5) Figure S3: The text claims that overexpression of Zic3 alone is sufficient to induce the cone-like photoreceptor precursor cells as well as horizontal cell-like precursors, but this is not clear in Figure S3A nor in any other figure. Similarly, the effects of Pou2f1 overexpression are different in Figure S3A and Figure S3B. In Figure S3B, the effects described (increased presence of cone-like and horizontal-like precursors) are very clear, whereas it is not in Figure S3A. How are these experiments different?

(6) The analyses of Zic3 conditional mutants (Figure S4) reveal an increase in many cone, rod, and pan-photoreceptor genes with only a reduction in some cone genes. Thus, the overall conclusion that Zic3 is essential for cones while repressing rod genes doesn't seem to match this particular dataset.

(7) Throughout the text, the authors used the term "evolved". To substantiate this claim, it would be important to include sequence analyses or to rephrase to a more neutral term that does not imply evolutionary inference.

Reviewer #3 (Public review):

Summary:

The authors perform deep transcriptomic and epigenetic comparisons between mouse and 13-lined ground squirrel (13LGS) to identify mechanisms that drive rod vs cone-rich retina development. Through cross-species analysis, the authors find extended cone generation in 13LGS, gene expression within progenitor/photoreceptor precursor cells consistent with a lengthened cone window, and differential regulatory element usage. Two of the transcription factors, Mef2c and Zic3, were subsequently validated using OE and KO mouse lines to verify the role of these genes in regulating competence to generate cone photoreceptors.

Strengths:

Overall, this is an impactful manuscript with broad implications toward our understanding of retinal development, cell fate specification, and TF network dynamics across evolution and with the potential to influence our future ability to treat vision loss in human patients. The generation of this rich new dataset profiling the transcriptome and epigenome of the 13LGS is a tremendous addition to the field that assuredly will be useful for numerous other investigations and questions of a variety of interests. In this manuscript, the authors use this dataset and compare it to data they previously generated for mouse retinal development to identify 2 new regulators of cone generation and shed insights into their regulation and their integration into the network of regulatory elements within the 13LGS compared to mouse.

Weaknesses:

(1) The authors chose to omit several cell classes from analyses and visualizations that would have added to their interpretations. In particular, I worry that the omission of 13LGS rods, early RPCs, and early NG from Figures 2C, D, and F is notable and would have added to the understanding of gene expression dynamics. In other words, (a) are these genes of interest unique to late RPCs or maintained from early RPCs, and (b) are rod networks suppressed compared to the mouse?

(2) The authors claim that the majority of cones are generated by late RPCs and that this is driven primarily by the enriched enhancer network around cone-promoting genes. With the temporal scRNA/ATACseq data at their disposal, the authors should compare early vs late born cones and RPCs to determine whether the same enhancers and genes are hyperactivated in early RPCs as well as in the 13LGS. This analysis will answer the important question of whether the enhancers activated/evolved to promote all cones, or are only and specifically activated within late RPCs to drive cone genesis at the expense of rods.

(3) The authors repeatedly use the term 'evolved' to describe the increased number of local enhancer elements of genes that increase in expression in 13LGS late RPCs and cones. Evolution can act at multiple levels on the genome and its regulation. The authors should consider analysis of sequence level changes between mouse, 13LGS, and other species to test whether the enhancer sequences claimed to be novel in the 13LGS are, in fact, newly evolved sequence/binding sites or if the binding sites are present in mouse but only used in late RPCs of the 13LGS.

(4) The authors state that 'Enhancer elements in 13LGS are predicted to be directly targeted by a considerably greater number of transcription factors than in mice'. This statement can easily be misread to suggest that all enhancers display this, when in fact, this is only the cone-promoting enhancers of late 13LGS RPCs. In a way, this is not surprising since these genes are largely less expressed in mouse vs 13LGS late RPCs, as shown in Figure 2. The manuscript is written to suggest this mechanism of enhancer number is specific to cone production in the 13LGS- it would help prove this point if the authors asked the opposite question and showed that mouse late RPCs do not have similar increased predicted binding of TFs near rod-promoting genes in C7-8.

Reviewer #1 (Public review):

In this important study, the authors develop a suite of machine vision tools to identify and align fluorescent neuronal recording images in space and time according to neuron identity and position. The authors provide compelling evidence for the speed and utility of these tools. While such tools have been developed in the past (including by the authors), the key advancement here is the speed and broad utility of these new tools. While prior approaches based on steepest descent worked, they required hundreds of hours of computational time, while the new approaches outlined here are >600-fold faster. The machine vision tools here should be immediately useful to readers specifically interested in whole-brain C. elegans data, but also for more general readers who may be interested in using BrainAlignNet for tracking fluorescent neuronal recordings from other systems.

I really enjoyed reading this paper. The authors had several ground truth examples to quantify the accuracy of their algorithms and identified several small caveats users should consider when using these tools. These tools were primarily developed for C. elegans, an animal with stereotyped development, but whose neurons can be variably located due to internal motion of the body. The authors provide several examples of how BrainAlignNet reliably tracked these neurons over space and time. Neuron identity is also important to track, and the authors showed how AutoCellLoader can reliably identify neurons based on their fluorescence in the NeuroPAL background. A challenge with NeuroPAL though, is the high expression of several fluorophores, which compromises behavioral fidelity. The authors provide some possible avenues where this problem can be addressed by expressing fewer fluorophores. While using all four channels provided the best performance, only using the tagRFP and CyOFP channels was sufficient for performance that was close to full performance using all 4 NeuroPAL channels. This result indicates that the development of future lines with less fluorophore expression could be sufficient for reliable neuronal identification, which would decrease the genetic load on the animal, but also open other fluorescent channels that could be used for tracking other fluorescent tools/markers. Even though these tools were developed for C. elegans specifically, they showed BrainAlignNet can be applied to other organisms as well (in their case, the cnidarian C. hemisphaerica), which broadens the utility of their tools.

Strengths:

(1) The authors have a wealth of ground-truth training data to compare their algorithms against, and provide a variety of metrics to assess how well their new tools perform against hand annotation and/or prior algorithms.

(2) For BrainAlignNet, the authors show how this tool can be applied to other organisms besides C. elegans.

(3) The tools are publicly available on GitHub, which includes useful README files and installation guidance.

Weaknesses:

(1) Most of the utility of these algorithms is for C. elegans specifically. Testing their algorithms (specifically BrainAlignNet) on more challenging problems, such as whole-brain zebrafish, would have been interesting. This is a very, very minor weakness, though.

(2) The tools are benchmarked against their own prior pipeline, but not against other algorithms written for the same purpose.

(3) Considerable pre-processing was done before implementation. Expanding upon this would improve accessibility of these tools to a wider audience.

Reviewer #3 (Public review):

Context:

Tracking cell trajectories in deformable organs, such as the head neurons of freely moving C. elegans, is a challenging task due to rapid, non-rigid cellular motion. Similarly, identifying neuron types in the worm brain is difficult because of high inter-individual variability in cell positions.

Summary:

In this study, the authors developed a deep learning-based approach for cell tracking and identification in deformable neuronal images. Several different CNN models were trained to: (1) register image pairs without severe deformation, and then track cells across continuous image sequences using multiple registration results combined with clustering strategies; (2) predict neuron IDs from multicolor-labeled images; and (3) perform clustering across multiple multicolor images to automatically generate neuron IDs.

Strengths:

Directly using raw images for registration and identification simplifies the analysis pipeline, but it is also a challenging task since CNN architectures often struggle to capture spatial relationships between distant cells. Surprisingly, the authors report very high accuracy across all tasks. For example, the tracking of head neurons in freely moving worms reportedly reached 99.6% accuracy, neuron identification achieved 98%, and automatic classification achieved 93% compared to human annotations.

Weaknesses:

(1) The deep networks proposed in this study for registration and neuron identification require dataset-specific training, due to variations in imaging conditions across different laboratories. This, in turn, demands a large amount of manually or semi-manually annotated training data, including cell centroid correspondences and cell identity labels, which reduces the overall practicality and scalability of the method.

(2) The cell tracking accuracy was not rigorously validated, but rather estimated using a biased and coarse approach. Specifically, the accuracy was assessed based on the stability of GFP signals in the eat-4-labeled channel. A tracking error was assumed to occur when the GFP signal switched between eat-4-negative and eat-4-positive at a given time point. However, this estimation is imprecise and only captures a small subset of all potential errors. Although the authors introduced a correction factor to approximate the true error rate, the validity of this correction relies on the assumption that eat-4 neurons are uniformly distributed across the brain - a condition that is unlikely to hold.

(3) Figure S1F demonstrates that the registration network, BrainAlignNet, alone is insufficient to accurately align arbitrary pairs of C. elegans head images. The high tracking accuracy reported is largely due to the use of a carefully designed registration sequence, matching only images with similar postures, and an effective clustering algorithm. Although the authors address this point in the Discussion section, the abstract may give the misleading impression that the network itself is solely responsible for the observed accuracy.

(4) The reported accuracy for neuron identification and automatic classification may be misleading, as it was assessed only on a subset of neurons labeled as "high-confidence" by human annotators. Although the authors did not disclose the exact proportion, various descriptions (such as Figure 4f) imply that this subset comprises approximately 60% of all neurons. While excluding uncertain labels is justifiable, the authors highlight the high accuracy achieved on this subset without clearly clarifying that the reported performance pertains only to neurons that are relatively easy to identify. Furthermore, they do not report what fraction of the total neuron population can be accurately identified using their methods-an omission of critical importance for prospective users.

Author response:

Reviewer #1 (Public review):

In this important study, the authors develop a suite of machine vision tools to identify and align fluorescent neuronal recording images in space and time according to neuron identity and position. The authors provide compelling evidence for the speed and utility of these tools. While such tools have been developed in the past (including by the authors), the key advancement here is the speed and broad utility of these new tools. While prior approaches based on steepest descent worked, they required hundreds of hours of computational time, while the new approaches outlined here are >600-fold faster. The machine vision tools here should be immediately useful to readers specifically interested in whole-brain C. elegans data, but also for more general readers who may be interested in using BrainAlignNet for tracking fluorescent neuronal recordings from other systems.

I really enjoyed reading this paper. The authors had several ground truth examples to quantify the accuracy of their algorithms and identified several small caveats users should consider when using these tools. These tools were primarily developed for C. elegans, an animal with stereotyped development, but whose neurons can be variably located due to internal motion of the body. The authors provide several examples of how BrainAlignNet reliably tracked these neurons over space and time. Neuron identity is also important to track, and the authors showed how AutoCellLoader can reliably identify neurons based on their fluorescence in the NeuroPAL background. A challenge with NeuroPAL though, is the high expression of several fluorophores, which compromises behavioral fidelity. The authors provide some possible avenues where this problem can be addressed by expressing fewer fluorophores. While using all four channels provided the best performance, only using the tagRFP and CyOFP channels was sufficient for performance that was close to full performance using all 4 NeuroPAL channels. This result indicates that the development of future lines with less fluorophore expression could be sufficient for reliable neuronal identification, which would decrease the genetic load on the animal, but also open other fluorescent channels that could be used for tracking other fluorescent tools/markers. Even though these tools were developed for C. elegans specifically, they showed BrainAlignNet can be applied to other organisms as well (in their case, the cnidarian C. hemisphaerica), which broadens the utility of their tools.

Strengths:

(1) The authors have a wealth of ground-truth training data to compare their algorithms against, and provide a variety of metrics to assess how well their new tools perform against hand annotation and/or prior algorithms.

(2) For BrainAlignNet, the authors show how this tool can be applied to other organisms besides C. elegans.

(3) The tools are publicly available on GitHub, which includes useful README files and installation guidance.

We thank the reviewer for noting these strengths of our study.

Weaknesses:

(1) Most of the utility of these algorithms is for C. elegans specifically. Testing their algorithms (specifically BrainAlignNet) on more challenging problems, such as whole-brain zebrafish, would have been interesting. This is a very, very minor weakness, though.

We appreciate the reviewer’s point that expanding to additional animal models would be valuable. In the study, we have so far tested our approaches on C. elegans and Jellyfish. Given that this is considered a ‘very, very minor weakness’ and that it does not directly affect the results or analyses in the paper, we think this might be better to address in future work.

(2) The tools are benchmarked against their own prior pipeline, but not against other algorithms written for the same purpose.

We agree that it would be valuable to benchmark other labs’ software pipelines on our datasets. We note that most papers in this area, which describe those pipelines, provide the same performance metrics that we do (accuracy of neuron identification, tracking accuracy, etc), so a crude, first-order comparison can be obtained by comparing the numbers in the papers. But, we agree that a rigorous head-to-head comparison would require applying these different pipelines to a common dataset. We considered performing these analyses, but we were concerned that using other labs’ software ‘off the shelf’ on our data might not represent those pipelines in their best light when compared to our pipeline that was developed with our data in mind. Data from different microscopy platforms can be surprisingly different and we wouldn’t want to perform an analysis that had this bias. Therefore, we feel that this comparison would be best pursued by all of these labs collaboratively (so that they can each provide input on how to run their software optimally). Indeed, this is an important area for future study. In this spirit, we have been sharing our eat-4::GFP datasets (that permit quantification of tracking accuracy) with other labs looking for additional ways to benchmark their tracking software.

We also note that there are not really any pipelines to directly compare against CellDiscoveryNet, as we are not aware of any other fully unsupervised approach for neuron identification in C. elegans.

(3) Considerable pre-processing was done before implementation. Expanding upon this would improve accessibility of these tools to a wider audience.

Indeed, some pre-processing was performed on images before registration and neuron identification -- understanding these nuances can be important. The pre-processing steps are described in the Results section and detailed in the Methods. They are also all available in our open-source software. For BrainAlignNet, the key steps were: (1) selecting image registration problems, (2) cropping, and (3) Euler alignment. Steps (1) and (3) were critically important and are extensively discussed in the Results and Discussion sections of our study (lines 142-144, 218-234, 318-323, 704-712). Step (2) is standard in image processing. For AutoCellLabeler and CellDiscoveryNet, the pre-processing was primarily to align the 4 NeuroPAL color channels to each other (i.e. make sure the blue/red/orange/etc channels for an animal are perfectly aligned). This is also just a standard image processing step to ensure channel alignment. Thus, the more “custom” pre-processing steps were extensively discussed in the study and the more “common” steps are still described in the Methods. The implementation of all steps is available in our open-source software.

Reviewer #2 (Public review):

Summary:

The paper introduced the pipeline to analyze brain imaging of freely moving animals: registering deforming tissues and maintaining consistent cell identities over time. The pipeline consists of three neural networks that are built upon existing models: BrainAlignNet for non-rigid registration, AutoCellLabeler for supervised annotation of over 100 neuronal types, and CellDiscoveryNet for unsupervised discovery of cell identities. The ambition of the work is to enable high-throughput and largely automated pipelines for neuron tracking and labeling in deforming nervous systems.

Strengths:

(1) The paper tackles a timely and difficult problem, offering an end-to-end system rather than isolated modules.

(2) The authors report high performance within their dataset, including single-pixel registration accuracy, nearly complete neuron linking over time, and annotation accuracy that exceeds individual human labelers.

(3) Demonstrations across two organisms suggest the methods could be transferable, and the integration of supervised and unsupervised modules is of practical utility.

We thank the reviewer for noting these strengths of our study.

Weaknesses:

(1) Lack of solid evaluation. Despite strong results on their own data, the work is not benchmarked against existing methods on community datasets, making it hard to evaluate relative performance or generality.

We agree that it would be valuable to benchmark many labs’ software pipelines on some common datasets, ideally from several different research labs. We note that most papers in this area, which describe the other pipelines that have been developed, provide the same performance metrics that we do (accuracy of neuron identification, tracking accuracy, etc), so a crude, first-order comparison can be obtained by comparing the numbers in the papers. But, we agree that a rigorous head-to-head comparison would require applying these different pipelines to a common dataset. We considered performing these analyses, but we were concerned that using other labs’ software ‘off the shelf’ and comparing the results to our pipeline (where we have extensive expertise) might bias the performance metrics in favor of our software. Therefore, we feel that this comparison would be best pursued by all of these labs collaboratively (so that they can each provide input on how to run their software optimally). Indeed, this is an important area for future study. In this spirit, we have been sharing our eat-4::GFP datasets (that permit quantification of tracking accuracy) with other labs looking for additional ways to benchmark their tracking software.

We also note that there are not really any pipelines to directly compare against CellDiscoveryNet, as we are not aware of any other fully unsupervised approach for neuron identification in C. elegans.

(2) Lack of novelty. All three models do not incorporate state-of-the-art advances from the respective fields. BrainAlignNet does not learn from the latest optical flow literature, relying instead on relatively conventional architectures. AutoCellLabeler does not utilize the advanced medNeXt3D architectures for supervised semantic segmentation. CellDiscoveryNet is presented as unsupervised discovery but relies on standard clustering approaches, with limited evaluation on only a small test set.

We appreciate that the machine learning field moves fast. Our goal was not to invent entirely novel machine learning tools, but rather to apply and optimize tools for a set of challenging, unsolved biological problems. We began with the somewhat simpler architectures described in our study and were largely satisfied with their performance. It is conceivable that newer approaches would perhaps lead to even greater accuracy, flexibility, and/or speed. But, oftentimes, simple or classical solutions can adequately resolve specific challenges in biological image processing.

Regarding CellDiscoveryNet, our claim of unsupervised training is precise: CellDiscoveryNet is trained end-to-end only on raw images, with no human annotations, pseudo-labels, external classifiers, or metadata used for training, model selection, or early stopping. The loss is defined entirely from the input data (no label signal). By standard usage in machine learning, this constitutes unsupervised (often termed “self-supervised”) representation learning. Downstream clustering is likewise unsupervised, consuming only image pairs registered by CellDiscoveryNet and neuron segmentations produced by our previously-trained SegmentationNet (which provides no label information).

(3) Lack of robustness. BrainAlignNet requires dataset-specific training and pre-alignment strategies, limiting its plug-and-play use. AutoCellLabeler depends heavily on raw intensity patterns of neurons, making it brittle to pose changes. By contrast, current state-of-the-art methods incorporate spatial deformation atlases or relative spatial relationships, which provide robustness across poses and imaging conditions. More broadly, the ANTSUN 2.0 system depends on numerous manually tuned weights and thresholds, which reduces reproducibility and generalizability beyond curated conditions.

Regarding BrainAlignNet: we agree that we trained on each species’ own data (worm, jellyfish) and we would suggest other labs working on new organisms to do the same based on our current state of knowledge. It would be fantastic if there was an alignment approach that generalized to all possible cases of non-rigid-registration in all animals – an important area for future study. We also agree that pre-alignment was critical in worms and jellyfish, which we discuss extensively in our study (lines 142-144, 318-321, 704-712).

Regarding AutoCellLabeler: the animals were not recorded in any standardized pose and were not aligned to each other beforehand – they were basically in a haphazard mix of poses and we used image augmentation to allow the network to generalize to other poses, as described in our study. It is still possible that AutoCellLabeler is somehow brittle to pose changes (e.g. perhaps extremely curved worms) – while we did not detect this in our analyses, we did not systematically evaluate performance across all possible poses. However, we do note that this network was able to label images taken from freely-moving worms, which by definition exhibit many poses (Figure 5D, lines 500-525); aggregating the network’s performance across freely-moving data points allowed it to nearly match its performance on high-SNR immobilized data. This suggests a degree of robustness of the AutoCellLabeler network to pose changes.

Regarding ANTSUN 2.0: we agree that there are some hyperparameters (described in our study) that affect ANTSUN performance. We agree that it would be worthwhile to fully automate setting these in future iterations of the software.

Evaluation:

To make the evaluation more solid, it would be great for the authors to (1) apply the new method on existing datasets and (2) apply baseline methods on their own datasets. Otherwise, without comparison, it is unclear if the proposed method is better or not. The following papers have public challenging tracking data: https://elifesciences.org/articles/66410, https://elifesciences.org/articles/59187, https://www.nature.com/articles/s41592-023-02096-3.

Please see our response to your point (1) under Weaknesses above.

Methodology:

(1) The model innovations appear incrementally novel relative to existing work. The authors should articulate what is fundamentally different (architectural choices, training objectives, inductive biases) and why those differences matter empirically. Ablations isolating each design choice would help.

There are other efforts in the literature to solve the neuron tracking and neuron identification problems in C. elegans (please see paragraphs 4 and 5 of our Introduction, which are devoted to describing these). However, they are quite different in the approaches that they use, compared to our study. For example, for neuron tracking they use t->t+1 methods, or model neurons as point clouds, etc (a variety of approaches have been tried). For neuron identification, they work on extracted features from images, or use statistical approaches rather than deep neural networks, etc (a variety of approaches have been tried). Our assessment is that each of these diverse approaches has strengths and drawbacks; we agree that a meta-analysis of the design choices used across studies could be valuable.

We also note that there are not really any pipelines to directly compare against CellDiscoveryNet, as we are not aware of any other fully unsupervised approach for neuron identification in C. elegans.

(2) The pipeline currently depends on numerous manually set hyperparameters and dataset-specific preprocessing. Please provide principled guidelines (e.g., ranges, default settings, heuristics) and a robustness analysis (sweeps, sensitivity curves) to show how performance varies with these choices across datasets; wherever possible, learn weights from data or replace fixed thresholds with data-driven criteria.

We agree that there are some ANTSUN 2.0 hyperparameters (described in our Methods section) that could affect the quality of neuron tracking. It would be worthwhile to fully automate setting these in future iterations of the software, ensuring that the hyperparameter settings are robust to variation in data/experiments.

Appraisal:

The authors partially achieve their aims. Within the scope of their dataset, the pipeline demonstrates impressive performance and clear practical value. However, the absence of comparisons with state-of-the-art algorithms such as ZephIR, fDNC, or WormID, combined with small-scale evaluation (e.g., ten test volumes), makes the strength of evidence incomplete. The results support the conclusion that the approach is useful for their lab's workflow, but they do not establish broader robustness or superiority over existing methods.

We wish to remind the reviewer that we developed BrainAlignNet for use in worms and jellyfish. These two animals have different distributions of neurons and radically different anatomy and movement patterns. Data from the two organisms was collected in different labs (Flavell lab, Weissbourd lab) on different types of microscopes (spinning disk, epifluorescence). We believe that this is a good initial demonstration that the approach has robustness across different settings.

Regarding comparisons to other labs’ C. elegans data processing pipelines, we agree that it will be extremely valuable to compare performance on common datasets, ideally collected in multiple different research labs. But we believe this should be performed collaboratively so that all software can be utilized in their best light with input from each lab, as described above. We agree that such a comparison would be very valuable.

Impact:

Even though the authors have released code, the pipeline requires heavy pre- and post-processing with numerous manually tuned hyperparameters, which limits its practical applicability to new datasets. Indeed, even within the paper, BrainAlignNet had to be adapted with additional preprocessing to handle the jellyfish data. The broader impact of the work will depend on systematic benchmarking against community datasets and comparison with established methods. As such, readers should view the results as a promising proof of concept rather than a definitive standard for imaging in deformable nervous systems.

Regarding worms vs jellyfish pre-processing: we actually had the exact opposite reaction to that of the reviewer. We were surprised at how similar the pre-processing was for these two very different organisms. In both cases, it was essential to (1) select appropriate registration problems to be solved; and (2) perform initialization with Euler alignment. Provided that these two challenges were solved, BrainAlignNet mostly took care of the rest. This suggests a clear path for researchers who wish to use this approach in another animal. Nevertheless, we also agree with the reviewer’s caution that a totally different use case could require some re-thinking or re-strategizing. For example, the strategy of how to select good registration problems could depend on the form of the animal’s movement.

Reviewer #3 (Public review):

Context:

Tracking cell trajectories in deformable organs, such as the head neurons of freely moving C. elegans, is a challenging task due to rapid, non-rigid cellular motion. Similarly, identifying neuron types in the worm brain is difficult because of high inter-individual variability in cell positions.

Summary:

In this study, the authors developed a deep learning-based approach for cell tracking and identification in deformable neuronal images. Several different CNN models were trained to: (1) register image pairs without severe deformation, and then track cells across continuous image sequences using multiple registration results combined with clustering strategies; (2) predict neuron IDs from multicolor-labeled images; and (3) perform clustering across multiple multicolor images to automatically generate neuron IDs.

Strengths:

Directly using raw images for registration and identification simplifies the analysis pipeline, but it is also a challenging task since CNN architectures often struggle to capture spatial relationships between distant cells. Surprisingly, the authors report very high accuracy across all tasks. For example, the tracking of head neurons in freely moving worms reportedly reached 99.6% accuracy, neuron identification achieved 98%, and automatic classification achieved 93% compared to human annotations.

We thank the reviewer for noting these strengths of our study.

Weaknesses:

(1) The deep networks proposed in this study for registration and neuron identification require dataset-specific training, due to variations in imaging conditions across different laboratories. This, in turn, demands a large amount of manually or semi-manually annotated training data, including cell centroid correspondences and cell identity labels, which reduces the overall practicality and scalability of the method.

We performed dataset-specific training for image registration and neuron identification, and we would encourage new users to do the same based on our current state of knowledge. This highlights how standardization of whole-brain imaging data across labs is an important issue for our field to address and that, without it, variations in imaging conditions could impact software utility. We refer the reviewer to an excellent study by Sprague et al. (2025) on this topic, which is cited in our study.

However, at the same time, we wish to note that it was actually reasonably straightforward to take the BrainAlignNet approach that we initially developed in C. elegans and apply it to jellyfish. Some of the key lessons that we learned in C. elegans generalized: in both cases, it was critical to select the right registration problems to solve and to preprocess with Euler registration for good initialization. Provided that those problems were solved, BrainAlignNet could be applied to obtain high-quality registration and trace extraction. Thus, our study provides clear suggestions on how to use these tools across multiple contexts.

(2) The cell tracking accuracy was not rigorously validated, but rather estimated using a biased and coarse approach. Specifically, the accuracy was assessed based on the stability of GFP signals in the eat-4-labeled channel. A tracking error was assumed to occur when the GFP signal switched between eat-4-negative and eat-4-positive at a given time point. However, this estimation is imprecise and only captures a small subset of all potential errors. Although the authors introduced a correction factor to approximate the true error rate, the validity of this correction relies on the assumption that eat-4 neurons are uniformly distributed across the brain - a condition that is unlikely to hold.

We respectfully disagree with this critique. We considered the alternative suggested by the reviewer (in their private comments to the authors) of comparing against a manually annotated dataset. But this annotation would require manually linking ~150 neurons across ~1600 timepoints, which would require humans to manually link neurons across timepoints >200,000 times for a single dataset. These datasets consist of densely packed neurons rapidly deforming over time in all 3 dimensions. Moreover, a single error in linking would propagate across timepoints, so the error tolerance of such annotation would be extremely low. Any such manually labeled dataset would be fraught with errors and should not be trusted. Instead, our approach relies on a simple, accurate assumption: GFP expression in a neuron should be roughly constant over a 16min recording (after bleach correction) and the levels will be different in different neurons when it is sparsely expressed. Because all image alignment is done in the red channel, the pipeline never “peeks” at the GFP until it is finished with neuron alignment and tracking. The eat-4 promoter was chosen for GFP expression because (a) the nuclei labeled by it are scattered across the neuropil in a roughly salt-and-pepper fashion – a mixture of eat-4-positive and eat-4-negative neurons are found throughout the head; and (b) it is in roughly 40% of the neurons, giving very good overall coverage. Our view is that this approach of labeling subsets of neurons with GFP should become the standard in the field for assessing tracking accuracy – it has a simple, accurate premise; is not susceptible to human labeling error; is straightforward to implement; and, since it does not require manual labeling, is easy to scale to multiple datasets. We do note that it could be further strengthened by using multiple strains each with different ‘salt-and-pepper’ GFP expression patterns.

(3) Figure S1F demonstrates that the registration network, BrainAlignNet, alone is insufficient to accurately align arbitrary pairs of C. elegans head images. The high tracking accuracy reported is largely due to the use of a carefully designed registration sequence, matching only images with similar postures, and an effective clustering algorithm. Although the authors address this point in the Discussion section, the abstract may give the misleading impression that the network itself is solely responsible for the observed accuracy.

Our tracking accuracy requires (a) a careful selection of registration problems, (b) highly accurate registration of the selected registration problems, and (c) effective clustering. We extensively discussed the importance of the choosing of the registration problems in the Results section (lines 218-234 and 318-321), Discussion section (lines 704-708), and Methods section (955-970 and 1246-1250) of our paper. We also discussed the clustering aspect in the Results section (lines 247-259), Discussion section (lines 708-712), and Methods section (lines 1162-1206). In addition, our abstract states that the BrainAlignNet needs to be “incorporated into an image analysis pipeline,” to inform readers that other aspects of image analysis need to occur (beyond BrainAlignNet) to perform tracking.

(4) The reported accuracy for neuron identification and automatic classification may be misleading, as it was assessed only on a subset of neurons labeled as "high-confidence" by human annotators. Although the authors did not disclose the exact proportion, various descriptions (such as Figure 4f) imply that this subset comprises approximately 60% of all neurons. While excluding uncertain labels is justifiable, the authors highlight the high accuracy achieved on this subset without clearly clarifying that the reported performance pertains only to neurons that are relatively easy to identify. Furthermore, they do not report what fraction of the total neuron population can be accurately identified using their methods-an omission of critical importance for prospective users.

The reviewer raises two points here: (1) whether AutoCellLabeler accuracy is impacted by ease of human labeling; and (2) what fraction of total neurons are identified. We address them one at a time.

Regarding (1), we believe that the reviewer overlooked an important analysis in our study. Indeed, to assess its performance, one can only compare AutoCellLabeler’s output against accurate human labels – there is simply no way around it. However, we noted that AutoCellLabeler was identifying some neurons with high confidence even when humans had low confidence or had not even tried to label the neurons (Fig. 4F). To test whether these were in fact accurate labels, we asked additional human labelers to spend extra time trying to label a random subset of these neurons (they were of course blinded to the AutoCellLabeler label). We then assessed the accuracy of AutoCellLabeler against these new human labels and found that they were highly accurate (Fig. 4H). This suggests that AutoCellLabeler has strong performance even when some human labelers find it challenging to label a neuron. However, we agree that we have not yet been able to quantify AutoCellLabeler performance on the small set of neuron classes that humans are unable to identify across datasets.

Regarding (2), we agree that knowing how many neurons are labeled by AutoCellLabeler is critical. For example, labeling only 3 neurons per animal with 100% accuracy isn’t very helpful. We wish to emphasize that we did not omit this information: we reported the number of neurons labeled for every network that we characterized in the study, alongside the accuracy of those labels (please see Figures 4I, 5A, and 6G; Figure 4I also shows the number of human labels per dataset, which the reviewer requested). We also showed curves depicting the tradeoff between accuracy and number of neurons labeled, which fully captures how we balanced accuracy and number of neurons labeled (Figures 5D and S4A). It sounds like the reviewer also wanted to know the total number of recorded neurons. The typical number of recorded neurons per dataset can also be found in the paper in Fig. 2E.

Author response:

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

Reviewer #1 (Public review): 

Summary: 

The study investigated how individuals living in urban slums in Salvador, Brazil, interact with environmental risk factors, particularly focusing on domestic rubbish piles, open sewers, and a central stream. The study makes use of the step selection functions using telemetry data, which is a method to estimate how likely individuals move towards these environmental features, differentiating among groups by gender, age, and leptospirosis serostatus. The results indicated that women tended to stay closer to the central stream while avoiding open sewers more than men. Furthermore, individuals who tested positive for leptospirosis tended to avoid open sewers, suggesting that behavioral patterns might influence exposure to risk factors for leptospirosis, hence ensuring more targeted interventions. 

Strengths: 

(1) The use of step selection functions to analyze human movement represents an innovative adaptation of a method typically used in animal ecology. This provides a robust quantitative framework for evaluating how people interact with environmental risk factors linked to infectious diseases (in this case, leptospirosis). 

(2) Detailed differentiation by gender and serological status allows for nuanced insights, which can help tailor targeted interventions and potentially improve public health measures in urban slum settings. 

(3) The integration of real-world telemetry data with epidemiological risk factors supports the development of predictive models that can be applied in future infectious disease research, helping to bridge the gap between environmental exposure and health outcomes. 

Weaknesses: 

(1) The sample size for the study was not calculated, although it was a nested cohort study. 

We thank Reviewer #1 for highlighting this weakness. We will make sure that this is explained in the next version of the manuscript. At the time of recruiting participants, we found no literature on how to perform a sample size calculation for movement studies involving GPS loggers and associated methods of analysis. Therefore, we aimed to recruit as many individuals as possible within the resource constraints of the study.  

“Participants who were already enrolled in the cohort study were recruited to take part in the movement analysis study. At the time of recruitment, we found no published scientific studies detailing how to perform sample size calculations for research using GPS data in humans. Therefore, we opted to use convenience sampling instead. A target of 30 people per study area, balanced by gender and blind to their serological status, was chosen for this study.” [Lines 163 - 169]

(2) The step‐selection functions, though a novel method, may face challenges in fully capturing the complexity of human decision-making influenced by socio-cultural and economic factors that were not captured in the study. 

We agree with Reviewer #1 that this model may fail to capture the full breadth of human decisionmaking when it comes to moving through local environments. We included a section discussing the aspect of violence and how this influences residents’ choices, along with some possibilities on how to record and account for this. Although it is outside of the scope of this study, we believe that coupling these quantitative methods with qualitative studies would provide a comprehensive understanding of movement in these areas.  

(3) The study's context is limited to a specific urban slum in Salvador, Brazil, which may reduce the generalizability of its findings to other geographical areas or populations that experience different environmental or socio-economic conditions. 

We thank the reviewer for highlighting this limitation. We have made this more clear in the discussion section: 

“As a result, the findings are biased towards the more represented individuals, limiting their generalisability. Additionally, all participants are from specific areas in Salvador, which may further limit the generalisability to similar contexts.” [Lines 561 - 564]

(4) The reliance on self-reported or telemetry-based movement data might include some inaccuracies or biases that could affect the precision of the selection coefficients obtained, potentially limiting the study's predictive power. 

We agree that telemetry data has inherent inaccuracies, which we have tried to account for by using only those data points within the study areas. We would like to clarify that there is no self-reported movement data used in this study. All movement data was collected using GPS loggers.  

(5) Some participants with less than 50 relocations within the study area were excluded without clear justification, see line 149. 

We found that the SSF models would not run properly if there weren’t enough relocations. Therefore, we decided to remove these individuals from the analysis. They are also removed from any descriptive statistics presented. We have now clarified this in the manuscript.  

“Individuals with less than 50 relocations within the study area were excluded from the analysis to ensure good model convergence. Details of these excluded individuals can be found in Supplementary Material I.” [Lines 183 – 186]

(6) Some figures are not clear (see Figure 4 A & B). 

We have improved the resolution of the image and believe it is more clear now. Please let us know if the resolution still is not clear enough.  

(7) No statement on conflict of interest was included, considering sponsorship of the study. 

The conflict of interest forms for each author were sent to eLife separately. I believe these should be made available upon publication, but please reach out if these need to be re-sent.  

Reviewer #2 (Public review): 

Summary: 

Pablo Ruiz Cuenca et al. conducted a GPS logger study with 124 adult participants across four different slum areas in Salvador, Brazil, recording GPS locations every 35 seconds for 48 hours. The aim of their study was to investigate step-selection models, a technique widely used in movement ecology to quantify contact with environmental risk factors for exposure to leptospires (open sewers, community streams, and rubbish piles). The authors built two different types of models based on distance and based on buffer areas to model human environmental exposure to risk factors. They show differences in movement/contact with these risk factors based on gender and seropositivity status. This study shows the existence of modest differences in contact with environmental risk factors for leptospirosis at small spatial scales based on socio-demographics and infection status. 

Strengths: 

The authors assembled a rich dataset by collecting human GPS logger data, combined with fieldrecorded locations of open sewers, community streams, and rubbish piles, and testing individuals for leptospirosis via serology. This study was able to capture fine-scale exposure dynamics within an urban environment and shows differences by gender and seropositive status, using a method novel to epidemiology (step selection). 

Weaknesses: 

Due to environmental data being limited to the study area, exposure elsewhere could not be captured, despite previous research by Owers et al. showing that the extent of movement was associated with infection risk. Limitations of step selection for use in studying human participants in an urban environment would need to be explicitly discussed. 

The environmental factors used in the study required research teams to visit the sites and map the locations. Given that individuals travelled throughout the city of Salvador, performing this task at a large scale would be unachievable. Therefore, we limited the data to only those points within the study area boundaries to avoid any biases from interactions with unrecorded environmental factors.  

Reviewing Editor Comments: 

The manuscript would benefit from clearer articulation of SSF assumptions, data exclusions, and buffer choices, as well as improvements in figure clarity, to strengthen its generalizability and impact. 

Please see replies to Reviewer #2 below regarding the assumptions (2.3), data exclusions (2.1) and buffer choices (2.2). We have improved Figure 4 clarity, please let us know if this is not sufficient.  

Reviewer #1 (Recommendations for the authors): 

(1) Provide comprehensive details on telemetry data collection for improved data quality and reproducibility. 

Details for this are included under the “Methods/GPS Data” section. We have included a sentence to explain that we used to GPS device manufacturer’s software to programme them. We believe this provides enough information on how to collect the data for reproducibility, but please let us know if there is further information that we could provide.  

“Individuals who consented to take part in this study were asked to wear GPS loggers for continuous periods of up to 48 hours, which could be repeated. The GPS loggers used were i-got U GT-600, set to record their location every 35 seconds. We used the manufacturer’s software to programme the devices. Data were collected between March and November 2022.” [Lines 172 - 176]

(2) Check all figures and improve on clarity (see Figure 4). 

We have updated Figure 4 and believe the resolution is better now. Please let us know if this it not the case from the readers perspective.  

(3) Revisit sentence structures to improve readability and reduce overly complex phrasing. 

We have reviewed the manuscript and made some changes to improve readability. 

Reviewer #2 (Recommendations for the authors): 

I thank Ruiz Cuenca et al. for putting together this interesting manuscript on the use of step selection functions for understanding exposure to leptospires in urban Brazil. I thoroughly enjoyed reading it and have a few suggestions that may improve the manuscript. 

I also apologise, but I was not able to find some of the supplementary materials, for instance, Supplementary Material I. That may have been my oversight. 

To eLife: These should have been included with the submitted manuscript file. Please let me know if it has to be resubmitted to eLife.

(1) Descriptive statistics 

Some more descriptive statistics would be helpful. For instance, what was the leptospirosis infection status of the six individuals who were removed due to having <50 points inside the area? As part of the analysis relies on exposure, defined as GPS locations within a 20m buffer of open sewers, community streams, and rubbish piles, it would be good to have some descriptive statistics around this. How many visits to these different sites did people make, and how did these statistics vary by study area, age, gender, and leptospirosis infection status? 

We thank Reviewer #2 for highlighting this. Thanks to their comment, we noticed a mistake in the code which excluded more individuals from the summary statistics table than were actually excluded from the full analysis. There were only 2 individuals that had less than 50 relocations across the whole day (5 am to 9 pm) which were excluded from further analysis. The mistake has been rectified and the summary statistics updated. (see table 1)

We have included the demographic details of excluded participants as a table in the supplementary material, which we have referenced to in the manuscript. We have also explained that the exclusion is to aid model convergence, as we found that too few relocations would result in SSF models not working properly.  

“Individuals with less than 50 relocations within the study area were excluded from the analysis to ensure good model convergence. Details of these excluded individuals can be found in Supplementary Material I.” [Lines 183 – 186]

We have also now included a table (Table 2),  to show more descriptive statistics of how much time individuals spent within each of the environmental buffers. 

(2) Definitions of buffers 

I was surprised that the authors chose a 20m buffer for each factor but 10m around the household.Could this be more clearly justified, especially given that there will be location errors in both the GPS location point and the GPS logger points? These buffers do appear quite small, particularly in an urban environment where obstruction from buildings can be expected to yield substantial GPS errors. 

The 20 meter buffer represents an intense interaction with the point of interest. This distance was decided after visiting the sites and seeing the points of interest in person. The 10 meter buffer accounts for the size of dwellings in these areas. We have included these explanations in the new manuscript:  

“The buffer rasters, one for each factor, were created using a 20 meter buffer around each reference point. The size of this buffer was decided after visiting the study areas and represented an area within which it could be considered a strong interaction with the point of interest.” [Lines 198 – 202]

“Buffer rasters were also created for each individual’s household location, with a 10 meter buffer around each location.This represented space within and immediately outside each house.  This buffer size accounted for the size of dwellings in these study areas.” [Lines 205 - 208]

(3) Assumptions of the step selection function 

Step selection functions (SSFs) rely on a number of assumptions. Whether these assumptions are met needs to be critically discussed within the article. (For a discussion of the assumptions, I am relying on points raised in this article: Integrated step selection analysis: bridging the gap between resource selection and animal movement (2015): Tal Avgar, Jonathan R. Potts, Mark A. Lewis, Mark S. Boyce, DOI: https://doi.org/10.1111/2041-210X.12528). 

First, SSFs typically assume each step is independent, conditional only on the previous step (Markovian process). This is violated in circular movements, for instance. Circular movements are highly likely in human movement as people will leave and return to their homes during the day. While this is partially addressed by conducting separate analyses by time of day, circular journeys can still exist within these segments. 

Second, SSFs do not account for goal-oriented behaviour like intentional destination-seeking. So, for instance, when someone executes a plan to visit a specific stream to fetch drinking water, such behaviour is poorly approximated using SSFs because SSFs compare observed steps to random alternatives drawn from a movement kernel, assuming movement is opportunistic rather than intentional. 

This is true of SSF that do not include movement attributes. However, in our SSF we have included both step lengths and turning angles, which, according to Avgar et al, should be enough to account for this goal-oriented behaviour. It may be clearer to call the model an integrated step selection function (iSSF), as they do in Avgar et al., which we can change in the next version of the manuscript.  

Third, turning angles in human movement are often sharp due to regular street layout, which can violate the assumptions of SSFs, which usually assume smooth, correlated movement. 

As this paper proposes SSFs as a novel method to measure exposure to environmentally transmitted pathogens, a discussion on the extent to which assumptions of SSFs are valid for this purpose should be included in the paper. 

We thank Reviewer #2 for highlighting these points. We have included a section discussing these assumptions in detail: 

“Additionally, these models have some underlying assumptions that may be violated in this study. Step-selection functions assume each step is independent, conditioned on the previous step. This can be violated by circular journeys. Although we attempted to account for these by analysing specific periods of the day, a higher temporal resolution of analysis may be needed if circular journeys are still present within each period. Another assumption is that movement is smooth through the environment. In urban environments this may not hold true, as street layouts may force sharp corners in movements. The effect of violating this assumption is not immediately clear and requires further methodological research to understand its significance. Finally, we assumed that by including movement characteristics (step lengths and turning angles) into our models, we were accounting for goal-oriented behaviour. These assumptions need to be considered in future studies that attempt to use step-selection functions to analyse human mobility.” [Lines 593 - 607]

(4) Abstract 

While it is highlighted in the abstract that this "study introduces a novel method for analysing human telemetry data in infectious disease research, providing critical insights for targeted interventions", I did not see any discussion about how the findings can inform interventions. 

We thank Reviewer #2 for highlighting this. We have now removed this wording from the abstract to avoid misunderstanding.  

(5) Effect sizes 

It would have helped me if there had been some discussion around the size of these effects. Especially for the distance-based models, the effects seem very small. Maybe this is a misinterpretation on my part, but it would help to contextualise if the observed effect were small or large. 

We agree with Reviewer #2 on this point and have now included a paragraph explaining that these effect sizes are indeed very small. We believe that this may be linked to the spatial scale of the rasters used (1 meter), as the selection coefficients represent changes with regards to increasing distances of 1 meter. This may not be that significant for human mobility. However, given the focus on analysing fine scale movement, we decided to keep the spatial scale of the rasters as small as possible. 

“It is important to highlight that the effect sizes of the selection coefficients for the distance based rasters are very small and could be considered negligible. This may be linked to the spatial scale used, as these values represent increases of 1 meter. A coarser scale may have produced larger effect sizes that may have been easier to conceptualise. However, given the focus on fine-scale movement, we decided to keep this spatial scale for the analysis.” [Lines 421 - 427]

Medulla and pons section controls our breathing

Part of our nervous system

Control over our breathing

Reviewer #2 (Public review):

This work presents theoretical results concerning the effect of punctuated mutation on multistep adaptation and empirical evidence for that effect in cancer. The empirical results seem to agree with the theoretical predictions. However, it is not clear how strong the effect should be on theoretical grounds, and there are other plausible explanations for the empirical observations.

For various reasons, the effect of punctuated mutation may be weaker than suggested by the theoretical and empirical analyses:

(1) The effect of punctuated mutation is much stronger when the first mutation of a two-step adaptation is deleterious (Figure 2). For double inactivation of a TSG, the first mutation--inactivation of one copy--would be expected to be neutral or slightly advantageous. The simulations depicted in Figure 4, which are supposed to demonstrate the expected effect for TSGs, assume that the first mutation is quite deleterious. This assumption seems inappropriate for TSGs, and perhaps the other synergistic pairs considered, and exaggerates the expected effects.

(2) More generally, parameter values affect the magnitude of the effect. The authors note, for example, that the relative effect decreases with mutation rate. They suggest that the absolute effect, which increases, is more important, but the relative effect seems more relevant and is what is assessed empirically.

(3) Routes to inactivation of both copies of a TSG that are not accelerated by punctuation will dilute any effects of punctuation. An example is a single somatic mutation followed by loss of heterozygosity. Such mechanisms are not included in the theoretical analysis nor assessed empirically. If, for example, 90% of double inactivations were the result of such mechanisms with a constant mutation rate, a factor of two effect of punctuated mutagenesis would increase the overall rate by only 10%. Consideration of the rate of apparent inactivation of just one TSG copy and of deletion of both copies would shed some light on the importance of this consideration.

Several factors besides the effects of punctuated mutation might explain or contribute to the empirical observations:

(1) High APOBEC3 activity can select for inactivation of TSGs (references in Butler and Banday 2023, PMID 36978147). This selective force is another plausible explanation for the empirical observations.

(2) Without punctuation, the rate of multistep adaptation is expected to rise more than linearly with mutation rate. Thus, if APOBEC signatures are correlated with a high mutation rate due to the action of APOBEC, this alone could explain the correlation with TSG inactivation.

(3) The nature of mutations caused by APOBEC might explain the results. Notably, one of the two APOBEC mutation signatures, SBS13, is particularly likely to produce nonsense mutations. The authors count both nonsense and missense mutations, but nonsense mutations are more likely to inactivate the gene, and hence to be selected.

Reviewer #1 (Public review):

Summary:

This study focuses on characterizing the EEG correlates of item-specific proportion congruency effects. In particular, two types of learned associations are characterized. One being associations between stimulus features and control states (SC), and the other being stimulus features and responses (SR). Decoding methods are used to identify SC and SR correlates and to determine whether they have similar topographies and dynamics.

The results suggest SC and SR associations are simultaneously coactivated and have shared topographies, with the inference being that these associations may share a common generator.

Strengths:

Fearless, creative use of EEG decoding to test tricky hypotheses regarding latent associations.

Nice idea to orthogonalize the ISPC condition (MC/MI) from stimulus features.

Weaknesses:

(1) I'm relatively concerned that these results may be spurious. I hope to be proven wrong, but I would suggest taking another look at a few things.

While a nice idea in principle, the ISPC manipulation seems to be quite confounded with the trial number. E.g., color-red is MI only during phase 2, and is MC primarily only during Phase 3 (since phase 1 is so sparsely represented). In my experience, EEG noise is highly structured across a session and easily exploited by decoders. Plus, behavior seems quite different between Phase 2 and Phase 3. So, it seems likely that the classes you are asking the decoder to separate are highly confounded with temporally structured noise.

I suggest thinking of how to handle this concern in a rigorous way. A compelling way to address this would be to perform "cross-phase" decoding, however I am not sure if that is possible given the design.

The time courses also seem concerning. What are we to make of the SR and SC timecourses, which have aggregate decoding dynamics that look to be <1Hz?

Some sanity checks would be one place to start. Time courses were baselined, but this is often not necessary with decoding; it can cause bias (10.1016/j.jneumeth.2021.109080), and can mask deeper issues. What do things look like when not baselined? Can variables be decoded when they should not be decoded? What does cross-temporal decoding look like - everything stable across all times, etc.?

(2) The nature of the shared features between SR and SC subspaces is unclear.

The simulation is framed in terms of the amount of overlap, revealing the number of shared dimensions between subspaces. In reality, it seems like it's closer to 'proportion of volume shared', i.e., a small number of dominant dimensions could drive a large degree of alignment between subspaces.

What features drive the similarity? What features drive the distinctions between SR and SC? Aside from the temporal confounds I mentioned above, is it possible that some low-dimensional feature, like EEG congruency effect (e.g., low-D ERPs associated with conflict), or RT dynamics, drives discriminability among these classes? It seems plausible to me - all one would need is non-homogeneity in the size of the congruency effect across different items (subject-level idiosyncracies could contribute: 10.1016/j.neuroimage.2013.03.039).

(3) The time-resolved within-trial correlation of RSA betas is a cool idea, but I am concerned it is biased. Estimating correlations among different coefficients from the same GLM design matrix is, in general, biased, i.e., when the regressors are non-orthogonal. This bias comes from the expected covariance of the betas and is discussed in detail here (10.1371/journal.pcbi.1006299). In short, correlations could be inflated due to a combination of the design matrix and the structure of the noise. The most established solution, to cross-validate across different GLM estimations, is unfortunately not available here. I would suggest that the authors think of ways to handle this issue.

(4) Are results robust to running response-locked analyses? Especially the EEG-behavior correlation. Could this be driven by different RTs across trials & trial-types? I.e., at 400 ms post-stim onset, some trials would be near or at RT/action execution, while others may not be nearly as close, and so EEG features would differ & "predict" RT.

(5) I suggest providing more explanation about the logic of the subspace decoding method - what trialtypes exactly constitute the different classes, why we would expect this method to capture something useful regarding ISPC, & what this something might be. I felt that the first paragraph of the results breezes by a lot of important logic.

In general, this paper does not seem to be written for readers who are unfamiliar with this particular topic area. If authors think this is undesirable, I would suggest altering the text.

Reviewer #2 (Public review):

Summary:

In this paper, the authors used a multi-level modelling approach to reanalyse trial data from Takeda's Phase III randomised control trial investigating the efficacy of the TAK-003 vaccine against dengue. The aim of the paper is to refine uncertainty by incorporating all the available data into the model and pooling across stratifications that are correlated. A major challenge in constructing a likelihood that allows for data available at differing levels of aggregation by group and outcome, and at different time intervals. This is done by first supposing that the data is available without aggregation for all groups, outcomes and time points, and then marginalising over the aggregated levels. The model is validated using simulations and then applied to trial data from Takeda. Results appear to corroborate those of Takeda with reduced uncertainty in the estimates.

Strengths:

The main strength of the paper is the multi-level modelling approach. It is a particularly natural one for this setting. One reason for this, as discussed in the paper, is that correlations across stratifications can arise when there are similarities in their underlying causal structure. It is more realistic to model this nested data structure hierarchically. Another reason, also well discussed in the paper, is the reduction in uncertainty you get when you pool estimates across related groups. Multi-level modelling is also beneficial when group sizes are different. For example, there were too few cases of DENV-4 from seronegatives, which resulted in hospitalised disease for the original analysis to produce estimates, but by using multi-level modelling, this paper can produce estimates. The modelling framework developed in this paper will be simple to extend to further trial data collected in the future.

Another strength is that it is reanalysing existing trial data, which is both cost-effective and beneficial for scientific reproducibility. This approach also helps to assess the robustness of conclusions about the efficacy of the TAK-003 vaccine to use of different analytical methods.

The paper is well-written. The tables and figures presented in this paper are particularly informative. Protection conferred by the vaccine varies depending upon which variant a person is exposed to, their serostatus, and time since vaccination. The analysis presented supports the discussed conclusions. Comparisons between the results of this paper and the results of the original trial analysis are also shown and demonstrate a reduction in the uncertainty of parameter estimates, as desired.

Weaknesses:

The weakness of the paper is that it reports per-exposure protection instead of vaccine efficacy. This is methodologically sound, but it does limit the comparability of this study with the original trial analyses, which reported vaccine efficacy. It is therefore unclear whether the reduction in uncertainty observed is due solely to the multi-level modelling approach or whether it may be due in part to the parameters of interest being slightly different.

Reviewer #2 (Public review):

Summary:

Across two experiments, this work presents a novel spatial predictive inference paradigm that facilitates the investigation of meta-learning across multiple environments with distinct statistics, as well as more local learning from sequences of observations within an environment. The authors present behavioral data indicating that people can indeed learn to distinguish between noise levels and calibrate their learning rates accordingly across environments, even on initial trials when revisiting an environment. They complement their behavioral results with computational modeling, further bolstering claims of both local and global adaptation. Additional fMRI results support the role of OFC in this meta-learning process, with central OFC activity reflecting similarity between environments. This similarity emerges over time with task experience. Holistically, this paradigm and these data add to our understanding of how humans dynamically adapt their behavior on different timescales.

Strengths:

The novel paradigm represents a clever and creative expansion of spatial predictive inference tasks. The cover story was well chosen to facilitate an intuitive understanding of both the differences between environments and the estimation of the mean within environments.

Additionally, the authors present complementary results from two experiments, which strengthen the behavioral findings. This is especially effective as the initial experiment's results were a bit noisy, and the modifications within the second experiment increased both power and the specificity/accuracy of participant predictions. Taken together, the behavioral results provide convincing evidence that participants did distinguish environments based on their underlying statistics and adapted their initial behavior accordingly.

Beyond this, the combination of behavioral results, computational modeling, and neuroimaging enhances the impact of the work. It paints a fuller picture of whether and how humans meta-learn the global statistics of environments, and this is an important direction for the field of adaptive learning.

Weaknesses:

(1) The authors make the distinction between meta-learned "global" learning rates and within-environment learning rate adaptation in response to "local" fluctuations/observations. Though the experimental paradigm is novel, there are certainly links to prior work - for instance, though change point structures don't entail revisiting unique environments, they do require meta-learning from environmental statistics that is distinct from transient local adaptation to prediction errors. This tendency to increase one's learning rate after large prediction errors is appropriate in change point environments, though, as is true in this study, the amount of increase should be dependent on. This represents a similar kind of slower-timescale learning or reuse of more "global" parameters, and can be seen to different extents in prior work. It might benefit readers if the authors were to link the current work to previous research more explicitly to draw clearer connections between the approaches and findings.

(2) Throughout much of the paper, the authors refer to the distinctions between environments primarily as differences in "initial learning rates" or "environment-specific learning rates." This is particularly prominent when discussing fMRI results. Though the optimal initial learning rate did differ across environments, this was the result of differences in underlying task statistics. It will be important to clarify this throughout the text, because of the confounds between task statistics and initial learning rate (and to some extent, the position on the screen), it is not possible to separate the impact of these specific variables. This is also relevant to understanding the justification for using methods like RSA to test whether brain regions represent task states similarly. If the main hypothesis is that neural activity reflects the (initial) learning rate itself, then a univariate analysis approach would seem more natural.

(3) For the neuroimaging results in particular, the specificity of some of the results (e.g. ventral striatum showing an effect of prediction error only in the low noise condition in the second half of task experience, only on the first trial) is a bit surprising. Additional justification of or context for these results would be useful to help readers gauge how expected or surprising these findings are.

(4) There are some methodological details that are unclear (e.g., how were the positions of the crabs selected relative to the location they emerged from? Looking at Figure 1C, it looks like the crabs spread out unevenly, and that the single position they emerge from is not necessarily at the center of the crab locations.) Additional detail and clarity would help address some unanswered questions (more details below).

This quote lays out a detailed way to understand prior restraint, focusing on the timing and method of stopping speech rather than its content. It also separates it from consequences that happen after the fact and connects the idea to both legal reasoning and everyday understanding, making it easier to see how the concept really works in practice.

Reviewer #1 (Public review):

In this well-written and timely manuscript, Rieger et al. introduce Squidly, a new deep learning framework for catalytic residue prediction. The novelty of the work lies in the aspect of integrating per-residue embeddings from large protein language models (ESM2) with a biology-informed contrastive learning scheme that leverages enzyme class information to rationally mine hard positive/negative pairs. Importantly, the method avoids reliance on the use of predicted 3D structures, enabling scalability, speed, and broad applicability. The authors show that Squidly outperforms existing ML-based tools and even BLAST in certain settings, while an ensemble with BLAST achieves state-of-the-art performance across multiple benchmarks. Additionally, the introduction of the CataloDB benchmark, designed to test generalization at low sequence and structural identity, represents another important contribution of this work.

I have only some minor comments:

(1) The manuscript acknowledges biases in EC class representation, particularly the enrichment for hydrolases. While CataloDB addresses some of these issues, the strong imbalance across enzyme classes may still limit conclusions about generalization. Could the authors provide per-class performance metrics, especially for underrepresented EC classes?

(2) An ablation analysis would be valuable to demonstrate how specific design choices in the algorithm contribute to capturing catalytic residue patterns in enzymes.

(3) The statement that users can optionally use uncertainty to filter predictions is promising but underdeveloped. How should predictive entropy values be interpreted in practice? Is there an empirical threshold that separates high- from low-confidence predictions? A demonstration of how uncertainty filtering shifts the trade-off between false positives and false negatives would clarify the practical utility of this feature.

(4) The excerpt highlights computational efficiency, reporting substantial runtime improvements (e.g., 108 s vs. 5757 s). However, the comparison lacks details on dataset size, hardware/software environment, and reproducibility conditions. Without these details, the speedup claim is difficult to evaluate. Furthermore, it remains unclear whether the reported efficiency gains come at the expense of predictive performance.

(5) Given the well-known biases in public enzyme databases, the dataset is likely enriched for model organisms (e.g., E. coli, yeast, human enzymes) and underrepresents enzymes from archaea, extremophiles, and diverse microbial taxa. Would this limit conclusions about Squidly's generalisability to less-studied lineages?

List all folders/files created/modified over the last two month by the owner/curator of the Peergos Name: hyperpost

Gyuri Lajos Lead Envisioneer for the Indy Learning Commons on the IndyWeb

Annotate this listing for providing publicly shared information related to non public documents/folders

add secret links with passwords and expiry dates to provide controlled access to information or anchoring threaded conversations

Yes Indywiki is the Way

the alternative that completes it to o bbecome what it always dreamed to be but failed to become

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Reply to the reviewers

1. General Statements

We thank the reviewers for providing thoughtful and constructive feedback, which will help us improve the clarity and rigor of the paper. On balance, the reviews were positive. Reviewer 1 mentioned that “This is a strong manuscript with few problems and all important findings well justified, indeed this is a nicely polished…..high-quality manuscript,” and that “this paper makes a major breakthrough, showing that cell autonomous defects in hTSCs are very likely at the heart of the pathology observed in GIN-prone murine mutants.” Reviewer 3 stated that “The study is well designed, and the manuscript is very well written. The conclusions are supported by the evidence presented.” Reviewer 2 was less enthusiastic, with main concerns being that “The paper is mostly descriptive and often quite confusing leaving one not much closer to understanding the mechanistic basis for the interesting sex-biased semi-lethal phenotype.” and felt that figure titles/section headers overstated the results, and finally recommended to improve some technical aspects and tempering conclusions. The proposed edits we think address most issues raised by the reviewers either with re-writing or adding data as described below.

In response to reviewer #1 comments:

Major comments:

• I am confused as to the basis of the sex-skewing phenomenon? Is the problem that lack of maternally loaded WT Mcm4 worsens the phenotype, or is the issue that Mcm4C3/C3 dams are less able to retain pregnancies, perhaps being a more inflammatory environment? Also, while there quite consistent evidence for reduced viability of Mcm4C3/C3McmGt/+ progeny, especially for female progeny, how confident can we be that the genotype of the dam vs. sire is important? Notably on a Ddx58 background, the progeny of the Mcm4C3/C3 sire included seven live male Mcm4C3/C3McmGt/+ but no female.

Regarding the first point (sex skewing only when female is C3/C3), we also suspected either: 1) the maternal uterine environment, or 2) reduced oocyte quality. Although not reported in this manuscript, we tested #1 by performing embryo transfer experiments. Transferring 2-cell stage embryos from sex-skewing mating to WT females did not rescue the sex-bias. We then examined oocytes from C3/C3 females. We found evidence for compromised mitochondria and transcriptome disruption. However, we are not sure why this happens (poor follicle support? Oocyte intrinsic phenomenon?). We are reserving these results and additional experiments for another paper, especially since this one mainly deals with GIN and placenta development. If the reviewers feel strongly that the embryo transfer data is crucial, we can include it.

Regarding how confident we are that the genotype of the dam vs. sire is important, this stems from our previous paper by McNairn et al 2019 (the percentage of female C3/C3 M2/+ from sex-skewing mating is 20% compared to 60% from the reciprocal mating), which was quite dramatic. Consistent with this, MCM levels were significantly reduced in the placentae only when the dam was C3/C3 and the sire C3/+ M2/+, but not in the reciprocal cross. The reviewer makes a good observation about the Ddx58 cross; we can only hypothesize that the mutation somehow sensitizes females in this scenario and will make mention of it in the revision. We also realize that we neglected to write in Methods that the Ddx58 allele was coisogenic in the C3H background.

• I'm not sure what Supplementary Figure 6 is showing (faster differentiation of C3 but less TGC?). Regardless, it's hard to draw too much conclusion from one not-very-pretty Western blot. This figure requires both additional replicates and a better explanation of how it fits with the other conclusions of the paper..

We hypothesized that the JZ defect observed in the semi-lethal genotype placentas could arise either from impaired maintenance of the progenitor pool or from reduced capacity of mutant trophoblast progenitors to differentiate into the JZ lineage. The blot in Supplementary Figure 6 was intended as a qualitative demonstration that mutant trophoblast stem cells can differentiate into JZ lineages. We recognize that the figure is not definitive and will revise the text to clarify its purpose. A replicate(s) of the Western will be performed as suggested.

• Supplementary Figure 7F-G is puzzling. Half of the mESCs have gamma-H2AX at all times, including most in S or G2 phase? In Figure S7E, do the quadrants correspond to being negative or positive for gamma-H2AX? At very least, IF images showing clear gamma-H2AX foci would be much more convincing.

The gates for γH2AX FACS analysis were established using negative controls lacking primary antibody. As reported previously, embryonic stem cells display high basal levels of γH2AX staining (Chuykin et al., Cell Cycle 2008; Turinetto et al., Stem Cells 2012; Ahuja et al., Nat Comm 2016), which likely explains the broad signal observed across cell cycle phases. Regardless, we will provide immunofluorescence staining of γH2Ax and foci count in our revision.

• The methods section is well detailed, but it would be ideal to clarify how many replicates each Western Blot or flow cytometry experiment is representative of.

Thanks for the suggestion. We will update this for Fig4 and Fig5.

Minor comments:

• Is it possible that cGAS-STING and RIG pathways act redundantly to cause inflammation and lethality, or that other innate immune components are involved? I don't expect the authors to make compound mutants to test this but at least this possibility should be discussed textually.

We appreciate the reviewer’s point, and had the same suspicion. Supporting this, we will add new RNA-seq analysis of Tmem173 KO placentas revealed elevated inflammatory gene expression compared to C3/C3 M2/+ controls, consistent with potential redundancy or feedback regulation. We will update in supplementary figures to reflect this.

In response to reviewer #2 comments:

Major comments:

A major concern throughout the paper is that conclusions are often overstating their data. The title of figure 2 is "placentae with replication stress have smaller junctional and labyrinth zones". However, there is no measure of replication stress in this figure, just a histological evaluation of the placentae from the different mutants. The title of figure 3 is "Impact of GIN on LZ is less than JZ," but there is no measure of GIN, but instead measurement of number of cells in cell cycle and some bulk RNA-seq analysis. Title of figure 4 is "TSCs with increased genomic instability exhibit abnormal phenotypes." Again there is no measure of GIN, but instead staining of derived TSCs for proliferation, cell death, and a TSC marker. Title of figure 5 is "DNA damage responses and G2/M checkpoint activation drive premature TSC differentiation." However, there does not appear to be a difference in gH2AX between the two mutant genotypes. Checkpoint proteins might be up, but need quantification and reproduction. > 4C is the only marker of differentiation. Importantly, all the analyses here are associations, not connections, so cannot use the word "drive". Similar issues can be raised with a number of the supplementary figures.

The Chaos3 (chromosome aberrations occurring spontaneously 3) model is a well-established system of intrinsic chronic replication stress and GIN. It is characterized by ~20 fold elevation of blood micronuclei (Shima et al., Nature 2007), a hallmark of GIN (Soxena et al., Mol Cell 2022); a destabilized MCM2-7 helicase prone to replication fork collapse (Bai et al., PLoS Genet 2016); and increased mitotic chromosome abnormalities and decreased dormant origins (Kawabata et al., Mol Cell 2011; Chuang et al., Nucleic Acid Res 2012) that are known to cause GIN and replication stress (Ibarra et al., PNAS 2008 ). Also, in our previous work (McNairn et al Nature 2019), we showed that placentae from C3/C3 dams exhibit significantly elevated γH2Ax as well as reduced MCM2 and MCM4 protein levels. In our current study, we also observe elevated γH2Ax in mutant TSCs (C3/C3 and C3/C3 M2/+), consistent with genomic instability. Nevertheless, we acknowledge that in TSCs, we did not formally demonstrate replications stress(RS), so where appropriate, we will advise figure titles, for example to say that “cells/placentae with a GIN or RS genotype.”

We acknowledge the reviewers concern regarding western blots. We will provide quantification and statistics in our revision.

1) A deeper analysis of the cell lines is likely to be the most fruitful path to reveal interesting mechanisms. It is very surprising that there is no phenotype in ESCs. Authors should check for increased apoptosis. Maybe the phenotypic cells are lost. Or do ESCs use different MCMs/mechanisms of DNA replication or are they better able to handle replication stress and GIN? How many passages were the TSCs and ESCs cultured for? Does GIN (i.e. aneuploidy, CNVs) develop in TSCs and ESCs with passaging? How do the MCM mutations impact the molecular identity of the ESC and TSC cells including their heterogeneity in the population.

We assessed apoptosis using cleaved caspase 3 flow cytometry in mutant ESCs and observed no difference compared to controls (we will add this data as Supplementary Fig. 7).

We believe there are intrinsic differences in TSCs and ESCs in their ability to respond to and counteract replication stress and DNA damage. ESCs are known to license more replication origins than somatic cells at a higher rate, which protects them from short G1-induced replication stress (Ahuja et al., Nat Comm 2016; Ge et al., Stem Cell Rep 2015; Matson et al., eLife 2017). Human placental cells physiologically exhibit high levels of mutation rate and chromosomal instability in vivo (Coorens et al., Nature 2021). Supporting this, Wang, D., et al (Nat Comm 2025) reported that several cell cycle and DDR regulators are differentially expressed in human TSCs vs human pluripotent stem cells. Whether such transcriptional differences directly contribute to functional outcomes remains to be determined.

All experiments in this study were conducted using early-passage ESCs and TSCs (i.e. Finally, we showed that close to 90% mutant ESCs are KLF4+ (a naive pluripotency marker) whereas EOMES+ cells were significantly reduced in TSCs carrying the GIN genotype (Fig. 4E–F and Supplementary Fig. 7), highlighting lineage-specific differences.

Minor Comments:

1) There is a lack of quantification and repeats for all Westerns. At minimum there should be three repeats for each experiment, quantification including normalization to a reference protein, and stats confirming any proposed differences between conditions.

We will update our revision with quantification and statistics for western blots.

2) I would recommend moving the results in supp table 1 to figure 1. While negative, they are the newer results. The results shown in current figure 1 are essentially a reproduction of their previous work.

The placental observations presented in Fig.1 are new. In particular, the placental and embryonic weight measurements graphed in Fig1B and C have not been published by our group. Fig1A reproduces our previous observation on embryo viability in GIN mutants (McNairn et al., Nature 2019), while the schematic was provided for better flow and readability given the complex mating schemes. We are agnostic on the Suppl Table 1. It could be changed to a new Table 1 in the main section depending on the journal.

In response to reviewer #3 comments:

Major Comments

While the inclusion of bulk RNAseq data of whole placental tissue is appreciated, the interpretation of the results is somewhat problematic, as it is acknowledged that the cell type composition of the placentas is drastically different between groups. Making conclusions based upon GSEA analysis of two different groups with drastically different cell type composition is somewhat misleading, as based on the results, it is a direct reflection of the cell types present. It would be more helpful to perform cell type deconvolution of the RNAseq data to estimate the proportion of each cell type within the bulk samples and compare that to what is seen histologically and not dive too deeply into the pathways since the results could just be a reflection of the cell types e.g. angiogenesis pathways from more endothelial cells. Additionally, the RNAseq data can be leveraged to look at expression of inflammatory genes by sex, which may show interesting patterns based on the other results.

We agree that the representation of cell types in the placenta is problematic especially for underrepresented genes. We propose to use the BayesPrism tool (Chu et al., Nat Cancer 2022) to deconvolute bulk RNA-seq for better representation of transcriptional changes in the placenta.

Section: GIN impairs trophoblast stem cell establishment and maintenance. To support the assertion in the first paragraph, beyond measuring apoptosis, it would be helpful at this stage to look at RNA expression levels indicative of the activation of DNA damage checkpoint genes

We have performed RNA-seq on mutant ESC and TSCs and are in the process of data analysis. We will update these results in the revision.

Please include additional methodological details in the methods section on the statistical analysis done for differential expression analysis. Specifically, what type of normalization was used, if lowly expressed genes were filtered out and at what cutoff, what statistical model was used (did you include covariates?), what comparisons were made? Did you stratify by sex? What cutoff was used for statistical significance? Did you perform multiple testing correction?

We will update RNA-Seq data analysis methods in our full revision.

2. Description of the revisions that have already been incorporated in the transferred manuscript

Reviewer #1 comments:

• Supplementary Table 1. would be enhanced greatly showing comparable tables for Mcm4C3/C3 x Mcm4C3/+McmGt/+ in mice without the Tmem173 or Ddx58 mutations. It is fine to recycle data from McNairn 2019 here, as long as the source is indicated, but a comparison is needed.

Thanks for pointing this out. We have updated this suggestion in Supp table 1.

• In Figure S3E-F, is the box above each graph supposed to show the genotype of the dam?

Yes. Thanks for pointing this out. We have added a description in the figure legend to make it clear.

• "Indeed, the placenta and embryo weights of E13.5 Mcm4C3/C3 Mcm2Gt/+ Mcm3Gt/+ animals were significantly improved vs. Mcm4C3/C3 Mcm2Gt/+ animals, rendering them similar to Mcm4C3/C3 littermates (Fig. 6A-C). The JZ (but not LZ) area in Mcm4C3/C3 Mcm2Gt/+ Mcm3Gt/+ placentae also increased to the level of Mcm4C3/C3 littermates (Fig. 6D-H)." There are two problems here. First, the figure calls are wrong. Second, the description of the data is not quite right, it looks like the C3/C3 and C3/C3 M2/+ M3/+ LZs are a similar size to each and are statistically indistinguishable.

Thanks for catching this. We have updated these in the main text.

*Reviewer #2 comments: *

Minor comment

• Need to review citations to figures. For example, no citations are made to figure 4a and 4c.

Thanks for catching this. We have updated the text.

Reviewer #3 comments:

Define the first use of >4C DNA content to help readers understand this potentially unfamiliar term.

We have edited this part to indicate cells with more than 4C DNA content for better clarity.

iDEP tool - please include citation to manuscript instead of link

We have updated this citation.

Check citations. Some citations to BioRxiv that are now published e.g. 13.

We have updated this citation.

3. Description of analyses that authors prefer not to carry out

Reviewer 2

2) Along similar lines, most of the in vivo phenotypic analyses are performed at E13.5, long after defects are likely beginning to express themselves especially given that they see phenotypes in the TSCs, which represent the polar TE of a E4.5. To understand the primary defects of the in vivo phenotype, they should be looking much earlier. Supplemental figure 5 is a start but represents a rather superficial analysis.

The peri-implantation period, namely E4.5, represents a “black box” of embryonic development given that this is a critical stage for implantation. Aside from being an extremely difficult stage to analyze technically, we don’t think it is essential to the conclusions (or doable in a timely manner), especially given the use of TSCs. If we complete EdU studies on E6.5 embryos, we will include them.

3) Fig. 6 would benefit from evidence that MCM3 mutant is rescuing MCM4 levels in the chromatin fraction of cells and the DNA damage phenotype.

The genetic evidence presented is strong, and although we didn’t do the suggested experiment, we feel that our previous studies (McNairn et al., Nature 2019 and Chuang et al., PLoS Genet 2010) on the effects of MCM3 as a nuclear export factor (as it is in yeast (Liku et al., Mol Biol Cell 2005)) are a reasonable basis for not repeating such experiments. Furthermore, we are no longer maintaining the Mcm3 line and it would take over a year to reconstitute and rebreed triple mutants.

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Referee #2

Evidence, reproducibility and clarity

The manuscript, "Chronic replication stress-mediated genomic instability disrupts placenta development in mice" by Munisha et al follows up a 2019 paper in Nature by the same group where they show that mutations to the MCM genes lead to a sex-skewed semi-lethal phenotype starting after embryonic day 9.5 and extending to birth. In the paper, they hypothesized that the semi-lethality is secondary to genomic instability (GIN) driven inflammation due to activation of the innate immune pathways sensing cytoplasmic DNA. In this paper, they start by disproving that hypothesis and then go on to present data arguing lethality is due to a placental development defect rather than inflammation. The paper is mostly descriptive and often quite confusing leaving one not much closer to understanding the mechanistic basis for the interesting sex-biased semi-lethal phenotype that was described in their original paper. The most interesting aspect of the paper is the derivation of TSC and ESCs and initial analysis suggesting that the TSCs are more sensitive to the MCM mutations, but the analysis is rather shallow. Importantly it is unclear how the phenotype explains the sex-skewing of the phenotype. Are the TSC phenotypes sex-skewed and if so why? Also, why is the JZ and especially GlyTCs most effected?

A major concern throughout the paper is that conclusions are often overstating their data. The title of figure 2 is "placentae with replication stress have smaller junctional and labyrinth zones". However, there is no measure of replication stress in this figure, just a histological evaluation of the placentae from the different mutants. The title of figure 3 is "Impact of GIN on LZ is less than JZ," but there is no measure of GIN, but instead measurement of number of cells in cell cycle and some bulk RNA-seq analysis. Title of figure 4 is "TSCs with increased genomic instability exhibit abnormal phenotypes." Again there is no measure of GIN, but instead staining of derived TSCs for proliferation, cell death, and a TSC marker. Title of figure 5 is "DNA damage responses and G2/M checkpoint activation drive premature TSC differentiation." However, there does not appear to be a difference in gH2AX between the two mutant genotypes. Checkpoint proteins might be up, but need quantification and reproduction. > 4C is the only marker of differentiation. Importantly, all the analyses here are associations, not connections, so cannot use the word "drive". Similar issues can be raised with a number of the supplementary figures.

Major Comments:

1) A deeper analysis of the cell lines is likely to be the most fruitful path to reveal interesting mechanisms. It is very surprising that there is no phenotype in ESCs. Authors should check for increased apoptosis. Maybe the phenotypic cells are lost. Or do ESCs use different MCMs/mechanisms of DNA replication or are they better able to handle replication stress and GIN? How many passages were the TSCs and ESCs cultured for? Does GIN (i.e. aneuploidy, CNVs) develop in TSCs and ESCs with passaging? How do the MCM mutations impact the molecular identity of the ESC and TSC cells including their heterogeneity in the population.

2) Along similar lines, most of the in vivo phenotypic analyses are performed at E13.5, long after defects are likely beginning to express themselves especially given that they see phenotypes in the TSCs, which represent the polar TE of a E4.5. To understand the primary defects of the in vivo phenotype, they should be looking much earlier. Supplemental figure 5 is a start but represents a rather superficial analysis.

3) Fig. 6 would benefit from evidence that MCM3 mutant is rescuing MCM4 levels in the chromatin fraction of cells and the DNA damage phenotype.

Minor Comments:

1) There is a lack of quantification and repeats for all Westerns. At minimum there should be three repeats for each experiment, quantification including normalization to a reference protein, and stats confirming any proposed differences between conditions.

2) I would recommend moving the results in supp table 1 to figure 1. While negative, they are the newer results. The results shown in current figure 1 are essentially a reproduction of their previous work.

3) Need to review citations to figures. For example, no citations are made to figure 4a and 4c.

Significance

As is, the study does not provide much new insight or understanding of how the MCM mutants are driving the sex-skewed semi-lethal phenotype. It would likely take much effort (months) to reach such a goal. However, without such effort, it is unclear what the significance of the story is. It does make the observation that the placenta appears to be impacted more severely and earlier than then the embryo, and that within the placenta, certain zones and cell types are more vulnerable. The reasons for these differential impacts are unclear though.

If the authors choose not to dig deeper as suggested in the major comments, then at a minimum it would be important to soften their conclusions as raised in the summary and at least perform experiments/edits proposed in minor comments.

Reviewer #2 (Public review):

Summary:

Wethington et al. investigated the mechanistic principles underlying antigen-specific proliferation and memory formation in mouse natural killer (NK) cells following exposure to mouse cytomegalovirus (MCMV), a phenomenon predominantly associated with CD8+ T cells. Using a stochastic modeling approach, the authors aimed to develop a quantitative model of NK cell clonal dynamics during MCMV infection. Starting from a single immature Ly49+CD27+ NK cell, a two-state linear model (with a death variant) explained the negative correlation between clone size at 8 dpi and the CD27+ fraction, but failed to reproduce the first and second moments of CD27+ and CD27− NK cell populations at 8 dpi. To address this limitation, the authors added an intermediate maturation state, yielding a three-stage model (CD27+Ly6C− → CD27−Ly6C− → CD27−Ly6C+) that fits the first and second moments under two constraints: CD27+ NK cells proliferate faster than CD27− NK cells, and clone size is negatively correlated with the CD27+ fraction (upper bound of −0.2). The model predicts high proliferation in the intermediate state and high death in mature CD27−Ly6C+ cells, and it was validated using Adams et al. (2021) NK reporter mice tracking CD27+/− populations after tamoxifen, allowing discrimination between bone marrow-derived and pre-existing peripheral NK cells. To test the prediction that mature CD27− NK cells have a higher death rate, the authors measured Ly49H+ NK cell viability in the mouse spleen at different time points post-MCMV infection. Data confirmed lower viability of mature (CD27−) than immature (CD27+) cells during days 4-8 post-infection, and a model variant supported that higher CD27− death increases their proportion in the dead cell compartment. Altogether, the authors propose a three-stage quantitative model of antigen-specific expansion and maturation of naïve Ly49H+ NK cells with the trajectory CD27+Ly6C− (immature) → CD27−Ly6C− (mature I) → CD27−Ly6C+ (mature II), highlighting high proliferation in the mature I state and increased death in the mature II state.

Strengths:

Models explaining correlations and first and second moments, supported by analytical investigations, stochastic simulations, and model selection, identify key processes in antigen-specific NK expansion and maturation. The work distinguishes expansion, contraction, and memory in NK cells from CD8+ T cells and informs NK therapy development.

Weaknesses (relating to initial submission):

The conclusions of this paper are largely supported by the available data. However, a comparative analysis with more recent works in the field would be desirable. Clarifications:

(1) Initial Conditions and Grassmann Data: The Grassmann data is used solely as a constraint, while the simulated values of CD27+/CD27− cells could have been directly fitted to the Grassmann data, which assumes a 1:1 ratio of CD27+/CD27− at t = 0. This would allow an alternative initial condition rather than starting from a single CD27+ cell.

(2) Correlation Coefficients in the Three-State Model: Although the parameter scan of the three-stage model (Figure 2) demonstrates the potential for negative correlations between colony size and the fraction of CD27+ cells, the calculated correlation coefficients using the fitted parameter values are not shown. Including these would validate that the fitted parameters lie in the negative-correlation regime.

(3) Viability Dynamics and Adaptive Response: The authors measured the time evolution of CD27+/− dynamics and viability over 30 days post-infection (Figure 4). It would be valuable to test whether the three-state model can reproduce the adaptive response of CD27− cells to MCMV infection, particularly the observed drop in CD27− viability at 5 dpi and its rebound at 8 dpi. Demonstrating this would test whether the model can simultaneously explain viability dynamics and moment dynamics, and would enable sensitivity analysis of CD27− viability with respect to model parameters.

Reviewer #2 (Public review):

Summary:

Meiotic recombination initiates with the formation of DNA double-strand break (DSB) formation, catalyzed by the conserved topoisomerase-like enzyme Spo11. Spo11 requires accessory factors that are poorly conserved across eukaryotes. Previous genetic studies have identified several proteins required for DSB formation in C. elegans to varying degrees; however, how these proteins interact with each other to recruit the DSB-forming machinery to chromosome axes remains unclear.

In this study, Raices et al. characterized the biochemical and genetic interactions among proteins that are known to promote DSB formation during C. elegans meiosis. The authors examined pairwise interactions using yeast two-hybrid (Y2H) and co-immunoprecipitation and revealed an interaction between a chromatin-associated protein HIM-17 and a transcription factor XND-1. They further confirmed the previously known interaction between DSB-1 and SPO-11 and showed that DSB-1 also interacts with a nematode-specific HIM-5, which is essential for DSB formation on the X chromosome. They also assessed genetic interactions among these proteins, categorizing them into four epistasis groups by comparing phenotypes in double vs. single mutants. Combining these results, the authors proposed a model of how these proteins interact with chromatin loops and are recruited to chromosome axes, offering insights into the process in C. elegans compared to other organisms.

Weaknesses:

This work relies heavily on Y2H, which is notorious for having high rates of false positives and false negatives. Although the interactions between HIM-17 and XND-1 and between DSB-1 and HIM-5 were validated by co-IP, the significance of these interactions was not tested in vivo. Cataloging Y2H and genetic interactions does not yield much more insight. The model proposed in Figure 4 is also highly speculative.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The manuscript by Raices et al., provides novel insights into the role and interactions between SPO-11 accessory proteins in C. elegans. The authors propose a model of meiotic DSBs regulation, critical to our understanding of DSB formation and ultimately crossover regulation and accurate chromosome segregation. The work also emphasizes the commonalities and species-specific aspects of DSB regulation.

Strengths:

This study capitalizes on the strengths of the C. elegans system to uncover genetic interactions between a large number of SPO-11 accessory proteins. In combination with physical interactions, the authors synthesize their findings into a model, which will serve as the basis for future work, to determine mechanisms of DSB regulation.

Weaknesses:

The methodology, although standard, lacks quantification. This includes the mass spectrometry data , along with the cytology. The work would also benefit from clarifying the role of the DSB machinery on the X chromosome versus the autosomes.

• We have uploaded the MS data and added a summary table with the number of peptides and coverage.

• We have added statistics to the comparisons of DAPI body counts.

• We have provided additional images of the change in HIM-5 localization

• We have quantified the overlap (or lack thereof) between XND-1 and HIM-17 and the DNA axis

Reviewer #2 (Public Review):

Summary:

Meiotic recombination initiates with the formation of DNA double-strand break (DSB) formation, catalyzed by the conserved topoisomerase-like enzyme Spo11. Spo11 requires accessory factors that are poorly conserved across eukaryotes. Previous genetic studies have identified several proteins required for DSB formation in C. elegans to varying degrees; however, how these proteins interact with each other to recruit the DSB-forming machinery to chromosome axes remains unclear.

In this study, Raices et al. characterized the biochemical and genetic interactions among proteins that are known to promote DSB formation during C. elegans meiosis. The authors examined pairwise interactions using yeast two-hybrid (Y2H) and co-immunoprecipitation and revealed an interaction between a chromatin-associated protein HIM-17 and a transcription factor XND-1. They further confirmed the previously known interaction between DSB-1 and SPO-11 and showed that DSB-1 also interacts with a nematodespecific HIM-5, which is essential for DSB formation on the X chromosome. They also assessed genetic interactions among these proteins, categorizing them into four epistasis groups by comparing phenotypes in double vs. single mutants. Combining these results, the authors proposed a model of how these proteins interact with chromatin loops and are recruited to chromosome axes, offering insights into the process in C. elegans compared to other organisms.

Weaknesses:

This work relies heavily on Y2H, which is notorious for having high rates of false positives and false negatives. Although the interactions between HIM-17 and XND-1 and between DSB-1 and HIM-5 were validated by co-IP, the significance of these interactions was not tested, and cataloging Y2H interactions does not yield much more insight.

We appreciate that the reviewer recognized the value of our IP data, but we beg to differ that we rely too heavily on the Y2H. We also provide genetic analysis on bivalent formation to support the physical interaction data. We do acknowledge that there are caveats with Y2H, however, including that a subset of the interactions can only be examined with proteins in one orientation due to auto-activation. While we acknowledge that it would be nice to have IP data for all of the proteins using CRISPR-tagged, functional alleles, these strains are not all feasible (e.g. no functional rec-1 tag has been made) and are beyond the scope of the current work.

Moreover, most experiments lack rigor, which raises serious concerns about whether the data convincingly supports the conclusions of this paper. For instance, the XND-1 antibody appears to detect a band in the control IP; however, there was no mention of the specificity of this antibody.

We previously showed the specificity of this antibody in its original publication showing lack of staining in the xnd-1 mutant by IF (Wagner et al., 2010). To further address this, however, we have now included a new supplementary figure (Figure S1) demonstrating the specificity of the XND-1 antibody by Western blot. The antibody detects a distinct band in extracts from wild-type (N2) worms, but this band is absent in two independent xnd-1 mutant strains. This confirms that the antibody specifically recognizes XND-1, supporting the validity of the IP results shown in the main figures.

Additionally, epistasis analysis of various genetic mutants is based on the quantification of DAPI bodies in diakinesis oocytes, but the comparisons were made without statistical analyses.

We have added statistical analysis to all datasets where quantification was possible, strengthening the rigor and interpretation of our findings.

For cytological data, a single representative nucleus was shown without quantification and rigorous analysis. The rationale for some experiments is also questionable (e.g. the rescue by dsb-2 mutants by him-5 transgenes in Figure 2), making the interpretation of the data unclear. Overall, while this paper claims to present "the first comprehensive model of DSB regulation in a metazoan", cataloging Y2H and genetic interactions did not yield any new insights into DSB formation without rigorous testing of their significance in vivo. The model proposed in Figure 4 is also highly speculative.

Regarding the cytology, we provide new images and quantification of HIM-17 and XND-1 overlap with the DNA axes. We also added full germ line images showing HIM-5 localization in wild type and dsb-1 mutants, to provide a more complete and representative view of the observed phenotype. To further support our findings, we’ve also included images demonstrating that this phenotype is consistently observed with both in live worm with the the him-5::GFP transgene and in fixed worms with an endogenously tagged version of HIM-5.

Reviewer #3 (Public Review):

During meiosis in sexually reproducing organisms, double-strand breaks are induced by a topoisomerase-related enzyme, Spo11, which is essential for homologous recombination, which in turn is required for accurate chromosome segregation. Additional factors control the number and genome-wide distribution of breaks, but the mechanisms that determine both the frequency and preferred location of meiotic DSBs remain only partially understood in any organism.

The manuscript presents a variety of different analyses that include variable subsets of putative DSB factors. It would be much easier to follow if the analyses had been more systematically applied. It is perplexing that several factors known to be essential for DSB formation (e.g., cohesins, HORMA proteins) are excluded from this analysis, while it includes several others that probably do not directly contribute to DSB formation (XND-1, HIM-17, CEP-1, and PARG-1).

We respectfully disagree with the reviewer’s statement regarding the selection of factors included in our analysis. In this work, our focus was specifically on SPO-11 accessory factors — proteins that directly interact with or regulate SPO-11 activity during doublestrand break formation. Cohesins and chromosome axis proteins (such as the HORMA domain proteins) are essential for establishing the correct chromosome architecture that supports DSB formation, but there is no evidence that they are direct accessory factors of SPO-11. Therefore, they were intentionally excluded from this study to maintain a clear and focused scope on proteins that more directly modulate SPO-11 function.

Conversely, XND-1, HIM-17, CEP-1, and PARG-1 have all been implicated in regulating aspects of SPO-11-mediated DSB formation or its immediate environment. Although their contributions mayinvolve broader chromatin or DNA damage response regulation, prior literature supports their inclusion as relevant modulators of SPO-11 activity, justifying their analysis within the context of this work.

The strongest claims seem to be that "HIM-5 is the determinant of X-chromosome-specific crossovers" and "HIM-5 coordinates the actions of the different accessory factors subgroups." Prior work had already shown that mutations in him-5 preferentially reduce meiotic DSBs on the X chromosome. While it is possible that HIM-5 plays a direct role in DSB induction on the X chromosome, the evidence presented here does not strongly support this conclusion. It is also difficult to reconcile this idea with evidence from prior studies that him-5 mutations predominantly prevent DSB formation on the sex chromosomes, while the protein localizes to autosomes.

HIM-5 is not the only protein that is autosomally enriched but preferentially affects the X chromosome: MES-4 and MRG-1 are both autosomally-enriched but influence silencing of the X chromosome. While HIM-5 appears autosomally-enriched, it does not appear to be autosomal-exclusive. While we would ideally perform ChIP to determine its localization on chromatin, this method for assaying DSB sites is likely insufficient to identify DSB sites which differ in each nucleus and for which there are no known hotspots in the worm.

him-5 mutants confer an ~50% reduction in total number of breaks and a very profound change in break dynamics (seen by RAD-51 foci (Meneely et al., 2012)). Since the autosomes receives sufficient breaks in this context to attain a crossover in >98% of nuclei, this indicates that the autosomes are much less profoundly impacted by loss of DSB functions than is the X chromosome. Indeed, prior data from co-author, Monica Colaiacovo, showed that fewer breaks occur on the X (Gao, 2015) likely resulting from differences in the chromatin composition of the X and autosome resulting from X chromosome silencing.

The conclusion that HIM-5 must be required for breaks on the X comes from the examination of DSB levels and their localization in different mutants that impair but do not completely abrogate breaks. In any situation where HIM-5 protein expression is affected (xnd-1, him-17, and him-5 null alleles), breaks on the X are reduced/ eliminated. By contrast, in dsb-2 mutants, where HIM-5 expression is unaffected, both X and autosomal breaks are impacted equally. As discussed above, in the absence of HIM-5 function, there are ~15 breaks/ nucleus. The Ppie1::him-5 transgene is expressed to lower levels than Phim-5::him-5, but in the best case, the ectopic expression of this protein should give a maximum of ~15 breaks (the total # of breaks is thought to be ~30/nucleus). By these estimates, Ppie-1::him-5; him-17 and him-5 null mutants have the same number of breaks. Yet, in the former case, breaks occur on the X; whereas in the latter they do not. The best explanation for this discrepancy is that HIM-5 is sufficient to recruits the DSB machinery to the X chromosome.

The one experiment that seems to elicit the conclusion that HIM-5 expression is sufficient for breaks on the X chromosome is flawed (see below). The conclusion that HIM-5 "coordinates the activities of the different accessory sub-groups" is not supported by data presented here or elsewhere.

We have reorganized the discussion to more directly address the reviewers’ concerns. We raise the possibility that HIM-5 has an important role in bringing together the SPO-11 and its interacting components (DSB-1/2/3) with the other DSB inducing factors, including those factors that regulating DSB timing (XND-1), coordination with the cell cycle (REC-1), association with the chromosome axis (PARG-1, MRE-11), and coupling to downstream resection and repair (MRE-11, CEP-1).  

This raises a natural question: if HIM-5 has such a central role, why are the phenotypes of HIM-5 so mild? We propose that while the loss of DSBs on the X appears mild, more profound effects are seen in the total number, timing, and placement of the DSBs across the genome- all of which are diminished or altered in the absence of HIM-5. The phenotypes of him-5 loss reminiscent of those observed in Prdm9-/- in mice where breaks are relocated to transcriptional start sites and show significant delay in formation. As with PRDM9, the comparatively subtle phenotypes of HIM-5 loss do not diminish its critical role in promoting proper DSB formation in most mammals.

Like most other studies that have examined DSB formation in C. elegans, this work relies on indirect assays, here limited to the cytological appearance of RAD-51 foci and bivalent chromosomes, as evidence of break formation or lack thereof. Unfortunately, neither of these assays has the power to reveal the genome-wide distribution or number of breaks. These assays have additional caveats, due to the fact that RAD-51 association with recombination intermediates and successful crossover formation both require multiple steps downstream of DSB induction, some of which are likely impaired in some of the mutants analyzed here. This severely limits the conclusions that can be drawn. Given that the goal of the work is to understand the effects of individual factors on DSB induction, direct physical assays for DSBs should be applied; many such assays have been developed and used successfully in other organisms.

We appreciate the reviewer’s thoughtful comments. We agree that RAD-51 foci are an indirect readout of DSB formation and that their dynamics can be influenced by defects in downstream repair processes. However, in C. elegans, the available methods for directly detecting DSBs are limited. Unlike other organisms, C. elegans lacks γH2AX, eliminating the possibility of using γH2AX as a DSB marker. TUNEL assays, while conceptually appealing, have proven unreliable and poorly reproducible in the germline context. Similarly, RPA foci do not consistently correlate with the number of DSBs and are influenced by additional processing steps.

Given these limitations, RAD-51 foci remain the most widely accepted surrogate for monitoring DSB formation in C. elegans. While we fully acknowledge the caveats associated with this approach — particularly the potential effects of downstream repair defects — RAD-51 analysis continues to provide valuable insight into DSB dynamics and regulation, especially when interpreted in combination with other phenotypic assessments.

Throughout the manuscript, the writing conflates the roles played by different factors that affect DSB formation in very different ways. XND-1 and HIM-17 have previously been shown to be transcription factors that promote the expression of many germline genes, including genes encoding proteins that directly promote DSBs. Mutations in either xnd-1 or him-17 result in dysregulation of germline gene expression and pleiotropic defects in meiosis and fertility, including changes in chromatin structure, dysregulation of meiotic progression, and (for xnd-1) progressive loss of germline immortality. It is thus misleading to refer to HIM-17 and XND-1 as DSB "accessory factors" or to lump their activities with those of other proteins that are likely to play more direct roles in DSB induction.

It is clear that we will not reach agreement about the direct vs indirect roles here of chromatin remodelers/transcription factors in break formation. In yeast, there is a precedent for SPP1 and in mouse for Prdm9, both of which could be described as transcription factors as well, as having roles in break formation by creating an open chromatin environment for the break machinery. We envision that these proteins function in the same fashion. The changes in histone acetylation in the xnd-1 mutants supports such a claim.

We do not know what the reviewer is referring to in statement that “XND-1 and HIM-17 have previously been shown to be transcription factors that promote the expression of many germline genes.” While the Carelli et al paper indeed shows a role for HIM-17 in expression of many germline genes, there is only one reference to XND-1 in this manuscript (Figure S3A) which shows that half of XND-1 binding sites overlap with the co-opted germline promoters. There is no transcriptional data at all on xnd-1 mutants, save our studies (referenced herein) that XND-1 regulates him-5 expression.

For example, statements such as the following sentence in the Introduction should be omitted or explained more clearly: "xnd-1 is also unique among the accessory factors in influencing the timing of DSBs; in the absence of xnd-1, there is precocious and rapid accumulation of DSBs as monitored by the accumulation of the HR strand-exchange protein RAD-51.

We are not sure what is confusing here. The distribution of RAD-51 foci is significantly altered in xnd-1 mutants and peak levels of breaks are achieved as nuclei leave the transition zone (Wagner et al., 2010; McClendon et al., 2016). There is no other mutation that causes this type of change in RAD-51 distribution.

"The evidence that HIM-17 promotes the expression of him-5 presented here corroborates data from other publications, notably the recent work of Carelli et al. (2022), but this conclusion should not be presented as novel here.

We have clarified this in the text. We note that this paper showed alterations in him-5 levels by RNA-Seq but they did not validate these results with quantitative RT-PCR. Thus, our studies do provide an important validation of their prior results.

The other factors also fall into several different functional classes, some of which are relatively well understood, based largely on studies in other organisms. The roles of RAD50 and MRE-11 in DSB induction have been investigated in yeast and other organisms as well as in several prior studies in C. elegans. DSB-1, DSB-2, and DSB-3 are homologs of relatively well-studied meiotic proteins in other organisms (Rec114 and Mei4) that directly promote the activity of Spo11, although the mechanism by which they do so is still unclear.

Whilst we agree that we understand some of the functions of the homologs, there are clearly examples in other processes of conserved proteins adopting unique regulatory function. We should not presume evolutionary conservation until proven. Indeed the comparison between the Mer2 proteins becomes particularly relevant here. For example, the RMM complex in plants does not contain PRD3, although this protein is thought to have function in DSB formation and repair (Lambing et al, 2022; Vrielynck et al., 2021; Thangavel et al., 2023). In Sordaria, as well, the Mer2 homolog has distinct functions (Tesse et al., 2017).  

Mutations in PARG-1 (a Poly-ADP ribose glycohydrolase) likely affect the regulation of polyADP-ribose addition and removal at sites of DSBs, which in turn are thought to regulate chromatin structure and recruitment of repair factors; however, there is no convincing evidence that PARG-1 directly affects break formation.

Our prior collaborative studies on PARG-1 showed that is has a non-catalytic function that promote DSBs that is independent of accumulation of PAR (Janisiw et al., 2020; Trivedi et al., 2022)

CEP-1 is a homolog of p53 and is involved in the DNA damage response in the germline, but again is unlikely to directly contribute to DSB induction.

We respectfully disagree with the reviewer’s statement. While CEP-1 is indeed a homolog of p53 and plays a major role in the DNA damage response, prior work from Brent Derry’s lab and from our group (Mateo et al., 2016) demonstrated that specific cep-1 separationof-function alleles affect DSB induction and/or repair pathway choice independently of canonical DNA damage checkpoint activation. In particular, defects in DSB formation observed in certain cep-1 mutants can be rescued by exogenous irradiation, supporting a direct or closely linked role in promoting DSB formation rather than merely responding to damage. Thus, based on these functional data, we considered CEP-1 a relevant factor to include in our analysis. We have now clarified this rationale in the revised manuscript.

HIM-5 and REC-1 do not have apparent homologs in other organisms and play poorly understood roles in promoting DSB induction. A mechanistic understanding of their functions would be of value to the field, but the current work does not shed light on this. A previous paper (Chung et al. G&D 2015) concluded that HIM-5 and REC-1 are paralogs arising from a recent gene duplication, based on genetic evidence for a partially overlapping role in DSB induction, as well as an argument based on the genomic location of these genes in different species; however, these proteins lack any detectable sequence homology and their predicted structures are also dissimilar (both are largely unstructured but REC-1 contains a predicted helical bundle lacking in HIM-5). Moreover, the data presented here do not reveal overlapping sets of genetic or physical interactions for the two genes/proteins. Thus, this earlier conclusion was likely incorrect, and this idea should not be restated uncritically here or used as a basis to interpret phenotypes.

Actually, there is quite good bioinformatic analysis that the rec-1 and him-5 loci evolved from a gene duplication and that each share features of the ancestral protein (Chung et al., 2015). We are sorry if the reviewer casts aspersions on the prior literature and analyses. The homology between these genes with the ancestral protein is near the same degree as dsb-1, dsb-2, or dsb-3 to their ancestral homologs (<17%).

DSB-1 was previously reported to be strictly required for all DSB and CO formation in C. elegans. Here the authors test whether the expression of HIM-5 from the pie-1 promoter can rescue DSB formation in dsb-1 mutants, and claim to see some rescue, based on an increase in the number of nuclei with one apparent bivalent (Figure 2C). This result seems to be the basis for the claim that HIM-5 coordinates the activities of other DSB proteins. However, this assay is not informative, and the conclusion is almost certainly incorrect. Notably, a substantial number of nuclei in the dsb-1 mutant (without Ppie-1::him-5) are reported as displaying a single bivalent (11 DAPI staining bodies) despite prior evidence that DSBs are absent in dsb-1 mutants; this suggests that the way the assay was performed resulted in false positives (bivalents that are not actually bivalents), likely due to inclusion of nuclei in which univalents could not be unambiguously resolved in the microscope. A slightly higher level of nuclei with a single unresolved pair of chromosomes in the dsb-1; Ppie-1::him-5 strain is thus not convincing evidence for rescue of DSBs/CO formation, and no evidence is presented that these putative COs are X-specific. The authors should provide additional experimental evidence - e.g., detection of RAD-51 and/or COSA-1 foci or genetic evidence of recombination - or remove this claim. The evidence that expression of Ppie-1::him-5 may partially rescue DSB abundance in dsb-2 mutants is hard to interpret since it is currently unknown why C. elegans expresses 2 paralogs of Rec114 (DSB-1 and DSB-2), and the age-dependent reduction of DSBs in dsb-2 mutants is not understood.

We have removed this claim in part because we have been unable to create the triple mutants strains to analyze COSA-1 foci.

To the point about 11 vs 12 DAPI bodies: the literature is actually replete with examples of 11 DAPI bodies vs 12 in mutants with no breaks:

Hinman al., 2021: null allele of dsb-3 has an average of 11.6 +/- 0.6 breaks;

Stamper et al, 2013, show just over 60% of dsb-1 nuclei with 12 DAPI bodies and 5-10% with 10 DAPI bodies. (Figure 1);

In addition, we also previously showed (Machovina et al., 2016) that a subset of meiotic nuclei have a single RAD-51 focus and can achieve a crossover. RAD-51 foci in spo-11 were also reported in Colaiacovo et al., 2003.

Several of the factors analyzed here, including XND-1, HIM-17, HIM-5, DSB-1, DSB-2, and DSB-3, have been shown to localize broadly to chromatin in meiotic cells. Coimmunoprecipitation of pairs of these factors, even following benzonase digestion, is not strong evidence to support a direct physical interaction between proteins.

Similarly, the super-resolution analysis of XND-1 and HIM-17 (Figure 1EF) does not reveal whether these proteins physically interact with each other, and does not add to our understanding of these proteins functions, since they are already known to bind to many of the same promoters. Promoters are also likely to be located in chromatin loops away from the chromosome axis, so in this respect, the localization data are also confirmatory rather than novel.

While the binding to promoters would be expected to be on DNA loops, that has not been definitively shown in the worm germ line. The supplemental data of the Carelli paper suggests that there are ~250 binding sites for each protein at these coopted promoters. This could not account for crossover map seen in C. elegans.

The reviewer states correct that we do not reveal that these proteins interact, but we have shown that the two proteins co-IP and have a Y2H interaction. This interaction is supporedt by a recent publication (Blazickova et al., 2025) corroborating this conclusion and identifies XND-1 in HIM-17 co-IPs also in the presence of benzonase. We do now show, however, by immuno-localization that the two proteins appear to be adjacent, but nonoverlapping. As now described in the text, AlphaFold 3 modeling and structural analysis suggests that the two proteins do interact directly and that the tagged 5’ end of HIM-17 used in our studies is likely to be at least 200nm from the putative XND-1 binding interface, a distance that is consistent with our confocal images showing frequent juxtaposition of the two proteins.

The phenotypic analysis of double mutant combinations does not seem informative. A major problem is that these different strains were only assayed for bivalent formation, which (as mentioned above) requires several steps downstream of DSB induction. Additionally, the basis for many of the single mutant phenotypes is not well understood, making it particularly challenging to interpret the effects of double mutants. Further, some of the interactions described as "synergistic" appear to be additive, not synergistic. While additive effects can be used as evidence that two genes work in different pathways, this can also be very misleading, especially when the function of individual proteins is unknown. I find that the classification of genes into "epistastasis groups" based on this analysis does not shed light on their functions and indeed seems in some cases to contradict what is known about their functions. ‘

As described above, each of the proteins analyzed is thought to have a direct role in regulating meiotic DSB formation and single mutant phenotypes are consistent with this interpretation. In almost all-if not all- of these cases, IR induced breaks suppress univalent phenotypes (or uncover a downstream repair defect (e.g. in mre-11)) supporting this conclusion. We have changed the terminology from “epistasis groups” since this is not strict epistasis, but rather, “functional groups”.  

The yeast two-hybrid (Y2H) data are only presented as a single colony. While it is understandable to use a 'representative' colony, it is ideal to include a dilution series for the various interactions, which is how Y2H data are typically shown.

The Y2H data are presented as spots on a plate and are from three to four individual transformants per interaction tested, and are not individual colonies. The experiment was repeated in triplicate from different transformations. We have now made this clearer in the materials and methods section. This approach has been successfully used to examine protein interactions in our prior manuscripts of yeast and human proteins [Gaines et al (2015) Nat. Comms 6:7834; Kondrashova et al (2017) Cancer Discovery 7:984; Garcin et al (2019) PLoS Genetics 15:e1008355; Bonilla et al (2021) eLife 1: e68080) Prakash et al (2022) PNAS 119: e2202727119, etc]

Additional (relatively minor) concerns about these data:

(1) Several interactions reported here seem to be detected in only one direction - e.g., MRE-11-AD/HIM-5-BD, REC-1-AD/XND-1-BD, and XND-1-AD/HIM-17-BD - while no interactions are seen with the reciprocal pairs of fusion proteins. I'm not sure if some of this is due to pasting "positive" colony images into the wrong position in the grid, but this should be addressed.

The asymmetry in the interactions observed is due to the well-known phenomenon in yeast two-hybrid (Y2H) assays where certain plasmids exhibit self-activation when fused in one orientation, making interpretation of reciprocal interactions challenging. In our experiment, some of the plasmids indeed showed self-activation in one direction, which likely accounts for the lack of interaction seen with the reciprocal pairs of fusion proteins. We have clarified this point in the Methods.

(2) DSB-3 was only assayed in pairwise combinations with a subset of other proteins; this should be explained; it is also unclear why the interaction grids are not symmetrical about the diagonal.

We have now completed the analysis by adding the interactions of DSB-3 with the remaining proteins that were missing from the initial set.

(3) I don't understand why the graphic summaries of Y2H data are split among 3 different figures (1, 2, and 3).

We chose to split the graphic summaries of the Y2H data across Figures 1, 2, and 3 because we felt this organization better aligns with the flow of the results presented in each figure. Each set of interactions is shown in the context of the specific experiments and findings discussed in those sections, which we believe helps provide a clearer and more logical presentation of the data.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Figure 1: B) The IP is difficult to interpret - there is a band of the corresponding size to XND-1 in the control lane calling into question the specificity of the IP/Western.

We added a supplemental figure with the specificity of the antibody showing that there is a background non-specific band.

C) More information about the mass spectrometry should be included. No indication of the number of times a peptide was identified, or the overall coverage of the identified proteins.

Done

This is important as in the results section (line 114) the authors indicate that there was "strong" interaction yet there is no way to assess this.

D) Why wasn't hatching measured in the him-5p::him-5; him-17(ok424) strain?

Great question. I guess we need to do this while back out for review. If anyone has suggestions of what to say here. Clearly we overlooked this point but do have the strain.

E) Quantification of the cytology should be included.

We have now quantified overlap between XND-1 and HIM-17

Figure 2: C) Statistics should be included.

Done

E) Quantification should be included for the cytology. I recommend changing the eals15 to HIM-5.

We included better images showing whole gonads instead of one or two nuclei. We were not sure what the reviewers want us to quantify here since the relocalization of the protein to the cytoplasm is very clear.

I have a general issue with the use of the term epistasis - this is used to order gene function based on different mutant phenotypes, usually with null alleles. While I think the authors have valid points with how they group the different SPO-11 accessory proteins, I do not think they should use the word epistasis, but rather genetic interactions.

We appreciate the reviewers thoughts on this matter and have removed the term epistasis and use functional groups or genetic interactions throughout the text.

Figure 4 and the nature of the X chromosome: First, I think it would help the non-C. elegans reader to include a little more information on the X chromosome with respect to its differences compared to the autosomes. I also think that, if possible, it would be beneficial to include a model of the X in Figure 4.

We have added more about X/autosome differences in the intro and during the discussion of HIM-5 function and have added a figure showing difference in the behavior of the X/autosomes during DSB/crossover formation.

Minor points:

Abstract: Given the findings of Silva and Smolikove on SPO-11 breaks, I recommend removing "early" from line 28 in the Abstract.

Done

Introduction (line 93): I think "biochemical studies" is a stretch here - I recommend "interaction studies".

Done

Results: (lines 160-161): mutations are not required for breaks. Line 172, there is a problem with the sentence.

Corrected

Reviewer #2 (Recommendations For The Authors):

Major comments:

(1) Figure 1B- The signal for XND-1 seems to appear both in the control and him-17::HA IP. Do the authors have tested the specificity of the XND-1 antibody?

We included a supplementary figure demonstrating the specificity of the XND-1 antibody by Western blot. This was also previously published (Wagner et al., 2010)

(2) Figure 1D - can the authors provide an explanation why the him-5p::him-5 transgene that drives a higher expression than pie-1p::him-5 fails to suppress the Him phenotype seen in him-17? What are the HIM-5 levels like in these two strains compared to N2 and him-17 null mutants? Can this information provide explanation for the differential effect of the him-5 transgenes?

We previously reported that him-5p::him-5 drives higher expression than pie-1p::him-5 (McClendon et al, 2016).

The reason that him-5p::him-5 does not rescue, despite higher wild type expression is that HIM-17 directly regulates expression of him-5. Since HIM-17 does not regulate the pie-1 promoter, the pie-1p::him-5 construct can at least partially suppress the him-17 mutation.

We have (hopefully) explained this better in the text.  

(3) Line 102- the subheading "HIM-5 is the essential factor for meiotic breaks in the Xchromosome" may not be appropriate for this section. This is what has previously been known. However, the results in Figure 1 demonstrate that a him-5 transgene can partially rescue the him-17 and ¬xnd-1 phenotype, but not that it is essential for meiotic DSB formation on X chromosomes.

We think some of the concern here is sematic and have changed the phraseology to say that HIM-5 is SUFFICIENT for DSBs on the X… which had not previously been shown.

Vis-à-vis the X chromosome, in all genetic backgrounds examined, the absence of HIM-5 consistently results in a complete lack of DSBs on the X. For instance, in dsb-2 mutants— where HIM-5 is still expressed—DSBs are reduced genome-wide, but the X chromosome occasionally retains breaks. In contrast, even a weak allele of him-17 results specifically in the loss of X chromosome breaks, underscoring a unique requirement for HIM-5 in promoting DSBs on the X. While Figure 1 shows that a him-5 transgene can partially rescue him-17 and xnd-1 phenotypes, the consistent observation that X breaks are absent without HIM-5 supports its classification as sufficient for DSB formation on the X chromosome.

(4) Figure 1E - please consider enlarging the images and showing multiple examples.

Done.

I also suggest that the authors perform a more rigorous analysis to support the conclusion that XND-1 and HIM-17 localize away from the axis by quantifying multiple images and doing line-scan analysis.

Provided. New images are provided in both, the main and supplemental figures, and quantification is included. There is no detectable overlap of the two protein with one another or the DNA axes (see quantification of overlap in Fig. 1).

(5) Line 162 - This is the first mention of DSB-1, DSB-2, and DSB-3 in the paper. DSB-1 and DSB-2 are Rec114 homologs in C. elegans (Tesse et al., 2017), while DSB-3 is a homolog of Mei4 (Hinman et al., 2021). These proteins should be properly introduced in the introduction with appropriate citations.

Done. We appreciate the reviewer pointing out that this was the first reference to these genes.

(6) Line 169 - the rationale for this experiment is unclear. Why did the Y2H interaction between HIM-5 and DSB-1 prompt the authors to test the rescue of dsb-1 or dsb-2 phenotypes by the ectopic expression of him-5? Do the authors have evidence that HIM-5 level is reduced in dsb-1 or dsb-2 mutants?

We have reorganized this section to better explain the motivation for looking at these interactions. We did see a difference in the localization in HIM-5 in the dsb-1 mutant animals and we did have a sense that HIM-5 was critical for breaks on the X. We reasoned that it could have independent functions in promoting breaks that were not yet appreciated so wanted to do this experiment.

(7) Line 172 - "very slightly reduced". This claim requires statistical analysis.

We added statistical analysis, but we also removed this claim.

(8) Figures 2C and 2D - Can the authors provide an explanation why the pie-1p::him-5 transgene fails to suppress the phenotypes in dsb-1, while the him-5p::him-5 trasgene can? Again, the rationale for these experiments is unclear. Because of this, the interpretation is also unclear.

The difference in rescue between the pie-1p::him-5 and him-5p::him-5 transgenes likely reflects differences in expression levels. As previously shown (McClendon et al., 2016), the him-5p::him-5 construct results in significantly higher expression of HIM-5 protein compared to pie-1p::him-5. This elevated expression likely explains its ability to partially rescue the dsb-1 phenotype. In contrast, the lower expression driven by the pie-1 promoter is insufficient to compensate for the absence of dsb-1 function. We have clarified the rationale and interpretation of these experiments in the revised manuscript to better reflect this point.

(9) Lines 184-185 - the data for endogenously tagged HIM-5::3xHA are not shown anywhere in the paper. This must be shown.

We have added this in the supplemental figures.

(10) Figure 2D and 2E - what does the localization of pie-1p::him-5::GFP (eaIs15) and him5p::him-5::GFP (eaIs4) look like in wild-type and dsb-1 mutants? Are the cytoplasmic aggregates caused by increased levels of HIM-5 expression? Can the differential behavior of him-5 transgenes provide explanation for differential rescues?

We now show both live and fixed images of Phim-5::him-5::gfp transgenes, as well as the localization of the endogenously HA-tagged HIM-5 locus (Figure 2 and S3). In all cases, the protein is initially nuclear and then absent from meiotic nuclei with similar timing. The Ppie1::him-5 transgene was very difficult to image due to low expression (even in wild type) so it not shown here. We presume it is the slightly elevated level of expression of the Phim5::him-5::gfp that can explain the differential rescue.

(11) Lines 221-222, where are the results shown? Please refer to Figure S3.

Done

(12) Figure S3 - these need statistical analyses.

Done

(13) Lines 230-231 - what about the rec-1; parg-1; cep-1 triple mutant?

This is an excellent suggestion and not one we have not yet pursued. Given the lack of strong phenotypes in all combination of double mutants, we prioritized other experiments . However, we agree that examining the rec-1; parg-1; cep-1 triple mutant would provide a valuable test of whether these factors act in the same pathway, and we appreciate the reviewer highlighting this potential future direction.

(14) Line 298 - I suggest the authors take a look at the Alphafold prediction of DSB-1/DSB-2/DSB-3 and the comparison to human and budding yeast Rec114/Mei4 complex in Guo et al., 2022 eLife, which could provide insights into the Y2H results.

We thank the reviewer for these comments and have indeed used these interactions and predicted homologies to zero in a region of interaction between these proteins that resembles what is seen in humans and yeast with a dimer of REC114 like proteins wraps stabilizing a central Mei4 helix . This is now shown in Figure 3H, I. Satisfyingly, this modeling predicts that a trimer comprised of 2 DSB-1 proteins with DSB-3 is more stable than a DSB1-DSB-2-DSB-3 trimer. This might explain why DSB-2 is not required in young adults and only becomes essential as DSB-1 levels drop in older animals (Rosu et al., 2013)

(15) Can the authors introduce mutations within the DSB-1 interfaces that disrupt the interaction to either SPO-11 or DSB-2?

We have begun to address this question by introducing targeted mutations within DSB-1. As shown in Figure 3E and 3F, mutations in the C-terminal region of DSB-1—which includes a core of four α-helices—disrupt its interaction with DSB-2 and DSB-3, but not with SPO-11. These findings suggest that the C-terminus mediates interactions specifically with DSB2 and DSB-3

(16) Line 323 - The him-5 phenotypes are too weak to support the idea that it serves as the linchpin for the whole DSB complex. Do the authors have an explanation for why him-5 mutants exhibit X-chromosome-specific DSB defects?

In response to the reviewer, above, and in the text, we have included a more detailed explanation of why we think HIM-5 has a key role in coordinating meiotic break formation. Although, identified for its role on the X, the phenotypes associated with DSB formation in the mutant are really quite pleiotropic and severe.

(17) Line 436 - C. elegans lacks DSB hotspots.

Removed

Minor comments:

(1) Figure 1A - please show the raw data for the yeast two-hybrid.

We show representative yeast colonies in Figure S3.

(2) It looks like the labeling for Figure 1B and 1C are switched.

Fixed.

(3) Figure 1B - what does the red box indicate? Please explain it in the legend.

It indicates the XND-1 band. We added that information in the legend.

(4) Figure 1C - in the legend, it was noted that the results are from GFP pulldowns of HIM17::GFP. However, the method for Figure 1B and the method section noted that HIM-17 was tagged with 3xHA, and the pull-down was performed using anti-HA affinity matrix. Please reconcile this discrepancy.

That’s because they were done in two different sets of experiments. For the IPs we used a HIM-17::HA strain and for the MS, a HIM-17::GFP strain.

(5) Also in Figure 1C - please call Table S2 in the main text when discussing the mass spec results. Also, it is not clear what HIM-17 and GFP indicate in the table. What makes CKU80 different from the other proteins listed under GFP? Please explain more clearly in the legend.

We have move the table to supplemental data where we have included all of the peptide counts and gene coverage. We have included in the revised method rationale for inclusion in this table which explains why CKU-80 differs.

(6) Line 527 - it is unclear what experiment was done for HIM-17. Please revise it to indicate that this is for "HIM-17 immunoprecipitation". Also please indicate the strain used for HIM17 pull-down (AV280?).

(7) Line 113- please be specific about how the HIM-17 IP was performed. Which epitope and strains are used for pull-downs?

This indeed was AV280. This has been added to the text and methods.

(8) Figure 1D- What does ND mean? In the text, it was stated that there was only a minor suppression of hatching rates. The hatching rate for him-5p::him-5; him-17 must have been measured, and the data must be presented.

ND does mean not determined. We have removed the statement about “minor suppression”. We only tested the overall population dynamics in the Phim-5::him-5;him17(ok424) and the DAPI body counts. The failure to suppress the latter suggests there would be no enect on hatching rates, although we did not test this directly. Since we had done this for the Ppie-1::him-5;him-17 strain, we provided this information to further support the claims of genetic rescue by ectopic expression.

(9) Line 151 - please specify that STED was used.

We have removed the STED images, and just show the confocal images with Lightning Processing.

(10) Figure 1E- the authors suggested that HIM-17 and XND-1 mainly localize to autosomes but not the X chromosome. However, there is not enough evidence that the chromosome excluded from HIM-17 staining is indeed an X chromosome.

(11) Figure 1E (Line 154) - what are the active chromatin markers examined? Where are the data?

We have previously shown that the chromosome lacking XND-1 staining is the X (Wagner et al., 2010). The X is heterochromatic and chromatin marks associated with active transcription, including H3K4me3 and HTZ-1 (a variant H2A), preferentially localize to autosomes, effectively anti-marking the X chromosome. As shown in the new Figure 1E, a single chromosome has very little XND-1 and HIM-17 associated proteins. This is the X chromosome.

(12) Line 172 - It should be a comma instead of the period after "In dsb-1 mutants".

Fixed

(13) Figure S3H-K - I suggest the authors indicate the alleles of mre-11 (null vs. iow1) on the graph, similarly to him-5(e1490) to avoid confusion.

Done

(14) Lines 294 and 600 - Guo et al. 2022 is now published in eLife. The authors must cite the published paper, not the preprint.

Fixed

(15) Line 407 - the reference Carelli et al., 2022 is missing.

Added

(16) Line 766 - please remove "is" before nuclear.

Done

Reviewer #3 (Recommendations For The Authors):

Major issues:

In my view, the most interesting mechanistic finding in the paper is the evidence that HIM-5 may not bind to chromatin in the absence of DSB-1. If validated, this would suggest that HIM-5 is likely to be directly involved in a process that promotes break formation, in contrast to factors such as HIM-17 and XND-1. It does not, however, support the idea that HIM-5 is at the top of a hierarchy of DSB factors, as it is interpreted here. More importantly, the data supporting this claim are unconvincing; only a single image of an unfixed gonad from an animal expressing HIM-5::GFP is shown. Immunofluorescence should be performed and the results must be quantified.

We have provided additional images of the HIM-5 relocalization to show that we observed this in both fixed and live worms with two different tagged strains. The exclusion from the nucleus is seen in all scenarios. Whether the protein now accumulates exclusively in the cytoplasm/ is destabilized is challenging to address with the fixed images due to the arbitrariness of defining “background” staining.

More generally, this type of analysis, looking at the interdependence of different factors for their association with chromosomes, is much more informative than the genetic interaction data presented in the paper, which does not seem to provide any mechanistic insights into the functions of the factors analyzed. The paper could potentially be greatly improved through a more extensive, systematic analysis of the interdependence of DSBpromoting factors for their localization to chromosomes.

We have at least added this for XND-1 and HIM-17 and show they are not interdependent for chromosome association. We also provide for the first time data on the localization of HIM-5 in the dsb-1 mutant. Many of the other interactions have already been shown in the literature and/or were not warranted base on the lack of genetic interaction we present here.

Minor issues:

The title is vague and inconclusive. A more concrete title summarizing the major findings would help readers to assess whether the work is of interest.

We have discussed the title extensively with all authors and all would like to keep the current title.

The authors claim that the expression of HIM-5 from a different promoter (Ppie-1::him-5) but not its endogenous promoter (Phim-5::him-5) can partially rescue the DSB defect in him-17 mutants. To support this claim, they should really quantify the germline expression of HIM-5 in wild-type, him-17, him-17; Ppie-1::him-5, and Phim-5::him-5; him-17.

We had previously reported the expression in the N2 background of both transgenes (McClendon et al., 2016)

Panel O appears to be missing from Figure S3.

Fixed

The evidence for chromosome fusions in cep-1; mre-11 mutants shown in S4D is not convincing and the claim should be removed unless stronger evidence can be obtained.

A clearer image has been added

The basis of the following statement is unclear: "Furthermore, rec-1;him-5 double mutants give an age-dependent severe loss of DSBs (like dsb-2 mutants) suggesting that the ancestral function of the protein may have a more profound effect on break formation." The manuscript does not seem to include data regarding age-dependent loss of DSBs and no other publication is cited to support this claim. The interpretation is also perplexing; I think that it may be predicated on the idea that REC-1 and HIM-5 are paralogs, but as stated above, this claim is not well supported and is likely specious.

We have added the reference. This was shown in Chung et al., 2013 – the paper that presented the cloning of the rec-1 locus.

RISC-V چیه؟

RISC-V (ریسک فایو) یک معماری مجموعه دستورالعمل (Instruction Set Architecture – ISA) برای پردازنده‌هاست. یعنی همون چیزی که مشخص می‌کنه یک CPU چه دستوراتی رو می‌تونه اجرا کنه (مثل جمع، ضرب، پرش، load/store و …).

نکات مهم:

1. RISC = Reduced Instruction Set Computer

2. یعنی مجموعه دستورالعمل‌هاش ساده و بهینه طراحی شده تا اجرای دستورات سریع‌تر باشه.

3. V = Version 5

4. عدد ۵ به نسل طراحی RISC برمی‌گرده، نه اینکه فقط ۵ دستور داشته باشه 🙂.

5. Open Source بودن

6. بزرگ‌ترین فرقش با معماری‌های معروف مثل x86 (اینتل/AMD) یا ARM (پردازنده‌های موبایل) اینه که RISC-V متن‌بازه.

7. یعنی هر کسی می‌تونه بدون پرداخت لایسنس، پردازنده بر اساس RISC-V طراحی کنه.

8. کاربردها

9. از میکروکنترلرهای خیلی کوچک (مثل Arduino)

10. تا سرورها و حتی سوپرکامپیوترها
11. چون انعطاف‌پذیره و می‌شه دستورالعمل‌های خاص رو بهش اضافه کرد.

مقایسه خیلی ساده:

• x86 (Intel/AMD): بسته، پیچیده، ولی خیلی قدرتمند.
• ARM: کم‌مصرف و محبوب در موبایل و تبلت، ولی نیاز به لایسنس داره.
• RISC-V: ساده، متن‌باز، قابل توسعه، و در حال رشد سریع در دنیا.

Dossier d'Information : La Méthode Réconciliations

Résumé

La méthode "Réconciliations" est une approche pédagogique innovante, conçue par Jérémie Fontanieu, professeur de sciences économiques et sociales (SES), et son ancien collègue David Benoit, professeur de mathématiques au lycée de Drancy.

Née en 2012 du constat de la démotivation des élèves, de l'épuisement des enseignants et de la rupture de communication entre l'école et les familles, cette méthode repose sur un principe fondamental : la création d'une alliance solide et proactive entre les professeurs et les parents.

Le protocole s'articule autour de deux piliers : un appel téléphonique à chaque famille avant même la rentrée scolaire pour établir un contact de confiance, suivi de l'envoi d'un SMS individualisé et hebdomadaire pour maintenir un dialogue constant tout au long de l'année.

En transformant les parents en "alliés indéfectibles", la méthode change la dynamique de la classe.

Les élèves, conscients de cette communication permanente, deviennent plus engagés et responsables, ce qui enclenche un cercle vertueux de progrès, d'encouragements et de réussite.

Les résultats sont probants : la classe de Jérémie Fontanieu affiche 100% de réussite au baccalauréat depuis l'année scolaire 2017-2018.

Au-delà des performances académiques, la méthode réduit considérablement le temps consacré à la discipline, diminue le sentiment d'isolement des professeurs et réconcilie les enseignants avec leur métier.

Développée de manière indépendante, sans soutien institutionnel, la méthode se diffuse via un collectif d'enseignants qui comptait 350 membres pour l'année 2023/2024, avec un objectif de 1000 participants.

Flexible, elle est appliquée avec succès de l'école primaire au lycée, dans des contextes socio-économiques variés, des zones prioritaires aux centres-villes et zones rurales.

1. Genèse et Contexte de la Méthode

La méthode Réconciliations est née d'une série de constats alarmants sur l'état du système éducatif, particulièrement exacerbés dans des contextes socio-économiques difficiles comme la Seine-Saint-Denis.

A. Les constats initiaux

Jérémie Fontanieu identifie plusieurs sources de frustration et d'échec qui ont motivé le développement de son approche :

• L'épuisement et le désespoir des enseignants : Particulièrement chez les plus jeunes, un sentiment d'abandon par l'institution face à la "violence de ce métier" et une impuissance face à des adolescents qui "gâchent leur potentiel".

Les professeurs se sentent seuls à porter toutes les responsabilités.

• La démotivation des élèves : Souvent "accros aux écrans et aux réseaux sociaux", les élèves manquent d'implication dans leur scolarité, ce qui accroît la frustration de leurs professeurs.

Ils ont "la flemme" ou manquent de confiance en eux.

• La rupture entre parents et enseignants : Une distance, voire une confrontation, entre ces deux pôles éducatifs, marquée par des "quiproquos et des failles" que les élèves exploitent.

Les parents, souvent tenus à l'écart, reçoivent des informations partielles de leurs enfants.

Les appels de l'école sont quasi systématiquement perçus comme des annonces de mauvaises nouvelles.

• Le sentiment d'abandon généralisé : En Seine-Saint-Denis, élèves et parents se sentent délaissés par l'Éducation nationale et la République, percevant l'école comme une "machine à broyer" incapable de les intégrer.

Ce sentiment est également partagé par les enseignants face à la pénurie structurelle, les faibles salaires et la dégradation des conditions de travail.

B. La déconstruction du "Mythe du prof héros"

Jérémie Fontanieu, dans son livre Le Mythe du prof héros, analyse une construction culturelle qu'il juge toxique.

Ce mythe, hérité des "hussards noirs de la République" du XIXe siècle, place l'enseignant sur un piédestal et lui attribue des capacités extraordinaires.

• Un mythe à double tranchant : Si l'idée de valoriser les professeurs est belle en apparence, elle conduit les parents à se décharger entièrement sur l'enseignant ("les laisser gérer").

• Une source de culpabilité : Pour les professeurs, cette attente irréaliste engendre un "sentiment de culpabilité de ne pas réussir à être à la hauteur du mythe".

• Un frein à l'implication parentale : Ce mythe dissuade les parents de s'impliquer, alors même qu'ils ont un rôle crucial à jouer.

Fontanieu insiste sur le fait que l'implication parentale n'est pas forcément intellectuelle mais relève de "l'attention morale", du soutien aux valeurs d'honnêteté et de courage, communes à l'éducation parentale et scolaire.

2. Principes Fondamentaux et Mécanisme Opérationnel

La méthode Réconciliations repose sur une stratégie de co-éducation proactive, simple et structurée, visant à faire des parents des partenaires centraux du processus éducatif.

A. Le socle : l'alliance parents-professeurs

L'idée centrale est de "nouer le dialogue" entre professeurs et parents pour créer un binôme solide.

En informant systématiquement les parents, l'enseignant change de statut aux yeux des élèves.

Ces derniers, réalisant que leurs parents et professeurs "sont devenus des amis", ne peuvent plus exploiter le manque de communication.

B. Le mécanisme en deux temps

La méthode s'appuie sur deux actions clés, mises en œuvre dès le début de l'année scolaire.

Étape

Description

Objectifs

1. L'appel téléphonique initial

L'enseignant contacte chaque famille personnellement "le 31 août ou le 1er septembre", avant même qu'un problème ne survienne.

• - Établir la confiance : Les parents, "positivement surpris", apprécient cette attention et deviennent rapidement des alliés.<br>\
• • Présenter la démarche : L'enseignant explique sa méthode et son intention de collaborer.<br>\
• • Dédramatiser la communication : L'appel n'est pas lié à une sanction, ce qui change la perception de l'école.

2. Les SMS hebdomadaires

Chaque semaine, un SMS individualisé est envoyé à chaque famille pour l'informer du comportement et du travail de l'enfant, "y compris lorsqu’il n’y a pas de problème".

• - Maintenir un dialogue constant : Assurer un suivi régulier et éviter les ruptures de communication.<br>
• - Valoriser et encourager : Les SMS permettent de féliciter les efforts et les progrès, renforçant la motivation.<br>\
• • Assurer la cohésion : Le fait que tous les élèves soient concernés, qu'ils soient en difficulté ou non, évite la stigmatisation et favorise la solidarité.

Jérémie Fontanieu souligne que cette approche est "contre-intuitive" car elle demande un investissement de travail supplémentaire en début d'année, mais que ce temps est "largement rentabilisé" par la suite.

3. Impacts et Résultats Observés

La mise en place de la méthode Réconciliations génère des effets positifs et mesurables sur l'ensemble des acteurs du système éducatif : élèves, enseignants et parents.

A. Sur les élèves

• Engagement accru : Constatant l'alliance entre parents et professeurs, les élèves deviennent "moins passifs et plus engagés" et prennent leurs responsabilités.

• Cercle vertueux de la réussite : L'investissement croissant des élèves entraîne des progrès, qui sont salués par les adultes (parents et professeurs).

Ces encouragements renforcent à leur tour l'implication, créant "une dynamique de réussite, de confiance et d'espoir pour tous".

• Amélioration des résultats scolaires : Depuis l'année scolaire 2017-2018, la classe de terminale de Jérémie Fontanieu affiche un taux de 100% de réussite au baccalauréat.

Les élèves terminent le programme en avance, ce qui leur laisse "beaucoup de temps pour réviser".

• Gain de confiance : La méthode permet aux élèves de réussir "là où ils pensaient ne pas en être capable", ce qui les encourage à poursuivre des études supérieures.

B. Sur les enseignants

• Réduction de la charge disciplinaire : Le partenariat avec les parents diminue les comportements perturbateurs. C'est "de l'énergie dépenser en moins à faire sa discipline".

• Fin de l'isolement : Les professeurs ne se sentent plus seuls à tout porter sur leurs épaules.

Ils trouvent un soutien précieux chez les parents, qui deviennent des "alliés indéfectibles" les protégeant des "violences du métier".

• Réconciliation avec le métier : La méthode permet aux enseignants de se consacrer à leur cœur de métier : l'enseignement. Jérémie Fontanieu évoque "une réconciliation entre nous, enseignants, et notre métier".

• Gestion du temps optimisée : Bien que l'approche puisse sembler chronophage, elle fait en réalité gagner "beaucoup de temps" en réduisant les conflits et en augmentant l'implication des élèves.

C. Sur les parents

• Partenaires actifs : Les parents deviennent des "acteurs clés" et des partenaires fiables, intégrés au processus éducatif.

• Influence positive : Ils réalisent l'influence qu'ils peuvent avoir, même sans compétences académiques spécifiques. Leur rôle est un levier d'attention morale et de soutien.

• Relation apaisée avec l'école : La communication régulière et positive transforme la relation, qui n'est plus basée sur la crainte des mauvaises nouvelles.

4. Le Collectif "Réconciliations" : Diffusion et Organisation

La méthode, initialement expérimentale, est aujourd'hui portée par un collectif d'enseignants en pleine croissance, qui fonctionne sur un modèle horizontal et indépendant.

A. Historique et croissance

• Développement : La méthode a été développée à partir de 2012 au lycée Eugène Delacroix de Drancy.

• Création du collectif : Après avoir constaté des résultats "suffisamment forts", le collectif est créé en 2021 pour partager la méthode.

• Expansion rapide : Le collectif est passé de 200 enseignants à 350 pour l'année 2023-2024, avec des projections autour de 500 pour 2024-2025.

• Objectif : Atteindre une masse critique de 1000 enseignants pour passer à une phase de diffusion à plus grande échelle via un site internet et un manuel.

B. Philosophie et indépendance

Le collectif revendique une indépendance totale et refuse "aucun soutien institutionnel". Jérémie Fontanieu explique ce choix : "Nous utilisons notre liberté pédagogique.

Nous sommes indépendants, et nous sommes très attachés à la diffusion horizontale de cette méthode, de professeur à professeur.

Nous grandissons plus lentement sans l’aide de l’État, mais de manière plus saine". La diffusion se fait principalement par le bouche-à-oreille.

C. Portée et applicabilité

La méthode Réconciliations démontre une grande flexibilité et s'adapte à divers contextes :

• Niveaux scolaires : Elle est appliquée de l'école primaire (CM2) jusqu'au lycée.

• Contextes géographiques et sociaux : Si elle est née en "quartier populaire", environ la moitié des enseignants qui l'utilisent travaillent en zones rurales ou dans des établissements de centre-ville, prouvant sa pertinence au-delà des zones d'éducation prioritaire.

5. Accès à la Méthode et Ressources Disponibles

Pour préserver la qualité de l'accompagnement, l'accès à la méthode est actuellement contrôlé par son fondateur en attendant que le collectif atteigne sa taille cible.

• Comment participer : Les enseignants intéressés doivent contacter directement Jérémie Fontanieu par email à l'adresse projet.reconciliations@gmail.com.

Les détails de la méthode restent "assez secrets" et ne sont pas communiqués publiquement pour le moment.

• Outils de soutien pour les membres : Les professeurs qui rejoignent le collectif ont accès à un ensemble d'outils pratiques :

• ◦ Vidéos et tutoriels (notamment pour la rédaction des SMS).   
• ◦ Groupes de discussion en ligne.   
• ◦ Visioconférences hebdomadaires.   
• ◦ Une rencontre annuelle pour un suivi et une formation continue.

• Ressources publiques : Pour en savoir plus sur la philosophie de la méthode, le public peut consulter :

• ◦ Le livre de Jérémie Fontanieu, "Le Mythe du prof héros".   
• ◦ Le film documentaire "Le Monde est à eux", sorti au cinéma en mars 2024.   
• ◦ Un deuxième documentaire disponible en ligne, montrant l'application de la méthode en écoles primaires et au collège.

This is huge.

Santé Mentale et Handicap : Synthèse de l'Audition de la Défenseure des droits

Résumé Exécutif

Ce document de synthèse présente les constats et analyses clés issus de l'audition de Claire Hédon, Défenseure des droits, devant la commission d'enquête sur les défaillances des politiques publiques en matière de santé mentale et de handicap. L'audition révèle une divergence critique entre les droits proclamés par la loi et leur application effective sur le terrain. Le handicap demeure le premier motif de saisine pour discrimination, signalant des failles systémiques dans des domaines essentiels tels que l'éducation, l'emploi et l'accessibilité. La situation de la santé mentale est jugée particulièrement alarmante, avec un système de soins insuffisant et cloisonné pour les adultes, et des conditions critiques pour les mineurs marqués par des délais d'attente insoutenables et des pratiques d'hospitalisation inadaptées. La Défenseure des droits soutient que le non-respect des droits fondamentaux, loin d'être une économie, engendre un coût social et financier élevé à long terme. La collecte de données fiables, l'application rigoureuse des textes existants et une approche décloisonnée sont identifiées comme des leviers indispensables pour remédier à ces défaillances.

1. Le Défenseur des droits : Un Observatoire des Défaillances Systémiques

L'institution du Défenseur des droits, par ses cinq domaines de compétence (droits des usagers des services publics, lutte contre les discriminations, droits de l'enfant, déontologie de la sécurité, protection des lanceurs d'alerte), constitue un observateur privilégié des carences des politiques publiques relatives au handicap et à la santé mentale.

Le Handicap comme Premier Motif de Discrimination

Claire Hédon souligne que le handicap est, depuis plusieurs années, le premier motif de saisine en matière de discrimination, ce qui témoigne de difficultés persistantes et généralisées.

• Statistiques Clés (2024) :

◦ Total des saisines pour discrimination : 5 679    ◦ Part concernant le handicap : 22 % (soit 1 249 réclamations)

Ces discriminations s'exercent dans de multiples domaines : emploi, scolarisation, accès à la santé, à la justice, aux loisirs, au sport et à la culture.

Le Coût du Non-Respect des Droits

La Défenseure des droits conteste l'idée selon laquelle l'application des droits fondamentaux représenterait un coût financier excessif. Elle affirme sa conviction que "c'est le non-respect des droits fondamentaux qui entraînera à terme un coût élevé pour la société". L'approche de l'institution se concentre sur "l'écart entre le droit annoncé et son effectivité", soulignant que les défaillances actuelles génèrent un coût social majeur.

2. La Santé Mentale : Une Crise des Droits Fondamentaux

L'audition met en lumière une crise profonde dans la prise en charge de la santé mentale en France, exacerbée par la crise sanitaire du Covid-19. Les politiques publiques sont jugées insuffisantes tant en quantité qu'en organisation.

2.1. Prise en Charge des Adultes : Un Système Insuffisant et Cloisonné

Le système de soins pour adultes souffre de faiblesses structurelles majeures :

• Offre de soins : Des offres trop faibles, des capacités d'hospitalisation limitées et des déserts médicaux.

• Organisation : Un système mal organisé et cloisonné entre les secteurs sanitaire et médico-social.

• Ressources : Des moyens qui stagnent alors que les besoins augmentent, rendant les conditions d'exercice indignes pour les soignants et les patients.

Conséquences pour les patients :

• Délais d'attente excessifs.

• Ruptures de soins fréquentes et errance sanitaire.

• Inégalités territoriales criantes, pénalisant particulièrement les personnes précaires.

Focus : La Situation Critique des Personnes Détenues

La santé mentale des personnes détenues est une préoccupation majeure, avec une surreprésentation des pathologies et troubles mentaux en milieu carcéral.

• Appels à la plateforme (3141) de juillet 2024 à juillet 2025 :

◦ 7,6 % des appels concernaient des difficultés d'accès aux soins (1 065 appels).    ◦ 106 appels portaient spécifiquement sur un risque suicidaire.

Causes identifiées :

1. La politique de désinstitutionnalisation : Menée sans un développement suffisant des services de proximité pour prendre le relais des services hospitaliers psychiatriques.

2. La diminution des déclarations d'irresponsabilité pénale : Conduisant au maintien en détention de personnes dont l'état de santé nécessiterait une prise en charge dans une structure de soins.

La Cour européenne des droits de l'homme (arrêt GC c. France, 2012) a qualifié cette situation de "traitement inhumain". Le manque de continuité des soins à la sortie de prison augmente par ailleurs le risque de récidive.

2.2. Santé Mentale des Mineurs : Une Situation Alarmante

Les données concernant la santé mentale des jeunes sont particulièrement inquiétantes. Une étude de 2025 (Institut Montaigne, Mutualité française, Institut Terram) révèle que 25 % des jeunes de 15 à 29 ans souffrent de dépression, un chiffre atteignant 39 % en outre-mer.

Carences Structurelles de la Pédopsychiatrie

• Absence de données fiables : Le manque de données agrégées sur le nombre d'enfants en attente de prise en charge "fragilise le pilotage de nos politiques publiques". Le rapport 2023 de la Cour des comptes estime que sur 1,6 million d'enfants souffrant d'un trouble psychique, seuls 50 à 53 % bénéficient de soins.

• Pénurie de médecins et inégalités territoriales : Malgré des infrastructures dans la moyenne européenne, le secteur est saturé. Les Centres Médico-Psychologiques (CMP) sont inégalement répartis, et les délais pour obtenir un premier rendez-vous dépassent souvent un an.

Pratiques Inadaptées et Atteintes aux Libertés

Des pratiques préoccupantes sont régulièrement signalées :

• Hospitalisation en services pour adultes : Des enfants et adolescents sont hospitalisés dans des services de psychiatrie adulte, une situation qui "ne fait que s'aggraver".

• Maintien par défaut : Des jeunes en situation de handicap sont maintenus en structure psychiatrique faute de places dans le secteur médico-social ou en protection de l'enfance.

• Recours à l'isolement et à la contention : Ces mesures de dernier recours sont utilisées de manière trop fréquente, souvent motivées par un manque de personnel. Pour les mineurs hospitalisés en "soins libres" (à la demande des parents mais sans leur propre consentement), il n'existe aucun contrôle systématique par un juge des libertés et de la détention (JLD), contrairement aux adultes.

Exemple emblématique : Une adolescente de 15 ans, atteinte d'autisme sévère, a été hospitalisée pendant plus de deux ans dans un service psychiatrique pour adultes, sans justification médicale. Elle était confinée dans une chambre d'isolement "plus de 20 heures par jour", déscolarisée et privée de soins somatiques essentiels.

3. Politiques du Handicap : Des Droits Proclamés mais Non Effectifs

Vingt ans après la loi de 2005, son application reste partielle et les obstacles à l'inclusion demeurent nombreux.

3.1. Éducation : Une Inclusion Inachevée

Bien que la loi de 2005 ait permis une augmentation du nombre d'enfants handicapés scolarisés, l'accès à une éducation de qualité reste difficile.

Catégorie de réclamation (2024)

Chiffres et pourcentages

Discrimination liée à l'éducation/formation

7 % des 5 679 saisines

Droits de l'enfant (majorité liée à la scolarisation)

30 % des 3 073 saisines

Obstacles persistants :

• Manque d'AESH : Malgré les créations de postes, les besoins ne sont pas couverts.

• Pause méridienne : La loi du 27 mai 2024 prévoyant la prise en charge par l'État de l'accompagnement sur le temps de la pause méridienne est "très loin d'être effective" en raison de blocages administratifs.

• Aménagement des examens : Une augmentation inquiétante des refus d'aménagement pour des élèves ou étudiants handicapés, parfois sous le prétexte paradoxal que "leurs résultats étaient bons".

3.2. Emploi : Premier Domaine de Discrimination

L'emploi est le principal secteur où s'exercent les discriminations liées au handicap. Sur les réclamations pour handicap en 2024, 21 % concernent l'emploi privé et 24 % l'emploi public. L'obligation d'emploi de 6 % ne suffit pas à garantir l'égalité de traitement.

Difficultés récurrentes :

• Aménagement tardif du poste de travail.

• Non-respect des préconisations du médecin du travail.

• Difficultés accrues dans le maintien dans l'emploi pour les personnes dont le handicap ou la maladie survient en cours de carrière.

3.3. Accessibilité : Un Retard Persistant

L'accessibilité, condition essentielle à la participation sociale, reste un point faible majeur.

• Transports : L'objectif de mise en accessibilité n'est pas atteint, la loi s'étant limitée aux "points d'arrêt prioritaires".

• Logement : Inquiétude face à l'assouplissement des règles d'accessibilité (loi ELAN).

• Numérique : La dématérialisation produit des effets ambivalents. Selon l'ARCOM, peu de sites publics atteignent 50 % d'accessibilité et seulement 5 % sont totalement conformes.

3.4. Aides à l'autonomie : Une Compensation Insuffisante et Inégale

Le droit à la compensation instauré par la loi de 2005 présente des limites flagrantes.

• La barrière des 60 ans : Une différence de traitement persiste selon que le handicap survient avant ou après 60 ans, la fusion des régimes prévue pour 2010 n'ayant jamais eu lieu.

• Limites de la PCH (Prestation de Compensation du Handicap) :

◦ L'aide humaine est limitée aux besoins essentiels.    ◦ Les aides techniques sont sous-financées.    ◦ La PCH parentalité est critiquée pour ses critères restrictifs.

4. Recommandations et Perspectives

L'audition se conclut sur plusieurs axes d'action prioritaires pour remédier aux défaillances constatées.

• Application des textes : L'urgence est "l'application pure et simple des textes votés par le Parlement", dont beaucoup sont en attente de décrets d'application.

• Données statistiques : Il est impératif de disposer de données fiables et agrégées (ex : nombre d'heures de scolarisation effectives, nombre de places manquantes en IME) pour piloter les politiques publiques.

• Décloisonnement : Renforcer la coordination entre les secteurs sanitaire, médico-social, éducatif et judiciaire est crucial pour assurer la fluidité des parcours.

• Priorité à la jeunesse : La santé mentale des jeunes, érigée en grande cause nationale 2025, nécessite une "véritable prise de conscience collective" et des moyens financiers adéquats, notamment pour les CMP.

• Formation : L'amélioration de la formation des professionnels (enseignants, AESH, employeurs, soignants) est essentielle pour faire évoluer les pratiques et les cultures professionnelles.

• Lutte contre la complexité administrative : Le "mille-feuille" des dispositifs doit être simplifié pour améliorer la lisibilité et l'accès aux droits pour les familles.

need a human's eyes on these things

Reviewer #2 (Public review):

Summary:

The authors quantify human fMRI BOLD responses in pulvinar and mediodorsal thalamic nuclei during a fear conditioning and extinction task across two days, in a large sample size (hundreds of participants). They show that the BOLD responses in these areas differentiate the conditioned (CS+) and safety (CS-) stimuli. Additionally, this changes with repeated trials, which could be a neural correlate of fear learning. They show that the anterior pulvinar is most correlated with the MD, and that this is not due to anatomical proximity. They perform graph analysis on the pulvinar subnuclei, which suggests that the medial pulvinar is a hub between the sensory (lateral/inferior) and associative (anterior) pulvinar. They show different patterns of thalamic activity across conditioning, extinction, recall, and renewal.

Strengths:

The data has a large sample size (n=293 in some measures, n=412 in others). This is a validated human fear conditioning/extinction task that Dr Milad's group has been working with for several years. Few labs have investigated the thalamus activity during fear conditioning and extinction, particularly with a large sample size. There is an independent replication of the pulvinar network structure (Figure 3), which suggests that the processing in the more sensory-related inferior and lateral pulvinar is relayed to the anterior pulvinar (and possibly thereby to more action-related prefrontal areas) via an intermediate step in the medial pulvinar - potentially a novel discovery, but that needs more validation.

Weaknesses:

(1) The authors cannot make causal claims about their results based on correlational neuroimaging evidence. Causal claims should be pared back. E.g., sentence 1 in the Results section: "The anterior pulvinar and MD contribute to early associative threat learning, as evidenced by increased functional activation in response to CS+ compared to CS- at the block level (Fig. 1b-c)." needs to be reworded to something like "The anterior pulvinar and MD have increased functional activation... This suggests that these areas may contribute to early associate threat learning."

(2) Figure 1: The fact that the difference in BOLD activity between CS+ and CS- goes away on the third trial is not addressed. This is a very large effect in the data.

(3) Figure 3: Could the observed network structure be due to anatomical proximity? Perhaps the authors should do an analogous analysis to what they did in Figure 2 for this intra-pulvinar analysis. This analysis doesn't take into account the indirect connections through corticothalamic and thalamocortical connections with the visual cortex and the pulvinar. There is an implicit assumption that there are interconnections between the pulvinar subnuclei, but there are few strong excitatory projections between these subnuclei to my knowledge. If visual areas are included in the graph, it would make things more complex, but would probably dramatically change the story. In this way, the message is somewhat constructed or arbitrary.

(3) In the results section describing Figures 4-7, there are no statistics supporting the claims made. There needs to be a set of graphs comparing the results across the study sessions and days, with statistical comparisons between the different experiments to confirm differences.

(4) Figure 7 does not include the major corticothalamic and thalamocortical projections from early, mid-level, and higher visual cortex to the different pulvinar nuclei. I doubt that there are strong direct projections between the pulvinar nuclei; rather, the functional connections are probably mediated through interconnections with cortical visual areas.

(5) Stylistic: There are a lot of hypotheses and interpretations presented in this primary literature paper, which may be better suited for a review or perspective piece.

(6) In the discussion, there is an assumption that the fMRI BOLD responses to CS+ and CS- need to be different to indicate that an area is processing these distinctly, but the BOLD signal can only detect large-scale changes in overall activity. It's easy to imagine that an area could be involved in processing these two stimuli distinctly without showing an overall difference in the gross amount of activity.

(7) There is strong evidence that the BOLD responses to the threat-related and safety-related stimuli are different, modest evidence for their claims of learning/plasticity in these pathways, and circumstantial evidence supporting their hypothesized graph network models. Overall, most of the claims made in the discussion are better considered possible interpretations rather than proven findings - this is not a criticism, as these experiments and subject matter are extremely complex.

This study continues to validate the power and utility of this in human fear conditioning/extinction paradigm, and extends this paradigm to investigating fear learning beyond the traditional limbic system pathways. It's possible that their models for the pulvinar nuclei interconnections could guide future neuromodulation or DBS studies that could provide more causal evidence for their hypotheses.

Reviewer #3 (Public review):

Summary:

The present work was aimed at investigating the specific contributions of thalamic nuclei to associative threat learning and extinction. Using fMRI, they examined activation patterns across pulvinar divisions, the lateral geniculate nucleus (LGN), and the mediodorsal thalamus (MD) during threat acquisition, extinction, and recall. Their goal was to uncover whether distinct thalamic systems support different modes of learning-automatic survival mechanisms versus more deliberate processes - and to propose a hierarchical pulvinar model of fear conditioning. They also try to refine current neuroanatomical models of threat learning and memory, highlighting the role of thalamic nuclei in it.

Strengths:

(1) Valuable theoretical elaboration and modeling regarding the differential role of pulvinar subdivisions on feedforward (inferior, lateral) and higher-order integration (anterior), and their functional interplay with other relevant subcortical and cortical structures in associative threat and extinction learning.

(2) Large sample sizes and multipronged analytical approaches were used for hypothesis testing.

(3) Exhaustive literature review in the field of associative threat, as well as regarding the role of thalamic nuclei and other brain structures in it.

Weaknesses:

(1) Several weaknesses should be pointed out regarding how fMRI data were collected, as well as decisions regarding how the fMRI data were preprocessed and analyzed:

a) fMRI data have low resolution (3 cubic mm), which certainly limits the examination of small nuclei such as the ones investigated here, and especially the examination of the LGN and inferior pulvinar.

b) fMRI was normalized to standard space. Analyzing the data in individual-subject space would have given you the options of avoiding altering every participant's brain and of using a probabilistic thalamic atlas that better adapts to each subject's brain and thalamic nuclei (see, for instance, Iglesias et al., 2018). This would have been ideal and would have given the authors more precision, especially considering the low resolution of the fMRI data and the size of the thalamic nuclei of interest.

c) On top of the two previous points, the authors decided to smooth the data to 6mm, which means that every single voxel within these small nuclei was blurred/mixed with the 2 immediately contiguous voxels (if they followed the standard SPM12 normalization resampling default which resamples, or upsamples the data in this case, to 2 x 2 x 2mm). Given the strong changes in structural connectivity and function that can occur, especially in the thalamus, on voxels of this size, this and the previous 2 decisions do not favor anatomical precision.

d) Motion during scanning was poorly controlled in the preprocessing. Including the motion parameters as covariates of no interest in the GLM does not fully guarantee that motion is not influencing the results, and that motion is not differentially influencing some experimental conditions more than others.

(2) It is not clearly indicated in the manuscript how many subjects and how many trials went into each of the analyses. It would be important to indicate this in the text and/or the figures.

(3) It is not clear either, why, given the large sample size, some of the results were not conducted using reproducibility strategies such as dividing the sample into 2 or 3 groups or using further cross-validation strategies.

(4) Limited testing of alternative hypotheses. The results clearly seem to be a selection of the findings supporting the hypotheses that the authors sought to confirm. (just one example: in the analysis reported in Figures 1-2; are there other correlations between the activation of the anterior pulvinar and MD with other pulvinar nuclei? only the MD-anterior Puv is reported).

(5) The manuscript does not contain a limitations subsection. Practically every study has limitations, and this one is not an exception. Better to tell the limitations to the readers upfront so they can factor them into their evaluation of the relevance of the manuscript and reported evidence.

(6) Data should be made available to the scientific community. Code too. Even if you just used standard fMRI toolboxes, any code used to run analyses will be helpful to the community, or if someone decides to try to replicate your findings.

Despite these weaknesses and what can be derived from them, this manuscript constitutes a valuable contribution to the field to start characterizing and conceptualizing the involvement of thalamic nuclei and their interactions with other brain regions in the associative threat learning circuitries. It also paves the road for further testing of the functional dynamics among these regions and circuitries, and modeling testing.

Document d'information : Enjeux et Défis de la Santé Mentale en France

Synthèse

L'audition devant la commission d'enquête de l'Assemblée nationale met en lumière une crise profonde et multidimensionnelle du système de santé mentale en France.

Les analyses des experts révèlent un fardeau économique et social colossal, estimé à 163 milliards d'euros, plaçant les maladies mentales au premier rang des dépenses de l'Assurance Maladie.

Ce coût est principalement tiré par les hospitalisations, qui représentent jusqu'à 85 % des dépenses directes pour des pathologies comme la schizophrénie, tandis que les coûts des médicaments, majoritairement génériqués, restent faibles.

Le système de soins psychiatriques est caractérisé par des défaillances systémiques majeures. La prévention est quasi inexistante, entraînant des retards de diagnostic dramatiques (plus de 10 ans pour les troubles bipolaires).

L'organisation des soins, jugée obsolète, reste hospitalo-centrée, inégalitaire sur le territoire et cloisonnée, notamment entre les soins somatiques et psychiatriques.

Ce cloisonnement a des conséquences mortelles, réduisant l'espérance de vie des patients de 15 à 20 ans, principalement à cause de maladies cardiovasculaires et de cancers non ou mal soignés.

Malgré ce tableau sombre, des innovations organisationnelles et technologiques ont prouvé leur efficacité.

Les "Centres Experts", par des bilans complets, réduisent significativement les hospitalisations et améliorent le pronostic des patients.

De même, des projets pilotes utilisant des outils numériques (expérimentation "Article 51") ont divisé par deux les tentatives de suicide et généré des économies substantielles.

Cependant, ces innovations peinent à être déployées à grande échelle en raison de freins structurels, d'un manque de vision stratégique et d'un sous-investissement chronique dans la recherche et le développement.

La psychiatrie souffre en parallèle d'une grave crise d'attractivité, exacerbant les pénuries de personnel et la saturation du système.

Enfin, la recherche et le pilotage des politiques publiques sont handicapés par un manque criant de données structurées, notamment sur le volet du handicap.

1. Le Fardeau Économique et Social des Maladies Mentales

L'analyse économique présentée par Isabelle Duranzaleski, professeur de médecine et docteur en économie, révèle l'empreinte considérable des pathologies psychiatriques sur la société française.

L'étude, qui reproduit des méthodologies internationales pour permettre les comparaisons, agrège plusieurs types de coûts pour obtenir un chiffre global.

1.1. Une Estimation Globale de 163 Milliards d'Euros

En combinant l'ensemble des coûts, l'étude arrive à un total de 163 milliards d'euros.

Ce chiffre, bien que sujet à des risques de double compte, a pour objectif principal d'alerter les décideurs publics sur l'ampleur du fardeau.

Il se décompose en quatre catégories principales :

1. Dépenses de l'Assurance Maladie : Les coûts directs des soins médicaux, qui placent les maladies mentales comme le premier ou deuxième poste de dépense avec le cancer.

2. Dépenses du secteur médico-social : Les coûts réels engagés pour l'accompagnement.

3. Pertes de production : Le "manque à gagner" pour la société lié à la morbidité et à la mortalité prématurée.

4. Perte de santé valorisée : Une estimation monétaire de la perte d'années de vie en bonne santé, calculée selon des standards internationaux.

1.2. La Prépondérance des Coûts d'Hospitalisation

Une analyse détaillée des dépenses directes pour les patients atteints de schizophrénie et de troubles bipolaires, menée via les Centres Experts, démontre que l'hospitalisation constitue la source principale des coûts.

• Pour la schizophrénie, 85 % des coûts directs sont liés aux hospitalisations.

• Pour les troubles bipolaires, ce chiffre s'élève à 78 %.

En comparaison, la part des médicaments est très faible, en raison de l'accès quasi-exclusif à des traitements génériqués et du manque d'accès aux innovations thérapeutiques.

Pathologie

Part des Hospitalisations

Part des Médicaments

Part des Consultations

Schizophrénie

85 %

9 %

6-7 %

Troubles Bipolaires

78 %

18 %

6-7 %

1.3. Le Coût Spécifique du Suicide

Une étude distincte, utilisant la même méthodologie, a évalué le coût du suicide et des tentatives de suicide, en incluant les coûts directs des soins, la perte de production et la perte d'années de vie valorisée.

• Coût du suicide : 18 milliards d'euros.

• Coût des tentatives de suicide : 5 milliards d'euros.

2. Défaillances Systémiques et Organisation des Soins

Les experts s'accordent sur un constat sévère : l'organisation actuelle des soins psychiatriques en France est en échec. Elle est marquée par un retard structurel, une inégalité d'accès et un cloisonnement préjudiciable.

2.1. L'Échec de la Prévention

Selon Marion Leboyer, professeur de psychiatrie, et Coralie Gandré, maîtresse de recherche à l'IRDES, le système de soins français présente des défaillances majeures aux trois niveaux de la prévention.

• Prévention primaire : Elle est quasi-inexistante, marquée par une méconnaissance profonde des maladies mentales dans la société et une méfiance envers la psychiatrie, ce qui entraîne une perte de chance considérable.

La France accuse un retard de 20 ans par rapport aux pays anglo-saxons dans la mise en place de mesures de prévention et de lutte contre la stigmatisation.

• Prévention secondaire : Elle se traduit par un retard au diagnostic catastrophique.

◦ Troubles bipolaires : Plus de 10 ans en moyenne entre les premiers symptômes et le diagnostic.    ◦ Schizophrénie : Une "durée de psychose non traitée" de 2 à 5 ans, alors que les cinq premières années sont cruciales pour la réponse au traitement.

Ce retard est attribué à un déficit de formation des médecins de première ligne et à un manque d'information du grand public.

• Prévention tertiaire : L'arrivée tardive dans le soin se fait majoritairement par les urgences, qui sont engorgées par des patients de plus en plus sévères, chroniques et polypathologiques.

Un patient sur quatre hospitalisé en psychiatrie y entre via les urgences, un indicateur international de mauvaise qualité de l'accès aux soins.

2.2. Un Modèle de Soins Obsolète, Inégal et Cloisonné

Le système français est décrit comme :

• Hospitalo-centré : L'approche reste centrée sur l'hôpital, malgré un virage ambulatoire précoce (80 % des prises en charge).

Un quart des lits en psychiatrie est occupé au long cours sans indication thérapeutique, souvent par défaut d'offre d'hébergement non médicalisé.

• Inégalitaire sur le territoire : L'accès aux soins varie considérablement d'une région à l'autre.

La prévalence de la schizophrénie et des troubles bipolaires est deux fois plus élevée dans les villes, suggérant la nécessité de politiques de soins adaptées aux facteurs de risque environnementaux (urbanicité, pollution, stress).

• Peu lisible et insuffisamment spécialisé : L'offre de soins est confuse pour les usagers et manque de spécialisation par pathologie, contrairement aux pratiques internationales.

• Manquant de vision stratégique : Les experts déplorent une absence de stratégie claire et de politique d'évaluation de l'organisation des soins.

2.3. La Fracture entre Soins Somatiques et Psychiatriques

Un des points les plus critiques soulevés est le cloisonnement total entre la psychiatrie et les autres spécialités médicales.

Les conséquences pour les patients sont dramatiques :

• Surmortalité massive : La première cause de mortalité n'est pas le suicide, mais les maladies cardiovasculaires et les cancers.

L'espérance de vie est réduite de 20 ans pour les femmes et 15 ans pour les hommes (données de M. Leboyer) ou de 13 ans pour les femmes et 16 ans pour les hommes (étude IRDES).

Le taux de mortalité est 2 à 3 fois supérieur à celui de la population générale.

• Sous-dépistage des comorbidités : Des pathologies comme le syndrome métabolique (hypertension, obésité, etc.) sont très fréquentes (24 % pour la schizophrénie, 38 % pour la dépression résistante, contre 10 % en population générale), mais 70 % à 90 % des patients ne sont ni dépistés, ni soignés.

• Moindre recours aux soins préventifs : Les patients ont moins accès au dépistage des cancers, à la vaccination, aux dentistes, ophtalmologues ou gynécologues.

• Retard au diagnostic pour le cancer : Les patients atteints de troubles psychiques sont diagnostiqués plus tardivement pour les cancers, reçoivent des prises en charge plus invasives mais moins intensives, ce qui augmente les pertes de chance.

3. Innovations et Levier d'Amélioration : Un Potentiel Sous-exploité

Face à ces défaillances, des innovations organisationnelles, numériques et thérapeutiques ont démontré leur efficacité, mais leur déploiement reste limité par de nombreux freins.

3.1. Les Centres Experts : Un Modèle d'Évaluation Efficace

Inspirés des modèles existants pour le cancer ou les maladies rares, les Centres Experts proposent un bilan complet (psychiatrique, cognitif, social, somatique) sur une journée en hôpital de jour.

• Impact démontré : Les études menées avant et après passage dans ces centres montrent une diminution significative du nombre de journées d'hospitalisation, une amélioration du pronostic, une meilleure adhérence au traitement et un meilleur dépistage des comorbidités somatiques.

• Objectif : L'objectif est de déployer ce dispositif sur tout le territoire national et de l'inscrire dans la liste des activités spécifiques nationales pour garantir un accès équitable à tous les patients.

3.2. Le Numérique au Service du Suivi (Expérimentation Article 51)

Un projet pilote a testé une innovation organisationnelle combinant un suivi par une infirmière de pratique avancée ("case manager") et des outils digitaux pour des patients bipolaires.

• Résultats positifs : L'évaluation par le ministère de la Santé a montré :

• ◦ Tentatives de suicide divisées par deux (de 7,6 % à 3,9 %).  
• ◦ Journées d'hospitalisation réduites (de 42 % à 24 %).  
• ◦ Impact économique démontré : un gain estimé à 3 500 € par patient et par an, grâce à la baisse des hospitalisations.

• Déploiement en attente : Malgré ces résultats, la décision de généraliser le dispositif est toujours en attente, suscitant l'inquiétude des patients et des soignants.

3.3. Freins à l'Innovation et à la Recherche

Plusieurs obstacles entravent la modernisation de la psychiatrie :

• Sous-investissement dans la recherche : La France ne consacre que 2 à 4 % de son budget de recherche biomédicale à la psychiatrie, l'un des taux les plus faibles des pays développés.

• Faible translation des découvertes : De nombreuses innovations (biomarqueurs sanguins, imagerie cérébrale, outils numériques) issues de la recherche peinent à être appliquées dans les soins courants.

• Accès limité aux nouvelles thérapies : Les patients français ont un accès moindre aux innovations thérapeutiques, qu'elles soient médicamenteuses (ex: antipsychotiques de 2e génération disponibles ailleurs en Europe mais pas en France) ou psychosociales.

4. Enjeux Transversaux et Difficultés Spécifiques

4.1. Crise d'Attractivité et Pénurie de Personnel

La psychiatrie fait face à une crise d'attractivité majeure, constituant un frein structurel à toute amélioration.

• Désaffection des jeunes médecins : La psychiatrie est choisie parmi les dernières disciplines par les internes, contrairement à des pays comme le Canada où elle est devenue un premier choix suite à des investissements massifs.

• Pénurie de personnel : La FHF (Fédération Hospitalière de France) estime qu'un quart des postes de psychiatres sont vacants dans plus de la moitié des établissements publics.

• Conséquences directes : Cette pénurie entraîne une fermeture massive de lits d'hospitalisation et une saturation de l'ensemble du système de soins (CMP surchargés, urgences engorgées).

4.2. Les Pratiques Coercitives et les Droits des Patients

La France se distingue par un recours élevé à des pratiques restrictives de liberté, avec des variations très importantes entre établissements qui interrogent l'équité des prises en charge.

• Soins sans consentement : Près de 100 000 personnes par an (soit 5% de la file active).

• Isolement : Près de 30 000 personnes par an.

• Contention mécanique : Près de 10 000 personnes par an. Ces chiffres placent la France dans une situation défavorable par rapport aux autres pays développés, avec une tendance à l'augmentation.

4.3. Le Manque Crucial de Données sur le Handicap

Maude Espagiac, spécialiste du handicap, souligne une difficulté majeure pour la recherche et le pilotage des politiques : l'accès aux données.

• Cloisonnement des données : Les systèmes d'information des secteurs médical, social et médico-social ne communiquent pas, ce qui empêche d'avoir une vision complète des parcours et des coûts.

• Identification des personnes : Il est très difficile d'identifier les personnes en situation de handicap dans les bases de données de santé existantes, et de mesurer les coûts non publics (reste à charge pour les familles, les aidants).

• Perspective d'amélioration : L'intégration annoncée des données des MDPH (Maisons Départementales des Personnes Handicapées) dans le Système National des Données de Santé (SNDS) d'ici 2026 est une avancée attendue depuis une décennie.

Joint Public Review:

This manuscript puts forward the provocative idea that a posttranslational feedback loop regulates daily and ultradian rhythms in neuronal excitability. The authors used in vivo long-term tip recordings of the long trichoid sensilla of male hawkmoths to analyze spontaneous spiking activity indicative of the ORNs' endogenous membrane potential oscillations. This firing pattern was disrupted by pharmacological blockade of the Orco receptor. They then use these recordings together with computational modeling to predict that Orco receptor neuron (ORN) activity is required for circadian, not ultradian, firing patterns. Orco did not show a circadian expression pattern in a qPCR experiment, and its conductance was proposed to be regulated by cyclic nucleotide levels. This evidence led the authors to conclude that a post-translational feedback loop (PTFL) clockwork, associated with the ORN plasma membrane, allows for temporal control of pheromone detection via the generation of multi-scale endogenous membrane potential oscillations. The findings will interest researchers in neurophysiology, circadian rhythms, and sensory biology. However, the manuscript has limited experimental evidence to support its central hypothesis and is undermined by several questionable assumptions that underlie their data analysis and model builds, as well as insufficient biological data, including critical controls to validate and/or fully justify the model the authors are proposing.

Strengths:

The study is notable for its combination of long-term in vivo tip recordings with computational modeling, which is technically challenging and adds weight to the authors' claims. The link between Orco, cyclic nucleotides, and circadian regulation is potentially important for sensory neuroscience, and the modeling framework itself - a stochastic Hodgkin-Huxley formulation that explicitly incorporates channel noise - is a solid and forward-looking contribution. Together, these elements make the study conceptually bold and of clear interest to circadian and olfactory biologists.

Major weaknesses:

At the same time, several limitations temper the conclusions. The pharmacological evidence relies on a single antagonist and concentration, without key controls. The circadian analysis is based on relatively small numbers of neurons, with rhythms detected only in subsets, and the alignment procedure used in constant darkness raises concerns of bias. The molecular evidence is sparse, with only three qPCR timepoints, and the model, while creative, rests on assumptions that are not yet fully supported by in vivo data.

Detailed comments are provided below:

(1) The role for Orco proposed in the authors' model largely stems from the effects seen following the administration of (a single dose) of the Orco antagonist, OLC15. However, this hypothesis is undercut by the lack of adequate pharmacological controls, including a basic multipoint OLC15 dose-response series in addition to the administration of blockers for the other channels that are embedded in their model, but which were ruled out as being involved in the modulation of biological rhythms. In addition, these studies would (ideally) also benefit from the inclusion of the same concentration (series) of an inactive OLC15 analog to better control for off-target effects.

(2) The expression pattern of Orco was assessed using qPCR at only three timepoints. Rhythmic transcripts can easily be missed with such sparse sampling (Hughes et al., 2017). A minimum of six evenly spaced timepoints across a 24-hour cycle would be required to confidently rule out circadian transcriptional regulation. In addition, the use of the timeless mRNA control from another study is not acceptable. Furthermore, qPCR analysis measures transcript abundance, not transcription, as the authors repeatedly state. Transcriptional studies would require nuclear run-off or, more recently, can be done with snRNAseq analysis. Taken together, these concerns undermine the authors' desire to rule out TTFL-based control that directly led them to implicate a PTTF-based model.

(3) The modelling presented is based on Orco as a ZT-dependent conductance tied to the cAMP oscillations that were reported by this group in the cockroach and from the presence and functionality in Manduca of homomeric Orco complexes that are devoid of tuning ORs. While these complexes have been generated in cell culture and other heterologous expression systems, as well as presumably exist in vivo in the Drosophila empty neuron and other tuning OR mutants, there is no evidence that these complexes exist in wild-type Manduca ORNs. While this doesn't necessarily undermine every aspect of their models, the authors should note the presence of Orco/OR complexes rather than Orco homomeric complexes.

(4) Some aspects of the authors' models, most notably the decision to phase align/optimize their DD and OLC15 recordings, are likely to bias their interpretations.

(5) The tip recordings from long trichoid sensilla are critical aspects of this study. These recordings were carried out on upper sensillar tips located on the distal-most second annulus. Since there are approximately 80 annuli on the Manduca antennae, it is unclear whether the recordings are representative of the antennal response.

(6) The authors do not provide any data in support of their cAMP/cGMP-based Orco gating, and the PTTF model proposed is somewhat disappointing. The model seems to be influenced by their long-held proposal that insect olfactory signaling has a critical metabotropic component involving cyclic nucleotides, PKC, etc, a view that may be influenced by the use of Orco homomeric complexes generated in HEK cells. Nevertheless, structural studies on Orco do not support a cyclic nucleotide binding site, although PKC-based phosphorylation has been implicated in the fine-tuning/adaptation of olfactory signaling.

(7) Because only 5/11 LD and 7/10 DD animals showed daily rhythms, with averages lacking clear daily modulation, the methods are not sufficiently reliable enough to reveal novel underlying mechanisms of circadian rhythm generation. The reported results are therefore not yet reliable or quantifiable. To quantify their results, the authors should apply tests for circadian rhythmicity using methods such as RAIN, JTK CYCLE, MetaCycle, or Echo. The use of FFT and Wavelet is applauded, but these methods do not have tests of significance for rhythms and can be biased when analyzing data in which there could only be 1-3 circadian cycles. Because the conclusions appear to be based on 11-12 neurons that were recorded for 2-4 days, the reader is concerned that the methods are not yet perfected to provide strong evidence for circadian regulation of spontaneous firing of ORNs. The average data (e.g., Figure 3Bii and 3Cii) highlight the apparent lack of daily rhythms. In summary, the results would be more compelling if more than 50% of the recordings had significant circadian amplitudes and with similar periods and phases.

(8) The statement that circadian patterns of ORN firing are lost with the Orco antagonist (OLC15) is not strongly supported. The manuscript should be revised to quantify how Orco changed circadian amplitude in the 12 recorded neurons. Measures of circadian amplitude can avoid confusing/vague statements like Line 394 "low and high frequency bands appeared to merge during the activity phase around ZT 0 in the animals that showed clear circadian rhythms (N = 5 of 11 in LD)". The conclusion that Orco blocks circadian firing appears to be contradicted by Figure 6, which indicates that ~6 of these neurons had circadian periods detected by wavelet. The manuscript would be strengthened with details about the specificity and reproducibility of the Orco antagonist. The authors quantify the gradual decrease in firing with the slope of a linear fit to estimate how the "effectiveness [of OLC15] increased over time." They conclude that the drug "obliterated circadian rhythms and attenuated the spontaneous activity in several, but not all experiments (N = 8 of 12)." The report would be greatly strengthened with corroborating data from additional Orco antagonists and additional doses of OLC15 (the authors use only 50 uM OLC15).

(9) The manuscript includes several statements that are more speculation than conclusion. For example, there is no evidence for tuning or plasticity in this report. Statements like the following should be removed or addressed with experiments that show changes in odor response specificity or sensitivity: "ORN signalosomes are highly plastic endogenous PTFL clocks comprising receptors for circadian and ultradian Zeitgebers that allow to tune into internal physiological and external environmental rhythms as basis for active sensing." (Discussion Line 622). The paper concludes that (line 380) "mean frequency of spontaneous spiking and the frequency of bursting expressed daily modulation, and are both most likely controlled via a circadian clock that targets the leak channel Orco." This is too bold given the available results.

(10) Because Orco conductance is modulated by cyclic nucleotides, it remains highly plausible that circadian regulation occurs upstream at the level of signaling pathways (e.g., calcium, calcium-binding proteins, GPCRs, cyclases, phosphodiesterases). The possibility that circadian oscillations of cyclic nucleotides are generated by the canonical TTFL mechanism has not been excluded. In fact, extensive work in Drosophila has demonstrated that the TTFL-based molecular clock proteins are required for circadian rhythms in olfaction.

(11) A defining feature of circadian oscillators is the feedback mechanism that generates a time delay (e.g., PERIOD/TIMELESS repressing their own transcription). While the authors describe how cyclic nucleotides can regulate Orco conductance, they do not provide a convincing explanation of how Orco activity could, in turn, feed back into the proposed PTFL to sustain oscillations. For these reasons, the authors should consider:

(a) Providing a broader discussion of non-TTFL models of circadian rhythms (e.g., redox cycles, post-translational modifications).

(b) Reassessing Orco expression using a higher-resolution temporal sampling ({greater than or equal to}6 timepoints per 24 h).

(c) Clarifying or revising the PTFL model to explicitly address how feedback would be achieved. Alternatively, the data may be more consistent with Orco conductance rhythms being regulated by post-translational mechanisms downstream of the canonical TTFL oscillator, as suggested by the Drosophila olfactory system literature.

Minor weaknesses:

(1) The authors should compare the firing patterns of ORN neurons to the bursts, clusters, and packets of retinal efferent spikes reported in Liu JS and Passaglia CL (2011; JBR). By comparing measures in moths to measures in Limulus, the authors might be able to address the question: Is the daily firing pattern of ORN neurons likely a conserved feature of circadian control of sensory sensitivity?

(2) The methods need further details. For example, it is unclear if or how single neuron activity was discriminated and whether the results were compromised by the relatively large environmental fluctuations in temperature (21-27oC), humidity (35-60%), or other cues known to modulate spontaneous firing.

Author response:

Joint Public Review

This manuscript puts forward the provocative idea that a posttranslational feedback loop regulates daily and ultradian rhythms in neuronal excitability. The authors used in vivo long-term tip recordings of the long trichoid sensilla of male hawkmoths to analyze spontaneous spiking activity indicative of the ORNs' endogenous membrane potential oscillations. This firing pattern was disrupted by pharmacological blockade of the Orco receptor. They then use these recordings together with computational modeling to predict that Orco receptor neuron (ORN) activity is required for circadian, not ultradian, firing patterns. Orco did not show a circadian expression pattern in a qPCR experiment, and its conductance was proposed to be regulated by cyclic nucleotide levels. This evidence led the authors to conclude that a post-translational feedback loop (PTFL) clockwork, associated with the ORN plasma membrane, allows for temporal control of pheromone detection via the generation of multi-scale endogenous membrane potential oscillations. The findings will interest researchers in neurophysiology, circadian rhythms, and sensory biology. However, the manuscript has limited experimental evidence to support its central hypothesis and is undermined by several questionable assumptions that underlie their data analysis and model builds, as well as insufficient biological data, including critical controls to validate and/or fully justify the model the authors are proposing.

We thank the reviewers for their thorough and thoughtful comments and believe that the manuscript will be much stronger once we incorporate the requested changes.

Please note that we used ORN as acronym for “olfactory receptor neuron” throughout the manuscript. ORNs contain odorant receptors (ORs), and in insects these ORs have to associate with the olfactory receptor co-receptor (Orco) in the cilium of the neuron to form functional OR-Orco complexes for odorant detection. Besides this chaperone function, Orco can form homomers with the potential to act as ionic pacemaker channels; a role which we explore in this study.

Strengths:

The study is notable for its combination of long-term in vivo tip recordings with computational modeling, which is technically challenging and adds weight to the authors' claims. The link between Orco, cyclic nucleotides, and circadian regulation is potentially important for sensory neuroscience, and the modeling framework itself - a stochastic Hodgkin-Huxley formulation that explicitly incorporates channel noise - is a solid and forward-looking contribution. Together, these elements make the study conceptually bold and of clear interest to circadian and olfactory biologists.

Major weaknesses:

At the same time, several limitations temper the conclusions. The pharmacological evidence relies on a single antagonist and concentration, without key controls. The circadian analysis is based on relatively small numbers of neurons, with rhythms detected only in subsets, and the alignment procedure used in constant darkness raises concerns of bias. The molecular evidence is sparse, with only three qPCR timepoints, and the model, while creative, rests on assumptions that are not yet fully supported by in vivo data.

Please see our responses to the detailed comments.

Detailed comments are provided below:

(1) The role for Orco proposed in the authors' model largely stems from the effects seen following the administration of (a single dose) of the Orco antagonist, OLC15. However, this hypothesis is undercut by the lack of adequate pharmacological controls, including a basic multipoint OLC15 dose-response series in addition to the administration of blockers for the other channels that are embedded in their model, but which were ruled out as being involved in the modulation of biological rhythms. In addition, these studies would (ideally) also benefit from the inclusion of the same concentration (series) of an inactive OLC15 analog to better control for off-target effects.

The Orco agonist VUAA1 (Jones et al., 2011) binds directly to Orco and increases the channel open time probability. In M. sexta hawkmoths, we have already published that VUAA 1 increases the low spontaneous activity of ORNs in a dose-dependent fashion (Nolte et al., 2016). Chen and Luetje (2012) systematically varied the chemical structure of VUAA1 to identify new Orco ligands and discovered 22 Orco Ligand Candidates (OLC) that either activated or inhibited Orco. In their heterologous expression system, Orco was most sensitive to inhibition by OLC15. Based on these results, we published a dose-response curve of OLC15 inhibition (1-100 µM) using in vivo tip recordings of pheromone-sensitive long trichoid sensilla of M. sexta (Nolte et al., 2016). In that study, we could also demonstrate that OLC15 antagonizes the VUAA1 activation of Orco.

Furthermore, we tested other published Orco antagonists in in vivo assays in intact hawkmoths, focusing on amiloride-derived antagonists, because we previously identified an amiloride-sensitive cation channel in hawkmoth ORNs. We found that, in contrast to OLC15, the amilorides HMA and MIA were not Orco-specific but instead affected different targets depending on time-of-day (Nolte et al., 2016). Based on those experiments and the dose-response curves we determined that the Orco agonist VUAA1 (Jones et al., 2011) and the Orco antagonist OLC15 (Chen and Luetje, 2012) worked best in hawkmoth ORNs to target Orco pharmacologically. Based on comparative tests with other published Orco antagonists we settled since then in all further experiments on a dose of 50 µM OLC15.

We will clarify the Methods section accordingly.

(2) The expression pattern of Orco was assessed using qPCR at only three timepoints. Rhythmic transcripts can easily be missed with such sparse sampling (Hughes et al., 2017). A minimum of six evenly spaced timepoints across a 24-hour cycle would be required to confidently rule out circadian transcriptional regulation. In addition, the use of the timeless mRNA control from another study is not acceptable. Furthermore, qPCR analysis measures transcript abundance, not transcription, as the authors repeatedly state. Transcriptional studies would require nuclear run-off or, more recently, can be done with snRNAseq analysis. Taken together, these concerns undermine the authors' desire to rule out TTFL-based control that directly led them to implicate a PTTF-based model.

We agree with the referees that more time points and a direct comparison between timeless and Orco mRNA levels should be included in this manuscript. We will include these additional qPCR experiments and edit the manuscript to make clear that we measure transcript abundance, but we will not perform snRNAseq analysis due to time- and financial constraints. We are currently working on the transcriptional control of Orco, both during ontogeny and throughout the day but this work in progress is beyond the scope of this manuscript.

(3) The modelling presented is based on Orco as a ZT-dependent conductance tied to the cAMP oscillations that were reported by this group in the cockroach and from the presence and functionality in Manduca of homomeric Orco complexes that are devoid of tuning ORs. While these complexes have been generated in cell culture and other heterologous expression systems, as well as presumably exist in vivo in the Drosophila empty neuron and other tuning OR mutants, there is no evidence that these complexes exist in wild-type Manduca ORNs. While this doesn't necessarily undermine every aspect of their models, the authors should note the presence of Orco/OR complexes rather than Orco homomeric complexes.

Our ELISAs found circadian oscillations in cAMP levels not only in antennae of the Madeira cockroach (Schendzielorz et al., 2014, 2012), but also in hawkmoth antennae (Schendzielorz et al., 2015). We will add the 2015 citation to the Modeling chapter in the Methods section to clarify this.

We agree with the referees that we cannot distinguish between Orco homo- and heteromers in the different compartments of our hawkmoth ORNs. Thus, as the referee suggests, we will add text regarding the presence and localization of OR-Orco heteromers. However, we have indications that Orco homomers could indeed be present in the hawkmoth ORNs. In a heterologous expression system, MsexOrco expression alone was sufficient to increase intracellular Ca²⁺ levels in response to VUAA1 application (Nolte et al., 2013). In differentiating primary cell cultures of hawkmoth antennae, Orco expression started during a developmental time window where ORNs did not yet express pheromone receptors, and Orco affected spontaneous activity (Nolte et al., 2016). Thus, Orco homomers are present in developing hawkmoth ORNs during a time window where ORNs already express spontaneous activity but cannot heteromerize with pheromone receptors. However, we do not know whether and in what ratio homo- and heteromers of Orco and ORs are present in the respective sensillum compartments of adult hawkmoths (Nolte et al., 2013; Stengl, 1994; Stengl and Hildebrand, 1990).

We will clarify our manuscript accordingly.

(4) Some aspects of the authors' models, most notably the decision to phase align/optimize their DD and OLC15 recordings, are likely to bias their interpretations.

It is consensus that insects display daily and circadian rhythms in pheromone-dependent mating, odor-gated feeding, and egg-laying behavior that phase-locks to environmental rhythms, corresponding with daily/circadian rhythms of sensory neuron physiology (e.g., Merlin et al., 2007; Rymer et al., 2007; Schendzielorz et al., 2015, 2012). However, circadian rhythms can be easily masked by stress, like the disturbances during a very challenging long-term recording experiment over several days. In addition, we observed in our animal raising facility that in LD 17:7 light-dark cycles the originally nocturnal hawkmoths M. sexta distribute their activity patterns over the course of the day, finding nocturnal as well as diurnal hawkmoths. Thus, light-dark cycles were not enough to ensure phase-synchronized behavioral rhythms, and it is very likely that the nocturnal hawkmoths rely heavily on pheromone/odor dependent synchronization as also found in other moth species (Ghosh et al., 2024). Here, we used isolated males that were never exposed to the female pheromones so that their circadian activity patterns readily disperse. Therefore, it became necessary in free-running conditions to first determine the respective behavioral rhythm for each animal, and then to phase-align their activity patterns to allow for statistical analysis. Otherwise, circadian differences would average out in a free-running population. As requested by the referees in point (7), we will use additional tests for rhythmicity in each of our recordings and revise the manuscript accordingly.

Assuming that hawkmoths need pheromone presence as additional Zeitgeber, we are currently working on a new set of experiments where we attempt to improve synchronization by exposure to LD cycles and pheromone before DD and OLC15 recordings. We will add these experiments to the manuscript.

(5) The tip recordings from long trichoid sensilla are critical aspects of this study. These recordings were carried out on upper sensillar tips located on the distal-most second annulus. Since there are approximately 80 annuli on the Manduca antennae, it is unclear whether the recordings are representative of the antennal response.

We think the reviewers might have misinterpreted our description of the recording site. In the Methods, we state that we clip off the 20 most distal annuli (leaving a stump of about 60 annuli) and insert the reference electrode into the flagellum up to the second annulus from the cut end, i.e., the recording site is located at 2/3 – 3/4 of the antenna length as seen from the head of the animal. We will make this more clear in the Methods section.

In addition, our lab did show with antibody stainings against Orco that apparently all ORNs that innervate long and short trichoid sensilla along the whole flagellum express the same staining pattern (Nolte et al., 2016). Furthermore, our patch clamp recordings of primary cell cultures of whole male antennae found largely overlapping ion channel populations across ORNs. This would indicate that all ORNs, whether they express pheromone- or general odorant receptors, could potentially share the same Orco-dependent spontaneous activity rhythms. In our lab, different experimenters from different years that recorded from long trichoid sensilla on different annuli did not detect obvious differences in neither the spontaneous activity nor the pheromone responses (c.f., Dolzer et al., 2003; Gawalek and Stengl, 2018; Schneider et al., 2025). Thus, it is very likely that we are reporting a general encoding mechanism that is not locally restricted along the antennal flagellum.

(5.1) The authors do not provide any data in support of their cAMP/cGMP-based Orco gating…

There are publications supporting cyclic nucleotide gating of Orco in Drosophila, but only after previous phosphorylation via protein kinase C (PKC; review: (Wicher and Miazzi, 2021)). Since Orco is very conserved among insect species, it is likely that these PKC and cGMP/cAMP-dependent regulations are present in other insect species. We are currently running thorough tip-recording experiments on the regulation of Orco gating, which are beyond the scope of this manuscript. However, we will add a set of experiments to this manuscript that demonstrates cAMP gating of Orco.

(5.2)… and the PTTF model proposed is somewhat disappointing.

For a detailed introduction of our PTFL membrane clock hypothesis please see our opinion paper (Stengl and Schneider, 2024).

(5.3) The model seems to be influenced by their long-held proposal that insect olfactory signaling has a critical metabotropic component involving cyclic nucleotides, PKC, etc, a view that may be influenced by the use of Orco homomeric complexes generated in HEK cells.

Indeed, we propose a metabotropic pheromone-transduction cascade, which in moths and cockroaches is based on G-protein-mediated activation of phospholipase C but not on adenylyl cyclase activation. Our hypothesis is not influenced by HEK cell heterologous expression studies of Orco but is supported by our own work comparing in vivo tip recordings of intact hawkmoths with patch clamp experiments on hawkmoth primary cell cultures of olfactory receptor neurons, which are able to respond to their species-specific pheromones in vitro ((Schneider et al., 2025; Stengl, 2010; Stengl and Funk, 2013; Wicher and Miazzi, 2021). In addition, a multitude of publications by other laboratories with in vivo and in vitro studies using physiological, genetic, and immunocytochemical assays all support a metabotropic signal transduction cascade in insect olfaction (reviews: Stengl, 2010; Stengl and Funk, 2013; Wicher and Miazzi, 2021). In contrast, the hypothesis suggesting a solely ionotropic pheromone- and general odor-dependent transduction cascade for all insect species is based on very sparse experimental evidence, based primarily on heterologous expression studies such as HEK cells that lack the insect’s WT molecular surroundings, and thus, cannot predict OR-Orco function in vivo. Furthermore, the ionotropic hypothesis is heavily based upon the argument that an inverse 7TM receptor cannot couple to G-proteins, which lacks careful backup via biochemical and structural studies. In addition, the ionotropic hypothesis lacks support via carefully performed physiological in vivo studies in different insect species that paid attention to analysis of the distinct kinetic components of ORN´s odor/pheromone responses and that employ physiological concentrations and durations of odor/pheromone stimuli (please see our most recent publication by Schneider et al. (2025)).

(5.4) Nevertheless, structural studies on Orco do not support a cyclic nucleotide binding site, although PKC-based phosphorylation has been implicated in the fine-tuning/adaptation of olfactory signaling.

While structural studies did not find evidence for conserved known cyclic nucleotide binding sites on Orco, this does not exclude the presence of so far unknown binding sites, or via sites that fold out only after a specific sequence of previous phosphorylations of the many phosphorylation sites on Orco. Indeed, physiological studies in Drosophila presented evidence for cyclic nucleotide dependence of Orco after previous PKC-dependent phosphorylation (Getahun et al., 2013). Our ongoing in vivo experiments in hawkmoths further corroborate a PKC- and cAMP-dependent modulation of Orco. These studies will be published in a follow-up publication.

(6) Because only 5/11 LD and 7/10 DD animals showed daily rhythms, with averages lacking clear daily modulation, the methods are not sufficiently reliable enough to reveal novel underlying mechanisms of circadian rhythm generation. The reported results are therefore not yet reliable or quantifiable. To quantify their results, the authors should apply tests for circadian rhythmicity using methods such as RAIN, JTK CYCLE, MetaCycle, or Echo. The use of FFT and Wavelet is applauded, but these methods do not have tests of significance for rhythms and can be biased when analyzing data in which there could only be 1-3 circadian cycles. Because the conclusions appear to be based on 11-12 neurons that were recorded for 2-4 days, the reader is concerned that the methods are not yet perfected to provide strong evidence for circadian regulation of spontaneous firing of ORNs. The average data (e.g., Figure 3Bii and 3Cii) highlight the apparent lack of daily rhythms. In summary, the results would be more compelling if more than 50% of the recordings had significant circadian amplitudes and with similar periods and phases.

The long-term tip-recordings of intact hawkmoths are very challenging and take a very long time to accomplish, thus, we are very happy that we succeeded in obtaining so many of them (N=34). Since 5/11 LD recordings and 7/10 DD recordings revealed daily/circadian rhythmicity and since many other physiological recordings at different ZTs of different members of our laboratory all revealed ZT-dependent pheromone-transduction we can be certain that the physiology of hawkmoth antennae is under strict circadian control. Please see also our response to (4) above commenting the phase-dispersal of activity rhythms observed in our experiments, as well as in the behavior of hawkmoth males in the mating cage.

Nevertheless, we will follow the advice of the referees to apply additional tests for significance of rhythms in spontaneous activity, and we are thankful for the tests suggested that we were not aware of.

(7) The statement that circadian patterns of ORN firing are lost with the Orco antagonist (OLC15) is not strongly supported. The manuscript should be revised to quantify how Orco changed circadian amplitude in the 12 recorded neurons. Measures of circadian amplitude can avoid confusing/vague statements like Line 394 “low and high frequency bands appeared to merge during the activity phase around ZT 0 in the animals that showed clear circadian rhythms (N = 5 of 11 in LD)”. The conclusion that Orco blocks circadian firing appears to be contradicted by Figure 6, which indicates that ~6 of these neurons had circadian periods detected by wavelet. The manuscript would be strengthened with details about the specificity and reproducibility of the Orco antagonist. The authors quantify the gradual decrease in firing with the slope of a linear fit to estimate how the “effectiveness [of OLC15] increased over time.” They conclude that the drug “obliterated circadian rhythms and attenuated the spontaneous activity in several, but not all experiments (N = 8 of 12).” The report would be greatly strengthened with corroborating data from additional Orco antagonists and additional doses of OLC15 (the authors use only 50 uM OLC15).

We will revise our data analysis, according to the valuable suggestions of the referees.

However, based upon our previous studies with other Orco antagonists and different doses of OLC15 (Nolte et al., 2016) we found that 50 µM OLC15 is the best Orco antagonist dose in M. sexta to target Orco-dependent modulation of spontaneous action potential activity of hawkmoth olfactory receptor neurons. Please see also our response to (1).

(8) The manuscript includes several statements that are more speculation than conclusion. For example, there is no evidence for tuning or plasticity in this report. Statements like the following should be removed or addressed with experiments that show changes in odor response specificity or sensitivity: "ORN signalosomes are highly plastic endogenous PTFL clocks comprising receptors for circadian and ultradian Zeitgebers that allow to tune into internal physiological and external environmental rhythms as basis for active sensing." (Discussion Line 622). The paper concludes that (line 380) "mean frequency of spontaneous spiking and the frequency of bursting expressed daily modulation, and are both most likely controlled via a circadian clock that targets the leak channel Orco." This is too bold given the available results.

We will revise the discussion accordingly and clarify which statements are supported via published evidence and which are predictions based upon our novel hypothesis published in our opinion paper (Stengl and Schneider, 2024).

(9.1) Because Orco conductance is modulated by cyclic nucleotides, it remains highly plausible that circadian regulation occurs upstream at the level of signaling pathways (e.g., calcium, calcium-binding proteins, GPCRs, cyclases, phosphodiesterases).

We agree with the referees that it is very likely that there are multiple layers of interconnected feedback cycles that control Orco localization and activity. Our novel hypothesis suggests interlocked TTFL and PTFL control of physiological circadian rhythms, not strictly hierarchical TTFL control, which would require a daily turnover of membrane proteins and transcriptional control via the established TTFL clock in insect ORNs. We currently search for TTFL control at all levels of odor/pheromone transduction using ZT-dependent transcriptomics in combination with qPCR and single nuclear transcriptomics, involving also all the molecules suggested by the referees. These studies are ongoing, are very time- and money-consuming, and are beyond the scope of this manuscript.

(9.2) The possibility that circadian oscillations of cyclic nucleotides are generated by the canonical TTFL mechanism has not been excluded. In fact, extensive work in Drosophila has demonstrated that the TTFL-based molecular clock proteins are required for circadian rhythms in olfaction.

Our experiments that test circadian TTFL control at different levels of the cAMP transduction cascade in hawkmoth antennae are on the way and are part of another publication. We will revise our discussion accordingly.

The experiments published for TTFL dependent control of Drosophila olfaction that we are aware of (Krishnan et al., 1999; Tanoue et al., 2004) do not exclude interlinked PTFL and TTFL clocks. Krishnan et al. (1999) demonstrate that the TTFL clock in antennal olfactory receptor neurons correlates with circadian rhythms in odor responses measured in electroantennogram (EAG) recordings, not in single sensillum recordings as in our experiments. EAG recordings comprise not only voltage responses of the olfactory sensory neurons but also voltage changes generated in non-neuronal antennal cells such as trichogen and tormogen cells that built the transepithelial potential gradient via vATPases that generates the high K⁺ concentration in the sensillum lymph (Jain et al., 2024; Klein, 1992; Thurm and Küppers, 1980). In addition, EAG recordings most likely contain responses of afferent neurons originating from somata in the brain that maintain central control of the antennae. Thus, EAG recordings are difficult to interpret.

(11) A defining feature of circadian oscillators is the feedback mechanism that generates a time delay (e.g., PERIOD/TIMELESS repressing their own transcription). While the authors describe how cyclic nucleotides can regulate Orco conductance, they do not provide a convincing explanation of how Orco activity could, in turn, feed back into the proposed PTFL to sustain oscillations. For these reasons, the authors should consider:

a) Providing a broader discussion of non-TTFL models of circadian rhythms (e.g., redox cycles, post-translational modifications).

We will revise the discussion accordingly.

b) Reassessing Orco expression using a higher-resolution temporal sampling ({greater than or equal to}6 timepoints per 24 h).

We will add those experiments to the revised version of the manuscript (see our response to (2)).

c) Clarifying or revising the PTFL model to explicitly address how feedback would be achieved. Alternatively, the data may be more consistent with Orco conductance rhythms being regulated by post-translational mechanisms downstream of the canonical TTFL oscillator, as suggested by the Drosophila olfactory system literature.

We will revise the manuscript accordingly.

Minor weaknesses:

(1) The authors should compare the firing patterns of ORN neurons to the bursts, clusters, and packets of retinal efferent spikes reported in Liu JS and Passaglia CL (2011; JBR). By comparing measures in moths to measures in Limulus, the authors might be able to address the question: Is the daily firing pattern of ORN neurons likely a conserved feature of circadian control of sensory sensitivity?

We will revise the discussion accordingly.

(2) The methods need further details. For example, it is unclear if or how single neuron activity was discriminated and whether the results were compromised by the relatively large environmental fluctuations in temperature (21-27oC), humidity (35-60%), or other cues known to modulate spontaneous firing.

We will clarify the Methods section.

References

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Dolzer J, Fischer K, Stengl M. 2003. Adaptation in pheromone-sensitive trichoid sensilla of the hawkmoth Manduca sexta. J Exp Biol 206:1575–1588. doi:10.1242/jeb.00302

Gawalek P, Stengl M. 2018. The Diacylglycerol Analogs OAG and DOG Differentially Affect Primary Events of Pheromone Transduction in the Hawkmoth Manduca sexta in a Zeitgebertime-Dependent Manner Apparently Targeting TRP Channels. Front Cell Neurosci 12:218. doi:10.3389/fncel.2018.00218

Getahun MN, Olsson SB, Lavista-Llanos S, Hansson BS, Wicher D. 2013. Insect Odorant Response Sensitivity Is Tuned by Metabotropically Autoregulated Olfactory Receptors. PLOS ONE 8:e58889. doi:10.1371/journal.pone.0058889

Ghosh S, Suray C, Bozzolan F, Palazzo A, Monsempès C, Lecouvreur F, Chatterjee A. 2024. Pheromone-mediated command from the female to male clock induces and synchronizes circadian rhythms of the moth Spodoptera littoralis. Curr Biol 34:1414-1425.e5. doi:10.1016/j.cub.2024.02.042

Jain K, Prelic S, Hansson BS, Wicher D. 2024. Expression of Drosophila melanogaster V-ATPases in Olfactory Sensillum Support Cells. Insects 15:1016. doi:10.3390/insects15121016

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Nolte A, Funk NW, Mukunda L, Gawalek P, Werckenthin A, Hansson BS, Wicher D, Stengl M. 2013. In situ Tip-Recordings Found No Evidence for an Orco-Based Ionotropic Mechanism of Pheromone-Transduction in Manduca sexta. PLOS ONE 8:e62648. doi:10.1371/journal.pone.0062648

Nolte A, Gawalek P, Koerte S, Wei H, Schumann R, Werckenthin A, Krieger J, Stengl M. 2016. No Evidence for Ionotropic Pheromone Transduction in the Hawkmoth Manduca sexta. PLOS ONE 11:e0166060. doi:10.1371/journal.pone.0166060

Rymer J, Bauernfeind AL, Brown S, Page TL. 2007. Circadian rhythms in the mating behavior of the cockroach, Leucophaea maderae. J Biol Rhythms 22:43–57. doi:10.1177/0748730406295462

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Schneider AC, Schröder K, Chang Y, Nolte A, Gawalek P, Stengl M. 2025. Hawkmoth Pheromone Transduction Involves G-Protein–Dependent Phospholipase Cβ Signaling. eNeuro 12:ENEURO.0376-24.2024. doi:10.1523/ENEURO.0376-24.2024

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Synthèse de l'Audition sur l'État de la Santé Mentale en France

Résuméf

L'audition à la commission d'enquête de l'Assemblée nationale a mis en lumière un consensus alarmant sur l'état désastreux de la psychiatrie en France, particulièrement en pédopsychiatrie, qualifiée de "désastre absolu".

Les experts, un pédopsychiatre-épidémiologiste et un psychanalyste-chercheur, s'accordent sur plusieurs points critiques : une dégradation continue du bien-être psychique de la population depuis 20 ans, une inversion préoccupante de la tendance à la baisse des suicides chez les jeunes depuis 2017, et une offre de soins totalement inadaptée face à une demande croissante, créant un "effet ciseau" dévastateur.

Les défaillances du système sont jugées systémiques et profondes, impliquant une responsabilité partagée entre les psychiatres (pour leurs certitudes passées), les administrations ("il nous flingue"), les directeurs d'hôpitaux (gestion par "tableau Excel") et des politiques publiques inadaptées.

La critique vise particulièrement le "New Public Management", qui applique des logiques de rentabilité au soin, et l'hégémonie d'une approche "scientiste" de l'Evidence-Based Medicine, jugée inefficace et inconsistante dans le champ de la santé mentale de l'enfant.

Des dispositifs comme "Mon Soutien Psy" sont cités comme des exemples de "leviers qu'il ne fallait pas activer".

Face à ce constat, les experts appellent à une réorientation radicale. Les leviers d'action prioritaires incluent la prévention, notamment la lutte contre la maltraitance infantile qui explique 50% des troubles futurs, et le renforcement des dispositifs institutionnels existants (CMP, CMPP, hôpitaux) plutôt que la création de nouvelles structures complexes.

La formation de thérapeutes qualifiés et la revalorisation des pratiques cliniques pluridisciplinaires sont essentielles.

Enfin, bien que le coût de l'inaction se chiffre en milliards d'euros, les experts soulignent que la solution n'est pas uniquement budgétaire mais réside dans une meilleure organisation des ressources existantes et dans des choix politiques courageux qui replacent la relation humaine et la complexité clinique au cœur du système de soin.

1. État des Lieux : Un Constat Alarmant

Les deux intervenants dressent un tableau extrêmement sombre de la situation psychiatrique en France, soulignant la nécessité de distinguer le "vécu anxio-dépressif" des pathologies cliniques et de se méfier des chiffres de prévalence bruts qui, pour des troubles dimensionnels comme la dépression, "ne veulent rien dire".

1.1. Tendances Épidémiologiques Inquiétantes

Le professeur Falissard, épidémiologiste, identifie une "quadruple interaction entre le temps, l'âge, le genre et la pathologie".

• Suicide et tentatives de suicide : Si la tendance globale sur 30 ans est à l'amélioration, une rupture nette est observée depuis 2017 chez les jeunes.

• ◦ L'amélioration s'est stoppée pour les suicides chez les jeunes.  
• ◦ Les suicides augmentent légèrement chez les jeunes filles.  
• ◦ Les tentatives de suicide montent de "façon spectaculaire" chez les jeunes filles depuis 2017.

• Vécu anxio-dépressif : Le ressenti subclinique mesuré par Santé publique France se dégrade "de façon homogène" depuis 20 ans, touchant toutes les tranches d'âge et tous les genres.

• Pédopsychiatrie : La situation est décrite comme un "désastre absolu". Certains départements n'ont plus de pédopsychiatres, rendant la permanence des soins impossible.

1.2. L'Effet Ciseau : Une Offre de Soins Débordée

M. Tonou met en évidence l'écart croissant entre l'augmentation de la demande et le déficit de l'offre de soins, créant un "effet ciseau" aux conséquences désastreuses.

• Délais d'attente : Les délais pour une consultation spécialisée sont "insupportables", allant de 6 à 18 mois.

À l'échelle d'un enfant de 3 à 6 ans, cela équivaut à un délai de 5 à 10 ans pour un adulte.

• Conséquences cliniques :

◦ Augmentation des hospitalisations en urgence.    ◦ Augmentation des passages à l'acte suicidaire.     ◦ Consommation accrue de psychotropes en pédiatrie, souvent hors autorisation de mise sur le marché (AMM).     ◦ Substitution des psychothérapies recommandées par le seul médicament, faute de moyens.

Selon les estimations, 13% de la population française serait concernée par un trouble mental, soit 1,5 million d'enfants et 9 millions de personnes au total.

2. Défaillances Systémiques et Critiques de la Gouvernance

Les experts s'accordent sur le fait que la crise actuelle est le résultat d'une série de défaillances à tous les niveaux du système. La responsabilité est "partagée" et les erreurs stratégiques des pouvoirs publics sont pointées du doigt.

2.1. Une Responsabilité Partagée

Le professeur Falissard insiste : "tout le monde est responsable du fait qu'aujourd'hui c'est une catastrophe".

Acteur

Critique

Les psychiatres

Ont eu le tort au 20e siècle de croire détenir une vérité unique (la psychanalyse), ce qui a nui aux patients, notamment dans l'autisme. Ce n'est plus le cas pour 90% des praticiens aujourd'hui.

L'Administration et la Tutelle

"Il ne nous aide pas, il nous flingue". Des réglementations absurdes (ex: conventions pour les orthophonistes en CMP) bloquent concrètement la prise en charge des enfants.

Le Délégué Interministériel (TND)

Est qualifié d'"antipsychiatre", accusé d'empêcher la construction de soins avec une "lubie scientiste" et une recherche de solutions simplistes à des problèmes complexes.

Les Directeurs d'Hôpitaux

Privilégient une gestion comptable ("Le tableau Excel il est bien rempli") au détriment de la qualité des soins, menant à des catastrophes (ex: aide-soignant d'orthopédie remplaçant une infirmière psy, conduisant à des abus sexuels).

Les Parents

Sont souvent "partie du problème", plaçant les soignants dans une situation complexe entre le devoir de protection de l'enfant et l'impératif d'inclure les parents dans le soin.

2.2. Erreurs Stratégiques de Gouvernance

M. Tonou identifie deux phénomènes majeurs qui ont sapé les fondements du soin psychique :

1. Le New Public Management : La gestion des structures publiques comme des entreprises, substituant "la rentabilité et le profit au principe fondateur des missions de services publics".

2. L'Evidence-Based Medicine (EBM) : Son déploiement pour évaluer la psychiatrie s'est révélé "tout à fait inconsistant dans le domaine de la santé mentale de l'enfant", avec "aucune avancée en terme de diagnostic, aucune avancée en terme de traitement".

Plusieurs initiatives sont citées comme des "archétypes de ce qu'il ne faudrait pas faire" :

• Le dispositif Mon Soutien Psy.

• La stratégie TND 2023-2027.

• Le cas des centres experts et de la Fondation FondaMental, critiqués pour leur approche qui dissocie le diagnostic du soin, leur absence de résultats concrets (zéro marqueur biologique trouvé) et les biais scientifiques de leurs études d'efficacité.

3. Prévention et Pistes d'Amélioration

Plutôt que de chercher des solutions simplistes, les experts appellent à revenir aux fondamentaux du soin, de la prévention et à mieux organiser les ressources existantes.

3.1. La Prévention comme Levier Principal

• Lutte contre la maltraitance : La prévention primaire, c'est "éviter les abus sur les enfants".

Cela "explique 50 % de la variance des problèmes psychiatriques des adolescents et des adultes après".

L'Aide Sociale à l'Enfance (ASE), qui devrait être le bras armé de cette prévention, est elle-même "un désastre absolu".

• Facteurs de risque sociaux : La lutte contre la pauvreté, la précarité et l'exclusion est une politique de prévention en santé mentale.

"Lorsque je propose une aide sociale à une famille en difficulté [...] je préviens aussi un risque de santé mentale".

• Milieu scolaire : Réduire le nombre d'élèves par classe (le seuil de 17 élèves est cité comme optimal pour l'apprentissage de la lecture) est un levier puissant pour la santé mentale des enfants.

3.2. Renforcer l'Existant et Former les Acteurs

La priorité n'est pas de créer de nouveaux dispositifs, mais de soutenir les institutions et les pratiques qui ont fait leurs preuves.

• Soutenir les institutions : "S'il y avait qu'une chose à retenir, c'est celle-là".

Il faut "rouvrir des places, des lits à l'hôpital, dans les services spécialisés, les CMP, les CMPP". Il faut cesser de "déshabiller le CMP" pour "abonder les centres experts".

• Repenser la formation :

◦ La question centrale n'est pas "quelle est la bonne thérapie ?" mais "qui est un bon thérapeute ?".

L'effet du clinicien est largement supérieur à l'effet spécifique de la thérapie.    *   ◦ Il n'existe pas de formation universitaire pour les psychothérapies en France.    *  ◦ Il faut recréer des formations intermédiaires pour les infirmiers, sur le modèle d'une spécialisation locale d'un an ("infirmiers plus un"), car le modèle des Infirmières en Pratique Avancée (IPA) est trop lourd et coûteux.

• Valoriser la diversité des approches : La spécificité française réside dans une grande diversité de pratiques (psychanalyse, thérapies familiales, psychothérapie institutionnelle) qui "font leur preuve dans la clinique". Toute tentative de réduire cette diversité "va se payer par du moins de soins".

4. Enjeux Économiques et Budgétaires

Le débat sur les moyens financiers révèle une tension entre la nécessité de prouver l'efficacité économique des investissements et la conviction que le soin aux plus vulnérables relève d'un "pacte démocratique" non quantifiable.

4.1. Le Coût de l'Inaction

M. Tonou propose une méthode pour chiffrer le "coût de l'inaction", qui se calculerait en milliards d'euros.

• Exemple de calcul : Pour un coût moyen estimé de 10 000 € par an et par patient, une meilleure prise en charge permettant de réduire ces coûts de 20% générerait une économie de 3,9 milliards d'euros en population pédiatrique et de 18 milliards d'euros en population générale.

• Finalité : Démontrer que "le coût de l'inaction est beaucoup plus élevé que des politiques volontaires et cohérentes".

4.2. Une Question d'Organisation plus que de Moyens ?

Le professeur Falissard adopte une posture provocatrice : "Ça n'est pas une question de moyen".

• Gains d'efficience : "Nous pourrions faire beaucoup mieux avec la même quantité d'argent". Il pointe l'argent alloué à des dispositifs coûteux et peu efficaces (ex: hospitalisations de semaine pour bilans TDAH) alors que les urgences ne sont pas financées.

• Inefficacité des études médico-économiques : Il affirme que les études prouvant le sous-financement de la psychiatrie "n'ont servi à rien".

• Injustice de l'allocation des ressources : Il dénonce une inégalité fondamentale : "On n'a pas le même argent selon les maladies qu'on a en France".

Des traitements à 2 millions d'euros sont remboursés pour certaines maladies rares, tandis qu'il n'y a "pas 3000 € pour une tentative de suicide chez une adolescente".

La rationalité économique ne s'applique pas à la psychiatrie en France.

La conclusion partagée est que la priorité absolue est de mieux organiser le système, en se basant sur les acteurs de terrain ("pas des gens en costard-cravate") pour redéfinir les priorités et optimiser les dépenses.

Reviewer #1 (Public review):

Summary:

This work asks the question of how different organelles and structures in the apicomplexan parasite Toxoplasma gondii are recycled and/or segregated to the daughter cells during cell replication. In particular, they consider an unusual cell structure called the residual body that links replicating cells during the intracellular infection stage of this parasite. The residual body has historically been considered a 'dumping ground' for unnecessary relics of the mother cell during division, but this notion is increasingly being revised. Indeed, cell replication in Toxoplasma is often misinterpreted as cell division (cytokinesis), but in fact, the cell replicates its organelles and structures to multiple 10s of copies in seemingly distinctly formed daughter cells, but cytokinesis is delayed for many such cycles and typically only occurs simultaneously with parasite egress from its host cell. The residual body is, in fact, the connection between these pre-cytokinetic replicated daughters, and effectively, this is still a single cell at this stage. The authors have previously shown that an actin network extends through the residual body between these daughter cells, and ER and mitochondria common to all cells are also linked through this structure. This study examining the fates of organelles during cell replication is timely for continuing our understanding of how this fascinating component of the cell participates in these processes. The authors use Halo-tags as their principal tool to track discrete populations of proteins, labelling their organelle locations, and this provides beautiful insight into these processes.

Strengths:

Using dyes conjugated to Halo tags, this work elegantly tracks the fates of proteins synthesised by an original 'mother' cell over several replication cycles of pre-cytokinetic 'daughters'. Using this tool, they show that some organelles are made intact just once and that some of these can be subsequently sorted to the daughters (micronemes and rhoptries) while others are dismantled (IMC) and the daughters must make their own. A third set of organelles (largely synthesis, sorting, and metabolic compartments) is divided and inherited, and new daughter-synthesised proteins are added to the preexisting maternal proteins in these structures. A role for actin and myosin is clearly demonstrated for micronemes and rhoptries, and this correlates with their relatively late inheritance into the developing daughters. Overall, this work gives clarity to the behaviours of several cell structures during replication and paves the way to a better understanding of the mechanisms that drive the differences between structures and the universality of these processes in other apicomplexan parasites.

Weaknesses:

In addressing the question of residual body participation in sorting of organelles, it would be useful to clearly define this structure and when and where it is delineated from the posterior of a mother cell during the formation of daughter structures. This might seem like a moot point, but it would give clarity to notions of recycling and 'reservoirs'. Mother cells retain their active invasion apparatus until very late in daughter formation, and the need for micronemes and rhoptries to be released from this service late in the process might explain why they are only then trafficked to the cell posterior and then into the daughters. So, is this a distinct 'residual body' body function/reservoir or just a spatial constraint of this sequence of daughter formation? In subsequent cell replications (4, 8, 16... stages), is there a separation between the residual body that links them all and the posterior of each new 'mother cell', and if so, when is this distinction lost? This is important because without a definition, we might be confusing different processes. Are rhoptries/micronemes that originate in one 'mother' able to be sorted to the 'daughters' from a distinct mother in this syncytium? If so, this would make it a sorting centre, but otherwise we could be just capturing the activities at the posterior of any given cell during replication. The authors' further thoughts on this would be very interesting.

The Group 2 structures are described as those that are divided between daughters and receive newly synthesised proteins that add to the maternal protein of these compartments. While this is a logical conclusion for several that are mentioned, where the maternal protein signal is seen to be depleted with replication (including for the apicoplast, ER, glideosome, and Golgi). Data for the addition of new proteins to these existing structures is actually only presented in direct support of this for the Golgi.

Reviewer #2 (Public review):

Summary:

Toxoplasma gondii is an obligate intracellular parasite and the causative agent of Toxoplasmosis. Parasite invasion into host cells, intracellular replication, and then egress, which results in the destruction of the infected cell, is central to pathogenicity. This manuscript focuses on understanding how maternal resources (in this case, cellular organelles) are shared between daughter parasites during cell division. Many organelles are single copy, meaning that division and inheritance by the daughters is crucial for successful replication. The major strength of this study was the use of a Halo-based pulse chase assay to characterize patterns of organelle inheritance. The results show that both microneme and rhoptries (secretory vesicles) previously thought to be synthesized de novo are inherited by daughter parasites. Thus, this paper adds new insight to our understanding of cell division in this important parasite.

Strengths:

This study demonstrated that pulse labeling of proteins can be used to monitor protein synthesis, turnover, and movement. This approach will be of great interest to the field. Using this method, the authors demonstrate three main modes of organelle inheritance.

(1) Organelles, where there are multiple copies (such as secretory vesicles, micronemes, and rhoptries), are divided between the daughter parasites, with additional contribution of newly formed vesicles. New and old material remain as separate entities in the cell.

(2) Single-copy organelles, which are expanded to include newly synthesized material prior to division, such as the Golgi and apicoplast.

(3) Cytoskeletal structures that are synthesized anew during each round of division. These studies provide more refined insight into patterns or organelle inheritance and demonstrate that secretory organelles are not made de novo during each round of division as previously thought. The paper has a logical flow, and overall, the data is presented in a clear and organized fashion.

Weaknesses:

(1) Descriptions of methodology and statistical analysis were incomplete.

(2) There are inconsistencies between the data in Figures 1 and 5. In Figure 1, a small amount of maternal IMC is visible in stage 2 parasites. Although this is a ~90% reduction, these parasites should be quantified as parasites with material IMC. However, the graph in Figure 5C indicates that no material parasites have GAPM1a, given that graph 5C is a binary measure (present vs. absent), one would expect a non-zero percent of parasites to have maternal material.

(3) The conclusion from Figure 6 was not justified based on the data. I agree with the author's conclusion that the accumulation of micronemes and rhoptries in the residual body was time-dependent. In Figure 6A, the signal observed in the residual body at times 6:30, 13, and 14 hours is not observed in subsequent time points. However, the fate of these micronemes and rhoptries is unclear. It cannot be concluded that these vesicles are recycled back to the mother. They could also have been degraded. In fact, the graphs of microneme inheritance in Figure 2B show a decrease in maternal signal from 100% to 80% between stages 1 and 2, indicating that some microneme degradation is taking place.

(4) To convincingly demonstrate that the redistribution of micronemes and rhoptries was due to recovery of MyoF protein levels after auxin washout, a Western blot should be performed to show MyoF protein levels over time. In addition, the decrease in mMIC2 protein levels in the residual body in Figure 8F should be measured and normalized for photobleaching. Both apical and basal signals appear to be reduced over the time course of imaging.

Analyse de la Politique de Santé Mentale et de Psychiatrie en France

Synthèse

Ce document de synthèse analyse l'état actuel et les perspectives d'évolution des politiques publiques de santé mentale et de psychiatrie en France, en se basant sur les auditions de la délégation ministérielle dédiée.

Il en ressort un constat central : après plus de trente ans de négligence, où la santé mentale a été le "parent pauvre des politiques publiques", un tournant majeur a été amorcé en 2018 avec la "Feuille de route santé mentale et psychiatrie".

Cette initiative marque une rupture, symbolisant un engagement politique et financier inédit pour rattraper des décennies de défaillances structurelles.

La crise Covid-19 a agi comme un révélateur, exacerbant les vulnérabilités préexistantes du système de soins (pénurie de soignants, hospitalo-centrisme, manque de prévention) et de la population (dégradation de la santé mentale des jeunes, des femmes et des précaires).

Le système actuel est décrit comme largement "illisible", constitué d'une "multitude de particularismes" et freiné par un modèle de financement "anesthésiant" qui décourage l'innovation.

Face à ces défis, une stratégie de transformation profonde est proposée, articulée autour de plusieurs axes fondamentaux :

1. Une gouvernance refondée et interministérielle : Inspirée du modèle du handicap, elle vise à coordonner l'ensemble des politiques publiques ayant un impact sur la santé mentale, en impliquant tous les ministères concernés.

2. Une programmation pluriannuelle : Pour sortir de la logique budgétaire annuelle (PLFSS) et donner de la visibilité financière et stratégique aux réformes sur le moyen et long terme.

3. Une organisation graduée des soins : Renforcer les premières lignes (médecine générale, psychologues, Maisons des adolescents) pour mieux orienter les patients et réserver les services de psychiatrie hautement spécialisés aux cas les plus complexes.

4. Des réformes structurelles du financement et des autorisations : Introduire des mécanismes incitatifs pour encourager l'innovation, les soins ambulatoires et la coopération entre tous les acteurs d'un territoire (public, privé, associatif).

L'enjeu humain reste crucial, avec une crise d'attractivité des métiers liée non seulement à la pénurie mais aussi à une "blessure morale" des soignants due à la perte de sens.

La dynamique actuelle, portée par une mobilisation politique et sociétale sans précédent, représente un "momentum" unique pour amplifier et accélérer ces réformes.

Analyse Détaillée

Un Héritage de Négligence Structurelle

Pendant plus de 30 ans, la santé mentale a été marginalisée dans les politiques publiques, une situation illustrée par plusieurs facteurs structurels :

• Le "parent pauvre" des politiques de santé : Ce statut s'est traduit par une considération "à part" des patients, des familles et des professionnels, tant dans le système de santé que dans la société.

• Une sous-valorisation financière : L'Objectif national de dépenses d'assurance maladie (Ondam) pour la psychiatrie a systématiquement progressé moins vite que l'Ondam pour la médecine, la chirurgie et l'obstétrique (MCO).

• Un pilotage financier "anesthésiant" : Le modèle de la dotation annuelle de fonctionnement (DAF) a largement contribué à freiner l'innovation et l'adaptation de l'offre de soins face à des besoins populationnels croissants.

• Une offre de soins illisible : L'absence de pilotage stratégique fort a conduit à une "multitude de particularismes" territoriaux, rendant le système complexe et difficile à naviguer pour les usagers, les familles et même les professionnels.

Des familles en sont réduites à déménager pour accéder à un secteur de psychiatrie jugé plus performant.

• Une faible culture des données probantes : Malgré l'existence de nombreuses études validées sur les prises en charge efficaces, le secteur a peu intégré ces "données probantes" dans ses pratiques.

La Feuille de Route de 2018 : Une Rupture et un Nouvel Élan

La "Feuille de route santé mentale et psychiatrie", lancée en 2018, est présentée comme une "rupture par rapport à une aboulie de plus de 30 ans".

Elle constitue le point de départ d'une politique de rattrapage, marquée par un engagement politique et financier inédit.

• Qualité et alignement international : La feuille de route est jugée de qualité et conforme aux standards internationaux.

• Enrichissements successifs : Elle a été constamment renforcée par des jalons importants comme le Ségur de la santé, les Assises de la santé mentale et de la psychiatrie, les Assises de la pédiatrie, et les annonces dans le cadre de la grande cause nationale.

• Continuité politique : Malgré une forte instabilité ministérielle (le délégué ministériel a "survécu à 10 ministres de la santé"), la feuille de route a été systématiquement reconduite et enrichie, assurant une forme de continuité dans l'action publique.

• Premiers effets visibles : Les réformes structurelles engagées commencent à porter leurs fruits, bien que leur plein impact ne soit attendu qu'à moyen terme.

Les Vulnérabilités Révélées par la Crise Sanitaire

La crise du Covid-19 a amplifié des fragilités structurelles anciennes, sans pour autant en être la cause première.

• Vulnérabilités du système de soins :

◦ Manque de soignants et départs de l'hôpital public.    ◦ Débits de formation insuffisants.    ◦ Un "hospitalo-centrisme" persistant.    ◦ Pauvreté de la santé primaire et des politiques de prévention.    ◦ Fortes hétérogénéités territoriales.

• Vulnérabilités de la population :

◦ Une dégradation de la santé mentale des jeunes, des femmes et des personnes en situation de précarité, un phénomène observé à l'échelle européenne et mondiale.

Vers un Changement de Paradigme : Stratégie et Réformes Proposées

Pour répondre à ces défis systémiques, un changement de paradigme est préconisé, s'appuyant sur des réformes profondes de la gouvernance, de la planification et de l'organisation des soins.

Refonder la Gouvernance sur un Modèle Interministériel

L'action sur les déterminants de la santé mentale (logement, emploi, lutte contre les violences, addictions) ne relève pas du seul ministère de la Santé. Une gouvernance interministérielle est donc jugée indispensable.

Structure Proposée

Fréquence

Objectif

Comité Interministériel

Annuelle

Définir, coordonner et évaluer les politiques publiques en faveur de la santé mentale, sur le modèle du Comité Interministériel du Handicap (CIH).

Conférence Nationale

Triennale

Débattre des orientations et des moyens des politiques de santé mentale, en réunissant l'ensemble des parties prenantes.

Comité des Parties Prenantes

À définir

Formaliser un organe consultatif pour assurer la participation active des usagers, familles, professionnels et autres acteurs.

Délégation Ministérielle

Renforcée

Renforcer ses moyens (objectif de 8 agents) pour lui confier la coordination et le pilotage de la feuille de route devenue interministérielle.

Instaurer une Programmation Pluriannuelle

Le cycle budgétaire annuel du Projet de Loi de Financement de la Sécurité Sociale (PLFSS) est un obstacle à la mise en œuvre de réformes structurelles.

La nécessité d'une vision à long terme est soulignée, avec un plaidoyer pour une loi de programmation qui garantirait une visibilité financière et stratégique sur plusieurs années.

Cette approche permettrait d'investir dans des actions de prévention dont les bénéfices, notamment en termes de "coût évité", ne sont mesurables que sur la durée.

Organiser l'Offre de Soins : Gradation et Coordination

Le système actuel est marqué par un héritage où "tout allait à la psychiatrie". La stratégie proposée vise à structurer une offre de soins graduée :

1. Renforcer les premières lignes : La médecine générale, les dispositifs comme "MonSoutienPsy" et les Maisons des adolescents doivent jouer un rôle de filtre pour les troubles légers à modérés et "refroidir" un certain nombre de situations.

2. Réserver la psychiatrie spécialisée : Le secteur de psychiatrie, avec ses équipes hautement spécialisées (psychiatres, psychologues, IPA, psychomotriciens), doit se concentrer sur les cas les plus complexes et graves.

3. Coordonner tous les acteurs : La réforme des autorisations vise à obliger les différents offreurs de soins d'un territoire (public sectorisé, public non sectorisé, privé) à sortir de leurs "couloirs de nage" pour s'articuler fonctionnellement et se répartir la charge des besoins.

4. Inciter à l'innovation : La réforme du mode de financement introduit des compartiments financiers incitatifs pour encourager les pratiques orientées vers le rétablissement, l'ambulatoire et les alternatives à l'hospitalisation, notamment sans consentement.

La Nécessité d'un "Grand Texte" Clarificateur

Un texte de loi est jugé crucial pour clarifier la politique de santé mentale, remettre de l'ordre dans les "particularismes" et définir des standards de prise en charge basés sur les données probantes.

Enjeux Cruciaux : Ressources Humaines et Évaluation

L'Attractivité des Métiers : Au-delà de la Pénurie

La crise de l'attractivité en psychiatrie est multifactorielle :

• Pénurie et burnout : La pénurie de personnel génère une surcharge de travail pour les équipes en place, créant un cercle vicieux.

• "Blessure morale" : Plus profondément, les soignants expriment une perte de sens. Ils sont confrontés à des situations qui violent leur éthique professionnelle (ex: maintenir un patient attaché pendant plusieurs jours sur un brancard faute de lit), ce qui génère une "blessure morale".

• Redonner du sens : Les dispositifs innovants (ex: équipes mobiles de crise financées par le Fonds d'Innovation Organisationnelle en Psychiatrie - FIOP) rencontrent moins de difficultés de recrutement car ils s'inscrivent dans un projet clair et porteur de sens.

• Formation : La réforme du DES de psychiatrie, en instaurant un passage obligatoire en pédopsychiatrie plus tôt dans le cursus, vise à améliorer l'attractivité de cette spécialité. Pour les infirmières, un renforcement des modules de santé mentale dans la formation initiale et un meilleur accompagnement à la prise de poste ("onboarding") sont des pistes explorées.

Développer une Culture de l'Évaluation

La France est jugée en retard sur l'évaluation de ses politiques publiques.

• Le coût de l'inaction : Des études, notamment anglo-saxonnes, démontrent les coûts socio-économiques phénoménaux de la non-prise en charge des troubles mentaux (estimés à 163 milliards d'euros par an en France par la Fondation FondaMental).

• L'argument du "coût évité" : Investir dans la prévention est économiquement vertueux. Par exemple, il est démontré qu'1€ investi dans des soins psychologiques de première ligne permet d'économiser entre 1,4€ et 1,6€. Cet argument peine cependant à être pris en compte dans les arbitrages budgétaires annuels.

• Évaluation des politiques publiques : Un ensemble d'indicateurs a été mis au point pour suivre les effets de la feuille de route, ce qui constitue une exception. Il reste à développer cette culture au niveau local pour évaluer l'efficacité des dispositifs innovants financés par appels à projets.

Synthèse de l'audition de Santé publique France sur la santé mentale et le handicap

Résumé

L'audition de Santé publique France devant la commission d'enquête met en lumière une dégradation persistante de la santé mentale de la population française depuis la pandémie de Covid-19, touchant particulièrement les jeunes de 18 à 24 ans et les femmes.

Les données de surveillance révèlent une prévalence élevée des troubles dépressifs et anxieux, avec un décalage majeur entre les besoins et le recours effectif aux soins.

Près de la moitié des adultes ayant connu un épisode dépressif caractérisé n'ont eu aucun recours thérapeutique.

Les principaux freins identifiés sont le coût des consultations, la difficulté à se confier et le manque d'information.

Concernant les personnes en situation de handicap, l'agence souligne une lacune importante dans les données de surveillance, rendant difficile la caractérisation fiable de leur état de santé et de leur prise en charge.

L'accès aux données des Maisons Départementales des Personnes Handicapées (MDPH) est identifié comme un levier majeur d'amélioration.

Face à ces constats, Santé publique France insiste sur l'importance cruciale de la prévention.

Les stratégies préconisées incluent le renforcement des compétences psychosociales dès l'enfance, la lutte contre la stigmatisation via des campagnes d'information et la promotion de la santé mentale positive. Des dispositifs comme le programme "Vigilance", qui a démontré un retour sur investissement positif (€1 investi pour €2 économisés en coûts de santé), sont mis en avant comme des modèles à généraliser pour une approche économiquement vertueuse de la santé publique.

1. Rôle et Méthodes de Surveillance de Santé publique France

Santé publique France, agence de santé publique créée en 2016, fonde ses missions sur un triple objectif :

1. Anticiper et répondre aux crises sanitaires, notamment par la gestion des stocks stratégiques de produits de santé et la mobilisation de la réserve sanitaire.

2. Surveiller l'état de santé de la population sur l'ensemble du territoire, y compris ultramarin, en couvrant les maladies infectieuses, chroniques et les expositions environnementales.

3. Développer la prévention et promouvoir la santé.

Pour la surveillance de la santé mentale, l'agence s'appuie sur plusieurs sources de données complémentaires :

• Les enquêtes en population générale :

• ◦ Réalisées sur des échantillons aléatoires, elles utilisent des questionnaires et des échelles de santé mentale pour évaluer l'état de la population sans poser de diagnostic individuel.  

• ◦ Exemples notables : le Baromètre de Santé publique France (adultes), l'enquête ENABI (enfants de 3 à 11 ans, réalisée en milieu scolaire en 2022), et l'enquête EnCLASS (collégiens).   

• ◦ Pendant la crise sanitaire, l'enquête Coviprêve a permis un suivi plus rapide, bien que moins détaillé.

• Les bases de données médico-administratives :

◦ Le Système National des Données de Santé (SNDS) est une source majeure d'informations.  

◦ Les données des services d'urgence (motifs de passage) et de SOS Médecins permettent un suivi en temps réel de certains indicateurs comme les troubles anxieux ou les tentatives de suicide.

L'ensemble de ces données permet d'obtenir une "photographie en vie réelle" de la santé mentale des Français, contribuant à l'élaboration de stratégies de prévention et de campagnes d'information.

2. État des Lieux de la Santé Mentale en France

2.1. Une Dégradation Post-Covid Durable

La surveillance épidémiologique confirme une dégradation nette de la santé mentale de la population française par rapport à la période pré-Covid.

• Populations les plus touchées : Les jeunes adultes de 18 à 24 ans, les jeunes filles et les femmes en général présentent les indicateurs les plus dégradés.

• Persistance : Les différents indicateurs de santé mentale se maintiennent à un niveau élevé, sans retour aux niveaux d'avant la crise sanitaire. Les causes sont multifactorielles (éco-anxiété, système économique, mais aussi une plus grande déclaration due à une libération de la parole).

2.2. Données Clés sur la Prévalence des Troubles

Les enquêtes récentes fournissent des chiffres préoccupants :

Population Cible

Indicateur

Donnée Chiffrée

Source (Année)

Adultes (18-79 ans)

Épisode dépressif caractérisé (12 derniers mois)

1 adulte sur 6

Baromètre SPF (2024)

Adultes (18-79 ans)

Trouble anxieux (12 derniers mois)

6 % de la population

Baromètre SPF (2024)

Enfants (6-11 ans)

Trouble probable de la santé mentale

Plus d'1 enfant sur 10

ENABI (2022)

Enfants (tous âges)

Consultation d'un professionnel pour des raisons psychologiques/d'apprentissage

1 enfant sur 5

ENABI (2022)

Collégiens

Consultation d'un psychiatre au cours de leur vie

1 tiers des collégiens

EnCLASS

2.3. Le Non-Recours aux Soins : Un Enjeu Majeur

Un décalage important est observé entre les besoins exprimés ou mesurés et le recours effectif à une prise en charge.

• Épisodes dépressifs : Près de la moitié (50 %) des personnes déclarant un épisode dépressif n'ont eu "aucun recours thérapeutique" (ni professionnel, ni traitement).

• Troubles anxieux : Cette proportion est de 1 personne sur 3.

• Profils concernés : Le non-recours aux soins est plus élevé chez les hommes que chez les femmes.

• Situations critiques : Près de 40 % des personnes déclarant une tentative de suicide ne se sont pas présentées à l'hôpital et n'ont pas consulté de professionnel de santé par la suite.

2.4. Les Freins à la Consultation

L'enquête Coviprêve a permis d'identifier les principaux obstacles au recours aux soins en santé mentale :

• 1. Le prix de la consultation (cité par près de la moitié des répondants).

• 2. La difficulté à se confier ou la peur de ce qu'ils pourraient découvrir sur eux-mêmes.

• 3. Le manque d'information sur les professionnels et leur rôle.

• 4. La difficulté à obtenir un rendez-vous.

• 5. La peur que l'entourage l'apprenne (stigmatisation).

• 3. La Situation Spécifique des Personnes en Situation de Handicap

Santé publique France reconnaît un manque de données structurées concernant l'état de santé des personnes en situation de handicap.

• Limites de la surveillance : La surveillance de cette population n'entre pas "strictement" dans les missions de l'agence, bien qu'elle soit incluse dans les enquêtes générales.

• Difficultés de caractérisation : Il est difficile d'identifier et de caractériser de manière fiable ces personnes dans les bases de données. L'Allocation Adulte Handicapé (AAH) est le principal repère, mais elle ne couvre que les adultes en âge de travailler avec des handicaps reconnus comme sévères.

• Besoin crucial de données : L'agence attend avec impatience la remontée des données des Maisons Départementales des Personnes Handicapées (MDPH) dans le SNDS, ce qui constituerait un "saut qualitatif et quantitatif" pour mieux orienter les politiques publiques.

• Vulnérabilités observées : Les données existantes montrent que les bénéficiaires de l'AAH sont "proportionnellement plus concernés par des événements cardiovasculaires graves".

4. Stratégies de Prévention et Pistes d'Amélioration

Face à ces constats, Santé publique France place la prévention au cœur de sa stratégie.

4.1. Axes de Prévention

• Prévention primaire :

◦ Compétences psychosociales (CPS) : Développer dès le plus jeune âge (école, associations sportives) des capacités à gérer le stress, communiquer, résoudre des problèmes.

Cette approche, inspirée des modèles anglo-saxons, est de plus en plus acceptée et intégrée, notamment par l'Éducation Nationale.  

◦ Promotion de la santé mentale positive : Informer sur les comportements protecteurs (activité physique, sommeil, altruisme, pensée positive) au même titre que la santé physique.

• Lutte contre la stigmatisation :

• ◦ Mener des campagnes d'information pour dédramatiser les troubles psychiques.  

• ◦ Mettre à disposition des ressources grand public comme le site santémentaleinfoservice.fr.

• Prévention tertiaire (prévention de la récidive) :

◦ Le dispositif Vigilance, qui consiste à rappeler les personnes ayant fait une tentative de suicide six mois après leur passage aux urgences, a fait l'objet d'une évaluation médico-économique très positive. Il est en cours de déploiement dans toutes les régions.

4.2. Pistes d'Amélioration

Santé publique France identifie plusieurs axes pour améliorer la connaissance et l'action :

• Mieux caractériser les personnes en situation de handicap dans les bases médico-administratives et médico-sociales.

• Mieux documenter la santé et le rôle des aidants.

• Poursuivre et développer les enquêtes en milieu scolaire pour un dépistage précoce.

• Renforcer l'information sur les signes de souffrance psychique et les parcours de soins gradués.

5. Enjeux Économiques et Décisionnels

L'audition a souligné la dimension économique de la santé mentale et l'importance de convaincre les décideurs publics d'investir dans la prévention.

• Coût des troubles psychiques : Estimé à 109 milliards d'euros pour la société française, dont près de la moitié en perte de productivité.

• Retour sur investissement de la prévention :

• ◦ L'évaluation du dispositif Vigilance montre que 1 € investi permet d'économiser 2 € de coûts de santé, avec un coût moyen évité de 248 € par patient.  
• ◦ Santé publique France s'engage à évaluer de plus en plus le retour sur investissement de ses actions.

• Nécessité d'un plaidoyer : L'agence travaille au développement d'indicateurs sur le "fardeau de la maladie" (Global Burden of Disease) pour objectiver le poids des troubles sur la société et justifier les investissements en prévention.

Le manque de données fiables, notamment à un niveau territorial fin, reste un obstacle pour convaincre les acteurs locaux.

Briefing : Prise en Charge de la Santé Mentale et du Handicap en France

Résumé

Ce document de synthèse analyse les enjeux majeurs de la prise en charge de la santé mentale en France, en se basant sur les échanges tenus à l'Assemblée nationale.

Il en ressort un paradoxe central : malgré des efforts budgétaires significatifs et le déploiement de dispositifs structurants, le secteur de la psychiatrie est en proie à une crise profonde, principalement due à une pénurie critique de ressources humaines.

Les points à retenir sont les suivants :

1. Crise d'Attractivité Sévère : La psychiatrie souffre d'un déficit majeur d'attractivité, avec plus de 23 % de postes de praticiens hospitaliers vacants dans le secteur public et 30 % des postes d'internes non pourvus.

Cette pénurie, qualifiée de "cercle vicieux", entrave la capacité du système à répondre à la demande croissante.

2. Dissonance entre Investissements et Réalité de Terrain : Des financements conséquents ont été alloués via des programmes comme le Fonds d'Innovation Organisationnelle en Psychiatrie (FIOP) et des appels à projets pour la pédopsychiatrie.

Le dispositif "Mon Soutien Psy" a également permis de réaliser plus de 2,5 millions de séances.

Cependant, ces efforts se heurtent à une réalité marquée par des délais d'attente, des défauts de prise en charge et un manque de diagnostics.

3. Impératif du Repérage Précoce : Un consensus se dégage sur la nécessité de basculer d'une approche majoritairement curative vers une stratégie axée sur la prévention et le repérage précoce des troubles.

Les médecins généralistes, la santé scolaire et les maisons des adolescents sont identifiés comme des acteurs clés de cette stratégie, qui est perçue comme un levier de "coûts évités" majeur.

4. Angle Mort sur les Données et l'Évaluation :

Il existe un manque critique de données médico-économiques sur l'impact du non-dépistage précoce et des hospitalisations évitées.

Ce déficit de modélisation affaiblit les plaidoyers pour un investissement accru dans la prévention et le suivi post-hospitalisation.

5. Structuration des Parcours et Coordination Territoriale :

Les Projets Territoriaux de Santé Mentale (PTSM) sont considérés comme un outil essentiel pour améliorer la coordination des acteurs.

Leur renforcement et leur évaluation sont des priorités, tout comme le développement de pratiques innovantes pour fluidifier les parcours entre la ville et l'hôpital.

Analyse Détaillée des Thématiques

1. La Crise d'Attractivité des Métiers en Psychiatrie

Le principal frein à l'amélioration de l'offre de soins en santé mentale est la pénurie de personnel qualifié, en particulier de psychiatres.

• Constat d'une Pénurie Sévère :

• ◦ La ressource humaine en psychiatrie est qualifiée de "denrée rare".   
• ◦ Postes vacants : Plus de 23 % des postes de psychiatres dans les hôpitaux publics ne sont pas pourvus.  
• ◦ Déficit de formation : Chaque année, 30 % des postes d'internes en psychiatrie restent vacants.  
• ◦ Recours aux praticiens étrangers (Padu) : Même en doublant les postes offerts aux praticiens à diplôme hors Union européenne, le taux de vacance reste extrêmement élevé, atteignant 30 % à 50 % selon les régions.

• Un Cercle Vicieux : Cette crise d'attractivité crée un "cercle vicieux" : les étudiants en médecine réalisant leurs stages dans des services en sous-effectif sont peu enclins à choisir cette spécialité, ce qui perpétue la pénurie.

• Pistes de Solution Évoquées :

◦ Valoriser les stages : Mettre l'accent sur la qualité de l'encadrement des stagiaires pour améliorer l'image de la profession.  

◦ Flexibiliser l'exercice : Encourager et faciliter l'exercice mixte (ville-hôpital, public-privé) et le temps partagé, qui correspondent aux aspirations des jeunes médecins ne souhaitant plus un exercice unique et à temps plein.

Des verrous réglementaires ont été levés depuis 2020 pour faciliter l'exercice mixte ville-hôpital.  

◦ Formation des paramédicaux : La réforme du métier d'infirmier intègre une obligation de stage en psychiatrie d'une durée minimale.

Par ailleurs, plus de 540 infirmiers en pratique avancée (IPA) en santé mentale ont déjà été formés.

2. Dissonance entre Efforts Budgétaires et Réalité de Terrain

Des investissements financiers importants ont été réalisés, mais leurs effets sont encore insuffisants pour répondre à l'ampleur des besoins.

• Investissements Financiers Conséquents :

• ◦ Fonds d'Innovation Organisationnelle en Psychiatrie (FIOP) : Depuis 2019, 288 millions d'euros ont été mobilisés pour accompagner 268 projets innovants. Le fonds a été reconduit en 2025.  
• ◦ Pédopsychiatrie et Psychiatrie Périnatale : Un appel à projets a permis de financer 435 projets, avec des crédits annuels compris entre 20 et 35 millions d'euros. 
• ◦ Dispositif "Mon Soutien Psy" : Fin 2024, le dispositif comptait plus de 4 100 psychologues conventionnés et avait bénéficié à près de 480 000 patients (dont 26 % de mineurs), pour un total de 2,5 millions de séances réalisées.

• Difficultés Persistantes sur le Terrain :

◦ Malgré ces chiffres, une "dissonance" est constatée entre les efforts budgétaires et la réalité vécue par les usagers et les professionnels : délais d'attente prolongés, défauts de prise en charge et manque de diagnostics.  

◦ Il est souligné que le système reste trop focalisé sur le "curatif" au détriment du "préventif".

3. L'Impératif de la Prévention et du Repérage Précoce

Le repérage précoce est identifié comme un axe stratégique majeur pour éviter l'aggravation des troubles et les conséquences sociales et familiales associées.

• Un Axe Prioritaire : Le dépistage est considéré comme "un des axes forts qu'il nous faut développer". Une mission a été confiée à trois personnalités qualifiées pour formuler des recommandations sur ce sujet.

• Les Acteurs Clés du Repérage :

◦ Médecins généralistes : Ils sont en première ligne, assurant 76 % des premières consultations pour troubles psychiatriques et traitant 73 % des dépressions.

L'enjeu est de mieux les "outiller" et de renforcer le lien avec les spécialistes.  

◦ Santé scolaire : Une circulaire conjointe (Santé/Éducation Nationale) est en cours de rédaction pour formaliser des "circuits courts" entre les établissements scolaires et les Centres Médico-Psychologiques (CMP).  

◦ Maisons des Adolescents : Leur cahier des charges est en cours de rénovation pour y intégrer pleinement la dimension de repérage. Leurs moyens financiers seront renforcés de façon "considérable".

• L'Enjeu des "Coûts Évités" : L'investissement dans la prévention et le diagnostic précoce est présenté non seulement comme une plus-value pour les personnes concernées, mais aussi comme une source d'économies "majeures" pour la collectivité en évitant des prises en charge plus lourdes à long terme.

4. Structuration de l'Offre et Coordination des Parcours

L'organisation des soins sur les territoires et la fluidité des parcours patients sont des défis centraux.

• Projets Territoriaux de Santé Mentale (PTSM) :

◦ Considérés comme un "outil intéressant", ils mobilisent les acteurs du sanitaire, du social et du médico-social.  

◦ Chaque PTSM bénéficie d'un poste de coordinateur financé.  

◦ Une "deuxième génération" de PTSM est en préparation pour aller plus loin dans la structuration des parcours.  

◦ Une carte interactive des PTSM sera mise en ligne sur le site du ministère pour améliorer la lisibilité et le partage de bonnes pratiques.

• Défis de la Coordination :

◦ Post-hospitalisation : L'organisation des sorties d'hospitalisation psychiatrique présente des "vraies difficultés", entraînant des réhospitalisations au coût "relativement conséquent", un point déjà soulevé par la Cour des comptes en 2021. 

◦ Innovation organisationnelle : Le FIOP vise précisément à soutenir des projets qui testent de nouvelles organisations pour améliorer la coordination ville-hôpital et la graduation des soins.

5. Manque de Données et Nécessité d'Évaluation

Un "angle mort" important subsiste concernant les données chiffrées, ce qui freine l'optimisation de l'allocation des ressources.

• Absence de Modélisation Médico-Économique :

◦ Il y a un manque de données sur le "coût médico-économique du non-dépistage précoce" et sur les hospitalisations potentiellement évitées.  

◦ L'approche culturelle française est perçue comme moins avancée que dans les pays anglo-saxons sur l'utilisation d'outils comme les QALY/DALY pour prioriser les investissements.

• Évaluation des Politiques Publiques :

◦ Le FIOP comme modèle : Ce fonds est cité en exemple pour son processus d'évaluation "extrêmement rigoureuse", menée par des experts indépendants après trois ans de financement, pouvant mener à la pérennisation, la généralisation ou l'arrêt du projet.  

◦ L'évaluation des PTSM : Si une évaluation qualitative a été menée (le "Tour de France" de Franck Bélivier), un besoin d'évaluation plus systématique et comparative des performances est exprimé pour mieux identifier et diffuser les bonnes pratiques.

6. Enjeux Spécifiques à Certaines Populations

• Pédopsychiatrie :

◦ Le secteur est "assez dépourvu" en lits, ce qui conduit à des hospitalisations d'enfants dans des services pour adultes.  

◦ Une inquiétude est soulevée quant au risque de "surdiagnostic", en référence à un rapport de la Cour des comptes, appelant à une vision plus globale de l'accompagnement.

• Santé Mentale en Milieu Carcéral :

◦ Prévalence élevée : Environ 30 % des détenus présentent des troubles psychiatriques.

Pour beaucoup, l'incarcération représente le "premier contact avec le soin". 

◦ Crise d'attractivité aggravée : Les difficultés de recrutement sont "probablement pires" dans ce milieu. L'exemple du centre pénitentiaire de Fresnes, passé de 19 à 6 psychiatres, est emblématique. 

◦ Solutions : Le développement de postes à temps partagé est crucial pour attirer des praticiens. Une "feuille de route santé des personnes placées sous main de justice" co-pilotée par les ministères de la Santé et de la Justice vise à travailler sur cet enjeu.

Reviewer #1 (Public review):

Summary:

In this study, the authors employed comprehensive proteomics and transcriptomics analysis to investigate the systemic and organ-specific adaptations to IF in males. They found that shared biological signaling processes were identified across tissues, suggesting unifying mechanisms linking metabolic changes to cellular communication, which revealed both conserved and tissue-specific responses by which IF may optimize energy utilization, enhance metabolic flexibility, and promote cellular resilience.

Strengths:

This study detected multiple organs, including the liver, brain, and muscle, and revealed both conserved and tissue-specific responses to IF.

Weaknesses:

(1) Why did the authors choose the liver, brain, and muscle, but not other organs such as the heart and kidney? The latter are proven to be the largest consumers of ketones, which is also changed in the IF treatment of this study.

(2) The proteomics and transcriptomics analyses were only performed at 4 months. However, a strong correlation between IF and the molecular adaptations should be time point-dependent.

(3) The context lacks a "discussion" section, which would detail the significance and weaknesses of the study.

(4) There is no confirmation for the proteomic and transcriptomic profiling. For example, the important changes in proteomics could be further identified by a Western blot.

Reviewer #2 (Public review):

Summary:

Fan and colleagues measure proteomics and transcriptomics in 3 organs (liver, skeletal muscle, cerebral cortex) from male C57BL/6 mice to investigate whether intermittent fasting (IF; 16h daily fasting over 4 months) produces systemic and organ-specific adaptations.

They find shared signaling pathways, certain metabolic changes, and organ-specific responses that suggest IF might affect energy utilization, metabolic flexibility, while promoting resilience at the cellular level.

Strengths:

The fact that there are 3 organs and 2 -omics approaches is a strength of this study.

Weaknesses:

The analytical approach of the data generated by the present study is not well posed, because it doesn't help to answer key questions implicit in the experimental design. Consequently, the paper, as it is for now, reads as a mere description of results and not a response to specific questions.

The presentation of the figures, the knowledge of the literature, and the inclusion of only one sex (male) are all weaknesses.

Reviewer #3 (Public review):

Summary:

Fan et al utilize large omics data sets to give an overview of proteomic and gene expression changes after 4 months of intermittent fasting (IF) in liver, muscle, and brain tissue. They describe common and distinct pathways altered under IF across tissues using different analysis approaches. The main conclusions presented are the variability in responses across tissues with IF. Some common pathways were observed, but there were notable distinctions between tissues.

Strengths:

(1) The IF study was well conducted and ran out to 4 months, which was a nice long-term design.

(2) The multiomics approach was solid, and additional integrative analysis was complementary to illustrate the differential pathways and interactions across tissues.

(3) The authors did not overstep their conclusions and imply an overreached mechanism.

Weaknesses:

The weaknesses, which are minor, include the use of only male mice and the early start (6 weeks) of the IF treatment. See specifics in the recommendations section.

Author response:

Reviewer #1 (Public review):

Summary: 

In this study, the authors employed comprehensive proteomics and transcriptomics analysis to investigate the systemic and organ-specific adaptations to IF in males. They found that shared biological signaling processes were identified across tissues, suggesting unifying mechanisms linking metabolic changes to cellular communication, which revealed both conserved and tissue-specific responses by which IF may optimize energy utilization, enhance metabolic flexibility, and promote cellular resilience. 

Strengths: 

This study detected multiple organs, including the liver, brain, and muscle, and revealed both conserved and tissue-specific responses to IF.

We appreciate the recognition of the study’s strengths and the opportunity to clarify the points raised.

Weaknesses: 

(1) Why did the authors choose the liver, brain, and muscle, but not other organs such as the heart and kidney? The latter are proven to be the largest consumers of ketones, which is also changed in the IF treatment of this study.

We agree that the heart and kidney are critical organs in ketone metabolism. Our selection of the liver, brain, and muscle was guided by their distinct metabolic functions and relevance to systemic energy balance, neuroplasticity, and locomotor activity, key domains influenced by intermittent fasting (IF). These tissues also offer complementary perspectives on central and peripheral adaptations to IF. Notably, we have previously examined the effects of IF on the heart (eLife 12:RP89214), and we fully acknowledge the importance of the kidney. We intend to include it in future studies to broaden the scope and deepen our understanding of IF-induced systemic responses.

(2) The proteomics and transcriptomics analyses were only performed at 4 months. However, a strong correlation between IF and the molecular adaptations should be time point-dependent.

We appreciate this insightful comment. The 4-month time point was selected to capture long-term adaptations to IF, beyond acute or transitional effects. While we acknowledge that molecular responses to IF are time-dependent, our goal in this study was to establish a foundational understanding of sustained systemic and tissue-specific changes. We fully agree that a longitudinal approach would provide deeper insights into the temporal dynamics of IF-induced adaptations. To address this, we are currently undertaking a comprehensive 2-year study that is specifically designed to explore these time-dependent effects in greater detail.

(3) The context lacks a "discussion" section, which would detail the significance and weaknesses of the study.

We appreciate this observation. The manuscript was originally structured to emphasize results and interpretation within each section, but we recognize that a dedicated discussion section would enhance clarity and contextual depth. In the revised version, we will add a comprehensive discussion section addressing broader implications, limitations, and future directions of the study.

(4) There is no confirmation for the proteomic and transcriptomic profiling. For example, the important changes in proteomics could be further identified by a Western blot. 

We acknowledge the importance of orthogonal validation to support high-throughput findings. While our study primarily focused on uncovering systemic patterns through proteomic and transcriptomic profiling, we agree that targeted confirmation would strengthen the conclusions. To this end, we have included immunohistochemical validation of a key protein common to all three organs—Serpin A1C. Additionally, we are planning a dedicated follow-up study to expand functional validation of several key proteins identified in this manuscript, which will be pursued as a separate project.

Reviewer #2 (Public review): 

Summary: 

Fan and colleagues measure proteomics and transcriptomics in 3 organs (liver, skeletal muscle, cerebral cortex) from male C57BL/6 mice to investigate whether intermittent fasting (IF; 16h daily fasting over 4 months) produces systemic and organ-specific adaptations. 

They find shared signaling pathways, certain metabolic changes, and organ-specific responses that suggest IF might affect energy utilization, metabolic flexibility, while promoting resilience at the cellular level.

Strengths: 

The fact that there are 3 organs and 2 -omics approaches is a strength of this study. 

We appreciate the reviewer’s recognition of the breadth of our study design. By integrating proteomics and transcriptomics across three metabolically distinct organs, we aimed to provide a comprehensive view of systemic and tissue-specific adaptations to IF. This multi-organ, multi-omics approach was central to uncovering both conserved and divergent biological responses.

Weaknesses: 

(1) The analytical approach of the data generated by the present study is not well posed, because it doesn't help to answer key questions implicit in the experimental design. Consequently, the paper, as it is for now, reads as a mere description of results and not a response to specific questions.

We thank the reviewer for this important observation. Our initial aim was to establish a foundational atlas of molecular changes induced by IF across key organs. However, we recognize that clearer framing of the biological questions would enhance interpretability. In the revised manuscript, we will have restructured the introduction, results, and discussion to align more explicitly with specific hypotheses, particularly those related to energy metabolism, cellular resilience, and inter-organ signaling. We have also added targeted analyses and clarified how each dataset contributes to answering these questions.

(2) The presentation of the figures, the knowledge of the literature, and the inclusion of only one sex (male) are all weaknesses.

We appreciate this feedback and agree that these are important considerations. Regarding figure presentation, we will revise several figures for improved clarity, add more descriptive legends, and reorganize supplemental materials to better support the main findings. On the literature front, we will expand the discussion to include recent and relevant studies on IF, metabolic adaptation, and sex-specific responses. As for the use of only male mice, this was a deliberate choice to reduce hormonal variability and focus on establishing baseline molecular responses. We fully acknowledge the importance of sex as a biological variable and will soon be conducting studies in female mice to address this gap.

Reviewer #3 (Public review):

Summary: 

Fan et al utilize large omics data sets to give an overview of proteomic and gene expression changes after 4 months of intermittent fasting (IF) in liver, muscle, and brain tissue. They describe common and distinct pathways altered under IF across tissues using different analysis approaches. The main conclusions presented are the variability in responses across tissues with IF. Some common pathways were observed, but there were notable distinctions between tissues.

Strengths: 

(1) The IF study was well conducted and ran out to 4 months, which was a nice long-term design. 

(2) The multiomics approach was solid, and additional integrative analysis was complementary to illustrate the differential pathways and interactions across tissues. 

(3) The authors did not overstep their conclusions and imply an overreached mechanism. 

We sincerely thank the reviewer for acknowledging the strengths of our study design and analytical approach. We aimed to strike a careful balance between comprehensive data generation and cautious interpretation, and we appreciate the recognition that our conclusions were appropriately framed within the scope of the data.

Weaknesses: 

The weaknesses, which are minor, include the use of only male mice and the early start (6 weeks) of the IF treatment. See specifics in the recommendations section.

We appreciate the reviewer’s thoughtful comments. The decision to use male mice and initiate IF at 6 weeks was based on minimizing hormonal variability and capturing early adult metabolic programming. We acknowledge that sex and developmental timing are important biological variables. To address this, we are conducting parallel studies in female mice and evaluating IF initiated at later life stages. These follow-up investigations will help determine the extent to which sex and timing influence the molecular and physiological outcomes of IF.

Note de Synthèse : La Politique de Santé Mentale en France selon la Direction Générale de la Santé

Synthèse

Cette note synthétise les perspectives et les actions de la Direction Générale de la Santé (DGS) concernant la santé mentale en France, telles que présentées lors d'une audition parlementaire.

Le constat principal est une dégradation "nette et durable" de la santé mentale de la population depuis la crise du Covid-19, se manifestant par une hausse significative des troubles anxieux, dépressifs et des idées suicidaires, particulièrement chez les jeunes.

En 2023, 23 % des adultes déclaraient un niveau d'anxiété élevé et 16 % se disaient déprimés, des chiffres en nette augmentation depuis 2019.

Le rôle de la DGS se concentre sur la prévention et la promotion de la santé mentale, en amont de la prise en charge psychiatrique. Son action repose sur quatre leviers stratégiques :

1. Améliorer les connaissances et lutter contre la stigmatisation via des campagnes de communication et des actions locales.

2. Promouvoir les comportements bénéfiques, notamment par le développement des compétences psychosociales, l'amélioration du sommeil et la prévention de l'usage problématique des écrans.

3. Renforcer le repérage précoce à travers des programmes comme les "Premiers secours en santé mentale".

4. Déployer une stratégie nationale de prévention du suicide, s'appuyant sur des dispositifs éprouvés comme le numéro national 3114 et le programme de recontact Vigilance, qui réduit de 38 % le risque de récidive.

Malgré ces efforts, des défis majeurs persistent.

La commission parlementaire souligne le décalage entre un diagnostic largement partagé et la mise en œuvre concrète sur le terrain, due notamment à un manque de professionnels (médecins scolaires, psychologues).

Un débat central porte sur la faible culture de la prévention en France, qui privilégie historiquement le curatif, et sur la difficulté à sécuriser des financements pluriannuels pour des actions dont les bénéfices ne sont visibles qu'à long terme.

La "Grande Cause Nationale 2025" est perçue comme une opportunité importante mais dont le démarrage a été freiné par le contexte politique.

1. Rôle de la DGS et Contexte

La Direction Générale de la Santé (DGS) positionne la santé mentale au cœur de ses priorités, avec un bureau dédié.

Son action se distingue de celle de la Direction Générale de l'Offre de Soins (DGOS), qui gère la prise en charge psychiatrique.

La DGS se concentre exclusivement sur les politiques de prévention et de promotion de la santé mentale.

L'approche de la DGS est double :

• Intersectorielle : La santé mentale étant multifactorielle, elle est prise en compte dans tous les milieux de vie (école, travail, loisirs) en lien avec les ministères concernés.

• Populationnelle : Une attention particulière est portée aux publics les plus vulnérables, incluant les jeunes, les personnes âgées, les personnes en situation de handicap, en situation de précarité ou détenues.

Il est précisé que la prise en charge globale du handicap relève de la Direction Générale de la Cohésion Sociale (DGCS).

2. Diagnostic de la Santé Mentale en France : Un Constat Préoccupant

L'Impact "Net et Durable" de la Crise Sanitaire

La crise du Covid-19 a marqué un "véritable tournant", provoquant une dégradation significative et persistante de la santé mentale de la population française.

• Chez les adultes : Selon l'enquête CoviPrev de Santé publique France, en 2023 :

◦ 23 % des personnes interrogées déclaraient un niveau d'anxiété élevé (+6 points par rapport à 2019).    ◦ 16 % se disaient déprimées (+5 points par rapport à 2019).

• Populations vulnérables : Les femmes, les jeunes adultes, les personnes précaires et celles ayant des antécédents de troubles psychiques présentent des indicateurs de santé mentale durablement dégradés.

Le Suicide : Une Préoccupation Majeure

Le suicide demeure un indicateur alarmant en France.

• En 2022, le taux de suicide était de 13,3 pour 100 000 habitants, l'un des plus élevés d'Europe.

• Ce taux est trois fois plus élevé chez les hommes que chez les femmes.

• Après une baisse depuis les années 80, le taux a atteint un plateau sur lequel il est devenu difficile d'agir.

La Vulnérabilité Particulière des Jeunes et des Personnes en Situation de Handicap

• Les jeunes : Les indicateurs sont "particulièrement préoccupants".

Le nombre de passages aux urgences pour gestes suicidaires chez les 11-17 ans est en hausse.

En 2023, 86 femmes de 15 à 19 ans sur 100 000 ont été hospitalisées pour gestes auto-infligés, une hausse de 46 % par rapport à 2017.

• Personnes en situation de handicap : Elles présentent un risque suicidaire majoré.

Des études montrent un risque 7 à 10 fois plus important pour les personnes présentant des troubles du spectre autistique.

Des Causes Multifactorielles

Ces évolutions sont attribuées à une combinaison de facteurs :

• Environnementaux : L'éco-anxiété est une réalité, notamment chez les jeunes.

• Géopolitiques et économiques : Les conflits, les attentats et l'instabilité économique.

• Sociétaux : La pression scolaire, les usages numériques et l'exposition aux réseaux sociaux sont corrélés à une dégradation de la santé mentale des plus jeunes.

3. Les Levier d'Action de la Direction Générale de la Santé

Face à ces constats, la DGS déploie une stratégie de prévention et de promotion articulée autour de quatre axes principaux.

Axe 1 : Amélioration des Connaissances et Lutte contre la Stigmatisation

L'objectif est de lever les freins à l'accès aux soins, notamment l'auto-stigmatisation.

• Campagnes de communication : Santé publique France déploie des campagnes grand public et ciblées, avec un site internet dédié.

• Actions territorialisées : Les "Semaines d'information en santé mentale" (SISM) et les "Conseils locaux de santé mentale" (CLSM) sont déployés pour réunir localement élus, citoyens, associations et professionnels.

Axe 2 : Promotion des Comportements Bénéfiques à la Santé Mentale

• Développement des compétences psychosociales (CPS) : Une stratégie interministérielle (portée par 7 ministères) vise à développer dès le plus jeune âge des compétences comme l'estime de soi, la relation à l'autre et l'esprit critique pour renforcer la résilience.

• Qualité du sommeil : Une feuille de route interministérielle a été lancée, rappelant qu'un sommeil altéré double le risque de développer une dépression.

• Prévention de l'usage excessif des écrans : Des actions sont menées pour contrer la corrélation observée entre le temps d'écran, l'exposition à des contenus inadaptés et les troubles dépressifs chez les jeunes.

Axe 3 : Repérage Précoce des Troubles

• Premiers secours en santé mentale : Inspiré d'un programme australien, ce dispositif vise à former plus de 200 000 secouristes capables de repérer les situations de détresse dans leur entourage. Le ministre a annoncé un objectif porté à 300 000 formés d'ici 2027.

• Mon bilan prévention : Mis en place en 2023, ce dispositif invite les citoyens à des âges clés de la vie à faire un bilan global de leurs comportements en santé, incluant la santé mentale.

Axe 4 : Stratégie Nationale de Prévention du Suicide

Cette stratégie, pilotée par la DGS, a permis de mettre en place des dispositifs clés qui ont démontré leur efficacité.

Dispositif

Description

Données Clés et Résultats

3114

Numéro national d'appel pour la prévention du suicide, accessible 24/7.

Plus de 1000 appels par jour. Un budget de 23 millions d'euros.

Vigilance

Dispositif de recontact des personnes passées aux urgences pour une tentative de suicide.

Réduit de 38 % le risque de réitération suicidaire. Retour sur investissement de 2 € pour 1 € investi. Aujourd'hui généralisé à 17 régions.

Prévention de la contagion suicidaire

Plans d'action locaux menés avec les Agences Régionales de Santé (ARS) et les élus pour prévenir les phénomènes de contagion après un suicide.

Efficacité démontrée par des retours d'expérience qualitatifs.

4. Enjeux, Débats et Perspectives

La "Grande Cause Nationale 2025" : Une Opportunité Mitigée

Reconnue comme une opportunité indéniable, la mise en œuvre de la "Grande Cause" a subi un "retard à l'embrayage" en raison du contexte politique (changement de gouvernement). Cependant, elle a permis de :

• Relancer la mobilisation interministérielle sur des thématiques transversales.

• Prioriser et concrétiser des projets, comme la campagne grand public de Santé publique France.

• Labelliser plus de 750 projets locaux, démontrant une appropriation territoriale.

Le Défi des Moyens Humains et de la Mise en Œuvre Locale

Un consensus émerge sur le fait que le diagnostic est connu, mais que l'action sur le terrain manque cruellement de moyens.

• La prévention et le repérage précoce se heurtent à une pénurie de professionnels (infirmières scolaires, médecins scolaires, psychologues).

• Les dispositifs comme les Conseils Locaux de Santé Mentale (CLSM) et les Projets Territoriaux de Santé Mentale (PTSM) visent à améliorer la coordination locale, mais la marche reste haute.

Le Modèle Économique de la Prévention

Le débat met en lumière une tension structurelle dans le système de santé français.

• Faiblesse de l'investissement : Les dépenses de prévention en France représentent 2 à 3 % des dépenses de santé, un niveau bas comparé aux standards de l'OCDE. La France a historiquement privilégié une culture du soin curatif.

• Logique de court terme : Les décideurs politiques sont contraints par des arbitrages budgétaires annuels, alors que les retours sur investissement de la prévention s'étalent sur plusieurs années. Le coût sociétal total des suicides et tentatives de suicide a été estimé à 24 milliards d'euros en 2019.

• Débat sur la pluriannualité : La proposition d'une loi de programmation pluriannuelle pour la santé mentale est avancée pour garantir des investissements à long terme. La DGS exprime une réserve, soulignant que la multiplication de telles lois rigidifie la dépense publique.

Le Dispositif "Mon Soutien Psi"

Ce dispositif, qui permet une prise en charge de séances de psychologue, est reconnu comme un progrès pour lever les freins financiers.

Il est cependant noté qu'il a pu contribuer à une "fuite" des psychologues du secteur public (hôpitaux, Centres Médico-Psychologiques) vers le secteur libéral, affaiblissant la prise en charge des troubles plus lourds qui nécessitent une approche pluridisciplinaire.

5. Focus sur des Populations Spécifiques

• Personnes âgées : Le taux de suicide chez les 85-94 ans est de 35 pour 100 000, soit près du triple du taux de la population générale, un chiffre largement attribué à l'isolement.

• Jeunes : Le harcèlement scolaire est identifié comme un facteur de risque majeur. La DGS collabore étroitement avec l'Éducation Nationale pour déployer les programmes de compétences psychosociales afin de mieux armer les élèves.

• Agriculteurs : Cette population connaît des taux de suicide extrêmement élevés. Des dispositifs spécifiques comme les "sentinelles" sont déployés par la MSA dans le cadre du suivi du mal-être agricole.

This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giaf106), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer 1: Jianbo Jian

The authors provide a high-quality reference genome for the Ural Owl (Strix uralensis), these genomic resources are valuable for conservation and evolution. The manuscript is well-written, and the scientific story with cell culture for conservation is interesting. I have some questions or comments as following: 1、 in abstract， the N50 is contig or scaffold？ 2、For the GenomeScope analysis, the estimated genome size is 1.29 Gb with low heterozygosity (0.2%). The assembled genome size is 1.38 Gb. Could there be duplicated genome sequences in the assembly, or did the genome survey evaluation exclude some k-mers? What were the parameters used in GenomeScope2 (e.g., was the -h parameter set to its default value)? 3、How do you perform the decontamination？ 4、For the Hi-C contact map, due to some chromosomes are considerably larger while others are much smaller, it is suggested that the larger chromosomes could be displayed independently from the smaller ones to enhance clarity and interpretation.

This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giaf089), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer 3:Weihua Li

(1) In the abstract, the statement 'WaveSeekerNet achieves scores of up to the maximum 1.0 across all evaluation metrics, including F1-score (Macro Average)' appears to slightly deviate from the actual experimental results. (2) In data preprocessing, the reasoning behind selecting and keeping the earliest collected sequence when duplicate sequences are encountered should be explained. (3) Compared with Figure 4, Figure 5 demonstrates performance improvements in most cases, but why is this not observed for some results in Figure 4d? (4) Could the oversampling/undersampling methods employed in the study introduce any potential biases to the analysis? (5) Given that VADR can provide viral classification and annotation information—which serves as the benchmark in this study, what specific advantages does WaveSeekerNet offer for subtype classification? (6) The paper employs 10-fold cross-validation to assess generalizability, yet the data processing section describes a temporal split (pre-2020 for training). Could the "Model Training and Testing" section provide further clarification on this?

This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giaf089), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer 1:Will Dampier

The manuscript presented by Nguyen et al. is well written, well researched, and well executed. The use of this new "wavelet style" neural network shows both an increased training efficiency and improved accuracy at detecting influenza subtypes for surveillance. However, I think their comparison to a 'plain' Transformer model does not take advantage of the improvements in pre-training and transfer-learning that have become standard practice in deep-learning. I have also included some stylistic suggestions to improve the figures as presented. After addressing these comments, I believe that this will become a very strong manuscript.

Major Comments:

The authors present a comparison between their new wavelet architecture and a standard transformer architecture using a one-hot encoded vector of amino-acids. I believe that this is the correct 'null model' to compare your wavelet architecture to, however, it does not represent the 'state of the art' in utilizing transformers for sequence analysis. As I'm sure the authors are aware, the disadvantage of transformers is that they take an extensive amount of training (they note the transformer only models take 2-4X more training epochs to converge). However, the advantage they bring is that they can be extensively trained for one task and then transfer that learning to another related task. A number of models have been pre-trained on giant collections of proteins Asgari et al, https://doi.org/10.1371/journal.pone.0141287 and Rives et al https://doi.org/10.1073/pnas.2016239118 which then allow one to transfer that knowledge to different domains with fewer examples such as demonstrated in Dampier et al https://doi.org/10.3389/fviro.2022.880618. It would be interesting to see whether your wavelet model defeats these pre-trained models with transfer learning. If you showed that, you could argue that there is no need for the extensive expense of 'foundational models'.

The authors discuss that there is a significant imbalance in the training set and they used up-sampling and limiting to balance out the class representation. Since the classes are not equally represented, the model may not be equally able to predict each class. And the high metrics may only be a representation of its ability to predict the popular classes correctly. The authors should include an additional set of figures (supplemental is fine) that show the metrics broken out by Subtype. It would also be interesting to see a graph of the class-size (before up-sampling) vs F1-score (or another metric) on that class. This could provide lower-bounds for how many samples are needed to train the model.

Minor Comments:

Figures 3, 4, and 5: These would benefit from a linked y-axis. It is hard to compare across A/B/C/D when the axes have different y-limits.

This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giaf017), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer 2: Ann-Katrin Llarena

Nasr and colleagues present an, at times, well-written manuscript with an interesting and robust pipeline that includes well-known softwares (you must make sure to cite the authors of these). However, the manuscript is, quote "...a collection of Galaxy-based FAIR workflows employing state-of-the-art tools to detect and track pathogens from metagenomic Nanopore sequencing". Its repeated how well it works, they even compare it to other software in table 1 (without proper benchmark). These initial statements are however not supported by the findings. The Salmonelal from the spiked samples are, as expected from food matrix present in low quantity), difficult to do more than state that the genus is present, and only a fraction of the samples can actually "complete" the entire pipeline. Also, the benchmarking is not really benchmarking (compare and measure this software against other competing software). No such comparison is done, and even though the intention of PathoGFAIR as stated throughout the paper, is detection and analysis of metagenomic samples, the benchmarking is done on isolate based wgs. It is also evident that the authors are not microbiologists as the manuscript is riddled with taxonomical misunderstandings about the vast genus Salmonella and when to use capital letters and italics. I am also lacking a proper discussion here on the results found in the spiking experiment in light of current EU legislation on Salmonella. Can this pipeline help in this regard? Sensitivity and specificity metrics are also lacking.

Abstract: "foodborne pathogen data" / "metagenomic Nanopore pathogenic data" - suggest to rewrite, as what I think you are trying to say is " initially developed to detect foodborne pathogens from metagenomic nanopore data, the workflow can be used to detect any pathogen." "Colony-forming unit and Cycle Threshold values." rewrite sentence, I do not completely understand what you are trying to say. what is "sufficient colony forming units?" It will vary as well between pathogens (infection dose varies). You could rather state your sensitivity of the pipeline here - even though i think that sampling prep, library prep and seq influences that more than the bioinformatics. "In any sample": did you test all matrixes? "sample is isolated or incubated before seq" you cannot isolate a sample, but you isolate a bacteria from a sample. unprecise language.

Introduction: In general, its well written, but a bit unprecise here and there. The authors also rely a lot on the following words: "rapid" "accurate". "outbreaks and epidemics" - rewrite, these are the same. "efforts to mitigate their spread and ensure food safety" again, complementary terms - rewrite. "global public health authorities" we do have everything from local to global food safety and public health authorities, I think one should highlight this. There is a difference between for instance EFSA and ECDC. "isolation can be complex"? do you mean complicated or work intensive? "The utilisation of Nanopore sequencing data, as exemplified in studies like [7]," citing practices like this is not really reader friendly. Suggest to write what they actually did in seven (as for instance the detection of blah in blah as shown in 7). "Once (meta)genomics data has been generated, bioinformatics approaches enable the rapid and accurate detection"; repetition of chapter above. You write in the former chapter that "the utilisation of nanopore data" which also includes bioinformatics of course. SURPI and Sunbeam is freely available? https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-019-0658-x https://chiulab.ucsf.edu/surpi/

"PathoGFAIR: pathogen identification and tracking from metagenomics". Im not convinced that it can perform tracing in an outbreak where only a few SNPs are allowed. PathoGFAIR does not really speed up the process of sampling, does it. Actually, it takes more time to extract crude dna from a sample than to place it in a enrichment broth or do a dilution series, so the presteps are not really a part of this. "Tracking pathogens" - again, if species level is the lowest rank it can go to, its not enough to perform tracking.

Overview chapter "input data is seq data generated w nanopore" basecalling is not included in the workflow? How is this performed? It affects the quality of the reads, so its nice to know what you did. The chapter is very wordy, and contains a lot of fill-words with salespitches almost. I would recommend rewriting it, for instance: Chapter that starts with subsequently and describes the different workflows and how they work together can be compressed. And the last three sections are salespitching.

WF1: Preprocessing: How stringent filtering and quality control are implemented in the workflow? How good quality do you need for the wp2-4 to work sufficiently well? Did you test? Food vehicle animal? What is that - do you mean that if you extract dna from bovine meat, you map to bovine genome? "a tool ten times faster etc etc." is discussion and should be removed from what I think is materials and methods even though the title of the section is workflow 1. What is a food host? Kalamari database includes many foodborne pathogens, such as Shigella, E. coli, Campylobacter etc etc. how can you just remove all reads that match to this database? Table 1: Innuendo is based on isolate WGS, and not intended for WGS. Also, it has its own built in wgMLST schema employed using chewbbacca, so it definitely has allele-abased pathogen identification. Its intended for illumina data. Victors are strictly a platform to analyse virulence factors and not intended even for taxonomic profiling, and its webinterface doesn't work. IDseq has step-by-step guides available on their webpage, so I think that qualifies as a tutorial. You can also contact them (user support). I guess the same is true for OneCodex, as you actually pay for that one. So the table is unprecise at best and should be corrected (I didn't go through Submeam, SURPI or PAIPline specs to try to check if you got it correctly). Rewrite this. Further, I think you should only include systems / pipelines that are intended for metagenomics. You have a footnote * that I cannot see in the table as well.

WF2 taxonomy profiling: The first sentence needs rewriting. Two sentences from "Although Kraken2 is a tool design…….." belongs in discussion. WF3: Medaka consensus pipeline : "This task is performed using neural networks applied from a pileup of individual sequencing reads against a draft assembly. " what draft assembly did you use here to create a consensus sequence? Actually, its not polishing contigs, its assemblying them? Again, there is some descriptions of the software which belongs in the discussion, say the perks one gets from using this tool over the other. I do not however get how screening for virulence genes = pathogen identification. The thing is that in a complex food matrix or faecal samples from animals, things like stx phages will also be present. These are not stec pathogens unless the phage is inside an e.coli. How do you make sure of the host for such mobile genetic elements as these virulence and amr genes often are located on? Seeing as this is the basis of your pathogen detection?

WF4: A bit again on choosing software over the other that is discussion food. Wf4/wf5: I am worried about the reliance on snp based technics for nanopore reads. Is the quality good enough to achieve sufficiently robust results? Easily adaptable workflows Last section is repetition (about each wf operating independently) Use cases: Data generation: Please revise how to write Salmonella names correctly. They should be in italics for genus, species and subspecies names, while the serovar/serotype is non italic and capital letter. So the correct term would be: * Salmonella enterica subsp. enterica serovar Houtenae, or in short; Salmonella Houtenae. * The strain DSM554 is of serovar Typhimurium, and this should referenced like this: Salmonella enterica subsp. enterica serovar Typhimurium strain DSM 554 First two sentences are contradictory to eachother? Sentence starting "15 samples were incubated"; don't start sentence with number, it looks like 33.15 How much meat did you use? What CFU/g does these ct values translate too? Its important to know the sensitivity relative to legislation. The limit is zero in 100grams, but I don't assume you tested 100g? What does adaptive sampling mean? To exclude chicken DNA? The point v sentence under description of supplementary table t1 is a bit weird punctuation Gene-based pathogen identification: Working with meat to detect low abundance pathogenic bacteria is challenging without enrichment of the expected pathogen with selective methods. Just incubating it a x temperature might work for some bacteria, but others need special atmosphere (campylobacter, clostridia) and nutrients. How do you accommodate this? Figure 2 B: The grey bares samples ? why are they collapsed in the left corner? And shy are sdhA and mucD highlighted? Also, please put genes in italics. the grey bars on the right (y-axis) are not annotated? To which reference genome are the barplot in d referring to? I can see for instance in f that there is a number of snps or variants for the Houtenae and Typhimurium, but not Salamae, was the latter used as reference? "an AIDA autotransporter-like protein, only found in Enterica strain samples but not in samples spiked with Houtenae or Salamae strains." All these strains are of the subspecies enterica Figure 3: punctuations a bit off here and there. Why do you operate with cfu/ml? You added it to meat? It should be cfu/g? It would be nice with a presentation of the resistance panel of the three spiked strains before presenting the amr genes. "Similar but inverse relations are observed for CFU/mL value (Figure 3 C & D), with a threshold for VF and AMR gene detection at 106 ." cfu/ml of what? The rinse? Added ml? I don't even know how much meat were included in the dna extractions. "The further the samples are from these thresholds, the higher the number of VF genes and AMR genes identified. Indeed, the three top scattered dots with identified VF genes between 250 and 300 (Figure 3 A, C, E) are the samples with the highest number of reads, higher CFU/mL value, and a relatively lower Ct value compared to other samples." The tendency is ok, but not all. For instance, you have several exceptions here for both amr genes and vf genes. Maybe mark the dots after say spiked strain/enrichment or not?

Discussion bit here : "enerally, allowing samples to incubate for a short period before se quencing enhances microbial growth, resulting in higher CFU/mL values and lower Ct values. This increase in microbial concentra tion improves the efficiency of direct sequencing by providing more genetic material for analysis, facilitating faster and more accurate pathogen detection. "

Allele-based pathogen identification: "Salmonella enterica subspecies enterica serovar typhimarium (NC_003197.2)": see earlier comment on writing correct taxonomically for Salmonella. "However, given the diversity among Salmonella subspecies in the samples, a high number of complex variants and SNPs were anticipated. " You only operate with ONE subspecies of Salmonella - S. enterica subsp. enterica. That's the relevant subspecies, and contains over 2500 serovariants. I don't understand this process; in an outbreak setting you are dependent on tracing, i.e. showing that you isolates are clonal. Pathogfair relies on mapping to a reference genome, but that again relies on isolation of suspected isolate and building a high quality assembly for the allel-based pathogen identification to work. Its not enough to just show that you have that or that serotype, you will have to show that they are clonal (i.e. separated by a limited number of SNPs, say max 20 snps over the full length of the chromosome). This method cannot do this. Samples with prior pathogen isolation: Do understand you correctly that you now exstract dna from isolates? Not whole samples matrix? If so, how is this benchmarking a pipeline intended for metagenomics sequencing? If you were to extract dna from feces/ food and then use your pipeline, that would be benchmarking. However, this doesn't prove that your pipeline works as you intend it to/or claim that it does. How were the samples prepared? If isolates, extraction method and sequencing techniques? Species name is written non-capitalized first letter, so Campylobacter jejuni. All gene names should be italicized. Suggest rewriting sentence: The wet lab procedures performed to isolate and prepare these samples for sequencing adhered to standard microbiological techniques, including cultivation, enrich ment, and isolation steps" to reflect actual sequel; enrichment, cultivation and isolation and verification." Conclusion: If for use for solely isolates, I think assemblies are a better way to go than this pipeline; its more reliable for clonality analysis needed in outbreaks. "We further supported the scientific community by introducing new 46 benchmark samples, making them publicly available. This demonstrates our significant investment of time and resources, providing valuable assets for future research." There are now 82000 c. jejuni just on ncbi, of which 600 are complete. Salmonella genomes are clocking on 524500 assemblies on enterobase. The contribution of these strains are not because they are new samples, but because your isolates represent data from an underrepresented region of the world, namely Palestine.

Supplmentary figure s4 is cropped so that x-line annotation is not visible. SFigure 5 Midpoint root amr phylogenetic tree? Supplementary table 1: its unclear for me if you added this amount of bacteria or it was the result of after 1h or 24h enrichment. Also, I don't understand how much meat you used for the dna extraction. Same goes for ct values.

This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giaf017), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer 1: Federico Zambelli

The authors present PathoGFAIR, a set of Galaxy workflows for the metagenomic analysis of shotgun Nanopore sequencing from isolated and non-isolated pathogens in contaminated food samples. They complement their work by analysing and releasing two datasets, one from isolated and the other from non-isolated samples, with the primary objective of illustrating the potentiality of the workflows. These datasets could also be used as benchmarks for future works.

The manuscript is generally well-written, and the authors highlight the advantages of the proposed workflows in Table 1 by comparing them to similar solutions. The workflows are well integrated into the Galaxy network, are available on the three main usegalaxy instances, and provide a thorough tutorial through the Galaxy training platform. A notable advantage of PathoGFAIR over similar workflows is that, thanks to Galaxy, the final user can easily tailor them by replacing any tool in the workflow with others available in the Galaxy ecosystem. This also allows easy updates for the tools in the workflows.

A few minor points that, if addressed, in my opinion, could further strengthen the manuscript:

1 - The rationale behind the tool selection in each of the four workflows is not always clear. While insights are present for workflows 1 and 4, this is not true for workflows 2 and 3. The reader would benefit from understanding why one tool has been preferred over another for the same task, even more so, given the possibility to modify the workflows easily, when this preference could be the other way around in particular use cases or conditions.

2—One of the main factors for a successful metagenomic analysis is the correctness, completeness, and up-to-dateness of the reference data. The authors should briefly describe how PathoGFAIR addresses this in Galaxy.

3—While this workflow is clearly stated to be tailored for shotgun metagenomic sequencing, the authors contrast this approach only with targeted sequencing. Instead, they should also discuss the 16s rRNA metagenomic approach, for which Nanopore kits are available, and why PathoGFAIR has been limited to the analysis of shotgun data.

Synthèse de l'Audition de la Défenseure des droits sur la Santé Mentale et le Handicap

Résumé

L'audition de la Défenseure des droits devant la commission d'enquête de l'Assemblée nationale dresse un tableau alarmant des défaillances systémiques dans la prise en charge de la santé mentale et du handicap en France.

Le handicap constitue le premier motif de saisine pour discrimination (22 % des réclamations en 2024), soulignant un écart persistant entre les droits annoncés et leur effectivité.

Les politiques de santé mentale sont jugées gravement insuffisantes, tant pour les majeurs que pour les mineurs.

La situation est particulièrement critique en milieu carcéral, où la surreprésentation des troubles mentaux, conjuguée à la surpopulation et au manque de soins, conduit à des traitements qualifiés d'inhumains.

Pour les jeunes, les données sont alarmantes (25 % souffrent de dépression), mais les services de pédopsychiatrie sont saturés, avec des délais d'attente dépassant un an, et un manque criant de données fiables pour piloter les politiques publiques.

Le recours abusif à l'isolement et à la contention, notamment sur des mineurs hospitalisés en services pour adultes sans contrôle judiciaire, constitue une atteinte grave aux droits fondamentaux.

Concernant le handicap, la loi de 2005, bien qu'ayant permis des avancées, est loin d'être intégralement appliquée.

L'éducation inclusive reste un défi majeur, marqué par un manque d'AESH, des difficultés d'aménagement des examens et une absence de données précises sur le temps de scolarisation réel.

L'emploi demeure le premier domaine de discrimination, et l'accessibilité (transports, logement, numérique) accuse un retard considérable. Les aides à l'autonomie sont insuffisantes et inégalitaires, notamment en raison du maintien d'une barrière d'âge à 60 ans.

La Défenseure des droits insiste sur le fait que le non-respect des droits fondamentaux représente un coût social et économique élevé à terme, bien supérieur à celui d'un investissement dans la prévention et une prise en charge effective.

L'urgence est de commencer par appliquer les textes existants et de prendre conscience de la détresse de la jeunesse.

Introduction : Rôle et Observations du Défenseur des droits

L'institution du Défenseur des droits est un observateur privilégié des carences des politiques publiques, car la question des droits des personnes handicapées traverse l'intégralité de ses cinq missions :

• 1. Droits des usagers des services publics

• 2. Lutte contre les discriminations

• 3. Protection des droits des enfants

• 4. Déontologie des forces de sécurité

• 5. Protection des lanceurs d'alerte

L'institution est également chargée du suivi de l'application de la Convention internationale relative aux droits des personnes handicapées (CIDPH), ratifiée par la France en 2019.

Le Handicap : Premier Motif de Discrimination

Depuis plusieurs années, le handicap est le premier motif de saisine en matière de discrimination. Cette constance révèle une problématique structurelle profonde.

Année

Nombre total de saisines (Discrimination)

Part relative au handicap

Nombre de réclamations (Handicap)

2024

5 679

22%

1 249

Ces discriminations s'exercent dans de multiples domaines, incluant l'emploi, la scolarisation, la santé, la justice, les loisirs, le sport et la culture. L'institution se positionne comme un "très bon observatoire de ce qui ne va pas", mettant en lumière l'écart entre le droit annoncé et son effectivité sur le terrain.

L'Argument Central : Le Coût du Non-Respect des Droits

La Défenseure des droits conteste fermement l'idée que l'application des droits fondamentaux représenterait un coût financier trop important.

Elle soutient au contraire que "c'est le non-respect des droits fondamentaux qui entraînera à terme un coût élevé pour la société", un argument particulièrement pertinent en période d'incertitude budgétaire.

Les Défaillances dans la Prise en Charge de la Santé Mentale

La Situation des Personnes Majeures

La réponse des pouvoirs publics à la crise de la santé mentale, aggravée par la pandémie de Covid-19, reste insuffisante. Un Français sur trois sera confronté à un trouble psychiatrique au cours de sa vie. Les défaillances sont multiples :

• Quantitatives : Offres de soins trop faibles, capacités d'hospitalisation limitées, déserts médicaux.

• Organisationnelles : Système mal organisé et cloisonné entre le sanitaire et le médico-social.

• Humaines : Le secteur de la psychiatrie peine à recruter alors que les besoins augmentent.

Ces carences entraînent des atteintes graves et répétées aux droits fondamentaux, notamment le droit à la santé, avec des délais d'attente excessifs, des ruptures de soins et des inégalités territoriales criantes qui pénalisent les plus précaires.

Cas Spécifique : La Santé Mentale des Personnes Détenues

La santé mentale des personnes détenues se dégrade faute d'un accompagnement adapté.

• Statistiques : Entre juillet 2024 et juillet 2025, la plateforme d'appel pour les détenus (3141) a reçu 1 065 appels (7,6%) pour des difficultés d'accès aux soins et 106 appels spécifiques pour un risque suicidaire.

• Causes de la surreprésentation des troubles mentaux :

• 1. Une politique de désinstitutionnalisation qui a réduit les lits en psychiatrie sans développer de services de proximité en relais.  
• 2. Une diminution du nombre de personnes déclarées pénalement irresponsables, qui se retrouvent de ce fait en prison.

• Conséquence juridique : Maintenir en détention une personne nécessitant une prise en charge médicale revient à lui infliger des "traitements inhumains", comme l'a souligné la Cour européenne des droits de l'homme (arrêt GC c. France, 2012).

• Facteurs aggravants : La surpopulation carcérale et l'absence de continuité des soins à la sortie, qui augmente le risque de récidive.

La Situation des Mineurs

Les résultats d'une étude de 2025 (Institut Montaigne, Mutualité française, Institut Teram) sont jugés alarmants :

• 25% des jeunes (15-29 ans) souffrent de dépression.

• Ce chiffre atteint 39% dans les outre-mer, avec des pics à plus de 50% en Guyane, 44% en Martinique et 43% à Mayotte.

Problèmes Structurels Identifiés

1. Manque de données fiables : L'absence de données agrégées au niveau national sur le nombre d'enfants en attente de prise en charge fragilise le pilotage des politiques publiques.

Le rapport de la Cour des comptes de 2023 sur la pédopsychiatrie estime que sur 1,6 million d'enfants avec un trouble psychique, seuls 50 à 53% bénéficient de soins.

2. Inégalités territoriales et pénurie de médecins : La politique du "virage ambulatoire" a renforcé le rôle des Centres Médico-Psychologiques (CMP), mais leur répartition est inégale (10 par département en moyenne, avec de fortes disparités).

Les délais pour obtenir un premier rendez-vous dépassent souvent un an, ce qui est incompatible avec la nécessité d'une intervention rapide.

3. Prise en charge inadaptée : Une source d'inquiétude majeure est l'hospitalisation d'enfants et d'adolescents au sein de services psychiatriques pour adultes, souvent par défaut de solutions dans le secteur médico-social ou en protection de l'enfance.

Mesures d'Isolement et de Contention : Des Pratiques Abusives

Le manque d'effectifs conduit trop souvent à des restrictions injustifiées des libertés, telles que des mesures d'isolement et de contention.

• Pour les majeurs : Le contrôle systématique par un juge des libertés et de la détention (JLD) pour les hospitalisations sans consentement est jugé peu efficace.

Il repose principalement sur l'avis médical, et seules 10% des décisions aboutissent à une levée de la mesure. De plus, aucun contrôle judiciaire n'est prévu pour les soins ambulatoires sans consentement.

• Pour les mineurs : La situation est encore plus préoccupante. Un mineur hospitalisé à la demande de ses parents est placé sous le régime de "soins libres" et ne bénéficie d'aucun contrôle du JLD, même en cas d'isolement ou de contention.

Ce vide juridique constitue une atteinte grave à leurs droits fondamentaux.

Cas emblématique

Une adolescente de 15 ans, atteinte d'autisme sévère, a été hospitalisée pendant plus de deux ans dans un service psychiatrique pour adultes, faute de place en structure médico-sociale. Durant cette période, elle a été :

• Confinée dans une chambre d'isolement verrouillée plus de 20 heures par jour.

• Déscolarisée.

• Privée de soins somatiques essentiels (ex: soins dentaires). Cette situation, loin d'être un cas isolé, illustre les conséquences dramatiques du manque de solutions adaptées.

Les Lacunes des Politiques Publiques Relatives au Handicap

Éducation : Un Droit Garanti mais un Accès Difficile

La loi de 2005 a permis une impulsion mais n'est toujours pas intégralement appliquée.

• Statistiques : 30% des saisines relatives aux droits de l'enfant concernent la scolarisation d'enfants en situation de handicap.

• Obstacles persistants : Inadaptation des locaux et du matériel, rigidité des programmes, formation insuffisante des professionnels.

• Accompagnement (AESH) : Malgré la création de postes, le manque persiste. La loi du 27 mai 2024 prévoyant la prise en charge de l'accompagnement sur le temps méridien par l'État est "très loin d'être effective" en raison de blocages entre les collectivités et les académies.

• Aménagements des examens : Une augmentation inquiétante des réclamations a été constatée en 2024 concernant des refus d'aménagement pour des élèves ou étudiants, parfois au prétexte paradoxal que leurs résultats scolaires étaient bons.

Emploi : Premier Domaine de Discrimination

L'emploi est le domaine où s'exercent le plus de discriminations liées au handicap. Sur les 1 249 réclamations de 2024, 21% concernent l'emploi privé et 24% l'emploi public. L'obligation d'emploi de 6% ne suffit pas à garantir l'égalité de traitement.

• Difficultés récurrentes :

◦ Aménagement tardif du poste de travail.    ◦ Non-respect par l'employeur des préconisations du médecin du travail.    ◦ Difficultés de maintien dans l'emploi, menant à des licenciements ou des démissions forcées.

Accessibilité : Un Retard Important et Persistant

L'accessibilité est une condition essentielle à la participation sociale et à la jouissance des droits.

• Transports : La loi a été modifiée pour ne concerner que les "points d'arrêt prioritaires", ce qui est jugé insuffisant.

• Logement : L'assouplissement des règles via la loi ELAN est une source d'inquiétude.

• Accessibilité numérique : La dématérialisation a des effets ambivalents. Selon l'ARCOM, peu de sites publics atteignent 50% d'accessibilité et seulement 5% sont totalement conformes. L'ordonnance de septembre 2023 renforçant les sanctions est saluée, mais elle doit s'accompagner du maintien d'accueils physiques accessibles.

Aides à l'Autonomie : Insuffisantes et Inégales

Vingt ans après la loi de 2005, le droit à la compensation du handicap présente des limites flagrantes.

• Barrière de l'âge : Une différence de traitement persiste selon que le handicap survient avant ou après 60 ans. La fusion des régimes, prévue pour 2010, n'a pas eu lieu.

• Prestation de Compensation du Handicap (PCH) :

◦ L'aide humaine est limitée aux besoins essentiels, excluant la vie sociale.    ◦ Les aides techniques sont sous-financées.    ◦ La PCH parentalité (2021) est critiquée pour ses critères restrictifs et son forfait inadapté.

Conclusion et Recommandations Principales

La Défenseure des droits conclut en réaffirmant l'engagement de son institution et formule plusieurs pistes d'action prioritaires :

1. Application des textes existants : La première urgence, notamment pour le handicap, est "l'application pure et simple des textes votés par le parlement" et la publication des décrets d'application en attente.

2. Priorité à la jeunesse : La santé mentale, grande cause nationale en 2025, doit se traduire par une "véritable prise de conscience collective", en particulier pour les jeunes qui ne peuvent être laissés sans réponse. L'investissement dans les CMP et le dépistage précoce est essentiel.

3. Nécessité de données fiables : Il est impératif de collecter et d'agréger des données précises (ex: nombre d'heures de scolarisation effectives, nombre d'enfants en attente de place en IME) pour permettre un pilotage éclairé des politiques publiques.

4. Formation des acteurs : Une meilleure formation des employeurs sur "l'aménagement raisonnable", des enseignants et des professionnels de santé est indispensable pour faire évoluer les pratiques.

5. Abaisser la barrière d'âge de 60 ans : Il est nécessaire de mettre fin à cette distinction qui crée des inégalités de traitement injustifiées.

6. Décloisonner les systèmes : Améliorer l'articulation entre les secteurs sanitaire, médico-social et éducatif est crucial pour assurer une fluidité des parcours et éviter les ruptures de prise en charge.

Synthèse de l'Audition de Claire Hédon, Défenseure des droits

L'audition de Claire Hédon, Défenseure des droits, a été l'occasion de présenter le rapport annuel d'activité de son institution, soulignant son rôle crucial dans la protection et la promotion des droits et libertés en France.

Elle a exprimé une vive inquiétude face à la fragilisation des droits, exacerbée par un discours qui les présente comme des obstacles, ainsi que par la dématérialisation excessive des services publics et le désengagement de l'État dans les territoires.

Deux alertes majeures ont été mises en lumière : l'ampleur croissante des discriminations et les défaillances de l'administration numérique, notamment pour les étrangers.

1. Mission et Cadre d'Action de l'Institution

La Défenseure des droits a rappelé que son institution, inscrite dans la Constitution, a pour mission de "veiller au respect des droits et des libertés". Ses cinq domaines de compétence sont :

• La défense des droits des usagers dans leurs relations avec les services publics.
• La défense et la promotion des droits de l'enfant et de l'intérêt supérieur de l'enfant.
• La lutte contre les discriminations et la promotion de l'égalité.
• Le respect de la déontologie des forces de sécurité.
• L'information, l'orientation et la protection des lanceurs d'alerte.
• L'institution, forte de 256 agents professionnels du droit et de 620 délégués bénévoles répartis sur tout le territoire, a traité près de 141 000 réclamations en 2024. Hédon a souligné l'efficacité de la médiation, avec 80 % des réclamations traitées par cette voie et les trois quarts aboutissant à un règlement amiable, permettant d'éviter la judiciarisation des conflits. Elle a insisté sur l'indépendance de son rôle, qui lui permet de "dire et d'obtenir des avancées" et de "faire émerger des sujets dans le débat public".

2. Une Inquiétante Fragilisation des Droits

Claire Hédon a exprimé une profonde inquiétude quant à la "fragilisation et l'éloignement des services publics liés à une dématérialisation excessive, un désengagement de l'État dans les territoires", ce qui "conduisent immanquablement à une fragilisation et à un éloignement des droits".

Cette dynamique "mine l'effectivité des droits, génère d'ailleurs du ressentiment contre les institutions, génère aussi des tensions dans la société et abîme le sentiment d'appartenance à la République".

3. L'Ampleur Croissante des Discriminations

Les discriminations sont un "phénomène très préoccupant" dont l'ampleur "inquiète à la mesure d'ailleurs du non recours en la matière".

Elle a fourni des chiffres éloquents :

• 18 % de la population de 18 à 49 ans déclarait avoir été discriminée en 2020, contre 14 % en 2008.
• Un jeune sur trois (18-34 ans) a été victime de discrimination, selon le 14ème baromètre avec l'OIT.

• Hausse de 52 % du nombre de victimes de discrimination entre 2021 et 2022.

• Malgré ces chiffres, les réclamations auprès de l'institution ont baissé de 15 % en 2024, ce qui interpelle la Défenseure. Le "non recours" s'explique par la "peur des représailles, le sentiment d'inutilité, le découragement, les difficultés à établir les faits [et la] méconnaissance des droits".

Les discriminations liées à l'origine (cumulées avec nationalité, apparence physique et conviction religieuse) représentent 25 % des réclamations, avec un pic d'appels (+53%) en mai-juin 2024 pour des propos haineux.

Claire Hédon regrette un "essoufflement des politiques publiques" et que la "non-discrimination comme objectif politique a pratiquement disparu du débat public et des discours des décideurs qui préfèrent parler de diversité de lutte contre les discours de haine".

Elle a insisté sur l'importance de faire appliquer le droit existant plutôt que d'ajouter de nouveaux critères.

Des exemples concrets de médiations réussies ont été cités, comme l'accès à un logement décent ou le non-renouvellement de CDD lié à un état de grossesse.

4. Défaillances de la Dématérialisation et Droits des Étrangers